%0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e66959 %T Factors Influencing Information Distortion in Electronic Nursing Records: Qualitative Study %A Wang,Jianan %A Xu,Yihong %A Yang,Zhichao %A Zhang,Jie %A Zhang,Xiaoxiao %A Li,Wen %A Sun,Yushu %A Pan,Hongying %+ Department of Nursing, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 Qingchun East Road, Hangzhou, 310016, China, 86 13857188922, 3191016@zju.edu.cn %K information distortion %K electronic health record %K qualitative research %K ethics %K nursing %D 2025 %7 9.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Information distortion in nursing records poses significant risks to patient safety and impedes the enhancement of care quality. The introduction of information technologies, such as decision support systems and predictive models, expands the possibilities for using health data but also complicates the landscape of information distortion. Only by identifying influencing factors about information distortion can care quality and patient safety be ensured. Objective: This study aims to explore the factors influencing information distortion in electronic nursing records (ENRs) within the context of China’s health care system and provide appropriate recommendations to address these distortions. Methods: This qualitative study used semistructured interviews conducted with 14 nurses from a Class-A tertiary hospital. Participants were primarily asked about their experiences with and observations of information distortion in clinical practice, as well as potential influencing factors and corresponding countermeasures. Data were analyzed using inductive content analysis, which involved initial preparation, line-by-line coding, the creation of categories, and abstraction. Results: The analysis identified 4 categories and 10 subcategories: (1) nurse-related factors—skills, awareness, and work habits; (2) patient-related factors—willingness and ability; (3) operational factors—work characteristics and system deficiencies; and (4) organizational factors—management system, organizational climate, and team collaboration. Conclusions: Although some factors influencing information distortion in ENRs are similar to those observed in paper-based records, others are unique to the digital age. As health care continues to embrace digitalization, it is crucial to develop and implement strategies to mitigate information distortion. Regular training and education programs, robust systems and mechanisms, and optimized human resources and organizational practices are strongly recommended. %M 40202777 %R 10.2196/66959 %U https://www.jmir.org/2025/1/e66959 %U https://doi.org/10.2196/66959 %U http://www.ncbi.nlm.nih.gov/pubmed/40202777 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e59220 %T Implementation of Medication-Related Technology and Its Impact on Pharmacy Workflow: Real-World Evidence Usability Study %A Yu,Wei-Ning %A Cheng,Yih-Dih %A Hou,Yu-Chi %A Hsieh,Yow-Wen %+ , Department of Pharmacy, China Medical University Hospital, No 2, Yude Rd, North Dist, Taichung City, 404327, Taiwan, 886 422052121 ext 12272, tovis168@gmail.com %K medication error %K dispensing error %K medication-related technology %K pharmacy %K smart dispensing counter %D 2025 %7 27.3.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Medication errors constitute a major contributor to patient harm, driving up health care costs and representing a preventable cause of medical incidents. Over the past decade, many hospitals have integrated various medication-related technologies into their pharmacy operations. However, real-world evidence of the impact of these advanced systems on clinical prescription dispensing error rates remains limited. Objective: This study aims to prospectively detect and record the categories and rates of dispensing errors to illustrate how medication-related technologies, such as automated dispensing cabinet (ADC), barcode medication administration (BCMA), and smart dispensing counter (SDC), can be used to minimize dispensing errors. Methods: This study used a before-and-after design at a 2202-bed academic medical center in Taiwan to assess the impact of implementing medication-related technologies (ADC, BCMA, and SDC) on patient medication safety. Dispensing error rates were analyzed from January 1, 2017, to December 31, 2023, using data from the China Medical University Hospital Patient Safety Database. The study periods were defined as stage 0 (preintervention, January to November 2017), stage 1 (post-ADC intervention, December 2017 to June 2018), stage 2 (post-BCMA intervention, July 2018 to October 2020), and stage 3 (post-SDC intervention, November 2020 to December 2023). Medication errors were defined according to the National Coordinating Council for Medication Error Reporting and Prevention (NCC MERP). Chi-square or Fisher exact tests were used to analyze differences between intervention periods, with Bonferroni correction for multiple comparisons. Statistical significance was set at P<.05. Results: Following the introduction of medication-related technologies, the average dispensing error incidence rate significantly decreased by 39.68%, 44.44%, and 77.78%, from 0.0063% in stage 0 to 0.0038%, 0.0035%, and 0.0014% in stages 1, 2, and 3, respectively (P<.001). The frequency of “wrong drug” errors, the most common error type in stage 0, significantly decreased by 51.15%, 56.85%, and 81.26% in stages 1, 2, and 3, respectively. All error types, except for “wrong dosage form,” “wrong strength,” “wrong time,” and “others,” demonstrated statistically significant differences (P<.001). The majority of harm severities were categorized as “A” (no error; 97%-98.8%) and “B-D” (error, no harm; 1.2%-3%) according to the NCC MERP classification. The severity of “no error” (category A) significantly decreased at each stage (P<.001). Statistically significant differences in dispensing error rates were observed between all stages (P<.001), except between stages 2 and 1 (P>.99). Conclusions: This study provides significant evidence that the implementation of medication-related technologies, including ADC, BCMA, and SDC, effectively reduces dispensing errors in a hospital pharmacy setting. Specifically, we observed a substantial decrease in the average dispensing error rate across 3 stages of technology implementation. Importantly, this study appears to be the first to investigate the combined impact of these 3 specific technologies on dispensing error rates within a hospital pharmacy. %M 40019479 %R 10.2196/59220 %U https://www.jmir.org/2025/1/e59220 %U https://doi.org/10.2196/59220 %U http://www.ncbi.nlm.nih.gov/pubmed/40019479 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e64266 %T Perceived Trust and Professional Identity Threat in AI-Based Clinical Decision Support Systems: Scenario-Based Experimental Study on AI Process Design Features %A Ackerhans,Sophia %A Wehkamp,Kai %A Petzina,Rainer %A Dumitrescu,Daniel %A Schultz,Carsten %+ , Kiel Institute of Responsible Innovation, University of Kiel, Westring 425, Kiel, 24118, Germany, 49 431880479, ackerhans@bwl.uni-kiel.de %K artificial intelligence %K clinical decision support systems %K explainable artificial intelligence %K professional identity threat %K health care %K physicians %K perceptions %K professional identity %D 2025 %7 26.3.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Artificial intelligence (AI)–based systems in medicine like clinical decision support systems (CDSSs) have shown promising results in health care, sometimes outperforming human specialists. However, the integration of AI may challenge medical professionals’ identities and lead to limited trust in technology, resulting in health care professionals rejecting AI-based systems. Objective: This study aims to explore the impact of AI process design features on physicians’ trust in the AI solution and on perceived threats to their professional identity. These design features involve the explainability of AI-based CDSS decision outcomes, the integration depth of the AI-generated advice into the clinical workflow, and the physician’s accountability for the AI system-induced medical decisions. Methods: We conducted a 3-factorial web-based between-subject scenario-based experiment with 292 medical students in their medical training and experienced physicians across different specialties. The participants were presented with an AI-based CDSS for sepsis prediction and prevention for use in a hospital. Each participant was given a scenario in which the 3 design features of the AI-based CDSS were manipulated in a 2×2×2 factorial design. SPSS PROCESS (IBM Corp) macro was used for hypothesis testing. Results: The results suggest that the explainability of the AI-based CDSS was positively associated with both trust in the AI system (β=.508; P<.001) and professional identity threat perceptions (β=.351; P=.02). Trust in the AI system was found to be negatively related to professional identity threat perceptions (β=–.138; P=.047), indicating a partially mediated effect on professional identity threat through trust. Deep integration of AI-generated advice into the clinical workflow was positively associated with trust in the system (β=.262; P=.009). The accountability of the AI-based decisions, that is, the system required a signature, was found to be positively associated with professional identity threat perceptions among the respondents (β=.339; P=.004). Conclusions: Our research highlights the role of process design features of AI systems used in medicine in shaping professional identity perceptions, mediated through increased trust in AI. An explainable AI-based CDSS and an AI-generated system advice, which is deeply integrated into the clinical workflow, reinforce trust, thereby mitigating perceived professional identity threats. However, explainable AI and individual accountability of the system directly exacerbate threat perceptions. Our findings illustrate the complex nature of the behavioral patterns of AI in health care and have broader implications for supporting the implementation of AI-based CDSSs in a context where AI systems may impact professional identity. %M 40138691 %R 10.2196/64266 %U https://formative.jmir.org/2025/1/e64266 %U https://doi.org/10.2196/64266 %U http://www.ncbi.nlm.nih.gov/pubmed/40138691 %0 Journal Article %@ 2291-9279 %I JMIR Publications %V 13 %N %P e69021 %T Personal Protection Equipment Training as a Virtual Reality Game in Immersive Environments: Development Study and Pilot Randomized Controlled Trial %A Zhou,Liang %A Liu,Haoyang %A Fan,Mengjie %A Liu,Jiahao %A Yu,Xingyao %A Zhao,Xintian %A Zhang,Shaoxing %K virtual reality training %K nosocomial infections control %K visualization %K human computer interaction %K personal protection equipment %K PPE %D 2025 %7 20.3.2025 %9 %J JMIR Serious Games %G English %X Background: Proper donning and doffing of personal protection equipment (PPE) and hand hygiene in the correct spatial context of a health facility is important for the prevention and control of nosocomial infections. On-site training is difficult due to the potential infectious risks and shortages of PPE, whereas video-based training lacks immersion which is vital for the familiarization of the environment. Virtual reality (VR) training can support the repeated practice of PPE donning and doffing in an immersive environment that simulates a realistic configuration of a health facility. Objective: This study aims to develop and evaluate a VR simulation focusing on the correct event order of PPE donning and doffing, that is, the item and hand hygiene order in the donning and doffing process but not the detailed steps of how to don and doff an item, in an immersive environment that replicates the spatial zoning of a hospital. The VR method should be generic and support customizable sequencing of PPE donning and doffing. Methods: An immersive VR PPE training tool was developed by computer scientists and medical experts. The effectiveness of the immersive VR method versus video-based learning was tested in a pilot study as a randomized controlled trial (N=32: VR group, n=16; video-based training, n=16) using questionnaires on spatial-aware event order memorization questions, usability, and task workload. Trajectories of participants in the immersive environment were also recorded for behavior analysis and potential improvements of the real environment of the health facility. Results: Comparable sequence memorization scores (VR mean 79.38, SD 12.90 vs video mean 74.38, SD 17.88; P=.37) as well as National Aeronautics and Space Administration Task Load Index scores (VR mean 42.9, SD 13.01 vs video mean 51.50, SD 20.44; P=.16) were observed. The VR group had an above-average usability in the System Usability Scale (mean 74.78>70.0) and was significantly better than the video group (VR mean 74.78, SD 13.58 vs video mean 57.73, SD 21.13; P=.009). The analysis and visualization of trajectories revealed a positive correlation between the length of trajectories and the completion time, but neither correlated to the accuracy of the memorization task. Further user feedback indicated a preference for the VR method over the video-based method. Limitations of and suggestions for improvements in the study were also identified. Conclusions: A new immersive VR PPE training method was developed and evaluated against the video-based training. Results of the pilot study indicate that the VR method provides training quality comparable to video-based training and is more usable. In addition, the immersive experience of realistic settings and the flexibility of training configurations make the VR method a promising alternative to video instructions. %R 10.2196/69021 %U https://games.jmir.org/2025/1/e69021 %U https://doi.org/10.2196/69021 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e60728 %T Optimizing the Pharmacotherapy of Vascular Surgery Patients at Hospital Admission and Discharge (PHAROS): Protocol for a Quasi-Experimental Clinical Uncontrolled Trial %A Porubcova,Slavka %A Lajtmanova,Kristina %A Szmicsekova,Kristina %A Slezakova,Veronika %A Tomka,Jan %A Tesar,Tomas %+ Department of Organisation and Management of Pharmacy, Faculty of Pharmacy, Comenius University, Odbojarov 10, Bratislava, 832 32, Slovakia, 421 2 9016 9348, tesar@fpharm.uniba.sk %K pharmacotherapy %K hospital pharmacy %K vascular surgery %K patient safety %K risk reduction %K pharmacist-proposed interventions %D 2025 %7 19.3.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Patient safety is essential in pharmacotherapy, especially in surgical contexts, due to the elevated risk of drug-related complications. Vascular surgery patients are particularly susceptible because of their complex medication needs and underlying health conditions. Improved safety monitoring and targeted pharmaceutical care in collaboration with physicians are crucial to minimize these risks and enhance patient outcomes. Objective: This protocol evaluates whether structured pharmaceutical care interventions—including medication reconciliation, medication review, and patient education—can reduce the prevalence of drug-related problems at hospital admission and discharge in vascular surgery patients. Methods: This prospective, uncontrolled study was conducted over 1 year in the Vascular Surgery Department at the National Institute of Cardiovascular Diseases in Bratislava, Slovakia. The study included adult patients with carotid artery disease or lower extremity artery disease who were on 3 or more medications, with an estimated sample size of approximately 100 patients. The primary intervention involved 3 key changes in practice: medication reconciliation at both admission and discharge, where hospital pharmacists review and verify medication lists; medication review to identify and address drug-related problems; and patient education at discharge. Pharmacist-proposed interventions were documented and communicated to the physician for treatment adjustments. The primary outcome is the change in drug-related problem prevalence from hospital admission to discharge. Secondary outcomes include the acceptance rate of pharmacist recommendations and patient understanding of pharmacotherapy. Data collection involved documenting the number, type, and frequency of drug-related problems; the anatomical therapeutic chemical classification of medications associated with drug-related problems; and patients’ social, demographic, and clinical characteristics, with a focus on factors related to drug-related problems, comorbidities, and medication use. Data analysis will use the paired Wilcoxon signed-rank test to compare the prevalence of drug-related problems and medication counts between admission and discharge. Continuous variables will be presented as means (SDs), while categorical variables will be reported as counts and percentages. Patient understanding of pharmacotherapy will be evaluated using a 3-point scale, classifying understanding as good (2-3 points per medication), modest (1-2 points), or poor (0-1 point). Results: Recruitment began in September 2021 and concluded in August 2022. Data collection occurred continuously during hospital stays, capturing demographics, comorbidities, pharmacotherapy, and drug-related problems at admission and discharge. Important milestones included the initial data review, which began in August 2023 to assess recruitment and data quality, including an early evaluation of drug-related problems. The primary analysis was completed in January 2024, focusing on the reduction in drug-related problems, intervention acceptance, and patient understanding. The final report was to be prepared by June 2024, disseminating the findings on pharmacist-led intervention impacts. Conclusions: This study should demonstrate that pharmacist-led interventions in collaboration with physicians can reduce pharmacotherapy risks and optimize medicine management for patient safety. Trial Registration: ClinicalTrials.gov NCT04930302; https://clinicaltrials.gov/study/NCT04930302 International Registered Report Identifier (IRRID): RR1-10.2196/60728 %M 40106812 %R 10.2196/60728 %U https://www.researchprotocols.org/2025/1/e60728 %U https://doi.org/10.2196/60728 %U http://www.ncbi.nlm.nih.gov/pubmed/40106812 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 12 %N %P e66699 %T An AI-Based Clinical Decision Support System for Antibiotic Therapy in Sepsis (KINBIOTICS): Use Case Analysis %A Düvel,Juliane Andrea %A Lampe,David %A Kirchner,Maren %A Elkenkamp,Svenja %A Cimiano,Philipp %A Düsing,Christoph %A Marchi,Hannah %A Schmiegel,Sophie %A Fuchs,Christiane %A Claßen,Simon %A Meier,Kirsten-Laura %A Borgstedt,Rainer %A Rehberg,Sebastian %A Greiner,Wolfgang %K CDSS %K use case analysis %K technology acceptance %K sepsis %K infection %K infectious disease %K antimicrobial resistance %K clinical decision support system %K decision-making %K clinical support %K machine learning %K ML %K artificial intelligence %K AI %K algorithm %K model %K analytics %K predictive models %K deep learning %K early warning %K early detection %D 2025 %7 4.3.2025 %9 %J JMIR Hum Factors %G English %X Background: Antimicrobial resistances pose significant challenges in health care systems. Clinical decision support systems (CDSSs) represent a potential strategy for promoting a more targeted and guideline-based use of antibiotics. The integration of artificial intelligence (AI) into these systems has the potential to support physicians in selecting the most effective drug therapy for a given patient. Objective: This study aimed to analyze the feasibility of an AI-based CDSS pilot version for antibiotic therapy in sepsis patients and identify facilitating and inhibiting conditions for its implementation in intensive care medicine. Methods: The evaluation was conducted in 2 steps, using a qualitative methodology. Initially, expert interviews were conducted, in which intensive care physicians were asked to assess the AI-based recommendations for antibiotic therapy in terms of plausibility, layout, and design. Subsequently, focus group interviews were conducted to examine the technology acceptance of the AI-based CDSS. The interviews were anonymized and evaluated using content analysis. Results: In terms of the feasibility, barriers included variability in previous antibiotic administration practices, which affected the predictive ability of AI recommendations, and the increased effort required to justify deviations from these recommendations. Physicians’ confidence in accepting or rejecting recommendations depended on their level of professional experience. The ability to re-evaluate CDSS recommendations and an intuitive, user-friendly system design were identified as factors that enhanced acceptance and usability. Overall, barriers included low levels of digitization in clinical practice, limited availability of cross-sectoral data, and negative previous experiences with CDSSs. Conversely, facilitators to CDSS implementation were potential time savings, physicians’ openness to adopting new technologies, and positive previous experiences. Conclusions: Early integration of users is beneficial for both the identification of relevant context factors and the further development of an effective CDSS. Overall, the potential of AI-based CDSSs is offset by inhibiting contextual conditions that impede its acceptance and implementation. The advancement of AI-based CDSSs and the mitigation of these inhibiting conditions are crucial for the realization of its full potential. %R 10.2196/66699 %U https://humanfactors.jmir.org/2025/1/e66699 %U https://doi.org/10.2196/66699 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 13 %N %P e63601 %T Predicting Agitation-Sedation Levels in Intensive Care Unit Patients: Development of an Ensemble Model %A Dai,Pei-Yu %A Lin,Pei-Yi %A Sheu,Ruey-Kai %A Liu,Shu-Fang %A Wu,Yu-Cheng %A Wu,Chieh-Liang %A Chen,Wei-Lin %A Huang,Chien-Chung %A Lin,Guan-Yin %A Chen,Lun-Chi %K intensive care units %K ICU %K agitation %K sedation %K ensemble learning %K machine learning %K ML %K artificial intelligence %K AI %K patient safety %K efficiency %K automation %K ICU care %K ensemble model %K learning model %K explanatory analysis %D 2025 %7 26.2.2025 %9 %J JMIR Med Inform %G English %X Background: Agitation and sedation management is critical in intensive care as it affects patient safety. Traditional nursing assessments suffer from low frequency and subjectivity. Automating these assessments can boost intensive care unit (ICU) efficiency, treatment capacity, and patient safety. Objectives: The aim of this study was to develop a machine-learning based assessment of agitation and sedation. Methods: Using data from the Taichung Veterans General Hospital ICU database (2020), an ensemble learning model was developed for classifying the levels of agitation and sedation. Different ensemble learning model sequences were compared. In addition, an interpretable artificial intelligence approach, SHAP (Shapley additive explanations), was employed for explanatory analysis. Results: With 20 features and 121,303 data points, the random forest model achieved high area under the curve values across all models (sedation classification: 0.97; agitation classification: 0.88). The ensemble learning model enhanced agitation sensitivity (0.82) while maintaining high AUC values across all categories (all >0.82). The model explanations aligned with clinical experience. Conclusions: This study proposes an ICU agitation-sedation assessment automation using machine learning, enhancing efficiency and safety. Ensemble learning improves agitation sensitivity while maintaining accuracy. Real-time monitoring and future digital integration have the potential for advancements in intensive care. %R 10.2196/63601 %U https://medinform.jmir.org/2025/1/e63601 %U https://doi.org/10.2196/63601 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e68436 %T Machine Learning Approach to Identifying Wrong-Site Surgeries Using Centers for Medicare and Medicaid Services Dataset: Development and Validation Study %A Chen,Yuan-Hsin %A Lin,Ching-Hsuan %A Fan,Chiao-Hsin %A Long,An Jim %A Scholl,Jeremiah %A Kao,Yen-Pin %A Iqbal,Usman %A Li,Yu-Chuan Jack %+ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No. 301, Yuantong Rd. Zhonghe Dist, New Taipei City, 235, Taiwan, 886 266382736, jack@tmu.edu.tw %K patient safety %K wrong site surgery %K medical errors %K machine learning %K claim data %D 2025 %7 13.2.2025 %9 Original Paper %J JMIR Form Res %G English %X Background: Wrong-site surgery (WSS) is a critical but preventable medical error, often resulting in severe patient harm and substantial financial costs. While protocols exist to reduce wrong-site surgery, underreporting and inconsistent documentation continue to contribute to its persistence. Machine learning (ML) models, which have shown success in detecting medication errors, may offer a solution by identifying unusual procedure-diagnosis combinations. This study investigated whether an ML approach can effectively adapt to detect surgical errors. Objective: This study aimed to evaluate the transferability and effectiveness of an ML-based model for detecting inconsistencies within surgical documentation, particularly focusing on laterality discrepancies. Methods: We used claims data from the Centers for Medicare and Medicaid Services Limited Data Set (CMS-LDS) from 2017 to 2020, focusing on surgical procedures with documented laterality. We developed an adapted Association Outlier Pattern (AOP) ML model to identify uncommon procedure-diagnosis combinations, specifically targeting discrepancies in laterality. The model was trained on data from 2017 to 2019 and tested on 2020 orthopedic procedures, using ICD-10-PCS (International Classification of Diseases, Tenth Revision, Procedure Coding System) codes to distinguish body part and laterality. Test cases were classified based on alignment between procedural and diagnostic laterality, with 2 key subgroups (right-left and left-right mismatches) identified for evaluation. Model performance was assessed by comparing precision-recall curves and accuracy against rule-based methods. Results: The findings here included 346,382 claims, of which 2170 claims demonstrated with significant laterality discrepancies between procedures and diagnoses. Among patients with left-side procedures and right-side diagnoses (603/1106), 54.5% were confirmed as errors after clinical review. For right-side procedures with left-side diagnoses (541/1064), 50.8% were classified as errors. The AOP model identified 697 and 655 potentially unusual combinations in the left-right and right-left subgroups, respectively, with over 80% of these cases confirmed as errors following clinical review. Most confirmed errors involved discrepancies in laterality for the same body part, while nonerror cases typically involved general diagnoses without specified laterality. Conclusions: This investigation showed that the AOP model effectively detects inconsistencies between surgical procedures and diagnoses using CMS-LDS data. The AOP model outperformed traditional rule-based methods, offering higher accuracy in identifying errors. Moreover, the model’s transferability from medication-disease associations to procedure-diagnosis verification highlights its broad applicability. By improving the precision of identifying laterality discrepancies, the AOP model can reduce surgical errors, particularly in orthopedic care. These findings suggest that the model enhances patient safety and has the potential to improve clinical decision-making and outcomes. %M 39946709 %R 10.2196/68436 %U https://formative.jmir.org/2025/1/e68436 %U https://doi.org/10.2196/68436 %U http://www.ncbi.nlm.nih.gov/pubmed/39946709 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 12 %N %P e60273 %T The Effects of Presenting AI Uncertainty Information on Pharmacists’ Trust in Automated Pill Recognition Technology: Exploratory Mixed Subjects Study %A Kim,Jin Yong %A Marshall,Vincent D %A Rowell,Brigid %A Chen,Qiyuan %A Zheng,Yifan %A Lee,John D %A Kontar,Raed Al %A Lester,Corey %A Yang,Xi Jessie %+ , Industrial and Operations Engineering, University of Michigan, 1640 IOE, 1205 Beal Avenue, Ann Arbor, MI, 48105, United States, 1 7347630541, xijyang@umich.edu %K artificial intelligence %K human-computer interaction %K uncertainty communication %K visualization %K medication errors %K safety %K artificial intelligence aid %K pharmacists %K pill verification %K automation %D 2025 %7 11.2.2025 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Dispensing errors significantly contribute to adverse drug events, resulting in substantial health care costs and patient harm. Automated pill verification technologies have been developed to aid pharmacists with medication dispensing. However, pharmacists’ trust in such automated technologies remains unexplored. Objective: This study aims to investigate pharmacists’ trust in automated pill verification technology designed to support medication dispensing. Methods: Thirty licensed pharmacists in the United States performed a web-based simulated pill verification task to determine whether an image of a filled medication bottle matched a known reference image. Participants completed a block of 100 verification trials without any help, and another block of 100 trials with the help of an imperfect artificial intelligence (AI) aid recommending acceptance or rejection of a filled medication bottle. The experiment used a mixed subjects design. The between-subjects factor was the AI aid type, with or without an AI uncertainty plot. The within-subjects factor was the four potential verification outcomes: (1) the AI rejects the incorrect drug, (2) the AI rejects the correct drug, (3) the AI approves the incorrect drug, and (4) the AI approves the correct drug. Participants’ trust in the AI system was measured. Mixed model (generalized linear models) tests were conducted with 2-tailed t tests to compare the means between the 2 AI aid types for each verification outcome. Results: Participants had an average trust propensity score of 72 (SD 18.08) out of 100, indicating a positive attitude toward trusting automated technologies. The introduction of an uncertainty plot to the AI aid significantly enhanced pharmacists’ end trust (t28=–1.854; P=.04). Trust dynamics were influenced by AI aid type and verification outcome. Specifically, pharmacists using the AI aid with the uncertainty plot had a significantly larger trust increment when the AI approved the correct drug (t78.98=3.93; P<.001) and a significantly larger trust decrement when the AI approved the incorrect drug (t2939.72=–4.78; P<.001). Intriguingly, the absence of the uncertainty plot led to an increase in trust when the AI correctly rejected an incorrect drug, whereas the presence of the plot resulted in a decrease in trust under the same circumstances (t509.77=–3.96; P<.001). A pronounced “negativity bias” was observed, where the degree of trust reduction when the AI made an error exceeded the trust gain when the AI made a correct decision (z=–11.30; P<.001). Conclusions: To the best of our knowledge, this study is the first attempt to examine pharmacists’ trust in automated pill verification technology. Our findings reveal that pharmacists have a favorable disposition toward trusting automation. Moreover, providing uncertainty information about the AI’s recommendation significantly boosts pharmacists’ trust in AI aid, highlighting the importance of developing transparent AI systems within health care. %M 39932773 %R 10.2196/60273 %U https://humanfactors.jmir.org/2025/1/e60273 %U https://doi.org/10.2196/60273 %U http://www.ncbi.nlm.nih.gov/pubmed/39932773 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e65546 %T Digital Health Technology Interventions for Improving Medication Safety: Systematic Review of Economic Evaluations %A Insani,Widya Norma %A Zakiyah,Neily %A Puspitasari,Irma Melyani %A Permana,Muhammad Yorga %A Parmikanti,Kankan %A Rusyaman,Endang %A Suwantika,Auliya Abdurrohim %+ Department of Pharmacology and Clinical Pharmacy, Universitas Padjadjaran, Jl Raya Bandung Sumedang KM 21, Jatinangor, Sumedang, 45363, Indonesia, 62 7796200, widya.insani@unpad.ac.id %K digital health technology %K drug safety %K adverse drug events %K medication errors %K patient safety %D 2025 %7 5.2.2025 %9 Review %J J Med Internet Res %G English %X Background: Medication-related harm, including adverse drug events (ADEs) and medication errors, represents a significant iatrogenic burden in clinical care. Digital health technology (DHT) interventions can significantly enhance medication safety outcomes. Although the clinical effectiveness of DHT for medication safety has been relatively well studied, much less is known about the cost-effectiveness of these interventions. Objective: This study aimed to systematically review the economic impact of DHT interventions on medication safety and examine methodological challenges to inform future research directions. Methods: A systematic search was conducted across 3 major electronic databases (ie, PubMed, Scopus, and EBSCOhost). The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed for this systematic review. Two independent investigators conducted a full-text review after screening preliminary titles and abstracts. We adopted recommendations from the Panel on Cost-Effectiveness in Health and Medicine for data extraction. A narrative analysis was conducted to synthesize clinical and economic outcomes. The quality of reporting for the included studies was assessed using the CHEERS (Consolidated Health Economic Evaluation Reporting Standards) guidelines. Results: We included 13 studies that assessed the cost-effectiveness (n=9, 69.2%), cost-benefit (n=3, 23.1%), and cost-utility (n=1, 7.7%) of DHT for medication safety. Of the included studies, more than half (n=7, 53.9%) evaluated a clinical decision support system (CDSS)/computerized provider order entry (CPOE), 4 (30.8%) examined automated medication-dispensing systems, and 2 (15.4%) focused on pharmacist-led outreach programs targeting health care professionals. In 12 (92.3% ) studies, DHT was either cost-effective or cost beneficial compared to standard care. On average, DHT interventions reduced ADEs by 37.12% (range 8.2%-66.5%) and medication errors by 54.38% (range 24%-83%). The key drivers of cost-effectiveness included reductions in outcomes, the proportion of errors resulting in ADEs, and implementation costs. Despite a significant upfront cost, DHT showed a return on investment within 3-4.25 years due to lower cost related with ADE treatment and improved workflow efficiency. In terms of reporting quality, the studies were classified as good (n=10, 76.9%) and moderate (n=3, 23.1%). Key methodological challenges included short follow-up periods, the absence of alert compliance tracking, the lack of ADE and error severity categorization, and omission of indirect costs. Conclusions: DHT interventions are economically viable to improve medication safety, with a substantial reduction in ADEs and medication errors. Future studies should prioritize incorporating alert compliance tracking, ADE and error severity classification, and evaluation of indirect costs, thereby increasing clinical benefits and economic viability. %M 39909404 %R 10.2196/65546 %U https://www.jmir.org/2025/1/e65546 %U https://doi.org/10.2196/65546 %U http://www.ncbi.nlm.nih.gov/pubmed/39909404 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e59946 %T Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled Trial %A Tsai,Chuan-Ching %A Kim,Jin Yong %A Chen,Qiyuan %A Rowell,Brigid %A Yang,X Jessie %A Kontar,Raed %A Whitaker,Megan %A Lester,Corey %+ Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, 428 Church Street, Ann Arbor, MI, 48109, United States, 1 7346478849, lesterca@umich.edu %K CDSS %K eye-tracking %K medication verification %K uncertainty visualization %K AI helpfulness and accuracy %K artificial intelligence %K cognitive interactions %K clinical decision support system %K cognition %K pharmacists %K medication %K interaction %K decision-making %K cognitive processing %D 2025 %7 31.1.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Clinical decision support systems leveraging artificial intelligence (AI) are increasingly integrated into health care practices, including pharmacy medication verification. Communicating uncertainty in an AI prediction is viewed as an important mechanism for boosting human collaboration and trust. Yet, little is known about the effects on human cognition as a result of interacting with such types of AI advice. Objective: This study aimed to evaluate the cognitive interaction patterns of pharmacists during medication product verification when using an AI prototype. Moreover, we examine the impact of AI’s assistance, both helpful and unhelpful, and the communication of uncertainty of AI-generated results on pharmacists’ cognitive interaction with the prototype. Methods: In a randomized controlled trial, 30 pharmacists from professional networks each performed 200 medication verification tasks while their eye movements were recorded using an online eye tracker. Participants completed 100 verifications without AI assistance and 100 with AI assistance (either with black box help without uncertainty information or uncertainty-aware help, which displays AI uncertainty). Fixation patterns (first and last areas fixated, number of fixations, fixation duration, and dwell times) were analyzed in relation to AI help type and helpfulness. Results: Pharmacists shifted 19%-26% of their total fixations to AI-generated regions when these were available, suggesting the integration of AI advice in decision-making. AI assistance did not reduce the number of fixations on fill images, which remained the primary focus area. Unhelpful AI advice led to longer dwell times on reference and fill images, indicating increased cognitive processing. Displaying AI uncertainty led to longer cognitive processing times as measured by dwell times in original images. Conclusions: Unhelpful AI increases cognitive processing time in the original images. Transparency in AI is needed in “black box” systems, but showing more information can add a cognitive burden. Therefore, the communication of uncertainty should be optimized and integrated into clinical workflows using user-centered design to avoid increasing cognitive load or impeding clinicians’ original workflow. Trial Registration: ClinicalTrials.gov NCT06795477; https://clinicaltrials.gov/study/NCT06795477 %M 39888668 %R 10.2196/59946 %U https://www.jmir.org/2025/1/e59946 %U https://doi.org/10.2196/59946 %U http://www.ncbi.nlm.nih.gov/pubmed/39888668 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e59606 %T Occupational Infections Among Workers in Europe: Protocol for a Scoping Review %A Dini,Guglielmo %A Curti,Stefania %A Rahmani,Alborz %A Durando,Paolo %A Mattioli,Stefano %+ Occupational Medicine Unit, IRCCS Ospedale Policlinico San Martino, L.go R. Benzi 10, Building n. 3., Genoa, 16132, Italy, 1 010 3537472, guglielmo.dini@unige.it %K infection %K work-related %K biological hazard %K narrative synthesis %K Europe %K occupational infection %K worker %K scoping review %K infectious %K prevention and control %K occupational health %K epidemiology %K burden of disease %K phenomenon %D 2025 %7 24.1.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: Workers may be exposed to different infectious agents, putting them at risk of developing occupational diseases. This can occur in many ways, through deliberate use of specific microorganisms or through potential exposure from close contact with biological material. Infection prevention and control measures against biohazards can reduce the risk of infection among workers. During the last few decades, an increasing proportion of workers in Europe have been exposed to infectious biological agents in their workplace. Knowledge gaps on this topic in Europe have limited our understanding of the overall phenomenon in occupational settings. Objective: This study aims to understand the extent and type of evidence on the epidemiology of occupational or work-related infections caused by bacterial, viral, fungal, and parasitical agents in European countries, the factors affecting their occurrence among workers, and the burden of disease among workers due to occupational risk. Methods: The review will be conducted following the Joanna Briggs Institute methodology for scoping reviews and the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. This review will consider studies that include data on the epidemiology of occupational infections, risk factors and determinants, and burden of disease among workers employed in specific occupational sectors in European countries in the period 2010-2023. The search will include MEDLINE, Web of Science, and Scopus databases. Independent reviewers (including GD, SC, AR, PD, and SM) will screen the titles, abstracts, and full texts of the selected studies. Data extraction will be performed using a tool developed by the researchers. The data will be mapped and analyzed according to the type of extracted data. Results: The literature search through different scientific databases started in April 2024 and is expected to be completed by December 2024. The findings will be extracted using an ad hoc data extraction tool, and relevant results will be presented in narrative and tabular form. Conclusions: This scoping review aims to provide rigorous evidence to fill the knowledge gap in the epidemiology of occupational or work-related infections in European countries, the factors affecting their occurrence, and the burden of disease in different professional settings. Such findings could improve the understanding of this complex occupational phenomenon in the European context, enabling more accurate and up-to-date surveillance of infections incurred in the workplace. International Registered Report Identifier (IRRID): PRR1-10.2196/59606 %M 39855637 %R 10.2196/59606 %U https://www.researchprotocols.org/2025/1/e59606 %U https://doi.org/10.2196/59606 %U http://www.ncbi.nlm.nih.gov/pubmed/39855637 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e59203 %T Assessment of Geriatric Problems and Risk Factors for Delirium in Surgical Medicine: Protocol for Multidisciplinary Prospective Clinical Study %A Möllmann,Henriette Louise %A Alhammadi,Eman %A Boulghoudan,Soufian %A Kuhlmann,Julian %A Mevissen,Anica %A Olbrich,Philipp %A Rahm,Louisa %A Frohnhofen,Helmut %+ Department of Oral-, Maxillo- and Plastic Facial Surgery, Heinrich-Heine-University Duesseldorf, Moorenstraße 5, Düsseldorf, 40225, Germany, 49 15206802915, henriettelouise.moellmann@med.uni-duesseldorf.de %K delirium %K older patients %K perioperative assessment %K age-related surgical risk factors %K geriatric assessment %K gerontology %K aging %K surgical medicine %K surgical care %K surgery %K multidisciplinary %K prospective study %K perioperative %K screening %K palliative care %K health informatics %D 2025 %7 22.1.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: An aging population in combination with more gentle and less stressful surgical procedures leads to an increased number of operations on older patients. This collectively raises novel challenges due to higher age heavily impacting treatment. A major problem, emerging in up to 50% of cases, is perioperative delirium. It is thus vital to understand whether and which existing geriatric assessments are capable of reliably identifying risk factors, how high the incidence of delirium is, and whether the resulting management of these risk factors might lead to a reduced incidence of delirium. Objective: This study aimed to determine the frequency and severity of geriatric medical problems in elective patients of the Clinics of Oral and Maxillofacial Surgery, Vascular Surgery, and Orthopedics, General Surgery, and Trauma Surgery, revealing associations with the incidence of perioperative delirium regarding potential risk factors, and recording the long-term effects of geriatric problems and any perioperative delirium that might have developed later the patient’s life. Methods: We performed both pre- and postoperative assessments in patients of 4 different surgical departments who are older than 70 years. Patient-validated screening instruments will be used to identify risk factors. A geriatric assessment with the content of basal and instrumental activities of daily living (basal activities of daily living [Katz index], instrumental activities of daily living [Lawton and Brody score], cognition [6-item screener and clock drawing test], mobility [de Morton Mobility Index and Sit-to-Stand test], sleep [Pittsburgh Sleep Quality Index and Insomnia Severity Index/STOP-BANG], drug therapy [polypharmacy and quality of medication, Fit For The Aged classification, and anticholinergic burden score], and pain assessment and delirium risk (Delirium Risk Assessment Tool) will be performed. Any medical problems detected will be treated according to current standards, and no intervention is planned as part of the study. In addition, a telephone follow-up will be performed 3, 6, and 12 months after discharge. Results: Recruitment started in August 2022, with 421 patients already recruited at the time of submission. Initial analyses of the data are to be published at the end of 2024 or the beginning of 2025. Conclusions: In the current study, we investigate whether the risk factors addressed in the assessment are associated with an increase in the delirium rate. The aim is then to reduce this comprehensive assessment to the central aspects to be able to conduct targeted and efficient risk screening. Trial Registration: German Clinical Trials Registry DRKS00028614; https://www.drks.de/search/de/trial/DRKS00028614 International Registered Report Identifier (IRRID): DERR1-10.2196/59203 %M 39841510 %R 10.2196/59203 %U https://www.researchprotocols.org/2025/1/e59203 %U https://doi.org/10.2196/59203 %U http://www.ncbi.nlm.nih.gov/pubmed/39841510 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 11 %N %P e68046 %T Transforming Medical Education to Make Patient Safety Part of the Genome of a Modern Health Care Worker %A Lachman,Peter %A Fitzsimons,John %K patient safety %K psychological safety %K medical curriculum %K professional competence %K clinical competence %D 2025 %7 17.1.2025 %9 %J JMIR Med Educ %G English %X Medical education has not traditionally recognized patient safety as a core subject. To foster a culture of patient safety and enhance psychological safety, it is essential to address the barriers and facilitators that currently impact the development and delivery of medical education curricula. The aim of including patient safety and psychological safety competencies in education curricula is to insert these into the genome of the modern health care worker. %R 10.2196/68046 %U https://mededu.jmir.org/2025/1/e68046 %U https://doi.org/10.2196/68046 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 14 %N %P e60622 %T Interstep Variations of Stairways and Associations of High-Contrast Striping and Fall-Related Events: Observational Study %A Harper,Sara A %A Brown,Chayston %A Poulsen,Shandon L %A Barrett,Tyson S %A Dakin,Christopher J %+ Kinesiology Department, University of Alabama in Huntsville, 301 Sparkman Drive NW, Huntsville, AL, 35899, United States, 1 256 824 2184, sah0075@uah.edu %K stairs %K stairway safety %K riser height %K tread depth %K horizontal-vertical illusion %K fall risk %K fall prevention %K videos %K Monte Carlo simulation %K public health %K vision-based strategy %K health promotion %K adults %K geriatric %D 2025 %7 8.1.2025 %9 Original Paper %J Interact J Med Res %G English %X Background: Interstep variations in step riser height and tread depth within a stairway could negatively impact safe stair negotiation by decreasing step riser height predictability and, consequently, increasing stair users’ fall risk. Unfortunately, interstep variations in riser height and depth are common, particularly in older stairways, but its impact may be lessened by highlighting steps’ edges using a high-contrast stripe on the top front edge of each step. Objective: This study aimed to determine (1) if fall-related events are associated with greater interstep riser height and depth variations and (2) if such fall-related events are reduced in the presence of contrast-enhanced step edges compared with a control stairway. Methods: Stair users were video recorded on 2 public stairways in a university building. One stairway had black vinyl stripes applied to the step’s edges and black-and-white vertical stripes on the last and top steps’ faces. The stairway with striping was counterbalanced, with the striped stairway than a control, and the control with stripes. Each stair user recorded was coded for whether they experienced a fall-related event. A total of 10,000 samples (observations) of 20 fall-related events were drawn with 0.25 probability from each condition to determine the probability of observing a distribution with the constraints outlined by the hypotheses by a computerized Monte Carlo simulation. Results: In total, 11,137 individual stair user observations had 20 fall-related events. The flights that had 14 mm in interstep riser height variation and 38 mm in interstep depth variation were associated with 80% (16/20) of the fall-related events observed. Furthermore, 2 fall-related events were observed for low interstep variation with no striping, and 2 fall-related events were observed during low interstep variation with striping. A total of 20 fall-related events were observed, with 4 occurring on flights of stairs with low interstep variation. For stairs with high variability in step dimensions, 13 of 16 (81%) fall-related events occurred on the control stairway (no striping) compared with 3 of 16 (19%) on the high-contrast striping stairway. The distribution of fall-related events we observed between conditions likely did not occur by chance, with a probability of 0.04. Conclusions: These data support the premise that a vision-based strategy (ie, striping) may counteract fall risk associated with interstep riser height and tread depth variation. Possibly, perception and action elicited through the horizontal-vertical illusion (striping) may have a positive impact on the incidence of fall-related events in the presence of high interstep riser height and depth variation. The findings of this study suggest that contrast enhancement (ie, striping) may be a simple and effective way to reduce the risk of falls associated with interstep variation, highlighting the potential for this approach to make a significant impact on fall prevention efforts. %M 39773894 %R 10.2196/60622 %U https://www.i-jmr.org/2025/1/e60622 %U https://doi.org/10.2196/60622 %U http://www.ncbi.nlm.nih.gov/pubmed/39773894 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e60176 %T Work Systems Analysis of Emergency Nurse Patient Flow Management Using the Systems Engineering Initiative for Patient Safety Model: Applying Findings From a Grounded Theory Study %A Benjamin,Ellen %A Giuliano,Karen K %K patient flow %K throughput %K emergency department %K nursing %K emergency nursing %K organizing work %K cognitive work %K human factors %K ergonomics %K SEIPS model %D 2024 %7 10.12.2024 %9 %J JMIR Hum Factors %G English %X Background: Emergency nurses actively manage the flow of patients through emergency departments. Patient flow management is complex, cognitively demanding work that shapes the timeliness, efficiency, and safety of patient care. Research exploring nursing patient flow management is limited. A comprehensive analysis of emergency nursing work systems is needed to improve patient flow work processes. Objective: The aim of this paper is to describe the work system factors that impact emergency nurse patient flow management using the System Engineering Initiative for Patient Safety model. Methods: This study used grounded theory methodologies. Data were collected through multiple rounds of focus groups and interviews with 27 emergency nurse participants and 64 hours of participant observation across 4 emergency departments between August 2022 and February 2023. Data were analyzed using coding, constant comparative analysis, and memo-writing. Emergent themes were organized according to the first component of the System Engineering Initiative for Patient Safety model, the work system. Results: Patient flow management is impacted by diverse factors, including personal nursing characteristics; tools and technology; external factors; and the emergency department’s physical and socio-organizational environment. Participants raised concerns about the available technology’s functionality, usability, and accessibility; departmental capacity and layout; resource levels across the health care system; and interdepartmental teamwork. Other noteworthy findings include obscurity and variability across departments’ staff roles titles, functions, and norms; the degree of provider involvement in patient flow management decisions; and management’s enforcement of timing metrics. Conclusions: There are significant barriers to the work of emergency patient flow management. More research is needed to measure the impact of these human factors on patient flow outcomes. Collaboration between health care administrators, human factors engineers, and nurses is needed to improve emergency nurse work systems. %R 10.2196/60176 %U https://humanfactors.jmir.org/2024/1/e60176 %U https://doi.org/10.2196/60176 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e55185 %T A Prediction Model to Identify Clinically Relevant Medication Discrepancies at the Emergency Department (MED-REC Predictor): Development and Validation Study %A Van De Sijpe,Greet %A Gijsen,Matthias %A Van der Linden,Lorenz %A Strouven,Stephanie %A Simons,Eline %A Martens,Emily %A Persan,Nele %A Grootaert,Veerle %A Foulon,Veerle %A Casteels,Minne %A Verelst,Sandra %A Vanbrabant,Peter %A De Winter,Sabrina %A Spriet,Isabel %+ Pharmacy Department, University Hospitals Leuven, Herestraat 49, Leuven, 3000, Belgium, 32 16345969, greet.vandesijpe@uzleuven.be %K medication reconciliation %K medication discrepancy %K emergency department %K prediction model %K risk stratification %K MED-REC predictor %K MED-REC %K predictor %K patient %K medication %K hospital %K software-implemented prediction model %K software %K geographic validation %K geographic %D 2024 %7 27.11.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Many patients do not receive a comprehensive medication reconciliation, mostly owing to limited resources. We hence need an approach to identify those patients at the emergency department (ED) who are at increased risk for clinically relevant discrepancies. Objective: The aim of our study was to develop and externally validate a prediction model to identify patients at risk for at least 1 clinically relevant medication discrepancy upon ED presentation. Methods: A prospective, multicenter, observational study was conducted at the University Hospitals Leuven and General Hospital Sint-Jan Brugge-Oostende AV, Belgium. Medication histories were obtained from patients admitted to the ED between November 2017 and May 2022, and clinically relevant medication discrepancies were identified. Three distinct datasets were created for model development, temporal external validation, and geographic external validation. Multivariable logistic regression with backward stepwise selection was used to select the final model. The presence of at least 1 clinically relevant discrepancy was the dependent variable. The model was evaluated by measuring calibration, discrimination, classification, and net benefit. Results: We included 824, 350, and 119 patients in the development, temporal validation, and geographic validation dataset, respectively. The final model contained 8 predictors, for example, age, residence before admission, number of drugs, and number of drugs of certain drug classes based on Anatomical Therapeutic Chemical coding. Temporal validation showed excellent calibration with a slope of 1.09 and an intercept of 0.18. Discrimination was moderate with a c-index (concordance index) of 0.67 (95% CI 0.61-0.73). In the geographic validation dataset, the calibration slope and intercept were 1.35 and 0.83, respectively, and the c-index was 0.68 (95% CI 0.58-0.78). The model showed net benefit over a range of clinically reasonable threshold probabilities and outperformed other selection criteria. Conclusions: Our software-implemented prediction model shows moderate performance, outperforming random or typical selection criteria for medication reconciliation. Depending on available resources, the probability threshold can be customized to increase either the specificity or the sensitivity of the model. %M 39602806 %R 10.2196/55185 %U https://www.jmir.org/2024/1/e55185 %U https://doi.org/10.2196/55185 %U http://www.ncbi.nlm.nih.gov/pubmed/39602806 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e57486 %T Identification of a Susceptible and High-Risk Population for Postoperative Systemic Inflammatory Response Syndrome in Older Adults: Machine Learning–Based Predictive Model %A Mai,Haiyan %A Lu,Yaxin %A Fu,Yu %A Luo,Tongsen %A Li,Xiaoyue %A Zhang,Yihan %A Liu,Zifeng %A Zhang,Yuenong %A Zhou,Shaoli %A Chen,Chaojin %+ Department of Anesthesiology, The Third Affiliated Hospital of Sun Yat-sen University, 600 Tianhe Road, Tianhe District, Guangzhou, 510631, China, 86 020 85253333, chenchj28@mail.sysu.edu.cn %K older adult patients %K postoperative SIRS %K sepsis %K machine learning %K prediction model %D 2024 %7 22.11.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Systemic inflammatory response syndrome (SIRS) is a serious postoperative complication among older adult surgical patients that frequently develops into sepsis or even death. Notably, the incidences of SIRS and sepsis steadily increase with age. It is important to identify the risk of postoperative SIRS for older adult patients at a sufficiently early stage, which would allow preemptive individualized enhanced therapy to be conducted to improve the prognosis of older adult patients. In recent years, machine learning (ML) models have been deployed by researchers for many tasks, including disease prediction and risk stratification, exhibiting good application potential. Objective: We aimed to develop and validate an individualized predictive model to identify susceptible and high-risk populations for SIRS in older adult patients to instruct appropriate early interventions. Methods: Data for surgical patients aged ≥65 years from September 2015 to September 2020 in 3 independent medical centers were retrieved and analyzed. The eligible patient cohort in the Third Affiliated Hospital of Sun Yat-sen University was randomly separated into an 80% training set (2882 patients) and a 20% internal validation set (720 patients). We developed 4 ML models to predict postoperative SIRS. The area under the receiver operating curve (AUC), F1 score, Brier score, and calibration curve were used to evaluate the model performance. The model with the best performance was further validated in the other 2 independent data sets involving 844 and 307 cases, respectively. Results: The incidences of SIRS in the 3 medical centers were 24.3% (876/3602), 29.6% (250/844), and 6.5% (20/307), respectively. We identified 15 variables that were significantly associated with postoperative SIRS and used in 4 ML models to predict postoperative SIRS. A balanced cutoff between sensitivity and specificity was chosen to ensure as high a true positive as possible. The random forest classifier (RF) model showed the best overall performance to predict postoperative SIRS, with an AUC of 0.751 (95% CI 0.709-0.793), sensitivity of 0.682, specificity of 0.681, and F1 score of 0.508 in the internal validation set and higher AUCs in the external validation-1 set (0.759, 95% CI 0.723-0.795) and external validation-2 set (0.804, 95% CI 0.746-0.863). Conclusions: We developed and validated a generalizable RF model to predict postoperative SIRS in older adult patients, enabling clinicians to screen susceptible and high-risk patients and implement early individualized interventions. An online risk calculator to make the RF model accessible to anesthesiologists and peers around the world was developed. %M 39501984 %R 10.2196/57486 %U https://www.jmir.org/2024/1/e57486 %U https://doi.org/10.2196/57486 %U http://www.ncbi.nlm.nih.gov/pubmed/39501984 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e65728 %T Evaluation of an App-Based Mobile Triage System for Mass Casualty Incidents: Within-Subjects Experimental Study %A Schmollinger,Martin %A Gerstner,Jessica %A Stricker,Eric %A Muench,Alexander %A Breckwoldt,Benjamin %A Sigle,Manuel %A Rosenberger,Peter %A Wunderlich,Robert %+ University Department of Anesthesiology and Intensive Care Medicine, University Hospital Tübingen, Eberhard Karls University, Hoppe-Seyler-Str.3, Tübingen, 72076, Germany, 49 70712981116, Robert.Wunderlich@med.uni-tuebingen.de %K disaster medicine %K mass casualty incidents %K digitalization %K triage %K Germany %K mobile triage app %D 2024 %7 21.11.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Digitalization in disaster medicine holds significant potential to accelerate rescue operations and ultimately save lives. Mass casualty incidents demand rapid and accurate information management to coordinate effective responses. Currently, first responders manually record triage results on patient cards, and brief information is communicated to the command post via radio communication. Although this process is widely used in practice, it involves several time-consuming and error-prone tasks. To address these issues, we designed, implemented, and evaluated an app-based mobile triage system. This system allows users to document responder details, triage categories, injury patterns, GPS locations, and other important information, which can then be transmitted automatically to the incident commanders. Objective: This study aims to design and evaluate an app-based mobile system as a triage and coordination tool for emergency and disaster medicine, comparing its effectiveness with the conventional paper-based system. Methods: A total of 38 emergency medicine personnel participated in a within-subject experimental study, completing 2 triage sessions with 30 patient cards each: one session using the app-based mobile system and the other using the paper-based tool. The accuracy of the triages and the time taken for each session were measured. Additionally, we implemented the User Experience Questionnaire along with other items to assess participants’ subjective ratings of the 2 triage tools. Results: Our 2 (triage tool) × 2 (tool order) mixed multivariate analysis of variance revealed a significant main effect for the triage tool (P<.001). Post hoc analyses indicated that participants were significantly faster (P<.001) and more accurate (P=.005) in assigning patients to the correct triage category when using the app-based mobile system compared with the paper-based tool. Additionally, analyses showed significantly better subjective ratings for the app-based mobile system compared with the paper-based tool, in terms of both school grading (P<.001) and across all 6 scales of the User Experience Questionnaire (all P<.001). Of the 38 participants, 36 (95%) preferred the app-based mobile system. There was no significant main effect for tool order (P=.24) or session order (P=.06) in our model. Conclusions: Our findings demonstrate that the app-based mobile system not only matches the performance of the conventional paper-based tool but may even surpass it in terms of efficiency and usability. This advancement could further enhance the potential of digitalization to optimize processes in disaster medicine, ultimately leading to the possibility of saving more lives. %M 39474975 %R 10.2196/65728 %U https://www.jmir.org/2024/1/e65728 %U https://doi.org/10.2196/65728 %U http://www.ncbi.nlm.nih.gov/pubmed/39474975 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e52301 %T The Added Value of Using Video in Out-of-Hours Primary Care Telephone Triage Among General Practitioners: Cross-Sectional Survey Study %A Nebsbjerg,Mette Amalie %A Bomholt,Katrine Bjørnshave %A Vestergaard,Claus Høstrup %A Christensen,Morten Bondo %A Huibers,Linda %K primary health care %K after-hours care %K referral and consultation %K general practitioners %K triage %K remote consultation %K telemedicine %D 2024 %7 15.11.2024 %9 %J JMIR Hum Factors %G English %X Background: Many countries have introduced video consultations in primary care both inside and outside of office hours. Despite some relational and technical limitations, general practitioners (GPs) have reported the benefits of video use in the daytime as it provides faster and more flexible access to health care. Studies have indicated that video may be specifically valuable in out-of-hours primary care (OOH-PC), but additional information on the added value of video use is needed. Objective: This study aimed to investigate triage GPs’ perspectives on video use in GP-led telephone triage in OOH-PC by exploring their reasons for choosing video use and its effect on triage outcome, the decision-making process, communication, and invested time. Methods: We conducted a cross-sectional questionnaire study among GPs performing telephone triage in the OOH-PC service in the Central Denmark Region from September 5, 2022, until December 21, 2022. The questionnaire was integrated into the electronic patient registration system as a pop-up window appearing after every third video contact. This setup automatically linked background data on the contact, patient, and GP to the questionnaire data. We used descriptive analyses to describe reasons for and effects of video use and GP evaluation, stratified by patient age. Results: A total of 2456 questionnaires were completed. The most frequent reasons for video use were to assess the severity (n=1951, 79.4%), to increase the probability of self-care (n=1279, 52.1%), and to achieve greater certainty in decision-making (n=810, 33%) (multiple answers were possible for reasons of video use). In 61.9% (n=1516) of contacts, the triage GPs anticipated that the contact would have resulted in a different triage outcome if video had not been used. Use of video resulted in a downgrading of severity level in 88.3% (n=1338) of cases. Triage GPs evaluated the use of video as positive in terms of their decision-making process (n=2358, 96%), communication (n=2214, 90.1%), and invested time (n=2391, 97.3%). Conclusions: Triage GPs assessed that the use of video in telephone triage did affect their triage outcome, mostly by downgrading the level of care needed. The participating triage GPs found video in OOH-PC to be of added value, particularly in communication and the decision-making process. %R 10.2196/52301 %U https://humanfactors.jmir.org/2024/1/e52301 %U https://doi.org/10.2196/52301 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e59634 %T An Electronic Medical Record–Based Prognostic Model for Inpatient Falls: Development and Internal-External Cross-Validation %A Parsons,Rex %A Blythe,Robin %A Cramb,Susanna %A Abdel-Hafez,Ahmad %A McPhail,Steven %+ Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Queensland University of Technology, 60 Musk Ave, Kelvin Grove, 4059, Australia, 61 31380905, rex.parsons@hdr.qut.edu.au %K clinical prediction model %K falls %K patient safety %K prognostic %K electronic medical record %K EMR %K intervention %K hospital %K risk assessment %K clinical decision %K support system %K in-hospital fall %K survival model %K inpatient falls %D 2024 %7 13.11.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Effective fall prevention interventions in hospitals require appropriate allocation of resources early in admission. To address this, fall risk prediction tools and models have been developed with the aim to provide fall prevention strategies to patients at high risk. However, fall risk assessment tools have typically been inaccurate for prediction, ineffective in prevention, and time-consuming to complete. Accurate, dynamic, individualized estimates of fall risk for admitted patients using routinely recorded data may assist in prioritizing fall prevention efforts. Objective: The objective of this study was to develop and validate an accurate and dynamic prognostic model for inpatient falls among a cohort of patients using routinely recorded electronic medical record data. Methods: We used routinely recorded data from 5 Australian hospitals to develop and internally-externally validate a prediction model for inpatient falls using a Cox proportional hazards model with time-varying covariates. The study cohort included patients admitted during 2018-2021 to any ward, with no age restriction. Predictors used in the model included admission-related administrative data, length of stay, and number of previous falls during the admission (updated every 12 hours up to 14 days after admission). Model calibration was assessed using Poisson regression and discrimination using the area under the time-dependent receiver operating characteristic curve. Results: There were 1,107,556 inpatient admissions, 6004 falls, and 5341 unique fallers. The area under the time-dependent receiver operating characteristic curve was 0.899 (95% CI 0.88-0.91) at 24 hours after admission and declined throughout admission (eg, 0.765, 95% CI 0.75-0.78 on the seventh day after admission). Site-dependent overestimation and underestimation of risk was observed on the calibration plots. Conclusions: Using a large dataset from multiple hospitals and robust methods to model development and validation, we developed a prognostic model for inpatient falls. It had high discrimination, suggesting the model has the potential for operationalization in clinical decision support for prioritizing inpatients for fall prevention. Performance was site dependent, and model recalibration may lead to improved performance. %M 39536309 %R 10.2196/59634 %U https://www.jmir.org/2024/1/e59634 %U https://doi.org/10.2196/59634 %U http://www.ncbi.nlm.nih.gov/pubmed/39536309 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e64674 %T System-Wide, Electronic Health Record–Based Medication Alerts for Appropriate Prescribing of Direct Oral Anticoagulants: Pilot Randomized Controlled Trial %A Smith,Shawna N %A Lanham,Michael S M %A Seagull,F Jacob %A Fabbri,Morris %A Dorsch,Michael P %A Jennings,Kathleen %A Barnes,Geoffrey %+ Division of Cardiovascular Medicine, Department of Internal Medicine, University of Michigan, 1425 E Ann St, Ann Arbor, MI, 48109, United States, 1 8882871082, gbarnes@umich.edu %K direct oral anticoagulants %K electronic health record %K medication safety %K prescribing errors %K pilot randomized controlled trial %K alert system optimization %K clinical decision support %K EHR %K randomized controlled trial %K RCT %K oral anticoagulants %D 2024 %7 8.11.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: While direct oral anticoagulants (DOACs) have improved oral anticoagulation management, inappropriate prescribing remains prevalent and leads to adverse drug events. Antithrombotic stewardship programs seek to enhance DOAC prescribing but require scalable and sustainable strategies. Objective: We present a pilot, prescriber-level randomized controlled trial to assess the effectiveness of electronic health record (EHR)–based medication alerts in a large health system. Methods: The pilot assessed prescriber responses to alerts for initial DOAC prescription errors (apixaban and rivaroxaban). A user-centered, multistage design process informed alert development, emphasizing clear indication, appropriate dosing based on renal function, and drug-drug interactions. Alerts appeared whenever a DOAC was being prescribed in a way that did not follow package label instructions. Clinician responses measured acceptability, accuracy, feasibility, and utilization of the alerts. Results: The study ran from August 1, 2022, through April 30, 2023. Only 1 prescriber requested trial exclusion, demonstrating acceptability. The error rate for false alerts due to incomplete data was 6.6% (16/243). Two scenarios with alert design and/or execution errors occurred but were quickly identified and resolved, underlining the importance of a responsive quality assurance process in EHR-based interventions. Trial feasibility issues related to alert-data capture were identified and resolved. Trial feasibility was also assessed with balanced randomization of prescribers and the inclusion of various alerts across both medications. Assessing utilization, 34.2% (83/243) of the encounters (with 134 prescribers) led to a prescription change. Conclusions: The pilot implementation study demonstrated the acceptability, accuracy, feasibility, and estimates of the utilization of EHR-based medication alerts for DOAC prescriptions and successfully established just-in-time randomization of prescribing clinicians. This pilot study sets the stage for large-scale, randomized implementation evaluations of EHR-based alerts to improve medication safety. Trial Registration: ClinicalTrials.gov NCT05351749; https://clinicaltrials.gov/study/NCT05351749 %M 39514247 %R 10.2196/64674 %U https://formative.jmir.org/2024/1/e64674 %U https://doi.org/10.2196/64674 %U http://www.ncbi.nlm.nih.gov/pubmed/39514247 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e56949 %T Views and Uses of Sepsis Digital Alerts in National Health Service Trusts in England: Qualitative Study With Health Care Professionals %A Lazzarino,Runa %A Borek,Aleksandra J %A Honeyford,Kate %A Welch,John %A Brent,Andrew J %A Kinderlerer,Anne %A Cooke,Graham %A Patil,Shashank %A Gordon,Anthony %A Glampson,Ben %A Goodman,Philippa %A Ghazal,Peter %A Daniels,Ron %A Costelloe,Céire E %A Tonkin-Crine,Sarah %+ Nuffield Department of Primary Care Health Sciences, Medical Division, University of Oxford, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, United Kingdom, 44 01865 289300, r.lazzarino@ymail.com %K digital alerts %K electronic health records %K computerized clinical decision support systems %K sepsis %K patient deterioration %K decision-making %K secondary care %K emergency care %K intensive care %K England %K qualitative study %D 2024 %7 15.10.2024 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Sepsis is a common cause of serious illness and death. Sepsis management remains challenging and suboptimal. To support rapid sepsis diagnosis and treatment, screening tools have been embedded into hospital digital systems to appear as digital alerts. The implementation of digital alerts to improve the management of sepsis and deterioration is a complex intervention that has to fit with team workflow and the views and practices of hospital staff. Despite the importance of human decision-making and behavior in optimal implementation, there are limited qualitative studies that explore the views and experiences of health care professionals regarding digital alerts as sepsis or deterioration computerized clinician decision support systems (CCDSSs). Objective: This study aims to explore the views and experiences of health care professionals on the use of sepsis or deterioration CCDSSs and to identify barriers and facilitators to their implementation and use in National Health Service (NHS) hospitals. Methods: We conducted a qualitative, multisite study with unstructured observations and semistructured interviews with health care professionals from emergency departments, outreach teams, and intensive or acute units in 3 NHS hospital trusts in England. Data from both interviews and observations were analyzed together inductively using thematic analysis. Results: A total of 22 health care professionals were interviewed, and 12 observation sessions were undertaken. A total of four themes regarding digital alerts were identified: (1) support decision-making as nested in electronic health records, but never substitute professionals’ knowledge and experience; (2) remind to take action according to the context, such as the hospital unit and the job role; (3) improve the alerts and their introduction, by making them more accessible, easy to use, not intrusive, more accurate, as well as integrated across the whole health care system; and (4) contextual factors affecting views and use of alerts in the NHS trusts. Digital alerts are more optimally used in general hospital units with a lower senior decision maker:patient ratio and by health care professionals with experience of a similar technology. Better use of the alerts was associated with quality improvement initiatives and continuous sepsis training. The trusts’ features, such as the presence of a 24/7 emergency outreach team, good technological resources, and staffing and teamwork, favored a more optimal use. Conclusions: Trust implementation of sepsis or deterioration CCDSSs requires support on multiple levels and at all phases of the intervention, starting from a prego-live analysis addressing organizational needs and readiness. Advancements toward minimally disruptive and smart digital alerts as sepsis or deterioration CCDSSs, which are more accurate and specific but at the same time scalable and accessible, require policy changes and investments in multidisciplinary research. %M 39405513 %R 10.2196/56949 %U https://humanfactors.jmir.org/2024/1/e56949 %U https://doi.org/10.2196/56949 %U http://www.ncbi.nlm.nih.gov/pubmed/39405513 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e58144 %T Co-Designing Remote Patient Monitoring Technologies for Inpatients: Systematic Review %A Sumner,Jennifer %A Tan,Si Ying %A Wang,Yuchen %A Keck,Camille Hui Sze %A Xin Lee,Eunice Wei %A Chew,Emily Hwee Hoon %A Yip,Alexander Wenjun %+ Medical Affairs-Research, Alexandra Hospital, 378 Alexandra Road, Singapore, 159964, Singapore, 65 6908 2222, jennyssumner@gmail.com %K remote patient monitoring %K technology %K inpatient %K care transition %K systematic review %K health technology %K patient-centeredness %K technology use %K effectiveness %K study design %K assessment %K pilot testing %K health care %K technologies %K terminology %K quality and consistency %K telehealth %K telemonitoring %D 2024 %7 15.10.2024 %9 Review %J J Med Internet Res %G English %X Background: The co-design of health technology enables patient-centeredness and can help reduce barriers to technology use. Objective: The study objectives were to identify what remote patient monitoring (RPM) technology has been co-designed for inpatients and how effective it is, to identify and describe the co-design approaches used to develop RPM technologies and in which contexts they emerge, and to identify and describe barriers and facilitators of the co-design process. Methods: We conducted a systematic review of co-designed RPM technologies for inpatients or for the immediate postdischarge period and assessed (1) their effectiveness in improving health outcomes, (2) the co-design approaches used, and (3) barriers and facilitators to the co-design process. Eligible records included those involving stakeholders co-designing RPM technology for use in the inpatient setting or during the immediate postdischarge period. Searches were limited to the English language within the last 10 years. We searched MEDLINE, Embase, CINAHL, PsycInfo, and Science Citation Index (Web of Science) in April 2023. We used the Joanna Briggs Institute critical appraisal checklist for quasi-experimental studies and qualitative research. Findings are presented narratively. Results: We screened 3334 reports, and 17 projects met the eligibility criteria. Interventions were designed for pre- and postsurgical monitoring (n=6), intensive care monitoring (n=2), posttransplant monitoring (n=3), rehabilitation (n=4), acute inpatients (n=1), and postpartum care (n=1). No projects evaluated the efficacy of their co-designed RPM technology. Three pilot studies reported clinical outcomes; their risk of bias was low to moderate. Pilot evaluations (11/17) also focused on nonclinical outcomes such as usability, usefulness, feasibility, and satisfaction. Common co-design approaches included needs assessment or ideation (16/17), prototyping (15/17), and pilot testing (11/17). The most commonly reported challenge to the co-design process was the generalizability of findings, closely followed by time and resource constraints and participant bias. Stakeholders’ perceived value was the most frequently reported enabler of co-design. Other enablers included continued stakeholder engagement and methodological factors (ie, the use of flexible mixed method approaches and prototyping). Conclusions: Co-design methods can help enhance interventions’ relevance, usability, and adoption. While included studies measured usability, satisfaction, and acceptability—critical factors for successful implementation and uptake—we could not determine the clinical effectiveness of co-designed RPM technologies. A stronger commitment to clinical evaluation is needed. Studies’ use of diverse co-design approaches can foster stakeholder inclusivity, but greater standardization in co-design terminology is needed to improve the quality and consistency of co-design research. %M 39405106 %R 10.2196/58144 %U https://www.jmir.org/2024/1/e58144 %U https://doi.org/10.2196/58144 %U http://www.ncbi.nlm.nih.gov/pubmed/39405106 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e54977 %T Designing an Intervention to Improve Medication Safety for Nursing Home Residents Based on Experiential Knowledge Related to Patient Safety Culture at the Nursing Home Front Line: Cocreative Process Study %A Juhl,Marie Haase %A Soerensen,Ann Lykkegaard %A Vardinghus-Nielsen,Henrik %A Mortensen,Lea Sinding %A Kolding Kristensen,Jette %A Olesen,Anne Estrup %+ Department of Clinical Pharmacology, Aalborg University Hospital, Mølleparkvej 8a, Gartnerboligen, ground floor, Aalborg, 9000, Denmark, 45 26281305, aneso@dcm.aau.dk %K intervention development %K nursing home %K frontline professionals %K medication safety %K quality improvement %K patient safety culture %K experiential knowledge %K cocreation %K resilient health care systems %K safety II perspective %K human resources %K integrated knowledge translation %D 2024 %7 9.10.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Despite years of attention, avoiding medication-related harm remains a global challenge. Nursing homes provide essential health care for frail older individuals, who often experience multiple chronic diseases and polypharmacy, increasing their risk of medication errors. Evidence of effective interventions to improve medication safety in these settings is inconclusive. Focusing on patient safety culture is a potential key to intervention development as it forms the foundation for overall patient safety and is associated with medication errors. Objective: This study aims to develop an intervention to improve medication safety for nursing home residents through a cocreative process guided by integrated knowledge translation and experience-based codesign. Methods: This study used a cocreative process guided by integrated knowledge translation and experience-based co-design principles. Evidence on patient safety culture was used as an inspirational source for exploration of medication safety. Data collection involved semistructured focus groups to generate experiential knowledge (stage 1) to inform intervention design in a multidisciplinary workshop (stage 2). Research validation engaging different types of research expertise and municipal managerial representatives in finalizing the intervention design was essential. Acceptance of the final intervention for evaluation was aimed for through contextualization focused on partnership with a municipal advisory board. An abductive, rapid qualitative analytical approach to data analysis was chosen using elements from analyzing in the present, addressing the time-dependent, context-bound aspects of the cocreative process. Results: Experiential knowledge was represented by three main themes: (1) closed systems and gaps between functions, (2) resource interpretation and untapped potential, and (3) community of medication safety and surveillance. The main themes informed the design of preliminary intervention components in a multidisciplinary workshop. An intervention design process focused on research validation in addition to contextualization resulted in the Safe Medication in Nursing Home Residents (SAME) intervention covering (1) campaign material visualizing key roles and responsibilities regarding medication for nursing home residents and (2) “Medication safety reflexive spaces” focused on social and health care assistants. Conclusions: The cocreative process successfully resulted in the multifaceted SAME intervention, grounded in lived experiences shared by some of the most important (but often underrepresented in research) stakeholders: frontline health care professionals and representatives of nursing home residents. This study brought attention toward closed systems related to functions in medication management and surveillance, not only informing the SAME intervention design but as opportunities for further exploration in future research. Evaluation of the intervention is an important next step. Overall, this study represents an important contribution to the complex field of medication safety. International Registered Report Identifier (IRRID): RR2-10.2196/43538 %M 39383532 %R 10.2196/54977 %U https://formative.jmir.org/2024/1/e54977 %U https://doi.org/10.2196/54977 %U http://www.ncbi.nlm.nih.gov/pubmed/39383532 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e58380 %T Enhancing Patient Safety Through an Integrated Internet of Things Patient Care System: Large Quasi-Experimental Study on Fall Prevention %A Wen,Ming-Huan %A Chen,Po-Yin %A Lin,Shirling %A Lien,Ching-Wen %A Tu,Sheng-Hsiang %A Chueh,Ching-Yi %A Wu,Ying-Fang %A Tan Cheng Kian,Kelvin %A Hsu,Yeh-Liang %A Bai,Dorothy %+ School of Gerontology and Long-Term Care, College of Nursing, Taipei Medical University, No.250, Wuxing Street, Xinyi District, Taipei, 110, Taiwan, 886 227361661 ext 6332, dbai@tmu.edu.tw %K patient safety %K falls %K fall prevention %K fall risk %K sensors %K Internet of Things %K bed-exit alert %K motion-sensing mattress system %K care quality %K quality improvement %K ubiquitous health %K mHealth %D 2024 %7 3.10.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: The challenge of preventing in-patient falls remains one of the most critical concerns in health care. Objective: This study aims to investigate the effect of an integrated Internet of Things (IoT) smart patient care system on fall prevention. Methods: A quasi-experimental study design is used. The smart patient care system is an integrated IoT system combining a motion-sensing mattress for bed-exit detection, specifying different types of patient calls, integrating a health care staff scheduling system, and allowing health care staff to receive and respond to alarms via mobile devices. Unadjusted and adjusted logistic regression models were used to investigate the relationship between the use of the IoT system and bedside falls compared with a traditional patient care system. Results: In total, 1300 patients were recruited from a medical center in Taiwan. The IoT patient care system detected an average of 13.5 potential falls per day without any false alarms, whereas the traditional system issued about 11 bed-exit alarms daily, with approximately 4 being false, effectively identifying 7 potential falls. The bedside fall incidence during hospitalization was 1.2% (n=8) in the traditional patient care system ward and 0.1% (n=1) in the smart ward. We found that the likelihood of bedside falls in wards with the IoT system was reduced by 88% (odds ratio 0.12, 95% CI 0.01-0.97; P=.047). Conclusions: The integrated IoT smart patient care system might prevent falls by assisting health care staff with efficient and resilient responses to bed-exit detection. Future product development and research are recommended to introduce IoT into patient care systems combining bed-exit alerts to prevent inpatient falls and address challenges in patient safety. %M 39361417 %R 10.2196/58380 %U https://www.jmir.org/2024/1/e58380 %U https://doi.org/10.2196/58380 %U http://www.ncbi.nlm.nih.gov/pubmed/39361417 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 8 %N %P e51198 %T Harnessing the Power of Complementarity Between Smart Tracking Technology and Associated Health Information Technologies: Longitudinal Study %A Tao,Youyou %A Zhu,Ruilin %A Wu,Dezhi %+ Department of Management Science, Lancaster University, Bailrigg, Lancaster, LA1 4YX, United Kingdom, 44 1524592938, ruilin.zhu@lancaster.ac.uk %K health IT %K smart tracking technology %K mobile IT %K health information exchange %K electronic health record %K readmission risk %K complementarity effects %K mobile phone %D 2024 %7 1.10.2024 %9 Original Paper %J JMIR Form Res %G English %X Background: Smart tracking technology (STT) that was applied for clinical use has the potential to reduce 30-day all-cause readmission risk through streamlining clinical workflows with improved accuracy, mobility, and efficiency. However, previously published literature has inadequately addressed the joint effects of STT for clinical use and its complementary health ITs (HITs) in this context. Furthermore, while previous studies have discussed the symbiotic and pooled complementarity effects among different HITs, there is a lack of evidence-based research specifically examining the complementarity effects between STT for clinical use and other relevant HITs. Objective: Through a complementarity theory lens, this study aims to examine the joint effects of STT for clinical use and 3 relevant HITs on 30-day all-cause readmission risk. These HITs are STT for supply chain management, mobile IT, and health information exchange (HIE). Specifically, this study examines whether the pooled complementarity effect exists between STT for clinical use and STT for supply chain management, and whether symbiotic complementarity effects exist between STT for clinical use and mobile IT and between STT for clinical use and HIE. Methods: This study uses a longitudinal in-patient dataset, including 879,122 in-patient hospital admissions for 347,949 patients in 61 hospitals located in Florida and New York in the United States, from 2014 to 2015. Logistic regression was applied to assess the effect of HITs on readmission risks. Time and hospital fixed effects were controlled in the regression model. Robust standard errors (SEs) were used to account for potential heteroskedasticity. These errors were further clustered at the patient level to consider possible correlations within the patient groups. Results: The interaction between STT for clinical use and STT for supply chain management, mobile IT, and HIE was negatively associated with 30-day readmission risk, with coefficients of –0.0352 (P=.003), –0.0520 (P<.001), and –0.0216 (P=.04), respectively. These results indicate that the pooled complementarity effect exists between STT for clinical use and STT for supply chain management, and symbiotic complementarity effects exist between STT for clinical use and mobile IT and between STT for clinical use and HIE. Furthermore, the joint effects of these HITs varied depending on the hospital affiliation and patients’ disease types. Conclusions: Our results reveal that while individual HIT implementations have varying impacts on 30-day readmission risk, their joint effects are often associated with a reduction in 30-day readmission risk. This study substantially contributes to HIT value literature by quantifying the complementarity effects among 4 different types of HITs: STT for clinical use, STT for supply chain management, mobile IT, and HIE. It further offers practical implications for hospitals to maximize the benefits of their complementary HITs in reducing the 30-day readmission risk in their respective care scenarios. %M 39353192 %R 10.2196/51198 %U https://formative.jmir.org/2024/1/e51198 %U https://doi.org/10.2196/51198 %U http://www.ncbi.nlm.nih.gov/pubmed/39353192 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e49691 %T Inefficient Processes and Associated Factors in Primary Care Nursing: System Configuration Analysis %A Tarver,Willi L %A Savoy,April %A Patel,Himalaya %A Weiner,Michael %A Holden,Richard J %+ School of Industrial Engineering, Purdue University, 799 W. Michigan St. ET 201, Indianapolis, IN, 46202, United States, 1 3172782194, asavoy@purdue.edu %K health information technology %K mobile devices %K nursing and nursing systems %K outpatient care %K SEIPS 2.0 %K work-system analysis %D 2024 %7 30.9.2024 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Industrywide, primary care nurses’ work is increasing in complexity and team orientation. Mobile health information technologies (HITs) designed to aid nurses with indirect care tasks, including charting, have had mixed success. Failed introductions of HIT may be explained by insufficient integration into nurses’ work processes, owing to an incomplete or incorrect understanding of the underlying work systems. Despite this need for context, published evidence has focused more on inpatient settings than on primary care. Objective: This study aims to characterize nurses’ and health technicians’ perceptions of process inefficiencies in the primary care setting and identify related work system factors. Methods: Guided by the Systems Engineering Initiative for Patient Safety (SEIPS) 2.0 model, we conducted an exploratory work system analysis with a convenience sample of primary care nurses and health technicians. Semistructured contextual interviews were conducted in 2 sets of primary care clinics in the Midwestern United States, one in an urban tertiary care center and the other in a rural community-based outpatient facility. Using directed qualitative content analysis of transcripts, we identified tasks participants perceived as frequent, redundant, or difficult, related processes, and recommendations for improvement. In addition, we conducted configuration analyses to identify associations between process inefficiencies and work system factors. Results: We interviewed a convenience sample of 20 primary care nurses and 2 health technicians, averaging approximately 12 years of experience in their current role. Across sites, participants perceived 2 processes, managing patient calls and clinic walk-in visits, as inefficient. Among work system factors, participants described organizational and technological factors associated with inefficiencies. For example, new organization policies to decrease patient waiting invoked frequent, repetitive, and difficult tasks, including chart review and check-in using tablet computers. Participants reported that issues with policy implementation and technology usability contributed to process inefficiencies. Organizational and technological factors were also perceived among participants as the most adaptable. Suggested technology changes included new tools for walk-in triage and patient self-reporting of symptoms. Conclusions: In response to changes to organizational policy and technology, without compensative changes elsewhere in their primary care work system, participants reported process adaptations. These adaptations indicate inefficient work processes. Understanding how the implementation of organizational policies affects other factors in the primary care work system may improve the quality of such implementations and, in turn, increase the effectiveness and efficiency of primary care nurse processes. Furthermore, the design and implementation of HIT interventions should consider influential work system factors and their effects on work processes. %M 39348682 %R 10.2196/49691 %U https://humanfactors.jmir.org/2024/1/e49691 %U https://doi.org/10.2196/49691 %U http://www.ncbi.nlm.nih.gov/pubmed/39348682 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e55099 %T Triage Accuracy and the Safety of User-Initiated Symptom Assessment With an Electronic Symptom Checker in a Real-Life Setting: Instrument Validation Study %A Liu,Ville %A Kaila,Minna %A Koskela,Tuomas %+ Faculty of Medicine, University of Helsinki, Ruusulankatu 21 B 32, Helsinki, 00250, Finland, 358 400642104, villeliu@hotmail.com %K nurse triage %K emergency department triage %K triage %K symptom assessment %K health services accessibility %K telemedicine %K eHealth %K remote consultation %K eHealth %K primary health care %K primary care %K urgent care %K health services research %K health services %D 2024 %7 26.9.2024 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Previous studies have evaluated the accuracy of the diagnostics of electronic symptom checkers (ESCs) and triage using clinical case vignettes. National Omaolo digital services (Omaolo) in Finland consist of an ESC for various symptoms. Omaolo is a medical device with a Conformité Européenne marking (risk class: IIa), based on Duodecim Clinical Decision Support, EBMEDS. Objective: This study investigates how well triage performed by the ESC nurse triage within the chief symptom list available in Omaolo (anal region symptoms, cough, diarrhea, discharge from the eye or watery or reddish eye, headache, heartburn, knee symptom or injury, lower back pain or injury, oral health, painful or blocked ear, respiratory tract infection, sexually transmitted disease, shoulder pain or stiffness or injury, sore throat or throat symptom, and urinary tract infection). In addition, the accuracy, specificity, sensitivity, and safety of the Omaolo ESC were assessed. Methods: This is a clinical validation study in a real-life setting performed at multiple primary health care (PHC) centers across Finland. The included units were of the walk-in model of primary care, where no previous phone call or contact was required. Upon arriving at the PHC center, users (patients) answered the ESC questions and received a triage recommendation; a nurse then assessed their triage. Findings on 877 patients were analyzed by matching the ESC recommendations with triage by the triage nurse. Results: Safe assessments by the ESC accounted for 97.6% (856/877; 95% CI 95.6%-98.0%) of all assessments made. The mean of the exact match for all symptom assessments was 53.7% (471/877; 95% CI 49.2%-55.9%). The mean value of the exact match or overly conservative but suitable for all (ESC’s assessment was 1 triage level higher than the nurse’s triage) symptom assessments was 66.6% (584/877; 95% CI 63.4%-69.7%). When the nurse concluded that urgent treatment was needed, the ESC’s exactly matched accuracy was 70.9% (244/344; 95% CI 65.8%-75.7%). Sensitivity for the Omaolo ESC was 62.6% and specificity of 69.2%. A total of 21 critical assessments were identified for further analysis: there was no indication of compromised patient safety. Conclusions: The primary objectives of this study were to evaluate the safety and to explore the accuracy, specificity, and sensitivity of the Omaolo ESC. The results indicate that the ESC is safe in a real-life setting when appraised with assessments conducted by triage nurses. Furthermore, the Omaolo ESC exhibits the potential to guide patients to appropriate triage destinations effectively, helping them to receive timely and suitable care. International Registered Report Identifier (IRRID): RR2-10.2196/41423 %M 39326038 %R 10.2196/55099 %U https://humanfactors.jmir.org/2024/1/e55099 %U https://doi.org/10.2196/55099 %U http://www.ncbi.nlm.nih.gov/pubmed/39326038 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e50935 %T Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study %A Tabaie,Azade %A Tran,Alberta %A Calabria,Tony %A Bennett,Sonita S %A Milicia,Arianna %A Weintraub,William %A Gallagher,William James %A Yosaitis,John %A Schubel,Laura C %A Hill,Mary A %A Smith,Kelly Michelle %A Miller,Kristen %+ Center for Biostatistics, Informatics, and Data Science, MedStar Health Research Institute, 3007 Tilden Street NW, Washington, DC, 20008, United States, 1 202 244 9810, azade.tabaie@medstar.net %K diagnostic error %K electronic health records %K machine learning %K natural language processing %K NLP %K mortality %K hospital %K risk %K length of stay %K patient harm %K diagnostic %K EHR %D 2024 %7 26.8.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Diagnostic errors are an underappreciated cause of preventable mortality in hospitals and pose a risk for severe patient harm and increase hospital length of stay. Objective: This study aims to explore the potential of machine learning and natural language processing techniques in improving diagnostic safety surveillance. We conducted a rigorous evaluation of the feasibility and potential to use electronic health records clinical notes and existing case review data. Methods: Safety Learning System case review data from 1 large health system composed of 10 hospitals in the mid-Atlantic region of the United States from February 2016 to September 2021 were analyzed. The case review outcome included opportunities for improvement including diagnostic opportunities for improvement. To supplement case review data, electronic health record clinical notes were extracted and analyzed. A simple logistic regression model along with 3 forms of logistic regression models (ie, Least Absolute Shrinkage and Selection Operator, Ridge, and Elastic Net) with regularization functions was trained on this data to compare classification performances in classifying patients who experienced diagnostic errors during hospitalization. Further, statistical tests were conducted to find significant differences between female and male patients who experienced diagnostic errors. Results: In total, 126 (7.4%) patients (of 1704) had been identified by case reviewers as having experienced at least 1 diagnostic error. Patients who had experienced diagnostic error were grouped by sex: 59 (7.1%) of the 830 women and 67 (7.7%) of the 874 men. Among the patients who experienced a diagnostic error, female patients were older (median 72, IQR 66-80 vs median 67, IQR 57-76; P=.02), had higher rates of being admitted through general or internal medicine (69.5% vs 47.8%; P=.01), lower rates of cardiovascular-related admitted diagnosis (11.9% vs 28.4%; P=.02), and lower rates of being admitted through neurology department (2.3% vs 13.4%; P=.04). The Ridge model achieved the highest area under the receiver operating characteristic curve (0.885), specificity (0.797), positive predictive value (PPV; 0.24), and F1-score (0.369) in classifying patients who were at higher risk of diagnostic errors among hospitalized patients. Conclusions: Our findings demonstrate that natural language processing can be a potential solution to more effectively identifying and selecting potential diagnostic error cases for review and therefore reducing the case review burden. %M 39186764 %R 10.2196/50935 %U https://www.jmir.org/2024/1/e50935 %U https://doi.org/10.2196/50935 %U http://www.ncbi.nlm.nih.gov/pubmed/39186764 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e55466 %T Development of Automated Triggers in Ambulatory Settings in Brazil: Protocol for a Machine Learning–Based Design Thinking Study %A Herrera,Claire Nierva %A Gimenes,Fernanda Raphael Escobar %A Herrera,João Paulo %A Cavalli,Ricardo %+ Fundamental of Nursing, Ribeirão Preto College of Nursing, University of São Paulo, Avenida dos Bandeirantes, 3900 Campus Universitário - Bairro Monte Alegre Ribeirão Preto, Ribeirão Preto, 14040902, Brazil, 55 1633153381, claire.nierher@usp.br %K machine learning %K ambulatory care %K patient safety %K medical records systems %K computerized %K patient safety %K technology %K quality of care %K automated triggers %K limitation %K predict %K potential risk %K outpatient %K ambulatory patient %K walk-in %K adverse events %K evidence-based %K preventive %K low-income countries %K middle-income countries %K data %K scarcity %K standardization %K quality intervention %D 2024 %7 12.8.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: The use of technologies has had a significant impact on patient safety and the quality of care and has increased globally. In the literature, it has been reported that people die annually due to adverse events (AEs), and various methods exist for investigating and measuring AEs. However, some methods have a limited scope, data extraction, and the need for data standardization. In Brazil, there are few studies on the application of trigger tools, and this study is the first to create automated triggers in ambulatory care. Objective: This study aims to develop a machine learning (ML)–based automated trigger for outpatient health care settings in Brazil. Methods: A mixed methods research will be conducted within a design thinking framework and the principles will be applied in creating the automated triggers, following the stages of (1) empathize and define the problem, involving observations and inquiries to comprehend both the user and the challenge at hand; (2) ideation, where various solutions to the problem are generated; (3) prototyping, involving the construction of a minimal representation of the best solutions; (4) testing, where user feedback is obtained to refine the solution; and (5) implementation, where the refined solution is tested, changes are assessed, and scaling is considered. Furthermore, ML methods will be adopted to develop automated triggers, tailored to the local context in collaboration with an expert in the field. Results: This protocol describes a research study in its preliminary stages, prior to any data gathering and analysis. The study was approved by the members of the organizations within the institution in January 2024 and by the ethics board of the University of São Paulo and the institution where the study will take place. in May 2024. As of June 2024, stage 1 commenced with data gathering for qualitative research. A separate paper focused on explaining the method of ML will be considered after the outcomes of stages 1 and 2 in this study. Conclusions: After the development of automated triggers in the outpatient setting, it will be possible to prevent and identify potential risks of AEs more promptly, providing valuable information. This technological innovation not only promotes advances in clinical practice but also contributes to the dissemination of techniques and knowledge related to patient safety. Additionally, health care professionals can adopt evidence-based preventive measures, reducing costs associated with AEs and hospital readmissions, enhancing productivity in outpatient care, and contributing to the safety, quality, and effectiveness of care provided. Additionally, in the future, if the outcome is successful, there is the potential to apply it in all units, as planned by the institutional organization. International Registered Report Identifier (IRRID): PRR1-10.2196/55466 %M 39133913 %R 10.2196/55466 %U https://www.researchprotocols.org/2024/1/e55466 %U https://doi.org/10.2196/55466 %U http://www.ncbi.nlm.nih.gov/pubmed/39133913 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 13 %N %P e58635 %T Perception of Medication Safety–Related Behaviors Among Different Age Groups: Web-Based Cross-Sectional Study %A Lang,Yan %A Chen,Kay-Yut %A Zhou,Yuan %A Kosmari,Ludmila %A Daniel,Kathryn %A Gurses,Ayse %A Young,Richard %A Arbaje,Alicia %A Xiao,Yan %+ Department of Business, State University of New York at Oneonta, 108 Ravine Pkwy, Oneonta, NY, 13820, United States, 1 607 436 3251, yan.lang@oneonta.edu %K medication safety %K patient engagement %K aged adults %K survey %K Amazon Mechanical Turk %K medication %K engagement %K older adults %K elderly %K safety %K United States %K USA %K crowdsourcing %K community %K patient portal %K primary care %K medications %K safety behavior %K younger adults %K age %K correlation %K statistical test %D 2024 %7 12.8.2024 %9 Original Paper %J Interact J Med Res %G English %X Background: Previous research and safety advocacy groups have proposed various behaviors for older adults to actively engage in medication safety. However, little is known about how older adults perceive the importance and reasonableness of these behaviors in ambulatory settings. Objective: This study aimed to assess older adults’ perceptions of the importance and reasonableness of 8 medication safety behaviors in ambulatory settings and compare their responses with those of younger adults. Methods: We conducted a survey of 1222 adults in the United States using crowdsourcing to evaluate patient behaviors that may enhance medication safety in community settings. A total of 8 safety behaviors were identified based on the literature, such as bringing medications to office visits, confirming medications at home, managing medication refills, using patient portals, organizing medications, checking medications, getting help, and knowing medications. Respondents were asked about their perception of the importance and reasonableness of these behaviors on a 5-point Likert rating scale in the context of collaboration with primary care providers. We assessed the relative ranking of behaviors in terms of importance and reasonableness and examined the association between these dimensions across age groups using statistical tests. Results: Of 1222 adult participants, 125 (10.2%) were aged 65 years or older. Most participants were White, college-educated, and had chronic conditions. Older adults rated all 8 behaviors significantly higher in both importance and reasonableness than did younger adults (P<.001 for combined behaviors). Confirming medications ranked highest in importance (mean score=3.78) for both age groups while knowing medications ranked highest in reasonableness (mean score=3.68). Using patient portals was ranked lowest in importance (mean score=3.53) and reasonableness (mean score=3.49). There was a significant correlation between the perceived importance and reasonableness of the identified behaviors, with coefficients ranging from 0.436 to 0.543 (all P<.001). Conclusions: Older adults perceived the identified safety behaviors as more important and reasonable than younger adults. However, both age groups considered a behavior highly recommended by professionals as the least important and reasonable. Patient engagement strategies, common and specific to age groups, should be considered to improve medication safety in ambulatory settings. %M 39133905 %R 10.2196/58635 %U https://www.i-jmr.org/2024/1/e58635 %U https://doi.org/10.2196/58635 %U http://www.ncbi.nlm.nih.gov/pubmed/39133905 %0 Journal Article %@ 2561-7605 %I JMIR Publications %V 7 %N %P e57402 %T Toward Safe and Confident Silver Drivers: Interview Study Investigating Older Adults’ Driving Practices %A Kim,Sunyoung %A Sivangula,Phaneendra %+ Department of Library and Information Science, Rutgers University, 4 Huntington Street, New Brunswick, NJ, 08901, United States, 1 8489327585, sk1897@comminfo.rutgers.edu %K older adults %K driving %K transportation %K healthy aging %K aging in place %K quality of life %D 2024 %7 12.8.2024 %9 Original Paper %J JMIR Aging %G English %X Background: As the aging population in the United States continues to increase rapidly, preserving the mobility and independence of older adults becomes increasingly critical for enabling aging in place successfully. While personal vehicular transport remains a popular choice among this demographic due to its provision of independence and control over their lives, age-related changes may heighten the risk of common driving errors and diminish driving abilities. Objective: This study aims to investigate the driving practices of older adults and their efforts to maintain safe and confident driving habits. Specifically, we sought to identify the factors that positively and negatively influence older adults’ driving performance and confidence, as well as the existing efforts put into sustaining their driving abilities. Methods: We recruited 20 adults aged ≥65 years who remained active drivers during the recruitment from the greater New York area. Then, we conducted semistructured interviews with them to examine their perceptions, needs, and challenges regarding safe and confident driving. Results: Our findings uncovered a notable disparity between older adults’ self-perceived driving skills and the challenges they face, particularly caused by age-related limitations and health conditions such as vision and memory declines and medication routines. Drawing on these findings, we proposed strategies to bridge this gap and empower older adults to drive safely and confidently, including fostering a realistic understanding of their capabilities, encouraging open dialogue regarding their driving, encouraging regular assessments, and increasing awareness of available resources. Conclusions: This study uncovered a noticeable disparity between the perceived driving competence of older adults and the actual challenges they confront while driving. This divergence underscores a significant need for better support beyond the existing aid available to preserve older adults’ driving skills. We hope that our recommendations will offer valuable insights for practitioners and scholars committed to enhancing the overall well-being and quality of life for older adults as they age in their homes. %M 39133531 %R 10.2196/57402 %U https://aging.jmir.org/2024/1/e57402 %U https://doi.org/10.2196/57402 %U http://www.ncbi.nlm.nih.gov/pubmed/39133531 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e53427 %T Pediatric Sedation Assessment and Management System (PSAMS) for Pediatric Sedation in China: Development and Implementation Report %A Zhu,Ziyu %A Liu,Lan %A Du,Min %A Ye,Mao %A Xu,Ximing %A Xu,Ying %K electronic data capture %K information systems %K pediatric sedation %K sedation management %K workflow optimization %D 2024 %7 7.8.2024 %9 %J JMIR Med Inform %G English %X Background: Recently, the growing demand for pediatric sedation services outside the operating room has imposed a heavy burden on pediatric centers in China. There is an urgent need to develop a novel system for improved sedation services. Objective: This study aimed to develop and implement a computerized system, the Pediatric Sedation Assessment and Management System (PSAMS), to streamline pediatric sedation services at a major children’s hospital in Southwest China. Methods: PSAMS was designed to reflect the actual workflow of pediatric sedation. It consists of 3 main components: server-hosted software; client applications on tablets and computers; and specialized devices like gun-type scanners, desktop label printers, and pulse oximeters. With the participation of a multidisciplinary team, PSAMS was developed and refined during its application in the sedation process. This study analyzed data from the first 2 years after the system’s deployment. Implementation (Results): From January 2020 to December 2021, a total of 127,325 sedations were performed on 85,281 patients using the PSAMS database. Besides basic variables imported from Hospital Information Systems (HIS), the PSAMS database currently contains 33 additional variables that capture comprehensive information from presedation assessment to postprocedural recovery. The recorded data from PSAMS indicates a one-time sedation success rate of 97.1% (50,752/52,282) in 2020 and 97.5% (73,184/75,043) in 2021. The observed adverse events rate was 3.5% (95% CI 3.4%‐3.7%) in 2020 and 2.8% (95% CI 2.7%-2.9%) in 2021. Conclusions: PSAMS streamlined the entire sedation workflow, reduced the burden of data collection, and laid a foundation for future cooperation of multiple pediatric health care centers. %R 10.2196/53427 %U https://medinform.jmir.org/2024/1/e53427 %U https://doi.org/10.2196/53427 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e46407 %T Evaluating Artificial Intelligence in Clinical Settings—Let Us Not Reinvent the Wheel %A Cresswell,Kathrin %A de Keizer,Nicolette %A Magrabi,Farah %A Williams,Robin %A Rigby,Michael %A Prgomet,Mirela %A Kukhareva,Polina %A Wong,Zoie Shui-Yee %A Scott,Philip %A Craven,Catherine K %A Georgiou,Andrew %A Medlock,Stephanie %A Brender McNair,Jytte %A Ammenwerth,Elske %+ Usher Institute, The University of Edinburgh, Usher Building, 5-7 Little France Road, Edinburgh, EH16 4UX, United Kingdom, 44 131 650 6984, kathrin.cresswell@ed.ac.uk %K artificial intelligence %K evaluation %K theory %K patient safety %K optimisation %K health care %K optimization %D 2024 %7 7.8.2024 %9 Viewpoint %J J Med Internet Res %G English %X Given the requirement to minimize the risks and maximize the benefits of technology applications in health care provision, there is an urgent need to incorporate theory-informed health IT (HIT) evaluation frameworks into existing and emerging guidelines for the evaluation of artificial intelligence (AI). Such frameworks can help developers, implementers, and strategic decision makers to build on experience and the existing empirical evidence base. We provide a pragmatic conceptual overview of selected concrete examples of how existing theory-informed HIT evaluation frameworks may be used to inform the safe development and implementation of AI in health care settings. The list is not exhaustive and is intended to illustrate applications in line with various stakeholder requirements. Existing HIT evaluation frameworks can help to inform AI-based development and implementation by supporting developers and strategic decision makers in considering relevant technology, user, and organizational dimensions. This can facilitate the design of technologies, their implementation in user and organizational settings, and the sustainability and scalability of technologies. %M 39110494 %R 10.2196/46407 %U https://www.jmir.org/2024/1/e46407 %U https://doi.org/10.2196/46407 %U http://www.ncbi.nlm.nih.gov/pubmed/39110494 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e48156 %T AI as a Medical Device Adverse Event Reporting in Regulatory Databases: Protocol for a Systematic Review %A Kale,Aditya U %A Dattani,Riya %A Tabansi,Ashley %A Hogg,Henry David Jeffry %A Pearson,Russell %A Glocker,Ben %A Golder,Su %A Waring,Justin %A Liu,Xiaoxuan %A Moore,David J %A Denniston,Alastair K %+ Institute of Inflammation and Ageing, College of Medical and Dental Sciences, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom, 44 1213713243, a.denniston@bham.ac.uk %K adverse event %K artificial intelligence %K regulatory science %K regulatory database %K safety issue %K feedback %K health care product %K artificial intelligence health technology %K reporting system %K safety %K medical devices %K safety monitoring %K risks %K descriptive analysis %D 2024 %7 11.7.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: The reporting of adverse events (AEs) relating to medical devices is a long-standing area of concern, with suboptimal reporting due to a range of factors including a failure to recognize the association of AEs with medical devices, lack of knowledge of how to report AEs, and a general culture of nonreporting. The introduction of artificial intelligence as a medical device (AIaMD) requires a robust safety monitoring environment that recognizes both generic risks of a medical device and some of the increasingly recognized risks of AIaMD (such as algorithmic bias). There is an urgent need to understand the limitations of current AE reporting systems and explore potential mechanisms for how AEs could be detected, attributed, and reported with a view to improving the early detection of safety signals. Objective: The systematic review outlined in this protocol aims to yield insights into the frequency and severity of AEs while characterizing the events using existing regulatory guidance. Methods: Publicly accessible AE databases will be searched to identify AE reports for AIaMD. Scoping searches have identified 3 regulatory territories for which public access to AE reports is provided: the United States, the United Kingdom, and Australia. AEs will be included for analysis if an artificial intelligence (AI) medical device is involved. Software as a medical device without AI is not within the scope of this review. Data extraction will be conducted using a data extraction tool designed for this review and will be done independently by AUK and a second reviewer. Descriptive analysis will be conducted to identify the types of AEs being reported, and their frequency, for different types of AIaMD. AEs will be analyzed and characterized according to existing regulatory guidance. Results: Scoping searches are being conducted with screening to begin in April 2024. Data extraction and synthesis will commence in May 2024, with planned completion by August 2024. The review will highlight the types of AEs being reported for different types of AI medical devices and where the gaps are. It is anticipated that there will be particularly low rates of reporting for indirect harms associated with AIaMD. Conclusions: To our knowledge, this will be the first systematic review of 3 different regulatory sources reporting AEs associated with AIaMD. The review will focus on real-world evidence, which brings certain limitations, compounded by the opacity of regulatory databases generally. The review will outline the characteristics and frequency of AEs reported for AIaMD and help regulators and policy makers to continue developing robust safety monitoring processes. International Registered Report Identifier (IRRID): PRR1-10.2196/48156 %M 38990628 %R 10.2196/48156 %U https://www.researchprotocols.org/2024/1/e48156 %U https://doi.org/10.2196/48156 %U http://www.ncbi.nlm.nih.gov/pubmed/38990628 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e50437 %T Considerations for Quality Control Monitoring of Machine Learning Models in Clinical Practice %A Faust,Louis %A Wilson,Patrick %A Asai,Shusaku %A Fu,Sunyang %A Liu,Hongfang %A Ruan,Xiaoyang %A Storlie,Curt %+ Robert D and Patricia E Kern Center for the Science of Health Care Delivery, Mayo Clinic, Mayo Clinic, 200 First St. SW, Rochester, MN, 55905, United States, 1 (507) 284 2511, Faust.Louis@mayo.edu %K artificial intelligence %K machine learning %K implementation science %K quality control %K monitoring %K patient safety %D 2024 %7 28.6.2024 %9 Viewpoint %J JMIR Med Inform %G English %X Integrating machine learning (ML) models into clinical practice presents a challenge of maintaining their efficacy over time. While existing literature offers valuable strategies for detecting declining model performance, there is a need to document the broader challenges and solutions associated with the real-world development and integration of model monitoring solutions. This work details the development and use of a platform for monitoring the performance of a production-level ML model operating in Mayo Clinic. In this paper, we aimed to provide a series of considerations and guidelines necessary for integrating such a platform into a team’s technical infrastructure and workflow. We have documented our experiences with this integration process, discussed the broader challenges encountered with real-world implementation and maintenance, and included the source code for the platform. Our monitoring platform was built as an R shiny application, developed and implemented over the course of 6 months. The platform has been used and maintained for 2 years and is still in use as of July 2023. The considerations necessary for the implementation of the monitoring platform center around 4 pillars: feasibility (what resources can be used for platform development?); design (through what statistics or models will the model be monitored, and how will these results be efficiently displayed to the end user?); implementation (how will this platform be built, and where will it exist within the IT ecosystem?); and policy (based on monitoring feedback, when and what actions will be taken to fix problems, and how will these problems be translated to clinical staff?). While much of the literature surrounding ML performance monitoring emphasizes methodological approaches for capturing changes in performance, there remains a battery of other challenges and considerations that must be addressed for successful real-world implementation. %M 38941140 %R 10.2196/50437 %U https://medinform.jmir.org/2024/1/e50437 %U https://doi.org/10.2196/50437 %U http://www.ncbi.nlm.nih.gov/pubmed/38941140 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e51614 %T Detecting Algorithmic Errors and Patient Harms for AI-Enabled Medical Devices in Randomized Controlled Trials: Protocol for a Systematic Review %A Kale,Aditya U %A Hogg,Henry David Jeffry %A Pearson,Russell %A Glocker,Ben %A Golder,Su %A Coombe,April %A Waring,Justin %A Liu,Xiaoxuan %A Moore,David J %A Denniston,Alastair K %+ Institute of Inflammation and Ageing, University of Birmingham, Edgbaston, Birmingham, B15 2TT, United Kingdom, 44 1213713264, a.denniston@bham.ac.uk %K patient safety %K adverse events %K randomized controlled trials %K medical device %K systematic review %K algorithmic %K artificial intelligence %K AI %K AI health technology %K safety %K algorithm error %D 2024 %7 28.6.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Artificial intelligence (AI) medical devices have the potential to transform existing clinical workflows and ultimately improve patient outcomes. AI medical devices have shown potential for a range of clinical tasks such as diagnostics, prognostics, and therapeutic decision-making such as drug dosing. There is, however, an urgent need to ensure that these technologies remain safe for all populations. Recent literature demonstrates the need for rigorous performance error analysis to identify issues such as algorithmic encoding of spurious correlations (eg, protected characteristics) or specific failure modes that may lead to patient harm. Guidelines for reporting on studies that evaluate AI medical devices require the mention of performance error analysis; however, there is still a lack of understanding around how performance errors should be analyzed in clinical studies, and what harms authors should aim to detect and report. Objective: This systematic review will assess the frequency and severity of AI errors and adverse events (AEs) in randomized controlled trials (RCTs) investigating AI medical devices as interventions in clinical settings. The review will also explore how performance errors are analyzed including whether the analysis includes the investigation of subgroup-level outcomes. Methods: This systematic review will identify and select RCTs assessing AI medical devices. Search strategies will be deployed in MEDLINE (Ovid), Embase (Ovid), Cochrane CENTRAL, and clinical trial registries to identify relevant papers. RCTs identified in bibliographic databases will be cross-referenced with clinical trial registries. The primary outcomes of interest are the frequency and severity of AI errors, patient harms, and reported AEs. Quality assessment of RCTs will be based on version 2 of the Cochrane risk-of-bias tool (RoB2). Data analysis will include a comparison of error rates and patient harms between study arms, and a meta-analysis of the rates of patient harm in control versus intervention arms will be conducted if appropriate. Results: The project was registered on PROSPERO in February 2023. Preliminary searches have been completed and the search strategy has been designed in consultation with an information specialist and methodologist. Title and abstract screening started in September 2023. Full-text screening is ongoing and data collection and analysis began in April 2024. Conclusions: Evaluations of AI medical devices have shown promising results; however, reporting of studies has been variable. Detection, analysis, and reporting of performance errors and patient harms is vital to robustly assess the safety of AI medical devices in RCTs. Scoping searches have illustrated that the reporting of harms is variable, often with no mention of AEs. The findings of this systematic review will identify the frequency and severity of AI performance errors and patient harms and generate insights into how errors should be analyzed to account for both overall and subgroup performance. Trial Registration: PROSPERO CRD42023387747; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387747 International Registered Report Identifier (IRRID): PRR1-10.2196/51614 %M 38941147 %R 10.2196/51614 %U https://www.researchprotocols.org/2024/1/e51614 %U https://doi.org/10.2196/51614 %U http://www.ncbi.nlm.nih.gov/pubmed/38941147 %0 Journal Article %@ 2369-3762 %I %V 10 %N %P e58758 %T Evaluation of ChatGPT-Generated Differential Diagnosis for Common Diseases With Atypical Presentation: Descriptive Research %A Shikino,Kiyoshi %A Shimizu,Taro %A Otsuka,Yuki %A Tago,Masaki %A Takahashi,Hiromizu %A Watari,Takashi %A Sasaki,Yosuke %A Iizuka,Gemmei %A Tamura,Hiroki %A Nakashima,Koichi %A Kunitomo,Kotaro %A Suzuki,Morika %A Aoyama,Sayaka %A Kosaka,Shintaro %A Kawahigashi,Teiko %A Matsumoto,Tomohiro %A Orihara,Fumina %A Morikawa,Toru %A Nishizawa,Toshinori %A Hoshina,Yoji %A Yamamoto,Yu %A Matsuo,Yuichiro %A Unoki,Yuto %A Kimura,Hirofumi %A Tokushima,Midori %A Watanuki,Satoshi %A Saito,Takuma %A Otsuka,Fumio %A Tokuda,Yasuharu %K atypical presentation %K ChatGPT %K common disease %K diagnostic accuracy %K diagnosis %K patient safety %D 2024 %7 21.6.2024 %9 %J JMIR Med Educ %G English %X Background: The persistence of diagnostic errors, despite advances in medical knowledge and diagnostics, highlights the importance of understanding atypical disease presentations and their contribution to mortality and morbidity. Artificial intelligence (AI), particularly generative pre-trained transformers like GPT-4, holds promise for improving diagnostic accuracy, but requires further exploration in handling atypical presentations. Objective: This study aimed to assess the diagnostic accuracy of ChatGPT in generating differential diagnoses for atypical presentations of common diseases, with a focus on the model’s reliance on patient history during the diagnostic process. Methods: We used 25 clinical vignettes from the Journal of Generalist Medicine characterizing atypical manifestations of common diseases. Two general medicine physicians categorized the cases based on atypicality. ChatGPT was then used to generate differential diagnoses based on the clinical information provided. The concordance between AI-generated and final diagnoses was measured, with a focus on the top-ranked disease (top 1) and the top 5 differential diagnoses (top 5). Results: ChatGPT’s diagnostic accuracy decreased with an increase in atypical presentation. For category 1 (C1) cases, the concordance rates were 17% (n=1) for the top 1 and 67% (n=4) for the top 5. Categories 3 (C3) and 4 (C4) showed a 0% concordance for top 1 and markedly lower rates for the top 5, indicating difficulties in handling highly atypical cases. The χ2 test revealed no significant difference in the top 1 differential diagnosis accuracy between less atypical (C1+C2) and more atypical (C3+C4) groups (χ²1=2.07; n=25; P=.13). However, a significant difference was found in the top 5 analyses, with less atypical cases showing higher accuracy (χ²1=4.01; n=25; P=.048). Conclusions: ChatGPT-4 demonstrates potential as an auxiliary tool for diagnosing typical and mildly atypical presentations of common diseases. However, its performance declines with greater atypicality. The study findings underscore the need for AI systems to encompass a broader range of linguistic capabilities, cultural understanding, and diverse clinical scenarios to improve diagnostic utility in real-world settings. %R 10.2196/58758 %U https://mededu.jmir.org/2024/1/e58758 %U https://doi.org/10.2196/58758 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e55357 %T Identifying Interventions to Improve Diagnostic Safety in Emergency Departments: Protocol for a Participatory Design Study %A Seo,Woosuk %A Park,Sun Young %A Zhang,Zhan %A Singh,Hardeep %A Pasupathy,Kalyan %A Mahajan,Prashant %+ School of Information, University of Michigan, 500 South State Street, Ann Arbor, MI, 48109, United States, 1 2063109264, seow@umich.edu %K emergency departments %K participatory design %K diagnostic process %K multilevel interventions %K sociotechnical interventions %K mobile phone %D 2024 %7 21.6.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Emergency departments (EDs) are complex and fast-paced clinical settings where a diagnosis is made in a time-, information-, and resource-constrained context. Thus, it is predisposed to suboptimal diagnostic outcomes, leading to errors and subsequent patient harm. Arriving at a timely and accurate diagnosis is an activity that occurs after an effective collaboration between the patient or caregiver and the clinical team within the ED. Interventions such as novel sociotechnical solutions are needed to mitigate errors and risks. Objective: This study aims to identify challenges that frontline ED health care providers and patients face in the ED diagnostic process and involve them in co-designing technological interventions to enhance diagnostic excellence. Methods: We will conduct separate sessions with ED health care providers and patients, respectively, to assess various design ideas and use a participatory design (PD) approach for technological interventions to improve ED diagnostic safety. In the sessions, various intervention ideas will be presented to participants through storyboards. Based on a preliminary interview study with ED patients and health care providers, we created intervention storyboards that illustrate different care contexts in which ED health care providers or patients experience challenges and show how each intervention would address the specific challenge. By facilitating participant group discussion, we will reveal the overlap between the needs of the design research team observed during fieldwork and the needs perceived by target users (ie, participants) in their own experience to gain their perspectives and assessment on each idea. After the group discussions, participants will rank the ideas and co-design to improve our interventions. Data sources will include audio and video recordings, design sketches, and ratings of intervention design ideas from PD sessions. The University of Michigan Institutional Review Board approved this study. This foundational work will help identify the needs and challenges of key stakeholders in the ED diagnostic process and develop initial design ideas, specifically focusing on sociotechnological ideas for patient-, health care provider–, and system-level interventions for improving patient safety in EDs. Results: The recruitment of participants for ED health care providers and patients is complete. We are currently preparing for PD sessions. The first results from design sessions with health care providers will be reported in fall 2024. Conclusions: The study findings will provide unique insights for designing sociotechnological interventions to support ED diagnostic processes. By inviting frontline health care providers and patients into the design process, we anticipate obtaining unique insights into the ED diagnostic process and designing novel sociotechnical interventions to enhance patient safety. Based on this study’s collected data and intervention ideas, we will develop prototypes of multilevel interventions that can be tested and subsequently implemented for patients, health care providers, or hospitals as a system. International Registered Report Identifier (IRRID): DERR1-10.2196/55357 %M 38904990 %R 10.2196/55357 %U https://www.researchprotocols.org/2024/1/e55357 %U https://doi.org/10.2196/55357 %U http://www.ncbi.nlm.nih.gov/pubmed/38904990 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e55571 %T Alarm Management in Intensive Care: Qualitative Triangulation Study %A Mosch,Lina %A Sümer,Meltem %A Flint,Anne Rike %A Feufel,Markus %A Balzer,Felix %A Mörike,Frauke %A Poncette,Akira-Sebastian %+ Institute of Medical Informatics, Charité – Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Charitéplatz 1, Berlin, 10117, Germany, 49 30450581 016, lina.mosch@charite.de %K digital health %K transdisciplinary research %K technological innovation %K patient-centered care %K qualitative %K ethnographic %K ethnography %K intensive care unit %K ICU %K intensive care %K German %K Germany %K Europe %K European %K interview %K interviews %K alarm %K alarms %K intelligent %K artificial intelligence %K grounded theory %K experience %K experiences %K attitude %K attitudes %K opinion %K opinions %K perception %K perceptions %K perspective %K perspectives %D 2024 %7 18.6.2024 %9 Original Paper %J JMIR Hum Factors %G English %X Background: The high number of unnecessary alarms in intensive care settings leads to alarm fatigue among staff and threatens patient safety. To develop and implement effective and sustainable solutions for alarm management in intensive care units (ICUs), an understanding of staff interactions with the patient monitoring system and alarm management practices is essential. Objective: This study investigated the interaction of nurses and physicians with the patient monitoring system, their perceptions of alarm management, and smart alarm management solutions. Methods: This explorative qualitative study with an ethnographic, multimethods approach was conducted in an ICU of a German university hospital. Using triangulation in data collection, 102 hours of field observations, 12 semistructured interviews with ICU staff members, and the results of a participatory task were analyzed. The data analysis followed an inductive, grounded theory approach. Results: Nurses and physicians reported interacting with the continuous vital sign monitoring system for most of their work time and tasks. There were no established standards for alarm management; instead, nurses and physicians stated that alarms were addressed through ad hoc reactions, a practice they viewed as problematic. Staff members’ perceptions of intelligent alarm management varied, but they highlighted the importance of understandable and traceable suggestions to increase trust and cognitive ease. Conclusions: Staff members’ interactions with the omnipresent patient monitoring system and its alarms are essential parts of ICU workflows and clinical decision-making. Alarm management standards and workflows have been shown to be deficient. Our observations, as well as staff feedback, suggest that changes are warranted. Solutions for alarm management should be designed and implemented with users, workflows, and real-world data at the core. %M 38888941 %R 10.2196/55571 %U https://humanfactors.jmir.org/2024/1/e55571 %U https://doi.org/10.2196/55571 %U http://www.ncbi.nlm.nih.gov/pubmed/38888941 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e55948 %T Efficacy and Safety of Remimazolam Versus Etomidate for Induction of General Anesthesia: Protocol for a Systematic Review and Meta-Analysis %A Zhao,Li %A Guo,Yiping %A Zhou,Xuelei %A Mao,Wei %A Chen,Linlin %A Xie,Ying %A Li,Linji %+ Department of Anesthesiology, The Second Clinical Medical College, North Sichuan Medical College, Nanchong Central Hospital, No. 97 Renmin South Road, Shunqing District, Nanchong, 637000, China, 86 15583009555, llj-stephen@163.com %K general anesthesia %K anesthesia induction %K postinduction hypotension %K remimazolam %K etomidate %K meta-analysis %D 2024 %7 12.6.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Postinduction hypotension (PIHO) is a hemodynamic abnormality commonly observed during the induction of general anesthesia. Etomidate is considered a safer drug for the induction of anesthesia because it has only minor adverse effects on the cardiovascular and pulmonary systems. Recent evidence indicates that the novel benzodiazepine remimazolam has minimal inhibitory effects on the circulation and respiration. However, the efficacy and safety of remimazolam versus etomidate in the induction of anesthesia are unclear. Objective: To further understand the potential of remimazolam in anesthesia induction, it is necessary to design a meta-analysis to compare its effects versus the classic safe anesthetic etomidate. The aim of this study is to determine which drug has more stable hemodynamics and a lower incidence of PIHO. Our study will also yield data on sedation efficiency, time to loss of consciousness, time to awakening, incidence of injection pain, and postoperative nausea and vomiting with the two drugs. Methods: We plan to search the Web of Science, Cochrane Library, Embase, PubMed, China National Knowledge Infrastructure, and Wanfang databases from the date of their creation until March 31, 2025. The language is limited to English and Chinese. The search terms are “randomized controlled trials,” “etomidate,” and “remimazolam.” The incidence of PIHO is the primary outcome measure. Secondary outcomes include depth of anesthesia after induction, sedation success rate, time to loss of consciousness, hemodynamic profiles, recovery time, incidence of injection pain, and postoperative nausea and vomiting. Reviews, meta-analyses, case studies, abstracts from conferences, and commentaries will not be included. The heterogeneity of the results will be evaluated by sensitivity and subgroup analyses. RevMan software and Stata software will be used for data analysis. We will evaluate the quality of included studies using version 2 of the Cochrane risk-of-bias tool. The confidence of the evidence will be assessed through the Grading of Recommendations, Assessments, Developments, and Evaluations system. Results: The protocol was registered in the international PROSPERO (Prospective Register of Systematic Reviews) registry in November 2023. As of June 2024, we have performed a preliminary article search and retrieval for further review. The review and analyses are expected to be completed in March 2025. We expect to submit manuscripts for peer review by the end of June 2025. Conclusions: By synthesizing the available evidence and comparing remimazolam and etomidate, we hope to provide valuable insights into the selection of anesthesia-inducing drugs to reduce the incidence of PIHO and improve patient prognosis. Trial Registration: PROSPERO CRD42023463120; https://tinyurl.com/333jb8bm International Registered Report Identifier (IRRID): PRR1-10.2196/55948 %M 38865185 %R 10.2196/55948 %U https://www.researchprotocols.org/2024/1/e55948 %U https://doi.org/10.2196/55948 %U http://www.ncbi.nlm.nih.gov/pubmed/38865185 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e55638 %T Assessing the Efficacy of the ARMOR Tool–Based Deprescribing Intervention for Fall Risk Reduction in Older Patients Taking Fall Risk–Increasing Drugs (DeFRID Trial): Protocol for a Randomized Controlled Trial %A Priyadarshini,Rekha %A Eerike,Madhavi %A Varatharajan,Sakthivadivel %A Ramaswamy,Gomathi %A Raj,Gerard Marshall %A Cherian,Jerin Jose %A Rajendran,Priyadharsini %A Gunasekaran,Venugopalan %A Rao,Shailaja V %A Konda,Venu Gopala Rao %+ Department of Pharmacology, All India Institute of Medical Sciences Bibinagar, Bibinagar, Hyderabad, 508126, India, 91 9941476332, dr.madhavieerike@gmail.com %K deprescribing %K geriatric %K fall risk–increasing drugs %K FRIDs %K ARMOR tool %K Assess, Review, Minimize, Optimize, and Reassess %K falls %K older patients %K fall risk %D 2024 %7 11.6.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Falls in older patients can lead to serious health complications and increased health care costs. Fall risk–increasing drugs (FRIDs) are a group of drugs that may induce falls or increase the tendency to fall (ie, fall risk). Deprescribing is the process of withdrawal from an inappropriate medication, supervised by a health care professional, with the goal of managing polypharmacy and improving outcomes. Objective: This study aims to assess the effectiveness of a deprescribing intervention based on the Assess, Review, Minimize, Optimize, and Reassess (ARMOR) tool in reducing the risk of falls in older patients and evaluate the cost-effectiveness of deprescribing FRIDs. Methods: This is an open-label, parallel-group randomized controlled academic trial. Individuals aged 60-80 years who are currently taking 5 or more prescribed drugs, including at least 1 FRID, will be recruited. Demographic data, medical conditions, medication lists, orthostatic hypotension, and fall history details will be collected. Fall concern will be assessed using the Fall Efficacy Scale, and fall risk will be assessed by the Timed Up and Go test and Tinetti Performance-Oriented Mobility Assessment tool. In this study, all treating physicians will be randomized using a stratified randomization method based on seniority. Randomized physicians will do deprescribing with the ARMOR tool for patients on FRIDs. Participants will maintain diaries, and monthly phone follow-ups will be undertaken to monitor falls and adverse events. Physical assessments will be performed to evaluate fall risk every 3 months for a year. The rationality of prescription drugs will be evaluated using the World Health Organization’s core indicators. Results: The study received a grant from the Indian Council of Medical Research–Safe and Rational Use of Medicine in October 2023. The study is scheduled to commence in April 2024 and conclude by 2026. Efficacy will be measured by fall frequency and changes in fall risk scores. Cost-effectiveness analysis will also include the incremental cost-effectiveness ratio calculation. Adverse events related to deprescription will be recorded. Conclusions: This trial will provide essential insights into the efficacy of the ARMOR tool in reducing falls among the geriatric population who are taking FRIDs. Additionally, it will provide valuable information on the cost-effectiveness of deprescribing practices, offering significant implications for improving the well-being of older patients and optimizing health care resource allocation. The findings from this study will be pertinent for health care professionals, policy makers, and researchers focused on geriatric care and fall prevention strategies. Trial Registration: Clinical Trials Registry – India CTRI/2023/12/060516; https://ctri.nic.in/Clinicaltrials/pubview2.php International Registered Report Identifier (IRRID): PRR1-10.2196/55638 %M 38861709 %R 10.2196/55638 %U https://www.researchprotocols.org/2024/1/e55638 %U https://doi.org/10.2196/55638 %U http://www.ncbi.nlm.nih.gov/pubmed/38861709 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e50274 %T Trust but Verify: Lessons Learned for the Application of AI to Case-Based Clinical Decision-Making From Postmarketing Drug Safety Assessment at the US Food and Drug Administration %A Ball,Robert %A Talal,Andrew H %A Dang,Oanh %A Muñoz,Monica %A Markatou,Marianthi %+ Office of Surveillance and Epidemiology, Center for Drug Evaluation and Research, US Food and Drug Administration, 10903 New Hampshire Avenue, Silver Spring, MD, 20993, United States, 1 301 796 2380, robert.ball@fda.hhs.gov %K drug safety %K artificial intelligence %K machine learning %K natural language processing %K causal inference %K case-based reasoning %K clinical decision support %D 2024 %7 6.6.2024 %9 Viewpoint %J J Med Internet Res %G English %X Adverse drug reactions are a common cause of morbidity in health care. The US Food and Drug Administration (FDA) evaluates individual case safety reports of adverse events (AEs) after submission to the FDA Adverse Event Reporting System as part of its surveillance activities. Over the past decade, the FDA has explored the application of artificial intelligence (AI) to evaluate these reports to improve the efficiency and scientific rigor of the process. However, a gap remains between AI algorithm development and deployment. This viewpoint aims to describe the lessons learned from our experience and research needed to address both general issues in case-based reasoning using AI and specific needs for individual case safety report assessment. Beginning with the recognition that the trustworthiness of the AI algorithm is the main determinant of its acceptance by human experts, we apply the Diffusion of Innovations theory to help explain why certain algorithms for evaluating AEs at the FDA were accepted by safety reviewers and others were not. This analysis reveals that the process by which clinicians decide from case reports whether a drug is likely to cause an AE is not well defined beyond general principles. This makes the development of high performing, transparent, and explainable AI algorithms challenging, leading to a lack of trust by the safety reviewers. Even accounting for the introduction of large language models, the pharmacovigilance community needs an improved understanding of causal inference and of the cognitive framework for determining the causal relationship between a drug and an AE. We describe specific future research directions that underpin facilitating implementation and trust in AI for drug safety applications, including improved methods for measuring and controlling of algorithmic uncertainty, computational reproducibility, and clear articulation of a cognitive framework for causal inference in case-based reasoning. %M 38842929 %R 10.2196/50274 %U https://www.jmir.org/2024/1/e50274 %U https://doi.org/10.2196/50274 %U http://www.ncbi.nlm.nih.gov/pubmed/38842929 %0 Journal Article %@ 2291-9694 %I %V 12 %N %P e53625 %T A Machine Learning Application to Classify Patients at Differing Levels of Risk of Opioid Use Disorder: Clinician-Based Validation Study %A Eguale,Tewodros %A Bastardot,François %A Song,Wenyu %A Motta-Calderon,Daniel %A Elsobky,Yasmin %A Rui,Angela %A Marceau,Marlika %A Davis,Clark %A Ganesan,Sandya %A Alsubai,Ava %A Matthews,Michele %A Volk,Lynn A %A Bates,David W %A Rozenblum,Ronen %K opioid-related disorders %K opioid use disorder %K machine learning %K artificial intelligence %K electronic health record %K clinical decision support %K model validation %K patient medication safety %K medication safety %K clinical decision %K decision making %K decision support %K patient safety %K opioid use %K drug use %K opioid safety %K medication %K OUD %K EHR %K AI %D 2024 %7 4.6.2024 %9 %J JMIR Med Inform %G English %X Background: Despite restrictive opioid management guidelines, opioid use disorder (OUD) remains a major public health concern. Machine learning (ML) offers a promising avenue for identifying and alerting clinicians about OUD, thus supporting better clinical decision-making regarding treatment. Objective: This study aimed to assess the clinical validity of an ML application designed to identify and alert clinicians of different levels of OUD risk by comparing it to a structured review of medical records by clinicians. Methods: The ML application generated OUD risk alerts on outpatient data for 649,504 patients from 2 medical centers between 2010 and 2013. A random sample of 60 patients was selected from 3 OUD risk level categories (n=180). An OUD risk classification scheme and standardized data extraction tool were developed to evaluate the validity of the alerts. Clinicians independently conducted a systematic and structured review of medical records and reached a consensus on a patient’s OUD risk level, which was then compared to the ML application’s risk assignments. Results: A total of 78,587 patients without cancer with at least 1 opioid prescription were identified as follows: not high risk (n=50,405, 64.1%), high risk (n=16,636, 21.2%), and suspected OUD or OUD (n=11,546, 14.7%). The sample of 180 patients was representative of the total population in terms of age, sex, and race. The interrater reliability between the ML application and clinicians had a weighted kappa coefficient of 0.62 (95% CI 0.53-0.71), indicating good agreement. Combining the high risk and suspected OUD or OUD categories and using the review of medical records as a gold standard, the ML application had a corrected sensitivity of 56.6% (95% CI 48.7%-64.5%) and a corrected specificity of 94.2% (95% CI 90.3%-98.1%). The positive and negative predictive values were 93.3% (95% CI 88.2%-96.3%) and 60.0% (95% CI 50.4%-68.9%), respectively. Key themes for disagreements between the ML application and clinician reviews were identified. Conclusions: A systematic comparison was conducted between an ML application and clinicians for identifying OUD risk. The ML application generated clinically valid and useful alerts about patients’ different OUD risk levels. ML applications hold promise for identifying patients at differing levels of OUD risk and will likely complement traditional rule-based approaches to generating alerts about opioid safety issues. %R 10.2196/53625 %U https://medinform.jmir.org/2024/1/e53625 %U https://doi.org/10.2196/53625 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e57878 %T Patient Partnership Tools to Support Medication Safety in Community-Dwelling Older Adults: Protocol for a Nonrandomized Stepped Wedge Clinical Trial %A Xiao,Yan %A Fulda,Kimberley G %A Young,Richard A %A Hendrix,Z Noah %A Daniel,Kathryn M %A Chen,Kay Yut %A Zhou,Yuan %A Roye,Jennifer L %A Kosmari,Ludmila %A Wilson,Joshua %A Espinoza,Anna M %A Sutcliffe,Kathleen M %A Pitts,Samantha I %A Arbaje,Alicia I %A Chui,Michelle A %A Blair,Somer %A Sloan,Dawn %A Jackson,Masheika %A Gurses,Ayse P %+ College of Nursing and Health Innovation, University of Texas at Arlington, 411 S. Nedderman Drive, Arlington, TX, 76019, United States, 1 817 272 5781, yan.xiao@uta.edu %K primary care %K medication safety %K communication %K patient engagement %K human factors %K medication %K safety %K engagement %K support %K community dwelling %K older adults %K elderly %K protocol %K patient safety %K self-management %K ambulatory %K medications %K tool %K tools %K effective %K medication safety %K data collection %K decision %K decision making %K care %K self-efficacy %K engagement tool %D 2024 %7 29.4.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Preventable harms from medications are significant threats to patient safety in community settings, especially among ambulatory older adults on multiple prescription medications. Patients may partner with primary care professionals by taking on active roles in decisions, learning the basics of medication self-management, and working with community resources. Objective: This study aims to assess the impact of a set of patient partnership tools that redesign primary care encounters to encourage and empower patients to make more effective use of those encounters to improve medication safety. Methods: The study is a nonrandomized, cross-sectional stepped wedge cluster-controlled trial with 1 private family medicine clinic and 2 public safety-net primary care clinics each composing their own cluster. There are 2 intervention sequences with 1 cluster per sequence and 1 control sequence with 1 cluster. Cross-sectional surveys will be taken immediately at the conclusion of visits to the clinics during 6 time periods of 6 weeks each, with a transition period of no data collection during intervention implementation. The number of visits to be surveyed will vary by period and cluster. We plan to recruit patients and professionals for surveys during 405 visits. In the experimental periods, visits will be conducted with two partnership tools and associated clinic process changes: (1) a 1-page visit preparation guide given to relevant patients by clinic staff before seeing the provider, with the intention to improve communication and shared decision-making, and (2) a library of short educational videos that clinic staff encourage patients to watch on medication safety. In the control periods, visits will be conducted with usual care. The primary outcome will be patients’ self-efficacy in medication use. The secondary outcomes are medication-related issues such as duplicate therapies identified by primary care providers and assessment of collaborative work during visits. Results: The study was funded in September 2019. Data collection started in April 2023 and ended in December 2023. Data was collected for 405 primary care encounters during that period. As of February 15, 2024, initial descriptive statistics were calculated. Full data analysis is expected to be completed and published in the summer of 2024. Conclusions: This study will assess the impact of patient partnership tools and associated process changes in primary care on medication use self-efficacy and medication-related issues. The study is powered to identify types of patients who may benefit most from patient engagement tools in primary care visits. Trial Registration: ClinicalTrials.gov NCT05880368; https://clinicaltrials.gov/study/NCT05880368 International Registered Report Identifier (IRRID): DERR1-10.2196/57878 %M 38684080 %R 10.2196/57878 %U https://www.researchprotocols.org/2024/1/e57878 %U https://doi.org/10.2196/57878 %U http://www.ncbi.nlm.nih.gov/pubmed/38684080 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e48218 %T Effective Communication Supported by an App for Pregnant Women: Quantitative Longitudinal Study %A Kötting,Lukas %A Anand-Kumar,Vinayak %A Keller,Franziska Maria %A Henschel,Nils Tobias %A Lippke,Sonia %+ Psychology and Methods, School of Business, Social & Decision Sciences, Constructor University Bremen gGmbH, Campus Ring 1, Bremen, 28759, Germany, 49 421 200 4730, s.lippke@jacobs-university.de %K clinical care %K health action process approach %K HAPA %K intention %K communication behavior %K patient safety %K patient education %K internet intervention %K dropout %K digital health %K behavior change %K prediction %K obstetric %K pregnant women %K pregnancy %K safe communication %K health behaviors %K obstetric care %D 2024 %7 26.4.2024 %9 Original Paper %J JMIR Hum Factors %G English %X Background: In the medical field of obstetrics, communication plays a crucial role, and pregnant women, in particular, can benefit from interventions improving their self-reported communication behavior. Effective communication behavior can be understood as the correct transmission of information without misunderstanding, confusion, or losses. Although effective communication can be trained by patient education, there is limited research testing this systematically with an app-based digital intervention. Thus, little is known about the success of such a digital intervention in the form of a web-app, potential behavioral barriers for engagement, as well as the processes by which such a web-app might improve self-reported communication behavior. Objective: This study fills this research gap by applying a web-app aiming at improving pregnant women’s communication behavior in clinical care. The goals of this study were to (1) uncover the potential risk factors for early dropout from the web-app and (2) investigate the social-cognitive factors that predict self-reported communication behavior after having used the web-app. Methods: In this study, 1187 pregnant women were recruited. They all started to use a theory-based web-app focusing on intention, planning, self-efficacy, and outcome expectancy to improve communication behavior. Mechanisms of behavior change as a result of exposure to the web-app were explored using stepwise regression and path analysis. Moreover, determinants of dropout were tested using logistic regression. Results: We found that dropout was associated with younger age (P=.014). Mechanisms of behavior change were consistent with the predictions of the health action process approach. The stepwise regression analysis revealed that action planning was the best predictor for successful behavioral change over the course of the app-based digital intervention (β=.331; P<.001). The path analyses proved that self-efficacy beliefs affected the intention to communicate effectively, which in turn, elicited action planning and thereby improved communication behavior (β=.017; comparative fit index=0.994; Tucker–Lewis index=0.971; root mean square error of approximation=0.055). Conclusions: Our findings can guide the development and improvement of apps addressing communication behavior in the following ways in obstetric care. First, such tools would enable action planning to improve communication behavior, as action planning is the key predictor of behavior change. Second, younger women need more attention to keep them from dropping out. However, future research should build upon the gained insights by conducting similar internet interventions in related fields of clinical care. The focus should be on processes of behavior change and strategies to minimize dropout rates, as well as replicating the findings with patient safety measures. Trial Registration: ClinicalTrials.gov identifier: NCT03855735; https://classic.clinicaltrials.gov/ct2/show/NCT03855735 %M 38669073 %R 10.2196/48218 %U https://humanfactors.jmir.org/2024/1/e48218 %U https://doi.org/10.2196/48218 %U http://www.ncbi.nlm.nih.gov/pubmed/38669073 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e56262 %T TAILR (Nursing-Sensitive Events and Their Association With Individual Nurse Staffing Levels) Project: Protocol for an International Longitudinal Multicenter Study %A Bachnick,Stefanie %A Unbeck,Maria %A Ahmadi Shad,Maryam %A Falta,Katja %A Grossmann,Nicole %A Holle,Daniela %A Bartakova,Jana %A Musy,Sarah N %A Hellberg,Sarah %A Dillner,Pernilla %A Atoof,Fatemeh %A Khorasanizadeh,Mohammadhossein %A Kelly-Pettersson,Paula %A Simon,Michael %+ Department of Nursing Science, University of Applied Sciences, Gesundheitscampus 6 – 8, Bochum, 44801, Germany, 49 234 77727 748, stefanie.bachnick@hs-gesundheit.de %K adverse events %K electronic health record %K hospital care %K no-harm incidents %K nursing care %K nursing-sensitive events %K nurse staffing %K patient safety %K systematic record review %D 2024 %7 22.4.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Nursing-sensitive events (NSEs) are common, accounting for up to 77% of adverse events in hospitalized patients (eg, fall-related harm, pressure ulcers, and health care–associated infections). NSEs lead to adverse patient outcomes and impose an economic burden on hospitals due to increased medical costs through a prolonged hospital stay and additional medical procedures. To reduce NSEs and ensure high-quality nursing care, appropriate nurse staffing levels are needed. Although the link between nurse staffing and NSEs has been described in many studies, appropriate nurse staffing levels are lacking. Existing studies describe constant staffing exposure at the unit or hospital level without assessing patient-level exposure to nurse staffing during the hospital stay. Few studies have assessed nurse staffing and patient outcomes using a single-center longitudinal design, with limited generalizability. There is a need for multicenter longitudinal studies with improved potential for generalizing the association between individual nurse staffing levels and NSEs. Objective: This study aimed (1) to determine the prevalence, preventability, type, and severity of NSEs; (2) to describe individual patient-level nurse staffing exposure across hospitals; (3) to assess the effect of nurse staffing on NSEs in patients; and (4) to identify thresholds of safe nurse staffing levels and test them against NSEs in hospitalized patients. Methods: This international multicenter study uses a longitudinal and observational research design; it involves 4 countries (Switzerland, Sweden, Germany, and Iran), with participation from 14 hospitals and 61 medical, surgery, and mixed units. The 16-week observation period will collect NSEs using systematic retrospective record reviews. A total of 3680 patient admissions will be reviewed, with 60 randomly selected admissions per unit. To be included, patients must have been hospitalized for at least 48 hours. Nurse staffing data (ie, the number of nurses and their education level) will be collected daily for each shift to assess the association between NSEs and individual nurse staffing levels. Additionally, hospital data (ie, type, teaching status, and ownership) and unit data (ie, service line and number of beds) will be collected. Results: As of January 2024, the verification process for the plausibility and comprehensibility of patients’ and nurse staffing data is underway across all 4 countries. Data analyses are planned to be completed by spring 2024, with the first results expected to be published in late 2024. Conclusions: This study will provide comprehensive information on NSEs, including their prevalence, preventability, type, and severity, across countries. Moreover, it seeks to enhance understanding of NSE mechanisms and the potential impact of nurse staffing on these events. We will evaluate within- and between-hospital variability to identify productive strategies to ensure safe nurse staffing levels, thereby reducing NSEs in hospitalized patients. The TAILR (Nursing-Sensitive Events and Their Association With Individual Nurse Staffing Levels) study will focus on the optimization of scarce staffing resources. International Registered Report Identifier (IRRID): DERR1-10.2196/56262 %M 38648083 %R 10.2196/56262 %U https://www.researchprotocols.org/2024/1/e56262 %U https://doi.org/10.2196/56262 %U http://www.ncbi.nlm.nih.gov/pubmed/38648083 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e55794 %T Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models %A Nishioka,Satoshi %A Watabe,Satoshi %A Yanagisawa,Yuki %A Sayama,Kyoko %A Kizaki,Hayato %A Imai,Shungo %A Someya,Mitsuhiro %A Taniguchi,Ryoo %A Yada,Shuntaro %A Aramaki,Eiji %A Hori,Satoko %+ Division of Drug Informatics, Keio University Faculty of Pharmacy, 1-5-30 Shibakoen, Minato-ku, Tokyo, 105-8512, Japan, 81 3 5400 2650, satokoh@keio.jp %K cancer %K anticancer drug %K adverse event %K side effect %K patient-reported outcome %K patients’ voice %K patient-oriented %K patient narrative %K natural language processing %K deep learning %K pharmaceutical care record %K SOAP %D 2024 %7 16.4.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients’ subjective opinions (patients’ voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients’ narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients’ daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients. Objective: This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients’ concerns at pharmacies was also assessed. Methods: Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients’ concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs. Results: From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients’ daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. “Pain or numbness” (n=57, 36.3%), “fever” (n=46, 29.3%), and “nausea” (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients’ daily lives. Conclusions: Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients’ subjective information recorded in pharmaceutical care records accumulated during pharmacists’ daily work. %M 38625718 %R 10.2196/55794 %U https://www.jmir.org/2024/1/e55794 %U https://doi.org/10.2196/55794 %U http://www.ncbi.nlm.nih.gov/pubmed/38625718 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e50532 %T Assessment of Patient Safety in a Low-Resource Health Care System: Proposal for a Multimethod Study %A Haque,Ghazal %A Asif,Fozia %A Ahmed,Fasih Ali %A Ayub,Farwa %A Syed,Sabih ul Hassan %A Pradhan,Nousheen Akber %A Hameed,Malika %A Siddiqui,Muhammad Muneeb Ullah %A Mahmood,Shafaq %A Zaidi,Tahani %A Siddiqi,Sameen %A Latif,Asad %+ Department of Anesthesiology, Aga Khan University Medical College, Stadium Road, Karachi, 74800, Pakistan, 92 2134864639, asad.latif@aku.edu %K patient safety %K health systems %K quality assessment %K safety culture %K assessment %K healthcare delivery %K health system %K hospital %K low-middle-income countries %K research methodology %D 2024 %7 27.3.2024 %9 Proposal %J JMIR Res Protoc %G English %X Background: The high prevalence of adverse events (AEs) globally in health care delivery has led to the establishment of many guidelines to enhance patient safety. However, patient safety is a relatively nascent concept in low- and middle-income countries (LMICs) where health systems are already overburdened and underresourced. This is why it is imperative to study the nuances of patient safety from a local perspective to advocate for the judicious use of scarce public health resources. Objective: This study aims to assess the status of patient safety in a health care system within a low-resource setting, using a multipronged, multimethod approach of standardized methodologies adapted to the local setting. Methods: We propose purposive sampling to include a representative mix of public and private, rural and urban, and tertiary and secondary care hospitals, preferably those ascribed to the same hospital quality standards. Six different approaches will be considered at these hospitals including (1) focus group discussions on the status quo of patient safety, (2) Hospital Survey on Patient Safety Culture, (3) Hospital Consumer Assessment of Healthcare Providers and Systems, (4) estimation of incidence of AEs identified by patients, (5) estimation of incidence of AEs via medical record review, and (6) assessment against the World Health Organization’s Patient Safety Friendly Hospital Framework via thorough reviews of existing hospital protocols and in-person surveys of the facility. Results: The abovementioned studies collectively are expected to yield significant quantifiable information on patient safety conditions in a wide range of hospitals operating within LMICs. Conclusions: A multidimensional approach is imperative to holistically assess the patient safety situation, especially in LMICs. Our low-budget, non–resource-intensive research proposal can serve as a benchmark to conduct similar studies in other health care settings within LMICs. International Registered Report Identifier (IRRID): PRR1-10.2196/50532 %M 38536223 %R 10.2196/50532 %U https://www.researchprotocols.org/2024/1/e50532 %U https://doi.org/10.2196/50532 %U http://www.ncbi.nlm.nih.gov/pubmed/38536223 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e50676 %T Safety in Teletriage by Nurses and Physicians in the United States and Israel: Narrative Review and Qualitative Study %A Haimi,Motti %A Wheeler,Sheila Quilter %+ Rappaport Faculty of Medicine, Technion – Israel Institute of Technology, Efron 1, Bat Galim, Haifa, 3200003, Israel, 972 504557767, morx@netvision.net.il %K telephone triage %K teletriage %K telehealth %K telemedicine %K safety %K system error %K human error %K triage %K outcome %K patient safety %D 2024 %7 25.3.2024 %9 Original Paper %J JMIR Hum Factors %G English %X Background: The safety of telemedicine in general and telephone triage (teletriage) safety in particular have been a focus of concern since the 1970s. Today, telehealth, now subsuming teletriage, has a basic structure and process intended to promote safety. However, inadequate telehealth systems may also compromise patient safety. The COVID-19 pandemic accelerated rapid but uneven telehealth growth, both technologically and professionally. Within 5-10 years, the field will likely be more technologically advanced; however, these advances may still outpace professional standards. The need for an evidence-based system is crucial and urgent. Objective: Our aim was to explore ways that developed teletriage systems produce safe outcomes by examining key system components and questioning long-held assumptions. Methods: We examined safety by performing a narrative review of the literature using key terms concerning patient safety in teletriage. In addition, we conducted system analysis of 2 typical formal systems, physician led and nurse led, in Israel and the United States, respectively, and evaluated those systems’ respective approaches to safety. Additionally, we conducted in-depth interviews with representative physicians and 1 nurse using a qualitative approach. Results: The review of literature indicated that research on various aspects of telehealth and teletriage safety is still sparse and of variable quality, producing conflicting and inconsistent results. Researchers, possibly unfamiliar with this complicated field, use an array of poorly defined terms and appear to design studies based on unfounded assumptions. The interviews with health care professionals demonstrated several challenges encountered during teletriage, mainly making diagnosis from a distance, treating unfamiliar patients, a stressful atmosphere, working alone, and technological difficulties. However, they reported using several measures that help them make accurate diagnoses and reasonable decisions, thus keeping patient safety, such as using their expertise and intuition, using structured protocols, and considering nonmedical factors and patient preferences (shared decision-making). Conclusions: Remote encounters about acute, worrisome symptoms are time sensitive, requiring decision-making under conditions of uncertainty and urgency. Patient safety and safe professional practice are extremely important in the field of teletriage, which has a high potential for error. This underregulated subspecialty lacks adequate development and substantive research on system safety. Research may commingle terminology and widely different, ill-defined groups of decision makers with wide variation in decision-making skills, clinical training, experience, and job qualifications, thereby confounding results. The rapid pace of telehealth’s technological growth creates urgency in identifying safe systems to guide developers and clinicians about needed improvements. %M 38526526 %R 10.2196/50676 %U https://humanfactors.jmir.org/2024/1/e50676 %U https://doi.org/10.2196/50676 %U http://www.ncbi.nlm.nih.gov/pubmed/38526526 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 13 %N %P e56933 %T Definitions and Measurements for Atypical Presentations at Risk for Diagnostic Errors in Internal Medicine: Protocol for a Scoping Review %A Harada,Yukinori %A Kawamura,Ren %A Yokose,Masashi %A Shimizu,Taro %A Singh,Hardeep %+ Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 880 Kitakobayashi, Mibu, 321-0293, Japan, 81 282 86 1111, yharada@dokkyomed.ac.jp %K atypical presentations %K diagnostic errors %K internal medicine %K scoping review protocol %K atypical presentation %K high risk %K data extraction %K descriptive statistics %K criteria %K qualitative %K content analysis %K inductive approach %K clinical informatics %K clinical decision support %D 2024 %7 25.3.2024 %9 Protocol %J JMIR Res Protoc %G English %X Background: Atypical presentations have been increasingly recognized as a significant contributing factor to diagnostic errors in internal medicine. However, research to address associations between atypical presentations and diagnostic errors has not been evaluated due to the lack of widely applicable definitions and criteria for what is considered an atypical presentation. Objective: The aim of the study is to describe how atypical presentations are defined and measured in studies of diagnostic errors in internal medicine and use this new information to develop new criteria to identify atypical presentations at high risk for diagnostic errors. Methods: This study will follow an established framework for conducting scoping reviews. Inclusion criteria are developed according to the participants, concept, and context framework. This review will consider studies that fulfill all of the following criteria: include adult patients (participants); explore the association between atypical presentations and diagnostic errors using any definition, criteria, or measurement to identify atypical presentations and diagnostic errors (concept); and focus on internal medicine (context). Regarding the type of sources, this scoping review will consider quantitative, qualitative, and mixed methods study designs; systematic reviews; and opinion papers for inclusion. Case reports, case series, and conference abstracts will be excluded. The data will be extracted through MEDLINE, Web of Science, CINAHL, Embase, Cochrane Library, and Google Scholar searches. No limits will be applied to language, and papers indexed from database inception to December 31, 2023, will be included. Two independent reviewers (YH and RK) will conduct study selection and data extraction. The data extracted will include specific details about the patient characteristics (eg, age, sex, and disease), the definitions and measuring methods for atypical presentations and diagnostic errors, clinical settings (eg, department and outpatient or inpatient), type of evidence source, and the association between atypical presentations and diagnostic errors relevant to the review question. The extracted data will be presented in tabular format with descriptive statistics, allowing us to identify the key components or types of atypical presentations and develop new criteria to identify atypical presentations for future studies of diagnostic errors. Developing the new criteria will follow guidance for a basic qualitative content analysis with an inductive approach. Results: As of January 2024, a literature search through multiple databases is ongoing. We will complete this study by December 2024. Conclusions: This scoping review aims to provide rigorous evidence to develop new criteria to identify atypical presentations at high risk for diagnostic errors in internal medicine. Such criteria could facilitate the development of a comprehensive conceptual model to understand the associations between atypical presentations and diagnostic errors in internal medicine. Trial Registration: Open Science Framework; www.osf.io/27d5m International Registered Report Identifier (IRRID): DERR1-10.2196/56933 %M 38526541 %R 10.2196/56933 %U https://www.researchprotocols.org/2024/1/e56933 %U https://doi.org/10.2196/56933 %U http://www.ncbi.nlm.nih.gov/pubmed/38526541 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e41557 %T Exploring the Use of Persuasive System Design Principles to Enhance Medication Incident Reporting and Learning Systems: Scoping Reviews and Persuasive Design Assessment %A Oyibo,Kiemute %A Gonzalez,Paola A %A Ejaz,Sarah %A Naheyan,Tasneem %A Beaton,Carla %A O’Donnell,Denis %A Barker,James R %+ Department of Electrical Engineering and Computer Science, Lassonde Research Centre, York University, 4751 Keele Street, North York, ON, M3J 2N9, Canada, 1 416 736 5053, kiemute.oyibo@yorku.ca %K medication incident %K reporting system %K persuasive technology %K persuasive design %K medication %K persuasive system design %K pharmacy %K pharmaceutic %K pharmacology %K drug reporting %K drug event %K adverse event %K incident management %D 2024 %7 21.3.2024 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Medication incidents (MIs) causing harm to patients have far-reaching consequences for patients, pharmacists, public health, business practice, and governance policy. Medication Incident Reporting and Learning Systems (MIRLS) have been implemented to mitigate such incidents and promote continuous quality improvement in community pharmacies in Canada. They aim to collect and analyze MIs for the implementation of incident preventive strategies to increase safety in community pharmacy practice. However, this goal remains inhibited owing to the persistent barriers that pharmacies face when using these systems. Objective: This study aims to investigate the harms caused by medication incidents and technological barriers to reporting and identify opportunities to incorporate persuasive design strategies in MIRLS to motivate reporting. Methods: We conducted 2 scoping reviews to provide insights on the relationship between medication errors and patient harm and the information system–based barriers militating against reporting. Seven databases were searched in each scoping review, including PubMed, Public Health Database, ProQuest, Scopus, ACM Library, Global Health, and Google Scholar. Next, we analyzed one of the most widely used MIRLS in Canada using the Persuasive System Design (PSD) taxonomy—a framework for analyzing, designing, and evaluating persuasive systems. This framework applies behavioral theories from social psychology in the design of technology-based systems to motivate behavior change. Independent assessors familiar with MIRLS reported the degree of persuasion built into the system using the 4 categories of PSD strategies: primary task, dialogue, social, and credibility support. Results: Overall, 17 articles were included in the first scoping review, and 1 article was included in the second scoping review. In the first review, significant or serious harm was the most frequent harm (11/17, 65%), followed by death or fatal harm (7/17, 41%). In the second review, the authors found that iterative design could improve the usability of an MIRLS; however, data security and validation of reports remained an issue to be addressed. Regarding the MIRLS that we assessed, participants considered most of the primary task, dialogue, and credibility support strategies in the PSD taxonomy as important and useful; however, they were not comfortable with some of the social strategies such as cooperation. We found that the assessed system supported a number of persuasive strategies from the PSD taxonomy; however, we identified additional strategies such as tunneling, simulation, suggestion, praise, reward, reminder, authority, and verifiability that could further enhance the perceived persuasiveness and value of the system. Conclusions: MIRLS, equipped with persuasive features, can become powerful motivational tools to promote safer medication practices in community pharmacies. They have the potential to highlight the value of MI reporting and increase the readiness of pharmacists to report incidents. The proposed persuasive design guidelines can help system developers and community pharmacy managers realize more effective MIRLS. %M 38512325 %R 10.2196/41557 %U https://humanfactors.jmir.org/2024/1/e41557 %U https://doi.org/10.2196/41557 %U http://www.ncbi.nlm.nih.gov/pubmed/38512325 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e47685 %T The Acceptance and Use of Digital Technologies for Self-Reporting Medication Safety Events After Care Transitions to Home in Patients With Cancer: Survey Study %A Jiang,Yun %A Hwang,Misun %A Cho,Youmin %A Friese,Christopher R %A Hawley,Sarah T %A Manojlovich,Milisa %A Krauss,John C %A Gong,Yang %+ School of Nursing, University of Michigan, 400 North Ingalls Street, Ann Arbor, MI, 48109, United States, 1 734 763 3705, jiangyu@umich.edu %K digital technology %K patient safety %K patient participation %K patient-reported outcomes %K drug-related side effects and adverse reactions %D 2024 %7 8.3.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Actively engaging patients with cancer and their families in monitoring and reporting medication safety events during care transitions is indispensable for achieving optimal patient safety outcomes. However, existing patient self-reporting systems often cannot address patients’ various experiences and concerns regarding medication safety over time. In addition, these systems are usually not designed for patients’ just-in-time reporting. There is a significant knowledge gap in understanding the nature, scope, and causes of medication safety events after patients’ transition back home because of a lack of patient engagement in self-monitoring and reporting of safety events. The challenges for patients with cancer in adopting digital technologies and engaging in self-reporting medication safety events during transitions of care have not been fully understood. Objective: We aim to assess oncology patients’ perceptions of medication and communication safety during care transitions and their willingness to use digital technologies for self-reporting medication safety events and to identify factors associated with their technology acceptance. Methods: A cross-sectional survey study was conducted with adult patients with breast, prostate, lung, or colorectal cancer (N=204) who had experienced care transitions from hospitals or clinics to home in the past 1 year. Surveys were conducted via phone, the internet, or email between December 2021 and August 2022. Participants’ perceptions of medication and communication safety and perceived usefulness, ease of use, attitude toward use, and intention to use a technology system to report their medication safety events from home were assessed as outcomes. Potential personal, clinical, and psychosocial factors were analyzed for their associations with participants’ technology acceptance through bivariate correlation analyses and multiple logistic regressions. Results: Participants reported strong perceptions of medication and communication safety, positively correlated with medication self-management ability and patient activation. Although most participants perceived a medication safety self-reporting system as useful (158/204, 77.5%) and easy to use (157/204, 77%), had a positive attitude toward use (162/204, 79.4%), and were willing to use such a system (129/204, 63.2%), their technology acceptance was associated with their activation levels (odds ratio [OR] 1.83, 95% CI 1.12-2.98), their perceptions of communication safety (OR 1.64, 95% CI 1.08-2.47), and whether they could receive feedback after self-reporting (OR 3.27, 95% CI 1.37-7.78). Conclusions: In general, oncology patients were willing to use digital technologies to report their medication events after care transitions back home because of their high concerns regarding medication safety. As informed and activated patients are more likely to have the knowledge and capability to initiate and engage in self-reporting, developing a patient-centered reporting system to empower patients and their families and facilitate safety health communications will help oncology patients in addressing their medication safety concerns, meeting their care needs, and holding promise to improve the quality of cancer care. %M 38457204 %R 10.2196/47685 %U https://www.jmir.org/2024/1/e47685 %U https://doi.org/10.2196/47685 %U http://www.ncbi.nlm.nih.gov/pubmed/38457204 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 10 %N %P e49755 %T Assessing the Labeling Information on Drugs Associated With Suicide Risk: Systematic Review %A Jeon,Soo Min %A Lim,HyunJoo %A Cheon,Hyo-bin %A Ryu,Juhee %A Kwon,Jin-Won %+ BK21 FOUR Community-Based Intelligent Novel Drug Discovery Education Unit, College of Pharmacy and Research Institute of Pharmaceutical Sciences, 80 Daehak-ro, Daegu, 41566, Republic of Korea, 82 539508580, jwkwon@knu.ac.kr %K suicide %K adverse drug events %K review %K drug %K mental health %K systematic review %K drug induced suicide %K drug reaction %K substance use %K suicidal %K medication %K suicide symptoms %K suicidal risk %K drugs %K adverse drug event %D 2024 %7 30.1.2024 %9 Review %J JMIR Public Health Surveill %G English %X Background: Drug-induced suicide (DIS) is a severe adverse drug reaction (ADR). Although clinical trials have provided evidence on DIS, limited investigations have been performed on rare ADRs, such as suicide. Objective: We aimed to systematically review case reports on DIS to provide evidence-based drug information. Methods: We searched PubMed to obtain case reports regarding DIS published until July 2021. Cases resulting from drugs that are no longer used or are nonapproved, substance use, and suicidal intentions were excluded. The quality of each case report was assessed using the CASE (Case Reports) checklist. We extracted data regarding demographics, medication history, suicide symptoms, and symptom improvement and evaluated the causality of DIS using the Naranjo score. Furthermore, to identify the potential suicidal risk of the unknown drugs, we compared the results of the causality assessment with those of the approved drug labels. Results: In 83 articles, we identified 152 cases involving 61 drugs. Antidepressants were reported as the most frequent causative drugs of DIS followed by immunostimulants. The causality assessment revealed 61 cases having possible, 89 cases having probable, and 2 cases having definite relationships with DIS. For approximately 85% of suspected drugs, the risk of suicidal ADRs was indicated on the approved label; however, the approved labels for 9 drugs, including lumacaftor/ivacaftor, doxycycline, clozapine, dextromethorphan, adalimumab, infliximab, piroxicam, paclitaxel, and formoterol, did not provide information about these risks. Conclusions: We found several case reports involving drugs without suicide risk information on the drug label. Our findings might provide valuable insights into drugs that may cause suicidal ADRs. %M 38289650 %R 10.2196/49755 %U https://publichealth.jmir.org/2024/1/e49755 %U https://doi.org/10.2196/49755 %U http://www.ncbi.nlm.nih.gov/pubmed/38289650 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e53378 %T A Machine Learning Approach with Human-AI Collaboration for Automated Classification of Patient Safety Event Reports: Algorithm Development and Validation Study %A Chen,Hongbo %A Cohen,Eldan %A Wilson,Dulaney %A Alfred,Myrtede %+ Department of Mechanical & Industrial Engineering, Faculty of Applied Science & Engineering, University of Toronto, 27 King's College Cir, Toronto, ON, M5S 1A1, Canada, 1 4372154739, myrtede.alfred@utoronto.ca %K accident %K accidents %K black box %K classification %K classifier %K collaboration %K design %K document %K documentation %K documents %K explainability %K explainable %K human-AI collaboration %K human-AI %K human-computer %K human-machine %K incident reporting %K interface design %K interface %K interpretable %K LIME %K machine learning %K patient safety %K predict %K prediction %K predictions %K predictive %K report %K reporting %K safety %K text %K texts %K textual %K artificial intelligence %D 2024 %7 25.1.2024 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Adverse events refer to incidents with potential or actual harm to patients in hospitals. These events are typically documented through patient safety event (PSE) reports, which consist of detailed narratives providing contextual information on the occurrences. Accurate classification of PSE reports is crucial for patient safety monitoring. However, this process faces challenges due to inconsistencies in classifications and the sheer volume of reports. Recent advancements in text representation, particularly contextual text representation derived from transformer-based language models, offer a promising solution for more precise PSE report classification. Integrating the machine learning (ML) classifier necessitates a balance between human expertise and artificial intelligence (AI). Central to this integration is the concept of explainability, which is crucial for building trust and ensuring effective human-AI collaboration. Objective: This study aims to investigate the efficacy of ML classifiers trained using contextual text representation in automatically classifying PSE reports. Furthermore, the study presents an interface that integrates the ML classifier with the explainability technique to facilitate human-AI collaboration for PSE report classification. Methods: This study used a data set of 861 PSE reports from a large academic hospital’s maternity units in the Southeastern United States. Various ML classifiers were trained with both static and contextual text representations of PSE reports. The trained ML classifiers were evaluated with multiclass classification metrics and the confusion matrix. The local interpretable model-agnostic explanations (LIME) technique was used to provide the rationale for the ML classifier’s predictions. An interface that integrates the ML classifier with the LIME technique was designed for incident reporting systems. Results: The top-performing classifier using contextual representation was able to obtain an accuracy of 75.4% (95/126) compared to an accuracy of 66.7% (84/126) by the top-performing classifier trained using static text representation. A PSE reporting interface has been designed to facilitate human-AI collaboration in PSE report classification. In this design, the ML classifier recommends the top 2 most probable event types, along with the explanations for the prediction, enabling PSE reporters and patient safety analysts to choose the most suitable one. The LIME technique showed that the classifier occasionally relies on arbitrary words for classification, emphasizing the necessity of human oversight. Conclusions: This study demonstrates that training ML classifiers with contextual text representations can significantly enhance the accuracy of PSE report classification. The interface designed in this study lays the foundation for human-AI collaboration in the classification of PSE reports. The insights gained from this research enhance the decision-making process in PSE report classification, enabling hospitals to more efficiently identify potential risks and hazards and enabling patient safety analysts to take timely actions to prevent patient harm. %M 38271086 %R 10.2196/53378 %U https://humanfactors.jmir.org/2024/1/e53378 %U https://doi.org/10.2196/53378 %U http://www.ncbi.nlm.nih.gov/pubmed/38271086 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e52495 %T Characterizing and Comparing Adverse Drug Events Documented in 2 Spontaneous Reporting Systems in the Lower Mainland of British Columbia, Canada: Retrospective Observational Study %A Lau,Erica Y %A Cragg,Amber %A Small,Serena S %A Butcher,Katherine %A Hohl,Corinne M %+ Department of Emergency Medicine, University of British Columbia, 7th Floor, 828 West 10th Avenue Research Pavilion, Vancouver, BC, V5Z 1M9, Canada, 1 6048754111 ext 68926, erica.lau@ubc.ca %K adverse drug event reporting systems %K side effect %K side effects %K drug %K drugs %K pharmacy %K pharmacology %K pharmacotherapy %K pharmaceutic %K pharmaceutics %K pharmaceuticals %K pharmaceutical %K medication %K medications %K patient safety %K health information technology %K pharmacovigilance %K adverse %K safety %K HIT %K information system %K information systems %K reporting %K descriptive statistics %K monitoring %D 2024 %7 18.1.2024 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Robust adverse drug event (ADE) reporting systems are crucial to monitor and identify drug safety signals, but the quantity and type of ADEs captured may vary by system characteristics. Objective: We compared ADEs reported in 2 different reporting systems in the same jurisdictions, the Patient Safety and Learning System–Adverse Drug Reaction (PSLS-ADR) and ActionADE, to understand report variation. Methods: This retrospective observational study analyzed reports entered into PSLS-ADR and ActionADE systems between December 1, 2019, and December 31, 2022. We conducted a comprehensive analysis including all events from both reporting systems to examine coverage and usage and understand the types of events captured in both systems. We calculated descriptive statistics for reporting facility type, patient demographics, serious events, and most reported drugs. We conducted a subanalysis focused on adverse drug reactions to enable direct comparisons between systems in terms of the volume and events reported. We stratified results by reporting system. Results: We performed the comprehensive analysis on 3248 ADE reports, of which 12.4% (375/3035) were reported in PSLS-ADR and 87.6% (2660/3035) were reported in ActionADE. Distribution of all events and serious events varied slightly between the 2 systems. Iohexol, gadobutrol, and empagliflozin were the most common culprit drugs (173/375, 46.2%) in PSLS-ADR, while hydrochlorothiazide, apixaban, and ramipril (308/2660, 11.6%) were common in ActionADE. We included 2728 reports in the subanalysis of adverse drug reactions, of which 12.9% (353/2728) were reported in PSLS-ADR and 86.4% (2357/2728) were reported in ActionADE. ActionADE captured 4- to 6-fold more comparable events than PSLS-ADR over this study’s period. Conclusions: User-friendly and robust reporting systems are vital for pharmacovigilance and patient safety. This study highlights substantial differences in ADE data that were generated by different reporting systems. Understanding system factors that lead to varying reporting patterns can enhance ADE monitoring and should be taken into account when evaluating drug safety signals. %M 38236629 %R 10.2196/52495 %U https://humanfactors.jmir.org/2024/1/e52495 %U https://doi.org/10.2196/52495 %U http://www.ncbi.nlm.nih.gov/pubmed/38236629 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e51921 %T Designing Human-Centered AI to Prevent Medication Dispensing Errors: Focus Group Study With Pharmacists %A Zheng,Yifan %A Rowell,Brigid %A Chen,Qiyuan %A Kim,Jin Yong %A Kontar,Raed Al %A Yang,X Jessie %A Lester,Corey A %+ Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, 428 Church St, Ann Arbor, MI, 48109, United States, 1 734 647 8849, lesterca@umich.edu %K artificial intelligence %K communication %K design methods %K design %K development %K engineering %K focus groups %K human-computer interaction %K medication errors %K morbidity %K mortality %K patient safety %K safety %K SEIPS %K Systems Engineering Initiative for Patient Safety %K tool %K user-centered design methods %K user-centered %K visualization %D 2023 %7 25.12.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Medication errors, including dispensing errors, represent a substantial worldwide health risk with significant implications in terms of morbidity, mortality, and financial costs. Although pharmacists use methods like barcode scanning and double-checking for dispensing verification, these measures exhibit limitations. The application of artificial intelligence (AI) in pharmacy verification emerges as a potential solution, offering precision, rapid data analysis, and the ability to recognize medications through computer vision. For AI to be embraced, it must be designed with the end user in mind, fostering trust, clear communication, and seamless collaboration between AI and pharmacists. Objective: This study aimed to gather pharmacists’ feedback in a focus group setting to help inform the initial design of the user interface and iterative designs of the AI prototype. Methods: A multidisciplinary research team engaged pharmacists in a 3-stage process to develop a human-centered AI system for medication dispensing verification. To design the AI model, we used a Bayesian neural network that predicts the dispensed pills’ National Drug Code (NDC). Discussion scripts regarding how to design the system and feedback in focus groups were collected through audio recordings and professionally transcribed, followed by a content analysis guided by the Systems Engineering Initiative for Patient Safety and Human-Machine Teaming theoretical frameworks. Results: A total of 8 pharmacists participated in 3 rounds of focus groups to identify current challenges in medication dispensing verification, brainstorm solutions, and provide feedback on our AI prototype. Participants considered several teaming scenarios, generally favoring a hybrid teaming model where the AI assists in the verification process and a pharmacist intervenes based on medication risk level and the AI’s confidence level. Pharmacists highlighted the need for improving the interpretability of AI systems, such as adding stepwise checkmarks, probability scores, and details about drugs the AI model frequently confuses with the target drug. Pharmacists emphasized the need for simplicity and accessibility. They favored displaying only essential information to prevent overwhelming users with excessive data. Specific design features, such as juxtaposing pill images with their packaging for quick comparisons, were requested. Pharmacists preferred accept, reject, or unsure options. The final prototype interface included (1) checkmarks to compare pill characteristics between the AI-predicted NDC and the prescription’s expected NDC, (2) a histogram showing predicted probabilities for the AI-identified NDC, (3) an image of an AI-provided “confused” pill, and (4) an NDC match status (ie, match, unmatched, or unsure). Conclusions: In partnership with pharmacists, we developed a human-centered AI prototype designed to enhance AI interpretability and foster trust. This initiative emphasized human-machine collaboration and positioned AI as an augmentative tool rather than a replacement. This study highlights the process of designing a human-centered AI for dispensing verification, emphasizing its interpretability, confidence visualization, and collaborative human-machine teaming styles. %M 38145475 %R 10.2196/51921 %U https://formative.jmir.org/2023/1/e51921 %U https://doi.org/10.2196/51921 %U http://www.ncbi.nlm.nih.gov/pubmed/38145475 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e44768 %T Safety Evaluation in Iterative Development of Wearable Patches for Aripiprazole Tablets With Sensor: Pooled Analysis of Clinical Trials %A Jan,Michael %A Coppin-Renz,Antonia %A West,Robin %A Gallo,Christophe Le %A Cochran,Jeffrey M %A Heumen,Emiel van %A Fahmy,Michael %A Reuteman-Fowler,J Corey %+ Otsuka Pharma GmbH, Europa-Allee 52, Frankfurt am Main, 60327, Germany, 49 (0)151 6468280, acoppinrenz@otsuka-europe.com %K wearable sensor %K adhesive patch %K adverse events %K skin irritation %K product iteration %K mobile phone %K biocompatibility %K Abilify MyCite %K development %K sensors %K skin %K monitoring %K treatment %K schizophrenia %K bipolar disorder %K depressive disorder %K abrasions %K blisters %K dermatitis %K pain %K rash %D 2023 %7 12.12.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Wearable sensors in digital health may pose a risk for skin irritation through the use of wearable patches. Little is known about how patient- and product-related factors impact the risk of skin irritation. Aripiprazole tablets with sensor (AS, Abilify MyCite; Otsuka America Pharmaceutical, Inc) is a digital medicine system indicated for the treatment of patients with schizophrenia, bipolar I disorder, and major depressive disorder. AS includes aripiprazole tablets with an embedded ingestible event marker, a wearable sensor attached to the skin through a wearable patch, a smartphone app, and a web-based portal. To continuously improve the final product, successive iterations of wearable patches were developed, including raisin patch version 4 (RP4), followed by disposable wearable sensor version 5 (DW5), and then reusable wearable sensor version 2 (RW2). Objective: This analysis pooled safety data from clinical studies in adult participants using the RP4, DW5, and RW2 wearable patches of AS and evaluated adverse events related to the use of wearable patches. Methods: Safety data from 12 studies in adults aged 18-65 years from May 2010 to August 2020 were analyzed. All studies evaluated safety, with studies less than 2 weeks also specifically examining human factors associated with the use of the components of AS. Healthy volunteers or patients with schizophrenia, bipolar I disorder, or major depressive disorder were enrolled; those who were exposed to at least 1 wearable patch were included in the safety analysis. Adverse events related to the use of a wearable patch were evaluated. Abrasions, blisters, dermatitis, discoloration, erythema, irritation, pain, pruritus, rash, and skin reactions were grouped as skin irritation events (SIEs). All statistical analyses were descriptive. Results: The analysis included 763 participants (mean [SD] age 42.6 [12.9] years; White: n=359, 47.1%; and male: n=420, 55%). Participants were healthy volunteers (n=269, 35.3%) or patients with schizophrenia (n=402, 52.7%), bipolar I disorder (n=57, 7.5%), or major depressive disorder (n=35, 4.6%). Overall, 13.6% (104/763) of the participants reported at least 1 SIE, all of which were localized to the wearable patch site. Incidence of ≥1 patch-related SIEs was seen in 18.1% (28/155), 14.2% (55/387), and 9.2% (28/306) of participants who used RP4, DW5, and RW2, respectively. Incidence of SIE-related treatment discontinuation was low, which is reported by 1.9% (3/155), 3.1% (12/387), and 1.3% (4/306) of participants who used RP4, DW5, and RW2, respectively. Conclusions: The incidence rates of SIEs reported as the wearable patch versions evolved from RP4 through RW2 suggest that information derived from reported adverse events may have informed product design and development, which could have improved both tolerability and wearability of successive products. Trial Registration: Clinicaltrials.gov NCT02091882, https://clinicaltrials.gov/study/NCT02091882; Clinicaltrials.gov NCT02404532, https://clinicaltrials.gov/study/NCT02404532; Clinicaltrials.gov NCT02722967, https://clinicaltrials.gov/study/NCT02722967; Clinicaltrials.gov NCT02219009, https://clinicaltrials.gov/study/NCT02219009; Clinicaltrials.gov NCT03568500, https://clinicaltrials.gov/study/NCT03568500; Clinicaltrials.gov NCT03892889, https://clinicaltrials.gov/study/NCT03892889 %M 38085556 %R 10.2196/44768 %U https://formative.jmir.org/2023/1/e44768 %U https://doi.org/10.2196/44768 %U http://www.ncbi.nlm.nih.gov/pubmed/38085556 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 10 %N %P e49025 %T Role of Individual Clinician Authority in the Implementation of Informatics Tools for Population-Based Medication Management: Qualitative Semistructured Interview Study %A Ranusch,Allison %A Lin,Ying-Jen %A Dorsch,Michael P %A Allen,Arthur L %A Spoutz,Patrick %A Seagull,F Jacob %A Sussman,Jeremy B %A Barnes,Geoffrey D %+ Center for Bioethics and Social Sciences in Medicine, University of Michigan, 2800 Plymouth Rd, B14 G214, Ann Arbor, MI, 48109, United States, 1 734 763 0047, gbarnes@umich.edu %K direct oral anticoagulant %K population management %K implementation science %K medical informatics %K individual clinician authority %K electronic health record %K health records %K EHR %K EHRs %K implementation %K clotting %K clot %K clots %K anticoagulant %K anticoagulants %K dashboard %K DOAC %K satisfaction %K interview %K interviews %K pharmacist %K pharmacy %K pharmacology %K medication %K prescribe %K prescribing %D 2023 %7 24.10.2023 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Direct oral anticoagulant (DOAC) medications are frequently associated with inappropriate prescribing and adverse events. To improve the safe use of DOACs, health systems are implementing population health tools within their electronic health record (EHR). While EHR informatics tools can help increase awareness of inappropriate prescribing of medications, a lack of empowerment (or insufficient empowerment) of nonphysicians to implement change is a key barrier. Objective: This study examined how the individual authority of clinical pharmacists and anticoagulation nurses is impacted by and changes the implementation success of an EHR DOAC Dashboard for safe DOAC medication prescribing. Methods: We conducted semistructured interviews with pharmacists and nurses following the implementation of the EHR DOAC Dashboard at 3 clinical sites. Interview transcripts were coded according to the key determinants of implementation success. The intersections between individual clinician authority and other determinants were examined to identify themes. Results: A high level of individual clinician authority was associated with high levels of key facilitators for effective use of the DOAC Dashboard (communication, staffing and work schedule, job satisfaction, and EHR integration). Conversely, a lack of individual authority was often associated with key barriers to effective DOAC Dashboard use. Positive individual authority was sometimes present with a negative example of another determinant, but no evidence was found of individual authority co-occurring with a positive instance of another determinant. Conclusions: Increased individual clinician authority is a necessary antecedent to the effective implementation of an EHR DOAC Population Management Dashboard and positively affects other aspects of implementation. International Registered Report Identifier (IRRID): RR2-10.1186/s13012-020-01044-5 %M 37874636 %R 10.2196/49025 %U https://humanfactors.jmir.org/2023/1/e49025 %U https://doi.org/10.2196/49025 %U http://www.ncbi.nlm.nih.gov/pubmed/37874636 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 10 %N %P e49490 %T Perceived Patient Workload and Its Impact on Outcomes During New Cancer Patient Visits: Analysis of a Convenience Sample %A Elkefi,Safa %A Asan,Onur %+ School of Systems and Enterprises, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, United States, 1 4145264330, oasan@stevens.edu %K health care %K cancer patients’ workload %K trust %K satisfaction %K health information technology %D 2023 %7 18.8.2023 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Studies exploring the workload in health care focus on the doctors’ perspectives. The ecology of the health care environment is critical and different for doctors and patients. Objective: In this study, we explore the patient workload among newly diagnosed patients with cancer during their first visit and its impact on the patient’s perceptions of the quality of care (their trust in their doctors, their satisfaction with the care visits, their perception of technology use). Methods: We collected data from the Hackensack Meridian Health, John Theurer Cancer Center between February 2021 and May 2022. The technology use considered during the visit is related to doctors’ use of electronic health records. A total of 135 participants were included in the study. Most participants were 50-64 years old (n=91, 67.41%). A majority (n=81, 60%) of them were White, and only (n=16, 11.85%) went to graduate schools. Results: The findings captured the significant effect of overall workload on trust in doctors and perception of health IT use within the visits. On the other hand, the overall workload did not impact patients’ satisfaction during the visit. A total of 80% (n=108) of patients experienced an overall high level of workload. Despite almost 55% (n=75) of them experiencing a high mental load, 71.1% (n=96) reported low levels of effort, 89% (n=120) experienced low time pressure, 85.2% (n=115) experienced low frustration levels, and 69.6% (n=94) experienced low physical activity. The more overall workload patients felt, the less they trusted their doctors (odds ratio [OR] 0.059, 95% CI 0.001-2.34; P=.007). Low trust was also associated with the demanding mental tasks in the visits (OR 0.055, 95% CI 0.002-2.64; P<.001), the physical load (OR 0.194, 95% CI 0.004-4.23; P<.001), the time load (OR 0.183, 95% CI 0.02-2.35; P=.046) the effort needed to cope with the environment (OR 0.163, 95% CI 0.05-1.69; P<.001), and the frustration levels (OR 0.323, 95% CI 0.04-2.55; P=.03). The patients’ perceptions of electronic health record use during the visit were negatively impacted by the overall workload experienced by the patients (OR 0.315, 95% CI 0.08-6.35; P=.01) and the high frustration level experienced (OR 0.111, 95% CI 0.015-3.75; P<.001). Conclusions: The study’s findings established pathways for future research and have implications for cancer patients’ workload. Better technology design and use can minimize perceived workload, which might contribute to the trust relationship between doctors and patients in this critical environment. Future human factors work needs to explore the workload and driving factors in longitudinal studies and assess whether these workloads might contribute to unintended patient outcomes and medical errors. %M 37594798 %R 10.2196/49490 %U https://humanfactors.jmir.org/2023/1/e49490 %U https://doi.org/10.2196/49490 %U http://www.ncbi.nlm.nih.gov/pubmed/37594798 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 10 %N %P e44089 %T Developing Implementation Strategies to Support the Uptake of a Risk Tool to Aid Physicians in the Clinical Management of Patients With Syncope: Systematic Theoretical and User-Centered Design Approach %A Rouleau,Geneviève %A Thiruganasambandamoorthy,Venkatesh %A Wu,Kelly %A Ghaedi,Bahareh %A Nguyen,Phuong Anh %A Desveaux,Laura %+ Institute for Health System Solutions and Virtual Care, Women’s College Hospital, 76, Grenville Street, Toronto, ON, M5S1B2, Canada, 1 438 392 1857, genevieve.rouleau02@uqo.ca %K emergency medicine %K physicians %K qualitative research %K risk management %K syncope %K user-centered design %D 2023 %7 13.6.2023 %9 Original Paper %J JMIR Hum Factors %G English %X Background: The Canadian Syncope Risk Score (CSRS) was developed to improve syncope management in emergency department settings. Evidence-based tools often fail to have the intended impact because of suboptimal uptake or poor implementation. Objective: In this paper, we aimed to describe the process of developing evidence-based implementation strategies to support the deployment and use of the CSRS in real-world emergency department settings to improve syncope management among physicians. Methods: We followed a systematic approach for intervention development, including identifying who needs to do what differently, identifying the barriers and enablers to be addressed, and identifying the intervention components and modes of delivery to overcome the identified barriers. We used the Behaviour Change Wheel to guide the selection of implementation strategies. We engaged CSRS end users (ie, emergency medicine physicians) in a user-centered design approach to generate and refine strategies. This was achieved over a series of 3 qualitative user-centered design workshops lasting 90 minutes each with 3 groups of emergency medicine physicians. Results: A total of 14 physicians participated in the workshops. The themes were organized according to the following intervention development steps: theme 1—identifying and refining barriers and theme 2—identifying the intervention components and modes of delivery. Theme 2 was subdivided into two subthemes: (1) generating high-level strategies and developing strategies prototypes and (2) refining and testing strategies. The main strategies identified to overcome barriers included education in the format of meetings, videos, journal clubs, and posters (to address uncertainty around when and how to apply the CSRS); the development of a web-based calculator and integration into the electronic medical record (to address uncertainty in how to apply the CSRS); a local champion (to address the lack of team buy-in); and the dissemination of evidence summaries and feedback through email communications (to address a lack of evidence about impact). Conclusions: The ability of the CSRS to effectively improve patient safety and syncope management relies on broad buy-in and uptake across physicians. To ensure that the CSRS is well positioned for impact, a comprehensive suite of strategies was identified to address known barriers. %M 37310783 %R 10.2196/44089 %U https://humanfactors.jmir.org/2023/1/e44089 %U https://doi.org/10.2196/44089 %U http://www.ncbi.nlm.nih.gov/pubmed/37310783 %0 Journal Article %@ 2561-9128 %I JMIR Publications %V 6 %N %P e34453 %T Patient Safety of Perioperative Medication Through the Lens of Digital Health and Artificial Intelligence %A Ye,Jiancheng %+ Feinberg School of Medicine, Northwestern University, 633 N Saint Clair St, Chicago, IL, 60611, United States, 1 312 503 3690, jiancheng.ye@u.northwestern.edu %K perioperative medicine %K patient safety %K anesthesiology %K human factors %K medication errors %K digital health %K health information technology %D 2023 %7 31.5.2023 %9 Viewpoint %J JMIR Perioper Med %G English %X Perioperative medication has made significant contributions to enhancing patient safety. Nevertheless, administering medication during this period still poses considerable safety concerns, with many errors being detected only after causing significant physiological disturbances. The intricacy of medication administration in the perioperative setting poses specific challenges to patient safety. To address these challenges, implementing potential strategies and interventions is critical. One such strategy is raising awareness and revising educational curricula regarding drug safety in the operating room. Another crucial strategy is recognizing the importance of redundancy and multiple checks in the operating room as a hallmark of medication safety, which is not a common practice. Digital health technologies and artificial intelligence (AI) also offer the potential to improve perioperative medication safety. Computerized physician order entry systems, electronic medication administration records, and barcode medication administration systems have been proven to reduce medication errors and improve patient safety. By implementing these strategies and interventions, health care professionals can enhance the safety of perioperative medication administration and improve patient outcomes. %M 37256663 %R 10.2196/34453 %U https://periop.jmir.org/2023/1/e34453 %U https://doi.org/10.2196/34453 %U http://www.ncbi.nlm.nih.gov/pubmed/37256663 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e43538 %T Safe Medication in Nursing Home Residents Through the Development and Evaluation of an Intervention (SAME): Protocol for a Fully Integrated Mixed Methods Study With a Cocreative Approach %A Juhl,Marie Haase %A Soerensen,Ann Lykkegaard %A Kristensen,Jette Kolding %A Johnsen,Søren Paaske %A Olesen,Anne Estrup %+ Department of Clinical Pharmacology, Aalborg University Hospital, Gartnerboligen, groundfloor, Mølleparkvej 8a, Aalborg, 9000, Denmark, 45 97664376, aneso@rn.dk %K protocols and guidelines %K medication safety %K cocreation %K user involvement %K health and safety within primary care %K research in nursing home care %K mixed methods %D 2023 %7 31.3.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: Medication safety is increasingly challenging patient safety in growing aging populations. Developing positive patient safety cultures is acknowledged as a primary goal to improve patient safety, but evidence on the interventions to do so is inconclusive. Nursing home residents are often cognitively and physically impaired and are therefore highly reliant on frontline health care providers. Thus, interventions to improve medication safety of nursing home residents through patient safety culture among providers are needed. Using cocreative partnerships, integrating knowledge of residents and their relatives, and ensuring managerial support could be beneficial. Objective: The primary aim of the Safe Medication of Nursing Home Residents Through Development and Evaluation of an Intervention (SAME) study is to improve medication safety for nursing home residents through developing an intervention by gaining experiential knowledge of patient safety culture in cocreative partnerships, integrating knowledge of residents and their relatives, and ensuring managerial support. Methods: The fully integrated mixed method study will be conducted using an integrated knowledge translation approach. Patient safety culture within nursing homes will first be explored through qualitative focus groups (stage 1) including nursing home residents, their relatives, and frontline health care providers. This will inform the development of an intervention in a multidisciplinary panel (stage 2) including cocreators representing the medication management process across the health care system. Evaluation of the intervention will be done in a randomized controlled trial set at nursing homes (stage 3). The primary outcome will be changes in the mean scale score of an adapted version of the Danish “Safety Attitudes Questionnaire” (SAQ-DK) for use in nursing homes. Patient safety–related outcomes will be collected through Danish health registers to assess safety issues and effects, including medication, contacts to health care, diagnoses, and mortality. Finally, a mixed methods analysis on patient safety culture in nursing homes will be done (stage 4), integrating qualitative data (stage 1) and quantitative data (stage 3) to comprehensively understand patient safety culture as a key to medication safety. Results: The SAME study is ongoing. Focus groups were carried out from April 2021 to September 2021 and the workshop in September 2021. Baseline SAQ-DK data were collected in January 2022 with expected follow-up in January 2023. Final data analysis is expected in spring 2024. Conclusions: The SAME study will help not only to generate evidence on interventions to improve medication safety of nursing home residents through patient safety culture but also to give insight into possible impacts of using cocreativity to guide the development. Thus, findings will address multiple gaps in evidence to guide future patient safety improvement efforts within primary care settings of political and scientific scope. Trial Registration: ClinicalTrials.gov NCT04990986; https://clinicaltrials.gov/ct2/show/NCT04990986 International Registered Report Identifier (IRRID): DERR1-10.2196/43538 %M 37000508 %R 10.2196/43538 %U https://www.researchprotocols.org/2023/1/e43538 %U https://doi.org/10.2196/43538 %U http://www.ncbi.nlm.nih.gov/pubmed/37000508 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 10 %N %P e43729 %T Evaluating the Usability of an Emergency Department After Visit Summary: Staged Heuristic Evaluation %A Barton,Hanna J %A Salwei,Megan E %A Rutkowski,Rachel A %A Wust,Kathryn %A Krause,Sheryl %A Hoonakker,Peter LT %A Dail,Paula vW %A Buckley,Denise M %A Eastman,Alexis %A Ehlenfeldt,Brad %A Patterson,Brian W %A Shah,Manish N %A King,Barbara J %A Werner,Nicole E %A Carayon,Pascale %+ Wisconsin Institute for Healthcare Systems Engineering, University of Wisconsin-Madison, 3139 Engineering Centers Building, 1550 Engineering Drive, Madison, WI, 53706, United States, 1 6083586120, hbarton@wisc.edu %K patient safety %K heuristic evaluation %K usability %K emergency medicine %K safety %K emergency %K human factors engineering %K usability %K discharge summary %K documentation %K heuristic %D 2023 %7 9.3.2023 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Heuristic evaluations, while commonly used, may inadequately capture the severity of identified usability issues. In the domain of health care, usability issues can pose different levels of risk to patients. Incorporating diverse expertise (eg, clinical and patient) in the heuristic evaluation process can help assess and address potential negative impacts on patient safety that may otherwise go unnoticed. One document that should be highly usable for patients—with the potential to prevent adverse outcomes—is the after visit summary (AVS). The AVS is the document given to a patient upon discharge from the emergency department (ED), which contains instructions on how to manage symptoms, medications, and follow-up care. Objective: This study aims to assess a multistage method for integrating diverse expertise (ie, clinical, an older adult care partner, and health IT) with human factors engineering (HFE) expertise in the usability evaluation of the patient-facing ED AVS. Methods: We conducted a three-staged heuristic evaluation of an ED AVS using heuristics developed for use in evaluating patient-facing documentation. In stage 1, HFE experts reviewed the AVS to identify usability issues. In stage 2, 6 experts of varying expertise (ie, emergency medicine physicians, ED nurses, geriatricians, transitional care nurses, and an older adult care partner) rated each previously identified usability issue on its potential impact on patient comprehension and patient safety. Finally, in stage 3, an IT expert reviewed each usability issue to identify the likelihood of successfully addressing the issue. Results: In stage 1, we identified 60 usability issues that violated a total of 108 heuristics. In stage 2, 18 additional usability issues that violated 27 heuristics were identified by the study experts. Impact ratings ranged from all experts rating the issue as “no impact” to 5 out of 6 experts rating the issue as having a “large negative impact.” On average, the older adult care partner representative rated usability issues as being more significant more of the time. In stage 3, 31 usability issues were rated by an IT professional as “impossible to address,” 21 as “maybe,” and 24 as “can be addressed.” Conclusions: Integrating diverse expertise when evaluating usability is important when patient safety is at stake. The non-HFE experts, included in stage 2 of our evaluation, identified 23% (18/78) of all the usability issues and, depending on their expertise, rated those issues as having differing impacts on patient comprehension and safety. Our findings suggest that, to conduct a comprehensive heuristic evaluation, expertise from all the contexts in which the AVS is used must be considered. Combining those findings with ratings from an IT expert, usability issues can be strategically addressed through redesign. Thus, a 3-staged heuristic evaluation method offers a framework for integrating context-specific expertise efficiently, while providing practical insights to guide human-centered design. %M 36892941 %R 10.2196/43729 %U https://humanfactors.jmir.org/2023/1/e43729 %U https://doi.org/10.2196/43729 %U http://www.ncbi.nlm.nih.gov/pubmed/36892941 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 10 %N %P e39114 %T Intensive Care Unit Physicians’ Perspectives on Artificial Intelligence–Based Clinical Decision Support Tools: Preimplementation Survey Study %A van der Meijden,Siri L %A de Hond,Anne A H %A Thoral,Patrick J %A Steyerberg,Ewout W %A Kant,Ilse M J %A Cinà,Giovanni %A Arbous,M Sesmu %+ Department of Intensive Care Medicine, Leiden University Medical Center, Albinusdreef 2, Leiden, 2333 ZA, Netherlands, 31 71 526 9111, S.L.van_der_meijden@lumc.nl %K intensive care unit %K hospital %K discharge %K artificial intelligence %K AI %K clinical decision support %K clinical support %K acceptance %K decision support %K decision-making %K digital health %K eHealth %K survey %K perspective %K attitude %K opinion %K adoption %K prediction %K risk %D 2023 %7 5.1.2023 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Artificial intelligence–based clinical decision support (AI-CDS) tools have great potential to benefit intensive care unit (ICU) patients and physicians. There is a gap between the development and implementation of these tools. Objective: We aimed to investigate physicians’ perspectives and their current decision-making behavior before implementing a discharge AI-CDS tool for predicting readmission and mortality risk after ICU discharge. Methods: We conducted a survey of physicians involved in decision-making on discharge of patients at two Dutch academic ICUs between July and November 2021. Questions were divided into four domains: (1) physicians’ current decision-making behavior with respect to discharging ICU patients, (2) perspectives on the use of AI-CDS tools in general, (3) willingness to incorporate a discharge AI-CDS tool into daily clinical practice, and (4) preferences for using a discharge AI-CDS tool in daily workflows. Results: Most of the 64 respondents (of 93 contacted, 69%) were familiar with AI (62/64, 97%) and had positive expectations of AI, with 55 of 64 (86%) believing that AI could support them in their work as a physician. The respondents disagreed on whether the decision to discharge a patient was complex (23/64, 36% agreed and 22/64, 34% disagreed); nonetheless, most (59/64, 92%) agreed that a discharge AI-CDS tool could be of value. Significant differences were observed between physicians from the 2 academic sites, which may be related to different levels of involvement in the development of the discharge AI-CDS tool. Conclusions: ICU physicians showed a favorable attitude toward the integration of AI-CDS tools into the ICU setting in general, and in particular toward a tool to predict a patient’s risk of readmission and mortality within 7 days after discharge. The findings of this questionnaire will be used to improve the implementation process and training of end users. %M 36602843 %R 10.2196/39114 %U https://humanfactors.jmir.org/2023/1/e39114 %U https://doi.org/10.2196/39114 %U http://www.ncbi.nlm.nih.gov/pubmed/36602843 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 12 %P e41834 %T Multifactor Quality and Safety Analysis of Antimicrobial Drugs Sold by Online Pharmacies That Do Not Require a Prescription: Multiphase Observational, Content Analysis, and Product Evaluation Study %A Mackey,Tim Ken %A Jarmusch,Alan K %A Xu,Qing %A Sun,Kunyang %A Lu,Aileen %A Aguirre,Shaden %A Lim,Jessica %A Bhakta,Simran %A Dorrestein,Pieter C %+ Global Health Program, Department of Anthropology, University of California, San Diego, 9500 Gilman Drive, MC: 0505, La Jolla, CA, 92093, United States, 1 9514914161, tkmackey@ucsd.edu %K online pharmacy %K antimicrobial resistance %K drug safety %K cyberpharmacies %K public health %K health website %K online health %K web surveillance %K patient safety %D 2022 %7 23.12.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Antimicrobial resistance is a significant global public health threat. However, the impact of sourcing potentially substandard and falsified antibiotics via the internet remains understudied, particularly in the context of access to and quality of common antibiotics. In response, this study conducted a multifactor quality and safety analysis of antibiotics sold and purchased via online pharmacies that did not require a prescription. Objective: The aim of this paper is to identify and characterize “no prescription” online pharmacies selling 5 common antibiotics and to assess the quality characteristics of samples through controlled test buys. Methods: We first used structured search queries associated with the international nonproprietary names of amoxicillin, azithromycin, amoxicillin and clavulanic acid, cephalexin, and ciprofloxacin to detect and characterize online pharmacies offering the sale of antibiotics without a prescription. Next, we conducted controlled test buys of antibiotics and conducted a visual inspection of packaging and contents for risk evaluation. Antibiotics were then analyzed using untargeted mass spectrometry (MS). MS data were used to determine if the claimed active pharmaceutical ingredient was present, and molecular networking was used to analyze MS data to detect drug analogs as well as possible adulterants and contaminants. Results: A total of 109 unique websites were identified that actively advertised direct-to-consumer sale of antibiotics without a prescription. From these websites, we successfully placed 27 orders, received 11 packages, and collected 1373 antibiotic product samples. Visual inspection resulted in all product packaging consisting of pill packs or blister packs and some concerning indicators of potential poor quality, falsification, and improper dispensing. Though all samples had the presence of stated active pharmaceutical ingredient, molecular networking revealed a number of drug analogs of unknown identity, as well as known impurities and contaminants. Conclusions: Our study used a multifactor approach, including web surveillance, test purchasing, and analytical chemistry, to assess risk factors associated with purchasing antibiotics online. Results provide evidence of possible safety risks, including substandard packaging and shipment, falsification of product information and markings, detection of undeclared chemicals, high variability of quality across samples, and payment for orders being defrauded. Beyond immediate patient safety risks, these falsified and substandard products could exacerbate the ongoing public health threat of antimicrobial resistance by circulating substandard product to patients. %M 36563038 %R 10.2196/41834 %U https://publichealth.jmir.org/2022/12/e41834 %U https://doi.org/10.2196/41834 %U http://www.ncbi.nlm.nih.gov/pubmed/36563038 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 4 %P e38411 %T Clinicians’ Perceptions of an Artificial Intelligence–Based Blood Utilization Calculator: Qualitative Exploratory Study %A Choudhury,Avishek %A Asan,Onur %A Medow,Joshua E %+ Industrial and Management Systems Engineering, Benjamin M Statler College of Engineering and Mineral Resources, West Virginia University, 1306 Evansdale Drive, PO Box 6107, Morgantown, WV, 26506-6107, United States, 1 3042939431, avishek.choudhury@mail.wvu.edu %K artificial intelligence %K human factors %K decision-making %K blood transfusion %K technology acceptance %K complications %K prevention %K decision support %K transfusion overload %K risk %K support %K perception %K safety %K usability %D 2022 %7 31.10.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: According to the US Food and Drug Administration Center for Biologics Evaluation and Research, health care systems have been experiencing blood transfusion overuse. To minimize the overuse of blood product transfusions, a proprietary artificial intelligence (AI)–based blood utilization calculator (BUC) was developed and integrated into a US hospital’s electronic health record. Despite the promising performance of the BUC, this technology remains underused in the clinical setting. Objective: This study aims to explore how clinicians perceived this AI-based decision support system and, consequently, understand the factors hindering BUC use. Methods: We interviewed 10 clinicians (BUC users) until the data saturation point was reached. The interviews were conducted over a web-based platform and were recorded. The audiovisual recordings were then anonymously transcribed verbatim. We used an inductive-deductive thematic analysis to analyze the transcripts, which involved applying predetermined themes to the data (deductive) and consecutively identifying new themes as they emerged in the data (inductive). Results: We identified the following two themes: (1) workload and usability and (2) clinical decision-making. Clinicians acknowledged the ease of use and usefulness of the BUC for the general inpatient population. The clinicians also found the BUC to be useful in making decisions related to blood transfusion. However, some clinicians found the technology to be confusing due to inconsistent automation across different blood work processes. Conclusions: This study highlights that analytical efficacy alone does not ensure technology use or acceptance. The overall system’s design, user perception, and users’ knowledge of the technology are equally important and necessary (limitations, functionality, purpose, and scope). Therefore, the effective integration of AI-based decision support systems, such as the BUC, mandates multidisciplinary engagement, ensuring the adequate initial and recurrent training of AI users while maintaining high analytical efficacy and validity. As a final takeaway, the design of AI systems that are made to perform specific tasks must be self-explanatory, so that the users can easily understand how and when to use the technology. Using any technology on a population for whom it was not initially designed will hinder user perception and the technology’s use. %M 36315238 %R 10.2196/38411 %U https://humanfactors.jmir.org/2022/4/e38411 %U https://doi.org/10.2196/38411 %U http://www.ncbi.nlm.nih.gov/pubmed/36315238 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 4 %P e37905 %T The Introduction of Robotics to an Outpatient Dispensing and Medication Management Process in Saudi Arabia: Retrospective Review of a Pharmacy-led Multidisciplinary Six Sigma Performance Improvement Project %A Al Nemari,Manal %A Waterson,James %+ Medication Management Solutions, Medical Affairs, Becton Dickinson, 11F Blue Bay Tower, Business Bay, Dubai, 52279, United Arab Emirates, 971 0566035154, james.waterson@bd.com %K inventory waste %K mislabeling events %K no-show returns %K inventory stock levels %K staff education %K task realignment %K outpatient %K Six Sigma %K medication management %K medication adherence %K risk %K pharmacy %K health care professional %K dispensing %K robotics %K automation %K pharmaceuticals %K inventory %D 2022 %7 11.10.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Outpatient pharmacy management aims for improved patient safety, improved quality of service, and cost reduction. The Six Sigma method improves quality by eliminating variability, with the goal of a nearly error-free process. Automation of pharmacy tasks potentially offers greater efficiency and safety. Objective: The goal was to measure the impact that integration of automation made to service, safety and efficiency, staff reallocation and reorientation, and workflow in the outpatient pharmacy department. The Six Sigma problem definition to be resolved was as follows: The current system of outpatient dispensing denies quality to patients in terms of waiting time and contact time with pharmacy professionals, incorporates risks to the patient in terms of mislabeling of medications and the incomplete dispensing of prescriptions, and is potentially wasteful in terms of time and resources. Methods: We described the process of introducing automation to a large outpatient pharmacy department in a university hospital. The Six Sigma approach was used as it focuses on continuous improvement and also produces a road map that integrates tracking and monitoring into its process. A review of activity in the outpatient department focused on non-value-added (NVA) pharmacist tasks, improving the patient experience and patient safety. Metrics to measure the impact of change were established, and a process map analysis with turnaround times (TATs) for each stage of service was created. Discrete events were selected for correction, improvement, or mitigation. From the review, the team selected key outcome metrics, including storage, picking and delivery dispensing rates, patient and prescription load per day, average packs and lines per prescription, and lines held. Our goal was total automation of stock management. We deployed 2 robotic dispensing units to feed 9 dispensing desks. The automated units were integrated with hospital information technology (HIT) that supports appointments, medication records, and prescriptions. Results: Postautomation, the total patient time in the department, including the time interacting with the pharmacist for medication education and counseling, dropped from 17.093 to 11.812 digital minutes, with an appreciable increase in patient-pharmacist time. The percentage of incomplete prescriptions dispensed versus orders decreased from 3.0% to 1.83%. The dispensing error rate dropped from 1.00% to 0.24%. Assessed via a “basket” of medications, wastage cost was reduced by 83.9%. During implementation, it was found that NVA tasks that were replaced by automated processes were responsible for an extensive loss of pharmacist time. The productivity ratio postautomation was 1.26. Conclusions: The Six Sigma methodology allowed for rapid transformation of the medication management process. The risk priority numbers (RPNs) for the “wrong patient-wrong medication error” reduced by a ratio of 5.25:1 and for “patient leaves unit with inadequate counseling” postautomation by 2.5:1. Automation allowed for ring-fencing of patient-pharmacist time. This time needs to be structured for optimal effectiveness. %M 36222805 %R 10.2196/37905 %U https://humanfactors.jmir.org/2022/4/e37905 %U https://doi.org/10.2196/37905 %U http://www.ncbi.nlm.nih.gov/pubmed/36222805 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 3 %P e39234 %T Electronic Diagnostic Support in Emergency Physician Triage: Qualitative Study With Thematic Analysis of Interviews %A Sibbald,Matthew %A Abdulla,Bashayer %A Keuhl,Amy %A Norman,Geoffrey %A Monteiro,Sandra %A Sherbino,Jonathan %+ McMaster Education Research, Innovation & Theory (MERIT) Program, Department of Medicine, McMaster University, 100 Main Street West, Hamilton, ON, L8P 1H6, Canada, 1 905 921 2101 ext 44477, matthew.sibbald@medportal.ca %K electronic differential diagnostic support %K clinical reasoning %K natural language processing %K triage %K diagnostic error %K human factors %K diagnosis %K diagnostic %K emergency %K artificial intelligence %K adoption %K attitude %K support system %K automation %D 2022 %7 30.9.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Not thinking of a diagnosis is a leading cause of diagnostic error in the emergency department, resulting in delayed treatment, morbidity, and excess mortality. Electronic differential diagnostic support (EDS) results in small but significant reductions in diagnostic error. However, the uptake of EDS by clinicians is limited. Objective: We sought to understand physician perceptions and barriers to the uptake of EDS within the emergency department triage process. Methods: We conducted a qualitative study using a research associate to rapidly prototype an embedded EDS into the emergency department triage process. Physicians involved in the triage assessment of a busy emergency department were provided the output of an EDS based on the triage complaint by an embedded researcher to simulate an automated system that would draw from the electronic medical record. Physicians were interviewed immediately after their experience. Verbatim transcripts were analyzed by a team using open and axial coding, informed by direct content analysis. Results: In all, 4 themes emerged from 14 interviews: (1) the quality of the EDS was inferred from the scope and prioritization of the diagnoses present in the EDS differential; (2) the trust of the EDS was linked to varied beliefs around the diagnostic process and potential for bias; (3) clinicians foresaw more benefit to EDS use for colleagues and trainees rather than themselves; and (4) clinicians felt strongly that EDS output should not be included in the patient record. Conclusions: The adoption of an EDS into an emergency department triage process will require a system that provides diagnostic suggestions appropriate for the scope and context of the emergency department triage process, transparency of system design, and affordances for clinician beliefs about the diagnostic process and addresses clinician concern around including EDS output in the patient record. %M 36178728 %R 10.2196/39234 %U https://humanfactors.jmir.org/2022/3/e39234 %U https://doi.org/10.2196/39234 %U http://www.ncbi.nlm.nih.gov/pubmed/36178728 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 8 %P e40445 %T A Novel Theory-Based Virtual Reality Training to Improve Patient Safety Culture in the Department of Surgery of a Large Academic Medical Center: Protocol for a Mixed Methods Study %A Mazur,Lukasz M %A Khasawneh,Amro %A Fenison,Christi %A Buchanan,Shawna %A Kratzke,Ian M %A Adapa,Karthik %A An,Selena J %A Butler,Logan %A Zebrowski,Ashlyn %A Chakravarthula,Praneeth %A Ra,Jin H %+ Division of Healthcare Engineering, Department of Radiation Oncology, School of Medicine, University of North Carolina, 101 Manning Drive, Chapel Hill, NC, 27713, United States, 1 9196169702, lmazur@med.unc.edu %K virtual reality training %K patient safety culture %K patient safety events %K sensemaking %K high reliability organizations %D 2022 %7 24.8.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Preventable surgical errors of varying degrees of physical, emotional, and financial harm account for a significant number of adverse events. These errors are frequently tied to systemic problems within a health care system, including the absence of necessary policies/procedures, obstructive cultural hierarchy, and communication breakdown between staff. We developed an innovative, theory-based virtual reality (VR) training to promote understanding and sensemaking toward the holistic view of the culture of patient safety and high reliability. Objective: We aim to assess the effect of VR training on health care workers’ (HCWs’) understanding of contributing factors to patient safety events, sensemaking of patient safety culture, and high reliability organization principles in the laboratory environment. Further, we aim to assess the effect of VR training on patient safety culture, TeamSTEPPS behavior scores, and reporting of patient safety events in the surgery department of an academic medical center in the clinical environment. Methods: This mixed methods study uses a pre-VR versus post-VR training study design involving attending faculty, residents, nurses, technicians of the department of surgery, and frontline HCWs in the operation rooms at an academic medical center. HCWs’ understanding of contributing factors to patient safety events will be assessed using a scale based on the Human Factors Analysis and Classification System. We will use the data frame theory framework, supported by a semistructured interview guide to capture the sensemaking process of patient safety culture and principles of high reliability organizations. Changes in the culture of patient safety will be quantified using the Agency for Healthcare Research and Quality surveys on patient safety culture. TeamSTEPPS behavior scores based on observation will be measured using the Teamwork Evaluation of Non-Technical Skills tool. Patient safety events reported in the voluntary institutional reporting system will be compared before the training versus those after the training. We will compare the Agency for Healthcare Research and Quality patient safety culture scores and patient safety events reporting before the training versus those after the training by using descriptive statistics and a within-subject 2-tailed, 2-sample t test with the significance level set at .05. Results: Ethics approval was obtained in May 2021 from the institutional review board of the University of North Carolina at Chapel Hill (22-1150). The enrollment of participants for this study will start in fall 2022 and is expected to be completed by early spring 2023. The data analysis is expected to be completed by July 2023. Conclusions: Our findings will help assess the effectiveness of VR training in improving HCWs’ understanding of contributing factors of patient safety events, sensemaking of patient safety culture, and principles and behaviors of high reliability organizations. These findings will contribute to developing VR training to improve patient safety culture in other specialties. %M 36001370 %R 10.2196/40445 %U https://www.researchprotocols.org/2022/8/e40445 %U https://doi.org/10.2196/40445 %U http://www.ncbi.nlm.nih.gov/pubmed/36001370 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 3 %P e36652 %T The Perceived Effectiveness of Secure Messaging for Medication Reconciliation During Transitions of Care: Semistructured Interviews With Patients %A Brady,Julianne E %A Linsky,Amy M %A Simon,Steven R %A Yeksigian,Kate %A Rubin,Amy %A Zillich,Alan J %A Russ-Jara,Alissa L %+ Center for Healthcare Organization and Implementation Research, VA Boston Healthcare System, 150 South Huntington Ave, Boston, MA, 02130, United States, 1 857 364 5110, julianne.brady@va.gov %K medication reconciliation %K patient portals %K telemedicine %K pharmacist-patient relationship %K medication errors %D 2022 %7 3.8.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Medication discrepancies can lead to adverse drug events and patient harm. Medication reconciliation is a process intended to reduce medication discrepancies. We developed a Secure Messaging for Medication Reconciliation Tool (SMMRT), integrated into a web-based patient portal, to identify and reconcile medication discrepancies during transitions from hospital to home. Objective: We aimed to characterize patients’ perceptions of the ease of use and effectiveness of SMMRT. Methods: We recruited 20 participants for semistructured interviews from a sample of patients who had participated in a randomized controlled trial of SMMRT. Interview transcripts were transcribed and then qualitatively analyzed to identify emergent themes. Results: Although most patients found SMMRT easy to view at home, many patients struggled to return SMMRT through secure messaging to clinicians due to technology-related barriers. Patients who did use SMMRT indicated that it was time-saving and liked that they could review it at their own pace and in the comfort of their own home. Patients reported SMMRT was effective at clarifying issues related to medication directions or dosages and that SMMRT helped remove medications erroneously listed as active in the patient’s electronic health record. Conclusions: Patients viewed SMMRT utilization as a positive experience and endorsed future use of the tool. Veterans reported SMMRT is an effective tool to aid patients with medication reconciliation. Adoption of SMMRT into regular clinical practice could reduce medication discrepancies while increasing accessibility for patients to help manage their medications. Trial Registration: ClinicalTrials.gov NCT02482025; https://clinicaltrials.gov/ct2/show/NCT02482025 %M 35921139 %R 10.2196/36652 %U https://humanfactors.jmir.org/2022/3/e36652 %U https://doi.org/10.2196/36652 %U http://www.ncbi.nlm.nih.gov/pubmed/35921139 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 8 %P e35726 %T Harnessing the Electronic Health Care Record to Optimize Patient Safety in Primary Care: Framework for Evaluating e–Safety-Netting Tools %A Black,Georgia Bell %A Bhuiya,Afsana %A Friedemann Smith,Claire %A Hirst,Yasemin %A Nicholson,Brian David %+ Department of Applied Health Research, University College London, 1-19 Torrington Place, London, WC1E 7HB, United Kingdom, 44 2031083157, g.black@ucl.ac.uk %K primary care %K patient safety %K electronic health record %K safety %K optimize %K framework %K evaluation %K tool %K diagnostic %K uncertainty %K management %K netting %K software %K criteria %D 2022 %7 1.8.2022 %9 Viewpoint %J JMIR Med Inform %G English %X The management of diagnostic uncertainty is part of every primary care physician’s role. e–Safety-netting tools help health care professionals to manage diagnostic uncertainty. Using software in addition to verbal or paper based safety-netting methods could make diagnostic delays and errors less likely. There are an increasing number of software products that have been identified as e–safety-netting tools, particularly since the start of the COVID-19 pandemic. e–Safety-netting tools can have a variety of functions, such as sending clinician alerts, facilitating administrative tasking, providing decision support, and sending reminder text messages to patients. However, these tools have not been evaluated by using robust research designs for patient safety interventions. We present an emergent framework of criteria for effective e–safety-netting tools that can be used to support the development of software. The framework is based on validated frameworks for electronic health record development and patient safety. There are currently no tools available that meet all of the criteria in the framework. We hope that the framework will stimulate clinical and public conversations about e–safety-netting tools. In the future, a validated framework would drive audits and improvements. We outline key areas for future research both in primary care and within integrated care systems. %M 35916722 %R 10.2196/35726 %U https://medinform.jmir.org/2022/8/e35726 %U https://doi.org/10.2196/35726 %U http://www.ncbi.nlm.nih.gov/pubmed/35916722 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 7 %P e37226 %T Patients’ Willingness and Ability to Identify and Respond to Errors in Their Personal Health Records: Mixed Methods Analysis of Cross-sectional Survey Data %A Lear,Rachael %A Freise,Lisa %A Kybert,Matthew %A Darzi,Ara %A Neves,Ana Luisa %A Mayer,Erik K %+ National Institute for Health Research Imperial Patient Safety Translational Research Centre, Institute of Global Health Innovation, Imperial College London, St Mary's Campus 10F Queen Elizabeth Queen Mother Wing, Praed Street, London, W2 1NY, United Kingdom, 44 020 7589 5111, r.lear12@imperial.ac.uk %K electronic health records %K personal health records %K patient participation %K errors %K patient safety %K digital health literacy %D 2022 %7 8.7.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Errors in electronic health records are known to contribute to patient safety incidents; however, systems for checking the accuracy of patient records are almost nonexistent. Personal health records (PHRs) enabling patient access to and interaction with the clinical records offer a valuable opportunity for patients to actively participate in error surveillance. Objective: This study aims to evaluate patients’ willingness and ability to identify and respond to errors in their PHRs. Methods: A cross-sectional survey was conducted using a web-based questionnaire. Patient sociodemographic data were collected, including age, sex, ethnicity, educational level, health status, geographical location, motivation to self-manage, and digital health literacy (measured using the eHealth Literacy Scale tool). Patients with experience of using the Care Information Exchange (CIE) portal, who specified both age and sex, were included in these analyses. The patients’ responses to 4 relevant survey items (closed-ended questions, some with space for free-text comments) were examined to understand their willingness and ability to identify and respond to errors in their PHRs. Multinomial logistic regression was used to identify patients’ characteristics that predict the ability to understand information in the CIE and willingness to respond to errors in their records. The framework method was used to derive themes from patients’ free-text responses. Results: Of 445 patients, 181 (40.7%) “definitely” understood the CIE information and approximately half (220/445, 49.4%) understood the CIE information “to some extent.” Patients with high digital health literacy (eHealth Literacy Scale score ≥26) were more confident in their ability to understand their records compared with patients with low digital health literacy (odds ratio [OR] 7.85, 95% CI 3.04-20.29; P<.001). Information-related barriers (medical terminology and lack of medical guidance or contextual information) and system-related barriers (functionality or usability and information communicated or displayed poorly) were described. Of 445 patients, 79 (17.8%) had noticed errors in their PHRs, which were related to patient demographic details, diagnoses, medical history, results, medications, letters or correspondence, and appointments. Most patients (272/445, 61.1%) wanted to be able to flag up errors to their health professionals for correction; 20.4% (91/445) of the patients were willing to correct errors themselves. Native English speakers were more likely to be willing to flag up errors to health professionals (OR 3.45, 95% CI 1.11-10.78; P=.03) or correct errors themselves (OR 5.65, 95% CI 1.33-24.03; P=.02). Conclusions: A large proportion of patients were able and willing to identify and respond to errors in their PHRs. However, some barriers persist that disproportionately affect the underserved groups. Further development of PHR systems, including incorporating channels for patient feedback on the accuracy of their records, should address the needs of nonnative English speakers and patients with lower digital health literacy. %M 35802397 %R 10.2196/37226 %U https://www.jmir.org/2022/7/e37226 %U https://doi.org/10.2196/37226 %U http://www.ncbi.nlm.nih.gov/pubmed/35802397 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 6 %P e36774 %T Re-engineering a Clinical Trial Management System Using Blockchain Technology: System Design, Development, and Case Studies %A Zhuang,Yan %A Zhang,Luxia %A Gao,Xiyuan %A Shae,Zon-Yin %A Tsai,Jeffrey J P %A Li,Pengfei %A Shyu,Chi-Ren %+ Institute for Data Science and Informatics, University of Missouri, 22G Heinkel Building, University of Missouri, Columbia, MO, 65211-2060, United States, 1 573 882 3884, shyuc@missouri.edu %K blockchain %K clinical trials %K clinical trial management system %K electronic data capture %K smart contract %D 2022 %7 27.6.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: A clinical trial management system (CTMS) is a suite of specialized productivity tools that manage clinical trial processes from study planning to closeout. Using CTMSs has shown remarkable benefits in delivering efficient, auditable, and visualizable clinical trials. However, the current CTMS market is fragmented, and most CTMSs fail to meet expectations because of their inability to support key functions, such as inconsistencies in data captured across multiple sites. Blockchain technology, an emerging distributed ledger technology, is considered to potentially provide a holistic solution to current CTMS challenges by using its unique features, such as transparency, traceability, immutability, and security. Objective: This study aimed to re-engineer the traditional CTMS by leveraging the unique properties of blockchain technology to create a secure, auditable, efficient, and generalizable CTMS. Methods: A comprehensive, blockchain-based CTMS that spans all stages of clinical trials, including a sharable trial master file system; a fast recruitment and simplified enrollment system; a timely, secure, and consistent electronic data capture system; a reproducible data analytics system; and an efficient, traceable payment and reimbursement system, was designed and implemented using the Quorum blockchain. Compared with traditional blockchain technologies, such as Ethereum, Quorum blockchain offers higher transaction throughput and lowers transaction latency. Case studies on each application of the CTMS were conducted to assess the feasibility, scalability, stability, and efficiency of the proposed blockchain-based CTMS. Results: A total of 21.6 million electronic data capture transactions were generated and successfully processed through blockchain, with an average of 335.4 transactions per second. Of the 6000 patients, 1145 were matched in 1.39 seconds using 10 recruitment criteria with an automated matching mechanism implemented by the smart contract. Key features, such as immutability, traceability, and stability, were also tested and empirically proven through case studies. Conclusions: This study proposed a comprehensive blockchain-based CTMS that covers all stages of the clinical trial process. Compared with our previous research, the proposed system showed an overall better performance. Our system design, implementation, and case studies demonstrated the potential of blockchain technology as a potential solution to CTMS challenges and its ability to perform more health care tasks. %M 35759315 %R 10.2196/36774 %U https://www.jmir.org/2022/6/e36774 %U https://doi.org/10.2196/36774 %U http://www.ncbi.nlm.nih.gov/pubmed/35759315 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 2 %P e33960 %T Factors Influencing Clinician Trust in Predictive Clinical Decision Support Systems for In-Hospital Deterioration: Qualitative Descriptive Study %A Schwartz,Jessica M %A George,Maureen %A Rossetti,Sarah Collins %A Dykes,Patricia C %A Minshall,Simon R %A Lucas,Eugene %A Cato,Kenrick D %+ Department of Biomedical Informatics, Columbia University, 622 West 168th Street, PH20 3720, New York, NY, 10032, United States, 1 212 305 5334, jms2468@cumc.columbia.edu %K clinical decision support systems %K machine learning %K inpatient %K nurses %K physicians %K qualitative research %D 2022 %7 12.5.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Clinician trust in machine learning–based clinical decision support systems (CDSSs) for predicting in-hospital deterioration (a type of predictive CDSS) is essential for adoption. Evidence shows that clinician trust in predictive CDSSs is influenced by perceived understandability and perceived accuracy. Objective: The aim of this study was to explore the phenomenon of clinician trust in predictive CDSSs for in-hospital deterioration by confirming and characterizing factors known to influence trust (understandability and accuracy), uncovering and describing other influencing factors, and comparing nurses’ and prescribing providers’ trust in predictive CDSSs. Methods: We followed a qualitative descriptive methodology conducting directed deductive and inductive content analysis of interview data. Directed deductive analyses were guided by the human-computer trust conceptual framework. Semistructured interviews were conducted with nurses and prescribing providers (physicians, physician assistants, or nurse practitioners) working with a predictive CDSS at 2 hospitals in Mass General Brigham. Results: A total of 17 clinicians were interviewed. Concepts from the human-computer trust conceptual framework—perceived understandability and perceived technical competence (ie, perceived accuracy)—were found to influence clinician trust in predictive CDSSs for in-hospital deterioration. The concordance between clinicians’ impressions of patients’ clinical status and system predictions influenced clinicians’ perceptions of system accuracy. Understandability was influenced by system explanations, both global and local, as well as training. In total, 3 additional themes emerged from the inductive analysis. The first, perceived actionability, captured the variation in clinicians’ desires for predictive CDSSs to recommend a discrete action. The second, evidence, described the importance of both macro- (scientific) and micro- (anecdotal) evidence for fostering trust. The final theme, equitability, described fairness in system predictions. The findings were largely similar between nurses and prescribing providers. Conclusions: Although there is a perceived trade-off between machine learning–based CDSS accuracy and understandability, our findings confirm that both are important for fostering clinician trust in predictive CDSSs for in-hospital deterioration. We found that reliance on the predictive CDSS in the clinical workflow may influence clinicians’ requirements for trust. Future research should explore the impact of reliance, the optimal explanation design for enhancing understandability, and the role of perceived actionability in driving trust. %M 35550304 %R 10.2196/33960 %U https://humanfactors.jmir.org/2022/2/e33960 %U https://doi.org/10.2196/33960 %U http://www.ncbi.nlm.nih.gov/pubmed/35550304 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 2 %P e31758 %T Assessing the Usability of a Clinical Decision Support System: Heuristic Evaluation %A Cho,Hwayoung %A Keenan,Gail %A Madandola,Olatunde O %A Dos Santos,Fabiana Cristina %A Macieira,Tamara G R %A Bjarnadottir,Ragnhildur I %A Priola,Karen J B %A Dunn Lopez,Karen %+ College of Nursing, University of Florida, 1225 Center Dr, Gainesville, FL, 32611, United States, 1 3522736347, hcho@ufl.edu %K usability %K heuristic %K clinical decision support %K electronic health record %K expert review %K evaluation %K user interface %K human-computer interaction %D 2022 %7 10.5.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Poor usability is a primary cause of unintended consequences related to the use of electronic health record (EHR) systems, which negatively impacts patient safety. Due to the cost and time needed to carry out iterative evaluations, many EHR components, such as clinical decision support systems (CDSSs), have not undergone rigorous usability testing prior to their deployment in clinical practice. Usability testing in the predeployment phase is crucial to eliminating usability issues and preventing costly fixes that will be needed if these issues are found after the system’s implementation. Objective: This study presents an example application of a systematic evaluation method that uses clinician experts with human-computer interaction (HCI) expertise to evaluate the usability of an electronic clinical decision support (CDS) intervention prior to its deployment in a randomized controlled trial. Methods: We invited 6 HCI experts to participate in a heuristic evaluation of our CDS intervention. Each expert was asked to independently explore the intervention at least twice. After completing the assigned tasks using patient scenarios, each expert completed a heuristic evaluation checklist developed by Bright et al based on Nielsen’s 10 heuristics. The experts also rated the overall severity of each identified heuristic violation on a scale of 0 to 4, where 0 indicates no problems and 4 indicates a usability catastrophe. Data from the experts’ coded comments were synthesized, and the severity of each identified usability heuristic was analyzed. Results: The 6 HCI experts included professionals from the fields of nursing (n=4), pharmaceutical science (n=1), and systems engineering (n=1). The mean overall severity scores of the identified heuristic violations ranged from 0.66 (flexibility and efficiency of use) to 2.00 (user control and freedom and error prevention), in which scores closer to 0 indicate a more usable system. The heuristic principle user control and freedom was identified as the most in need of refinement and, particularly by nonnursing HCI experts, considered as having major usability problems. In response to the heuristic match between system and the real world, the experts pointed to the reversed direction of our system’s pain scale scores (1=severe pain) compared to those commonly used in clinical practice (typically 1=mild pain); although this was identified as a minor usability problem, its refinement was repeatedly emphasized by nursing HCI experts. Conclusions: Our heuristic evaluation process is simple and systematic and can be used at multiple stages of system development to reduce the time and cost needed to establish the usability of a system before its widespread implementation. Furthermore, heuristic evaluations can help organizations develop transparent reporting protocols for usability, as required by Title IV of the 21st Century Cures Act. Testing of EHRs and CDSSs by clinicians with HCI expertise in heuristic evaluation processes has the potential to reduce the frequency of testing while increasing its quality, which may reduce clinicians’ cognitive workload and errors and enhance the adoption of EHRs and CDSSs. %M 35536613 %R 10.2196/31758 %U https://humanfactors.jmir.org/2022/2/e31758 %U https://doi.org/10.2196/31758 %U http://www.ncbi.nlm.nih.gov/pubmed/35536613 %0 Journal Article %@ 2563-6316 %I JMIR Publications %V 3 %N 2 %P e32902 %T Effects of Pharmacogenomic Testing in Clinical Pain Management: Retrospective Study %A Tagwerker,Christian %A Carias-Marines,Mary Jane %A Smith,David J %+ Alcala Testing and Analysis Services, 3703 Camino del Rio South, San Diego, CA, 92108, United States, 1 6194505870 ext 205, christian.tagwerker@alcalalabs.com %K pharmacogenomics %K pain management %K drug-drug interaction %K DDI %K pharmacy %K prescriptions %K genetics %K genomics %K drug-gene interaction %K pain %D 2022 %7 3.5.2022 %9 Original Paper %J JMIRx Med %G English %X Background: The availability of pharmacogenomic (PGx) methods to determine the right drug and dosage for individualized patient treatment has increased over the past decade. Adoption of the resulting PGx reports in a clinical setting and monitoring of clinical outcomes is a challenging and long-term commitment. Objective: This study summarizes an extended PGx deep sequencing panel intended for medication dosing and prescription guidance newly adopted in a pain management clinic. The primary outcome of this retrospective study reports the number of cases and types of drugs covered, for which PGx data appears to have assisted in optimal drug prescription and dosing. Methods: A PGx panel is described, encompassing 23 genes and 141 single-nucleotide polymorphisms or indels, combined with PGx dosing guidance and drug-gene interaction (DGI) and drug-drug interaction (DDI) reporting to prevent adverse drug reactions (ADRs). During a 2-year period, patients (N=171) were monitored in a pain management clinic. Urine toxicology, PGx reports, and progress notes were studied retrospectively for changes in prescription regimens before and after the PGx report was made available to the provider. An additional algorithm provided DGIs and DDIs to prevent ADRs. Results: Among patient PGx reports with medication lists provided (n=146), 57.5% (n=84) showed one or more moderate and 5.5% (n=8) at least one serious PGx interaction. A total of 96 (65.8%) patients showed at least one moderate and 15.1% (n=22) one or more serious DGIs or DDIs. A significant number of active changes in prescriptions based on the 102 PGx/DGI/DDI report results provided was observed for 85 (83.3%) patients for which a specific drug was either discontinued or switched within the defined drug classes of the report, or a new drug was added. Conclusions: Preventative action was observed for all serious interactions, and only moderate interactions were tolerated for the lack of other alternatives. This study demonstrates the application of an extended PGx panel combined with a customized informational report to prevent ADRs and improve patient care. %M 37725552 %R 10.2196/32902 %U https://med.jmirx.org/2022/2/e32902 %U https://doi.org/10.2196/32902 %U http://www.ncbi.nlm.nih.gov/pubmed/37725552 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 4 %P e31464 %T Overcoming Decisional Gaps in High-Risk Prescribing by Junior Physicians Using Simulation-Based Training: Protocol for a Randomized Controlled Trial %A Lauffenburger,Julie C %A DiFrancesco,Matthew F %A Barlev,Renee A %A Robertson,Ted %A Kim,Erin %A Coll,Maxwell D %A Haff,Nancy %A Fontanet,Constance P %A Hanken,Kaitlin %A Oran,Rebecca %A Avorn,Jerry %A Choudhry,Niteesh K %+ Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street Suite 3030, Boston, MA, 02120, United States, 1 6175258865, jlauffenburger@bwh.harvard.edu %K pragmatic trial %K behavioral science %K prescribing %K benzodiazepines %K antipsychotics %K impact evaluation %D 2022 %7 27.4.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Gaps between rational thought and actual decisions are increasingly recognized as a reason why people make suboptimal choices in states of heightened emotion, such as stress. These observations may help explain why high-risk medications continue to be prescribed to acutely ill hospitalized older adults despite widely accepted recommendations against these practices. Role playing and other efforts, such as simulation training, have demonstrated benefits to help people avoid decisional gaps but have not been tested to reduce overprescribing of high-risk medications. Objective: This study aims to evaluate the impact of a simulation-based training program designed to address decisional gaps on prescribing of high-risk medications compared with control. Methods: In this 2-arm pragmatic trial, we are randomizing at least 36 first-year medical resident physicians (ie, interns) who provide care on inpatient general medicine services at a large academic medical center to either intervention (simulation-based training) or control (online educational training). The intervention comprises a 40-minute immersive individual simulation training consisting of a reality-based patient care scenario in a simulated environment at the beginning of their inpatient service rotation. The simulation focuses on 3 types of high-risk medications, including benzodiazepines, antipsychotics, and sedative hypnotics (Z-drugs), in older adults, and is specifically designed to help the physicians identify their reactions and prescribing decisions in stressful situations that are common in the inpatient setting. The simulation scenario is followed by a semistructured debriefing with an expert facilitator. The trial’s primary outcome is the number of medication doses for any of the high-risk medications prescribed by the interns to patients aged 65 years or older who were not taking one of the medications upon admission. Secondary outcomes include prescribing by all providers on the care team, being discharged on 1 of the medications, and prescribing of related medications (eg, melatonin, trazodone), or the medications of interest for the control intervention. These outcomes will be measured using electronic health record data. Results: Recruitment of interns began on March 29, 2021. Recruitment for the trial ended in Q42021, with follow-up completed by Q12022. Conclusions: This trial will evaluate the impact of a simulation-based training program designed using behavioral science principles on prescribing of high-risk medications by junior physicians. If the intervention is shown to be effective, this approach could potentially be reproducible by others and for a broader set of behaviors. Trial Registration: ClinicalTrials.gov NCT04668248; https://clinicaltrials.gov/ct2/show/NCT04668248 International Registered Report Identifier (IRRID): PRR1-10.2196/31464 %M 35475982 %R 10.2196/31464 %U https://www.researchprotocols.org/2022/4/e31464 %U https://doi.org/10.2196/31464 %U http://www.ncbi.nlm.nih.gov/pubmed/35475982 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 6 %N 4 %P e36710 %T Critical Care Nurses’ Knowledge of Correct Line Types for Administration of Common Intravenous Medications: Assessment and Intervention Study %A Al-Jaber,Rania %A Samuda,Natalie %A Chaker,Ahmad %A Waterson,James %+ Medical Affairs, Medication Management Solutions, Becton Dickinson, 11F Blue Bay Tower, Business Bay, Dubai, 52279, United Arab Emirates, 971 0566035154, james.waterson@bd.com %K critical care, intravenous medication, compatibility, administration error, infusion maintenance, medication interaction %K knowledge %K survey %D 2022 %7 26.4.2022 %9 Original Paper %J JMIR Form Res %G English %X Background: There is a paucity of information in the literature on core nursing staff knowledge on the requirements of specific intravenous administration lines for medications regularly given in critical care. There is also a lack of well-researched and appropriate information in the literature for intravenous administration line selection, and the need for filtration, protection from light, and other line-material selection precautions for many critical and noncritical medications used in these settings to maintain their potency and efficacy. Objective: We aimed to assess the knowledge gap of clinicians with respect to intravenous administration line set material requirements for critical care medications. Methods: Data were drawn from a clinician knowledge questionnaire, a region-wide database of administered infusions, and regional data on standard and special intravenous administration line consumption for 1 year (2019-2020) from an enterprise resource planning system log. The clinician knowledge questionnaire was validated with 3 groups (n=35) and then released for a general survey of critical care nurses (n=72) by assessing response dispersal and interrater reliability (Cronbach α=.889). Correct answers were determined by referencing available literature, with consensus between the team’s pharmacists. Percentage deviations from correct answers (which had multiple possible selections) were calculated for control and test groups. We reviewed all 3 sources of information to identify the gap between required usage and real usage, and the impact of knowledge deficits on this disparity. Results: Percentage deviations from correct answers were substantial in the control groups and extensive in the test group for all medications tested (percentage deviation range –43% to 93%), with the exception of for total parenteral nutrition. Respondents scored poorly on questions about medications requiring light protection, and there was a difference of 2.75% between actual consumption of lines and expected consumption based on medication type requirement. Confusion over the requirements for low-sorbing lines, light protection of infusions, and the requirement for filtration of specific solutions was evident in all evidence sources. The consumption of low-sorbing lines (125,090/1,454,440, 8.60%) was larger than the regional data of medication usage data would suggest as being appropriate (15,063/592,392, 2.54%). Conclusions: There is no single source of truth for clinicians on the interactions of critical care intravenous medications and administration line materials, protection from light, and filtration. Nursing staff showed limited knowledge of these requirements. To reduce clinical variability in this area, it is desirable to have succinct easy-to-access information available for clinicians to make decisions on which administration line type to use for each medication. The study’s results will be used to formulate solutions for bedside delivery of accurate information on special intravenous line requirements for critical care medications. %M 35471247 %R 10.2196/36710 %U https://formative.jmir.org/2022/4/e36710 %U https://doi.org/10.2196/36710 %U http://www.ncbi.nlm.nih.gov/pubmed/35471247 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 11 %N 3 %P e30454 %T A Quality Improvement Emergency Department Surge Management Platform (SurgeCon): Protocol for a Stepped Wedge Cluster Randomized Trial %A Mariathas,Hensley H %A Hurley,Oliver %A Anaraki,Nahid Rahimipour %A Young,Christina %A Patey,Christopher %A Norman,Paul %A Aubrey-Bassler,Kris %A Wang,Peizhong Peter %A Gadag,Veeresh %A Nguyen,Hai V %A Etchegary,Holly %A McCrate,Farah %A Knight,John C %A Asghari,Shabnam %+ Centre for Rural Health Studies, Faculty of Medicine, Memorial University of Newfoundland, 300 Prince Philip Drive, St. John's, NL, A1B3V6, Canada, 1 709 777 2142, shabnam.asghari@med.mun.ca %K SurgeCon %K emergency department %K stepped wedge design %K cluster randomized trials %K wait time %D 2022 %7 24.3.2022 %9 Protocol %J JMIR Res Protoc %G English %X Background: Despite many efforts, long wait times and overcrowding in emergency departments (EDs) have remained a significant health service issue in Canada. For several years, Canada has had one of the longest wait times among the Organisation for Economic Co-operation and Development countries. From a patient’s perspective, this challenge has been described as “patients wait in pain or discomfort for hours before being seen at EDs.” To overcome the challenge of increased wait times, we developed an innovative ED management platform called SurgeCon that was designed based on continuous quality improvement principles to maintain patient flow and mitigate the impact of patient surge on ED efficiency. The SurgeCon quality improvement intervention includes a protocol-driven software platform, restructures ED organization and workflow, and aims to establish a more patient-centric environment. We piloted SurgeCon at an ED in Carbonear, Newfoundland and Labrador, and found that there was a 32% reduction in ED wait times. Objective: The primary objective of this trial is to determine the effects of SurgeCon on ED performance by assessing its impact on length of stay, the time to a physician’s initial assessment, and the number of patients leaving the ED without being seen by a physician. The secondary objectives of this study are to evaluate SurgeCon’s effects on patient satisfaction and patient-reported experiences with ED wait times and its ability to create better-value care by reducing the per-patient cost of delivering ED services. Methods: The implementation of the intervention will be assessed using a comparative effectiveness-implementation hybrid design. This type of hybrid design is known to shorten the amount of time associated with transitioning interventions from being the focus of research to being used for practice and health care services. All EDs with 24/7 on-site physician support (category A hospitals) will be enrolled in a 31-month, pragmatic, stepped wedge cluster randomized trial. All clusters (hospitals) will start with a baseline period of usual care and will be randomized to determine the order and timing of transitioning to intervention care until all hospitals are using the intervention to manage and operationalize their EDs. Results: Data collection for this study is continuing. As of February 2022, a total of 570 randomly selected patients have participated in telephone interviews concerning patient-reported experiences and patient satisfaction with ED wait times. The first of the 4 EDs was randomly selected, and it is currently using SurgeCon’s eHealth platform and applying efficiency principles that have been learned through training since September 2021. The second randomly selected site will begin intervention implementation in winter 2022. Conclusions: By assessing the impact of SurgeCon on ED services, we hope to be able to improve wait times and create better-value ED care in this health care context. Trial Registration: ClinicalTrials.gov NCT04789902; https://clinicaltrials.gov/ct2/show/NCT04789902 International Registered Report Identifier (IRRID): DERR1-10.2196/30454 %M 35323121 %R 10.2196/30454 %U https://www.researchprotocols.org/2022/3/e30454 %U https://doi.org/10.2196/30454 %U http://www.ncbi.nlm.nih.gov/pubmed/35323121 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 1 %P e30130 %T A Patient Outcomes–Driven Feedback Platform for Emergency Medicine Clinicians: Human-Centered Design and Usability Evaluation of Linking Outcomes Of Patients (LOOP) %A Strauss,Alexandra T %A Morgan,Cameron %A El Khuri,Christopher %A Slogeris,Becky %A Smith,Aria G %A Klein,Eili %A Toerper,Matt %A DeAngelo,Anthony %A Debraine,Arnaud %A Peterson,Susan %A Gurses,Ayse P %A Levin,Scott %A Hinson,Jeremiah %+ Department of Medicine, Johns Hopkins University School of Medicine, 600 N Wolfe St, Block 465, Baltimore, MD, 21287, United States, 1 6098280943, atstrauss13@gmail.com %K emergency medicine %K usability %K human-centered design %K health informatics %K feedback %K practice-based learning and improvement %K emergency room %K ER %K platform %K outcomes %K closed-loop learning %D 2022 %7 23.3.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: The availability of patient outcomes–based feedback is limited in episodic care environments such as the emergency department. Emergency medicine (EM) clinicians set care trajectories for a majority of hospitalized patients and provide definitive care to an even larger number of those discharged into the community. EM clinicians are often unaware of the short- and long-term health outcomes of patients and how their actions may have contributed. Despite large volumes of patients and data, outcomes-driven learning that targets individual clinician experiences is meager. Integrated electronic health record (EHR) systems provide opportunity, but they do not have readily available functionality intended for outcomes-based learning. Objective: This study sought to unlock insights from routinely collected EHR data through the development of an individualizable patient outcomes feedback platform for EM clinicians. Here, we describe the iterative development of this platform, Linking Outcomes Of Patients (LOOP), under a human-centered design framework, including structured feedback obtained from its use. Methods: This multimodal study consisting of human-centered design studios, surveys (24 physicians), interviews (11 physicians), and a LOOP application usability evaluation (12 EM physicians for ≥30 minutes each) was performed between August 2019 and February 2021. The study spanned 3 phases: (1) conceptual development under a human-centered design framework, (2) LOOP technical platform development, and (3) usability evaluation comparing pre- and post-LOOP feedback gathering practices in the EHR. Results: An initial human-centered design studio and EM clinician surveys revealed common themes of disconnect between EM clinicians and their patients after the encounter. Fundamental postencounter outcomes of death (15/24, 63% respondents identified as useful), escalation of care (20/24, 83%), and return to ED (16/24, 67%) were determined high yield for demonstrating proof-of-concept in our LOOP application. The studio aided the design and development of LOOP, which integrated physicians throughout the design and content iteration. A final LOOP prototype enabled usability evaluation and iterative refinement prior to launch. Usability evaluation compared to status quo (ie, pre-LOOP) feedback gathering practices demonstrated a shift across all outcomes from “not easy” to “very easy” to obtain and from “not confident” to “very confident” in estimating outcomes after using LOOP. On a scale from 0 (unlikely) to 10 (most likely), the users were very likely (9.5) to recommend LOOP to a colleague. Conclusions: This study demonstrates the potential for human-centered design of a patient outcomes–driven feedback platform for individual EM providers. We have outlined a framework for working alongside clinicians with a multidisciplined team to develop and test a tool that augments their clinical experience and enables closed-loop learning. %M 35319469 %R 10.2196/30130 %U https://humanfactors.jmir.org/2022/1/e30130 %U https://doi.org/10.2196/30130 %U http://www.ncbi.nlm.nih.gov/pubmed/35319469 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 1 %P e29019 %T Barriers to and Facilitators for Acceptance of Comprehensive Clinical Decision Support System–Driven Care Maps for Patients With Thoracic Trauma: Interview Study Among Health Care Providers and Nurses %A Jones,Emma K %A Banks,Alyssa %A Melton,Genevieve B %A Porta,Carolyn M %A Tignanelli,Christopher J %+ Department of Surgery, University of Minnesota, 420 Delaware St SE, Mayo Mail Code 195, Minneapolis, MN, 55455, United States, 1 6126261968, ctignane@umn.edu %K clinical decision support systems %K rib fractures %K trauma %K Unified Theory of Acceptance and Use of Technology %K human computer interaction %D 2022 %7 16.3.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Comprehensive clinical decision support (CDS) care maps can improve the delivery of care and clinical outcomes. However, they are frequently plagued by usability problems and poor user acceptance. Objective: This study aims to characterize factors influencing successful design and use of comprehensive CDS care maps and identify themes associated with end-user acceptance of a thoracic trauma CDS care map earlier in the process than has traditionally been done. This was a planned adaptive redesign stage of a User Acceptance and System Adaptation Design development and implementation strategy for a CDS care map. This stage was based on a previously developed prototype CDS care map guided by the Unified Theory of Acceptance and Use of Technology. Methods: A total of 22 multidisciplinary end users (physicians, advanced practice providers, and nurses) were identified and recruited using snowball sampling. Qualitative interviews were conducted, audio-recorded, and transcribed verbatim. Generation of prespecified codes and the interview guide was informed by the Unified Theory of Acceptance and Use of Technology constructs and investigative team experience. Interviews were blinded and double-coded. Thematic analysis of interview scripts was conducted and yielded descriptive themes about factors influencing the construction and potential use of an acceptable CDS care map. Results: A total of eight dominant themes were identified: alert fatigue (theme 1), automation (theme 2), redundancy (theme 3), minimalistic design (theme 4), evidence based (theme 5), prevent errors (theme 6), comprehensive across the spectrum of disease (theme 7), and malleability (theme 8). Themes 1 to 4 addressed factors directly affecting end users, and themes 5 to 8 addressed factors affecting patient outcomes. More experienced providers prioritized a system that is easy to use. Nurses prioritized a system that incorporated evidence into decision support. Clinicians across specialties, roles, and ages agreed that the amount of extra work generated should be minimal and that the system should help them administer optimal care efficiently. Conclusions: End user feedback reinforces attention toward factors that improve the acceptance and use of a CDS care map for patients with thoracic trauma. Common themes focused on system complexity, the ability of the system to fit different populations and settings, and optimal care provision. Identifying these factors early in the development and implementation process may facilitate user-centered design and improve adoption. %M 35293873 %R 10.2196/29019 %U https://humanfactors.jmir.org/2022/1/e29019 %U https://doi.org/10.2196/29019 %U http://www.ncbi.nlm.nih.gov/pubmed/35293873 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 1 %P e30655 %T A Remote Patient-Monitoring System for Intensive Care Medicine: Mixed Methods Human-Centered Design and Usability Evaluation %A Poncette,Akira-Sebastian %A Mosch,Lina Katharina %A Stablo,Lars %A Spies,Claudia %A Schieler,Monique %A Weber-Carstens,Steffen %A Feufel,Markus A %A Balzer,Felix %+ Institute of Medical Informatics, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Charitéplatz 1, Berlin, 10117, Germany, 49 30450 ext 651166, felix.balzer@charite.de %K digital health %K patient monitoring %K intensive care medicine %K intensive care unit %K technological innovation %K user-centered design %K usability %K user experience %K implementation science %K qualitative research %K interview %K mixed methods %K mobile phone %D 2022 %7 11.3.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Continuous monitoring of vital signs is critical for ensuring patient safety in intensive care units (ICUs) and is becoming increasingly relevant in general wards. The effectiveness of health information technologies such as patient-monitoring systems is highly determined by usability, the lack of which can ultimately compromise patient safety. Usability problems can be identified and prevented by involving users (ie, clinicians). Objective: In this study, we aim to apply a human-centered design approach to evaluate the usability of a remote patient-monitoring system user interface (UI) in the ICU context and conceptualize and evaluate design changes. Methods: Following institutional review board approval (EA1/031/18), a formative evaluation of the monitoring UI was performed. Simulated use tests with think-aloud protocols were conducted with ICU staff (n=5), and the resulting qualitative data were analyzed using a deductive analytic approach. On the basis of the identified usability problems, we conceptualized informed design changes and applied them to develop an improved prototype of the monitoring UI. Comparing the UIs, we evaluated perceived usability using the System Usability Scale, performance efficiency with the normative path deviation, and effectiveness by measuring the task completion rate (n=5). Measures were tested for statistical significance using a 2-sample t test, Poisson regression with a generalized linear mixed-effects model, and the N-1 chi-square test. P<.05 were considered significant. Results: We found 37 individual usability problems specific to monitoring UI, which could be assigned to six subcodes: usefulness of the system, response time, responsiveness, meaning of labels, function of UI elements, and navigation. Among user ideas and requirements for the UI were high usability, customizability, and the provision of audible alarm notifications. Changes in graphics and design were proposed to allow for better navigation, information retrieval, and spatial orientation. The UI was revised by creating a prototype with a more responsive design and changes regarding labeling and UI elements. Statistical analysis showed that perceived usability improved significantly (System Usability Scale design A: mean 68.5, SD 11.26, n=5; design B: mean 89, SD 4.87, n=5; P=.003), as did performance efficiency (normative path deviation design A: mean 8.8, SD 5.26, n=5; design B: mean 3.2, SD 3.03, n=5; P=.001), and effectiveness (design A: 18 trials, failed 7, 39% times, passed 11, 61% times; design B: 20 trials, failed 0 times, passed 20 times; P=.002). Conclusions: Usability testing with think-aloud protocols led to a patient-monitoring UI with significantly improved usability, performance, and effectiveness. In the ICU work environment, difficult-to-use technology may result in detrimental outcomes for staff and patients. Technical devices should be designed to support efficient and effective work processes. Our results suggest that this can be achieved by applying basic human-centered design methods and principles. Trial Registration: ClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173 %M 35275071 %R 10.2196/30655 %U https://humanfactors.jmir.org/2022/1/e30655 %U https://doi.org/10.2196/30655 %U http://www.ncbi.nlm.nih.gov/pubmed/35275071 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 9 %N 1 %P e33651 %T Usage and Usability of a National e-Library for Chemotherapy Regimens: Mixed Methods Study %A Fyhr,AnnSofie %A Persson,Johanna %A Ek,Åsa %+ Regional Cancer Centre South, Region Skåne, Medicon Village, Scheeletorget 1, Lund, SE-223 81, Sweden, 46 46 275 23 51, ann-sofie.fyhr@skane.se %K chemotherapy regimens %K user evaluation %K standardization %K patient safety %K chemotherapy %K safety %K usability %K e-library %K medication errors %D 2022 %7 17.2.2022 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Accurate information about chemotherapy drugs and regimens is needed to reduce chemotherapy errors. A national e-library, as a common knowledge source with standardized chemotherapy nomenclature and content, was developed. Since the information in the library is both complex and extensive, it is central that the users can use the resource as intended. Objective: The aim of this study was to evaluate the usage and usability of an extensive e-library for chemotherapy regimens developed to reduce medication errors, support the health care staff in their work, and increase patient safety. Methods: To obtain a comprehensive evaluation, a mixed methods study was performed for a broad view of the usage, including a compilation of subjective views of the users (web survey, spontaneous user feedback, and qualitative interviews), analysis of statistics from the website, and an expert evaluation of the usability of the webpage. Results: Statistics from the website show an average of just over 2500 visits and 870 unique visitors per month. Most visits took place Mondays to Fridays, but there were 5-10 visits per day on weekends. The web survey, with 292 answers, shows that the visitors were mainly physicians and nurses. Almost 80% (224/292) of respondents searched for regimens and 90% (264/292) found what they were looking for and were satisfied with their visit. The expert evaluation shows that the e-library follows many existing design principles, thus providing some useful improvement suggestions. A total of 86 emails were received in 2020 with user feedback, most of which were from nurses. The main part (78%, 67/86) contained a question, and the rest had discovered errors mainly in some regimen. The interviews reveal that most hospitals use a computerized physician order entry system, and they use the e-library in various ways, import XML files, transfer information, or use it as a reference. One hospital without a system uses the administration schedules from the library. Conclusions: The user evaluation indicates that the e-library is used in the intended manner and that the users can interact without problems. Users have different needs depending on their profession and their workplace, and these can be supported. The combination of methods applied ensures that the design and content comply with the users’ needs and serves as feedback for continuous design and learning. With a broad national usage, the e-library can become a source for organizational and national learning and a source for continuous improvement of cancer care in Sweden. %M 35175199 %R 10.2196/33651 %U https://humanfactors.jmir.org/2022/1/e33651 %U https://doi.org/10.2196/33651 %U http://www.ncbi.nlm.nih.gov/pubmed/35175199 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 2 %P e23355 %T Habit and Automaticity in Medical Alert Override: Cohort Study %A Wang,Le %A Goh,Kim Huat %A Yeow,Adrian %A Poh,Hermione %A Li,Ke %A Yeow,Joannas Jie Lin %A Tan,Gamaliel %A Soh,Christina %+ Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore, 65 67904808, akhgoh@ntu.edu.sg %K alert systems %K habits %K electronic medical record %K health personnel alert fatigue %D 2022 %7 16.2.2022 %9 Original Paper %J J Med Internet Res %G English %X Background: Prior literature suggests that alert dismissal could be linked to physicians’ habits and automaticity. The evidence for this perspective has been mainly observational data. This study uses log data from an electronic medical records system to empirically validate this perspective. Objective: We seek to quantify the association between habit and alert dismissal in physicians. Methods: We conducted a retrospective analysis using the log data comprising 66,049 alerts generated from hospitalized patients in a hospital from March 2017 to December 2018. We analyzed 1152 physicians exposed to a specific clinical support alert triggered in a hospital’s electronic medical record system to estimate the extent to which the physicians’ habit strength, which had been developed from habitual learning, impacted their propensity toward alert dismissal. We further examined the association between a physician’s habit strength and their subsequent incidences of alert dismissal. Additionally, we recorded the time taken by the physician to respond to the alert and collected data on other clinical and environmental factors related to the alerts as covariates for the analysis. Results: We found that a physician’s prior dismissal of alerts leads to their increased habit strength to dismiss alerts. Furthermore, a physician’s habit strength to dismiss alerts was found to be positively associated with incidences of subsequent alert dismissals after their initial alert dismissal. Alert dismissal due to habitual learning was also found to be pervasive across all physician ranks, from junior interns to senior attending specialists. Further, the dismissal of alerts had been observed to typically occur after a very short processing time. Our study found that 72.5% of alerts were dismissed in under 3 seconds after the alert appeared, and 13.2% of all alerts were dismissed in under 1 second after the alert appeared. We found empirical support that habitual dismissal is one of the key factors associated with alert dismissal. We also found that habitual dismissal of alerts is self-reinforcing, which suggests significant challenges in disrupting or changing alert dismissal habits once they are formed. Conclusions: Habitual tendencies are associated with the dismissal of alerts. This relationship is pervasive across all levels of physician rank and experience, and the effect is self-reinforcing. %M 35171102 %R 10.2196/23355 %U https://www.jmir.org/2022/2/e23355 %U https://doi.org/10.2196/23355 %U http://www.ncbi.nlm.nih.gov/pubmed/35171102 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 1 %P e35225 %T Incidence of Diagnostic Errors Among Unexpectedly Hospitalized Patients Using an Automated Medical History–Taking System With a Differential Diagnosis Generator: Retrospective Observational Study %A Kawamura,Ren %A Harada,Yukinori %A Sugimoto,Shu %A Nagase,Yuichiro %A Katsukura,Shinichi %A Shimizu,Taro %+ Department of Diagnostic and Generalist Medicine, Dokkyo Medical University, 880 Kitakobayashi, Mibu, 321-0293, Japan, 81 282861111, shimizutaro7@gmail.com %K artificial intelligence %K automated medical history–taking %K diagnostic errors %K outpatient %K Safer Dx %D 2022 %7 27.1.2022 %9 Original Paper %J JMIR Med Inform %G English %X Background: Automated medical history–taking systems that generate differential diagnosis lists have been suggested to contribute to improved diagnostic accuracy. However, the effect of these systems on diagnostic errors in clinical practice remains unknown. Objective: This study aimed to assess the incidence of diagnostic errors in an outpatient department, where an artificial intelligence (AI)–driven automated medical history–taking system that generates differential diagnosis lists was implemented in clinical practice. Methods: We conducted a retrospective observational study using data from a community hospital in Japan. We included patients aged 20 years and older who used an AI-driven, automated medical history–taking system that generates differential diagnosis lists in the outpatient department of internal medicine for whom the index visit was between July 1, 2019, and June 30, 2020, followed by unplanned hospitalization within 14 days. The primary endpoint was the incidence of diagnostic errors, which were detected using the Revised Safer Dx Instrument by at least two independent reviewers. To evaluate the effect of differential diagnosis lists from the AI system on the incidence of diagnostic errors, we compared the incidence of these errors between a group where the AI system generated the final diagnosis in the differential diagnosis list and a group where the AI system did not generate the final diagnosis in the list; the Fisher exact test was used for comparison between these groups. For cases with confirmed diagnostic errors, further review was conducted to identify the contributing factors of these errors via discussion among three reviewers, using the Safer Dx Process Breakdown Supplement as a reference. Results: A total of 146 patients were analyzed. A final diagnosis was confirmed for 138 patients and was observed in the differential diagnosis list from the AI system for 69 patients. Diagnostic errors occurred in 16 out of 146 patients (11.0%, 95% CI 6.4%-17.2%). Although statistically insignificant, the incidence of diagnostic errors was lower in cases where the final diagnosis was included in the differential diagnosis list from the AI system than in cases where the final diagnosis was not included in the list (7.2% vs 15.9%, P=.18). Conclusions: The incidence of diagnostic errors among patients in the outpatient department of internal medicine who used an automated medical history–taking system that generates differential diagnosis lists seemed to be lower than the previously reported incidence of diagnostic errors. This result suggests that the implementation of an automated medical history–taking system that generates differential diagnosis lists could be beneficial for diagnostic safety in the outpatient department of internal medicine. %M 35084347 %R 10.2196/35225 %U https://medinform.jmir.org/2022/1/e35225 %U https://doi.org/10.2196/35225 %U http://www.ncbi.nlm.nih.gov/pubmed/35084347 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 8 %N 1 %P e33311 %T Comparison of Online Patient Reviews and National Pharmacovigilance Data for Tramadol-Related Adverse Events: Comparative Observational Study %A Park,Susan %A Choi,So Hyun %A Song,Yun-Kyoung %A Kwon,Jin-Won %+ BK21 FOUR Community-Based Intelligent Novel Drug Discovery Education Unit, College of Pharmacy, Research Institute of Pharmaceutical Sciences, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu, 41566, Republic of Korea, 82 53 950 8580, jwkwon@knu.ac.kr %K drug safety %K pharmacovigilance %K tramadol %K social media %K adverse effect %D 2022 %7 4.1.2022 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Tramadol is known to cause fewer adverse events (AEs) than other opioids. However, recent research has raised concerns about various safety issues. Objective: We aimed to explore these new AEs related to tramadol using social media and conventional pharmacovigilance data. Methods: This study used 2 data sets, 1 from patients’ drug reviews on WebMD (January 2007 to January 2021) and 1 from the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS; January 2016 to December 2020). We analyzed 2062 and 29,350 patient reports from WebMD and FAERS, respectively. Patient posts on WebMD were manually assigned the preferred terms of the Medical Dictionary for Regulatory Activities. To analyze AEs from FAERS, a disproportionality analysis was performed with 3 measures: proportional reporting ratio, reporting odds ratio, and information component. Results: From the 869 AEs reported, we identified 125 new signals related to tramadol use not listed on the drug label that satisfied all 3 signal detection criteria. In addition, 20 serious AEs were selected from new signals. Among new serious AEs, vascular disorders had the largest signal detection criteria value. Based on the disproportionality analysis and patients’ symptom descriptions, tramadol-induced pain might also be an unexpected AE. Conclusions: This study detected several novel signals related to tramadol use, suggesting newly identified possible AEs. Additionally, this study indicates that unexpected AEs can be detected using social media analysis alongside traditional pharmacovigilance data. %M 34982723 %R 10.2196/33311 %U https://publichealth.jmir.org/2022/1/e33311 %U https://doi.org/10.2196/33311 %U http://www.ncbi.nlm.nih.gov/pubmed/34982723 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 8 %N 4 %P e27171 %T Communicating Guideline Recommendations Using Graphic Narrative Versus Text-Based Broadcast Screensavers: Design and Implementation Study %A Sinnenberg,Lauren %A Umscheid,Craig A %A Shofer,Frances S %A Leri,Damien %A Meisel,Zachary F %+ Center for Emergency Care Policy and Research, University of Pennsylvania, Ravdin Ground, 3400 Spruce Street, Philadelphia, PA, 19104, United States, 1 215 746 5618, zfm@pennmedicine.upenn.edu %K medical informatics %K screensaver %K guideline dissemination %K graphic narratives %K health communication %K workstation %K clinical workstation %K guidelines %K medical education %K education %D 2021 %7 13.12.2021 %9 Original Paper %J JMIR Hum Factors %G English %X Background: The use of graphic narratives, defined as stories that use images for narration, is growing in health communication. Objective: The aim of this study was to describe the design and implementation of a graphic narrative screensaver (GNS) to communicate a guideline recommendation (ie, avoiding low-value acid suppressive therapy [AST] use in hospital inpatients) and examine the comparative effectiveness of the GNS versus a text-based screensaver (TBS) on clinical practice (ie, low-value AST prescriptions) and clinician recall. Methods: During a 2-year period, the GNS and the TBS were displayed on inpatient clinical workstations. The numbers of new AST prescriptions were examined in the four quarters before, the three quarters during, and the one quarter after screensavers were implemented. Additionally, an electronic survey was sent to resident physicians 1 year after the intervention to assess screensaver recall. Results: Designing an aesthetically engaging graphic that could be rapidly understood was critical in the development of the GNS. The odds of receiving an AST prescription on medicine and medicine subspecialty services after the screensavers were implemented were lower for all four quarters (ie, GNS and TBS broadcast together, only TBS broadcast, only GNS broadcast, and no AST screensavers broadcast) compared to the quarter prior to implementation (odds ratio [OR] 0.85, 95% CI 0.78-0.92; OR 0.89, 95% CI 0.82-0.97; OR 0.87, 95% CI 0.80-0.95; and OR 0.81, 95% CI 0.75-0.89, respectively; P<.001 for all comparisons). There were no statistically significant decreases for other high-volume services, such as the surgical services. These declines appear to have begun prior to screensaver implementation. When surveyed about the screensaver content 1 year later, resident physicians recalled both the GNS and TBS (43/70, 61%, vs 54/70, 77%; P=.07) and those who recalled the screensaver were more likely to recall the main message of the GNS compared to the TBS (30/43, 70%, vs 1/54, 2%; P<.001). Conclusions: It is feasible to use a graphic narrative embedded in a broadcast screensaver to communicate a guideline recommendation, but further study is needed to determine the impact of graphic narratives on clinical practice. %M 34264197 %R 10.2196/27171 %U https://humanfactors.jmir.org/2021/4/e27171 %U https://doi.org/10.2196/27171 %U http://www.ncbi.nlm.nih.gov/pubmed/34264197 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e27188 %T The Utility of Different Data Standards to Document Adverse Drug Event Symptoms and Diagnoses: Mixed Methods Study %A Chan,Erina %A Small,Serena S %A Wickham,Maeve E %A Cheng,Vicki %A Balka,Ellen %A Hohl,Corinne M %+ Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Research Institute, 828 West 10th Avenue, Vancouver, BC, V5Z 1M9, Canada, 1 604 875 4111 ext 55219, Serena.Small@ubc.ca %K adverse drug events %K health information technology %K data standards %D 2021 %7 10.12.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Existing systems to document adverse drug events often use free text data entry, which produces nonstandardized and unstructured data that are prone to misinterpretation. Standardized terminology may improve data quality; however, it is unclear which data standard is most appropriate for documenting adverse drug event symptoms and diagnoses. Objective: This study aims to compare the utility, strengths, and weaknesses of different data standards for documenting adverse drug event symptoms and diagnoses. Methods: We performed a mixed methods substudy of a multicenter retrospective chart review. We reviewed the research records of prospectively diagnosed adverse drug events at 5 Canadian hospitals. A total of 2 pharmacy research assistants independently entered the symptoms and diagnoses for the adverse drug events using four standards: Medical Dictionary for Regulatory Activities (MedDRA), Systematized Nomenclature of Medicine (SNOMED) Clinical Terms, SNOMED Adverse Reaction (SNOMED ADR), and International Classification of Diseases (ICD) 11th Revision. Disagreements between research assistants regarding the case-specific utility of data standards were discussed until a consensus was reached. We used consensus ratings to determine the proportion of adverse drug events covered by a data standard and coded and analyzed field notes from the consensus sessions. Results: We reviewed 573 adverse drug events and found that MedDRA and ICD-11 had excellent coverage of adverse drug event symptoms and diagnoses. MedDRA had the highest number of matches between the research assistants, whereas ICD-11 had the fewest. SNOMED ADR had the lowest proportion of adverse drug event coverage. The research assistants were most likely to encounter terminological challenges with SNOMED ADR and usability challenges with ICD-11, whereas least likely to encounter challenges with MedDRA. Conclusions: Usability, comprehensiveness, and accuracy are important features of data standards for documenting adverse drug event symptoms and diagnoses. On the basis of our results, we recommend the use of MedDRA. %M 34890351 %R 10.2196/27188 %U https://www.jmir.org/2021/12/e27188 %U https://doi.org/10.2196/27188 %U http://www.ncbi.nlm.nih.gov/pubmed/34890351 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 12 %P e30238 %T The Communicating Narrative Concerns Entered by Registered Nurses (CONCERN) Clinical Decision Support Early Warning System: Protocol for a Cluster Randomized Pragmatic Clinical Trial %A Rossetti,Sarah Collins %A Dykes,Patricia C %A Knaplund,Christopher %A Kang,Min-Jeoung %A Schnock,Kumiko %A Garcia Jr,Jose Pedro %A Fu,Li-Heng %A Chang,Frank %A Thai,Tien %A Fred,Matthew %A Korach,Tom Z %A Zhou,Li %A Klann,Jeffrey G %A Albers,David %A Schwartz,Jessica %A Lowenthal,Graham %A Jia,Haomiao %A Liu,Fang %A Cato,Kenrick %+ Department of Biomedical Informatics, Columbia University, 622 W 168th Street PH20, New York, NY, 10032, United States, 1 781 801 9211, sac2125@cumc.columbia.edu %K nursing documentation %K prediction %K early warning system %K deterioration %K clinical trial %K clinical decision support system %K natural language processing %D 2021 %7 10.12.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Every year, hundreds of thousands of inpatients die from cardiac arrest and sepsis, which could be avoided if those patients’ risk for deterioration were detected and timely interventions were initiated. Thus, a system is needed to convert real-time, raw patient data into consumable information that clinicians can utilize to identify patients at risk of deterioration and thus prevent mortality and improve patient health outcomes. The overarching goal of the COmmunicating Narrative Concerns Entered by Registered Nurses (CONCERN) study is to implement and evaluate an early warning score system that provides clinical decision support (CDS) in electronic health record systems. With a combination of machine learning and natural language processing, the CONCERN CDS utilizes nursing documentation patterns as indicators of nurses’ increased surveillance to predict when patients are at the risk of clinical deterioration. Objective: The objective of this cluster randomized pragmatic clinical trial is to evaluate the effectiveness and usability of the CONCERN CDS system at 2 different study sites. The specific aim is to decrease hospitalized patients’ negative health outcomes (in-hospital mortality, length of stay, cardiac arrest, unanticipated intensive care unit transfers, and 30-day hospital readmission rates). Methods: A multiple time-series intervention consisting of 3 phases will be performed through a 1-year period during the cluster randomized pragmatic clinical trial. Phase 1 evaluates the adoption of our algorithm through pilot and trial testing, phase 2 activates optimized versions of the CONCERN CDS based on experience from phase 1, and phase 3 will be a silent release mode where no CDS is viewable to the end user. The intervention deals with a series of processes from system release to evaluation. The system release includes CONCERN CDS implementation and user training. Then, a mixed methods approach will be used with end users to assess the system and clinician perspectives. Results: Data collection and analysis are expected to conclude by August 2022. Based on our previous work on CONCERN, we expect the system to have a positive impact on the mortality rate and length of stay. Conclusions: The CONCERN CDS will increase team-based situational awareness and shared understanding of patients predicted to be at risk for clinical deterioration in need of intervention to prevent mortality and associated harm. Trial Registration: ClinicalTrials.gov NCT03911687; https://clinicaltrials.gov/ct2/show/NCT03911687 International Registered Report Identifier (IRRID): DERR1-10.2196/30238 %M 34889766 %R 10.2196/30238 %U https://www.researchprotocols.org/2021/12/e30238 %U https://doi.org/10.2196/30238 %U http://www.ncbi.nlm.nih.gov/pubmed/34889766 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 12 %P e26407 %T Chinese-Named Entity Recognition From Adverse Drug Event Records: Radical Embedding-Combined Dynamic Embedding–Based BERT in a Bidirectional Long Short-term Conditional Random Field (Bi-LSTM-CRF) Model %A Wu,Hong %A Ji,Jiatong %A Tian,Haimei %A Chen,Yao %A Ge,Weihong %A Zhang,Haixia %A Yu,Feng %A Zou,Jianjun %A Nakamura,Mitsuhiro %A Liao,Jun %+ School of Science, China Pharmaceutical University, #639 Longmian Avenue, Jiangning District, Nanjing, 211198, China, 86 13952040425, liaojun@cpu.edu.cn %K deep learning %K BERT %K adverse drug reaction %K named entity recognition %K electronic medical records %D 2021 %7 1.12.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: With the increasing variety of drugs, the incidence of adverse drug events (ADEs) is increasing year by year. Massive numbers of ADEs are recorded in electronic medical records and adverse drug reaction (ADR) reports, which are important sources of potential ADR information. Meanwhile, it is essential to make latent ADR information automatically available for better postmarketing drug safety reevaluation and pharmacovigilance. Objective: This study describes how to identify ADR-related information from Chinese ADE reports. Methods: Our study established an efficient automated tool, named BBC-Radical. BBC-Radical is a model that consists of 3 components: Bidirectional Encoder Representations from Transformers (BERT), bidirectional long short-term memory (bi-LSTM), and conditional random field (CRF). The model identifies ADR-related information from Chinese ADR reports. Token features and radical features of Chinese characters were used to represent the common meaning of a group of words. BERT and Bi-LSTM-CRF were novel models that combined these features to conduct named entity recognition (NER) tasks in the free-text section of 24,890 ADR reports from the Jiangsu Province Adverse Drug Reaction Monitoring Center from 2010 to 2016. Moreover, the man-machine comparison experiment on the ADE records from Drum Tower Hospital was designed to compare the NER performance between the BBC-Radical model and a manual method. Results: The NER model achieved relatively high performance, with a precision of 96.4%, recall of 96.0%, and F1 score of 96.2%. This indicates that the performance of the BBC-Radical model (precision 87.2%, recall 85.7%, and F1 score 86.4%) is much better than that of the manual method (precision 86.1%, recall 73.8%, and F1 score 79.5%) in the recognition task of each kind of entity. Conclusions: The proposed model was competitive in extracting ADR-related information from ADE reports, and the results suggest that the application of our method to extract ADR-related information is of great significance in improving the quality of ADR reports and postmarketing drug safety evaluation. %M 34855616 %R 10.2196/26407 %U https://medinform.jmir.org/2021/12/e26407 %U https://doi.org/10.2196/26407 %U http://www.ncbi.nlm.nih.gov/pubmed/34855616 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 8 %N 4 %P e31214 %T Stakeholder Perspectives on an Inpatient Hypoglycemia Informatics Alert: Mixed Methods Study %A Mathioudakis,Nestoras %A Aboabdo,Moeen %A Abusamaan,Mohammed S %A Yuan,Christina %A Lewis Boyer,LaPricia %A Pilla,Scott J %A Johnson,Erica %A Desai,Sanjay %A Knight,Amy %A Greene,Peter %A Golden,Sherita H %+ Division of Endocrinology, Diabetes & Metabolism, Department of Medicine, Johns Hopkins University, 1830 E. Monument Street, Suite 333, Baltimore, MD, 21287, United States, 1 410 502 8089, nmathio1@jhmi.edu %K informatics alert %K clinical decision support %K hypoglycemia %K hospital %K inpatient %D 2021 %7 26.11.2021 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Iatrogenic hypoglycemia is a common occurrence among hospitalized patients and is associated with poor clinical outcomes and increased mortality. Clinical decision support systems can be used to reduce the incidence of this potentially avoidable adverse event. Objective: This study aims to determine the desired features and functionality of a real-time informatics alert to prevent iatrogenic hypoglycemia in a hospital setting. Methods: Using the Agency for Healthcare Research and Quality Five Rights of Effective Clinical Decision Support Framework, we conducted a mixed methods study using an electronic survey and focus group sessions of hospital-based providers. The goal was to elicit stakeholder input to inform the future development of a real-time informatics alert to target iatrogenic hypoglycemia. In addition to perceptions about the importance of the problem and existing barriers, we sought input regarding the content, format, channel, timing, and recipient for the alert (ie, the Five Rights). Thematic analysis of focus group sessions was conducted using deductive and inductive approaches. Results: A 21-item electronic survey was completed by 102 inpatient-based providers, followed by 2 focus group sessions (6 providers per session). Respondents universally agreed or strongly agreed that inpatient iatrogenic hypoglycemia is an important problem that can be addressed with an informatics alert. Stakeholders expressed a preference for an alert that is nonintrusive, accurate, communicated in near real time to the ordering provider, and provides actionable treatment recommendations. Several electronic medical record tools, including alert indicators in the patient header, glucose management report, and laboratory results section, were deemed acceptable formats for consideration. Concerns regarding alert fatigue were prevalent among both survey respondents and focus group participants. Conclusions: The design preferences identified in this study will provide the framework needed for an informatics team to develop a prototype alert for pilot testing and evaluation. This alert will help meet the needs of hospital-based clinicians caring for patients with diabetes who are at a high risk of treatment-related hypoglycemia. %M 34842544 %R 10.2196/31214 %U https://humanfactors.jmir.org/2021/4/e31214 %U https://doi.org/10.2196/31214 %U http://www.ncbi.nlm.nih.gov/pubmed/34842544 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 8 %N 4 %P e22325 %T Including the Reason for Use on Prescriptions Sent to Pharmacists: Scoping Review %A Mercer,Kathryn %A Carter,Caitlin %A Burns,Catherine %A Tennant,Ryan %A Guirguis,Lisa %A Grindrod,Kelly %+ Library, University of Waterloo, 200 University Avenue West, DC 1555, Waterloo, ON, N2L 3G1, Canada, 1 519 888 4567 ext 42659, kmercer@uwaterloo.ca %K patient safety %K human factors %K patient engagement %K multidisciplinary %D 2021 %7 25.11.2021 %9 Review %J JMIR Hum Factors %G English %X Background: In North America, although pharmacists are obligated to ensure prescribed medications are appropriate, information about a patient’s reason for use is not a required component of a legal prescription. The benefits of prescribers including the reason for use on prescriptions is evident in the current literature. However, it is not standard practice to share this information with pharmacists. Objective: Our aim was to characterize the research on how including the reason for use on a prescription impacts pharmacists. Methods: We performed an interdisciplinary scoping review, searching literature in the fields of health care, informatics, and engineering. The following databases were searched between December 2018 and January 2019: PubMed, Institute of Electrical and Electronics Engineers (IEEE), Association for Computing Machinery (ACM), International Pharmaceutical Abstracts (IPA), and EMBASE. Results: A total of 3912 potentially relevant articles were identified, with 9 papers meeting the inclusion criteria. The studies used different terminology (eg, indication, reason for use) and a wide variety of study methodologies, including prospective and retrospective observational studies, randomized controlled trials, and qualitative interviews and focus groups. The results suggest that including the reason for use on a prescription can help the pharmacist catch more errors, reduce the need to contact prescribers, support patient counseling, impact communication, and improve patient safety. Reasons that may prevent prescribers from adding the reason for use information are concerns about workflow and patient privacy. Conclusions: More research is needed to understand how the reason for use information should be provided to pharmacists. In the limited literature to date, there is a consensus that the addition of this information to prescriptions benefits patient safety and enables pharmacists to be more effective. Future research should use an implementation science or theory-based approach to improve prescriber buy-in and, consequently, adoption. %M 34842545 %R 10.2196/22325 %U https://humanfactors.jmir.org/2021/4/e22325 %U https://doi.org/10.2196/22325 %U http://www.ncbi.nlm.nih.gov/pubmed/34842545 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 11 %P e30704 %T Mitigating Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: Exploratory Mixed Methods Experiment %A Bickmore,Timothy W %A Ólafsson,Stefán %A O'Leary,Teresa K %+ Khoury College of Computer Sciences, Northeastern University, 360 Huntington Ave, 524 ISEC, Boston, MA, 02115, United States, 1 6173735477, t.bickmore@northeastern.edu %K conversational assistant %K conversational interface %K dialogue system %K medical error %K patient safety %K risk mitigation %K warnings %K disclaimers %K grounding %K explainability %K mobile phone %D 2021 %7 9.11.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Prior studies have demonstrated the safety risks when patients and consumers use conversational assistants such as Apple’s Siri and Amazon’s Alexa for obtaining medical information. Objective: The aim of this study is to evaluate two approaches to reducing the likelihood that patients or consumers will act on the potentially harmful medical information they receive from conversational assistants. Methods: Participants were given medical problems to pose to conversational assistants that had been previously demonstrated to result in potentially harmful recommendations. Each conversational assistant’s response was randomly varied to include either a correct or incorrect paraphrase of the query or a disclaimer message—or not—telling the participants that they should not act on the advice without first talking to a physician. The participants were then asked what actions they would take based on their interaction, along with the likelihood of taking the action. The reported actions were recorded and analyzed, and the participants were interviewed at the end of each interaction. Results: A total of 32 participants completed the study, each interacting with 4 conversational assistants. The participants were on average aged 42.44 (SD 14.08) years, 53% (17/32) were women, and 66% (21/32) were college educated. Those participants who heard a correct paraphrase of their query were significantly more likely to state that they would follow the medical advice provided by the conversational assistant (χ21=3.1; P=.04). Those participants who heard a disclaimer message were significantly more likely to say that they would contact a physician or health professional before acting on the medical advice received (χ21=43.5; P=.001). Conclusions: Designers of conversational systems should consider incorporating both disclaimers and feedback on query understanding in response to user queries for medical advice. Unconstrained natural language input should not be used in systems designed specifically to provide medical advice. %M 34751661 %R 10.2196/30704 %U https://www.jmir.org/2021/11/e30704 %U https://doi.org/10.2196/30704 %U http://www.ncbi.nlm.nih.gov/pubmed/34751661 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 8 %N 4 %P e25453 %T Using Postmarket Surveillance to Assess Safety-Related Events in a Digital Rehabilitation App (Kaia App): Observational Study %A Jain,Deeptee %A Norman,Kevin %A Werner,Zachary %A Makovoz,Bar %A Baker,Turner %A Huber,Stephan %+ Department of Orthopaedic Surgery, Washington University in St. Louis, 660 South Euclid Avenue, St. Louis, MO, 63110, United States, 1 314 747 4950, deeptee.jain@gmail.com %K lower back pain %K digital therapeutics %K adverse event %K pain %K safety %K digital health %K multidisciplinary pain treatment %D 2021 %7 9.11.2021 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Low back pain (LBP) affects nearly 4 out of 5 individuals during their lifetime and is the leading cause of disability globally. Digital therapeutics are emerging as effective treatment options for individuals experiencing LBP. Despite the growth of evidence demonstrating the benefits of these therapeutics in reducing LBP and improving functional outcomes, little data has been systematically collected on their safety profiles. Objective: This study aims to evaluate the safety profile of a multidisciplinary digital therapeutic for LBP, the Kaia App, by performing a comprehensive assessment of reported adverse events (AEs) by users as captured by a standardized process for postmarket surveillance. Methods: All users of a multidisciplinary digital app that includes physiotherapy, mindfulness techniques, and education for LBP (Kaia App) from 2018 to 2019 were included. Relevant messages sent by users via the app were collected according to a standard operating procedure regulating postmarket surveillance of the device. These messages were then analyzed to determine if they described an adverse event (AE). Messages describing an AE were then categorized based on the type of AE, its seriousness, and its relatedness to the app, and they were described by numerical counts. User demographics, including age and gender, and data on app use were collected and evaluated to determine if they were risk factors for increased AE reporting. Results: Of the 138,337 active users of the Kaia App, 125 (0.09%) reported at least one AE. Users reported 0.00014 AEs per active day on the app. The most common nonserious AE reported was increased pain. Other nonserious AEs reported included muscle issues, unpleasant sensations, headache, dizziness, and sleep disturbances. One serious AE, a surgery, was reported. Details of the event and its connection to the intervention were not obtainable, as the user did not provide more information when asked to do so; therefore, it was considered to be possibly related to the intervention. There was no relationship between gender and AE reporting (P>.99). Users aged 25 to 34 years had reduced odds (odds ratio [OR] 0.31, 95% CI 0.08-0.95; P=.03) of reporting AEs, while users aged 55 to 65 years (OR 2.53, 95% CI 1.36-4.84, P=.002) and ≥75 years (OR 4.36, 95% CI 1.07-13.26; P=.02) had increased odds. AEs were most frequently reported by users who had 0 to 99 active days on the app, and less frequently reported by users with more active days on the app. Conclusions: This study on the Kaia App provides the first comprehensive assessment of reported AEs associated with real-world use of digital therapeutics for lower back pain. The overall rate of reported AEs was very low, but significant reporting bias is likely to be present. The AEs reported were generally consistent with those described for in-person therapies for LBP. %M 34751664 %R 10.2196/25453 %U https://humanfactors.jmir.org/2021/4/e25453 %U https://doi.org/10.2196/25453 %U http://www.ncbi.nlm.nih.gov/pubmed/34751664 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 11 %P e23789 %T A Fully Collaborative, Noteless Electronic Medical Record Designed to Minimize Information Chaos: Software Design and Feasibility Study %A Steinkamp,Jackson %A Sharma,Abhinav %A Bala,Wasif %A Kantrowitz,Jacob J %+ Hospital of the University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, United States, 1 215 662 4000, jacksonsteinkamp@gmail.com %K electronic medical records %K clinical notes %K information chaos %K information overload %K clinician burnout %K software design %K problem-oriented medical record %K medical records %K electronic records %K documentation %K clinical %K software %D 2021 %7 9.11.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Clinicians spend large amounts of their workday using electronic medical records (EMRs). Poorly designed documentation systems contribute to the proliferation of out-of-date information, increased time spent on medical records, clinician burnout, and medical errors. Beyond software interfaces, examining the underlying paradigms and organizational structures for clinical information may provide insights into ways to improve documentation systems. In particular, our attachment to the note as the major organizational unit for storing unstructured medical data may be a cause of many of the problems with modern clinical documentation. Notes, as currently understood, systematically incentivize information duplication and information scattering, both within a single clinician’s notes over time and across multiple clinicians’ notes. Therefore, it is worthwhile to explore alternative paradigms for unstructured data organization. Objective: The aim of this study is to demonstrate the feasibility of building an EMR that does not use notes as the core organizational unit for unstructured data and which is designed specifically to disincentivize information duplication and information scattering. Methods: We used specific design principles to minimize the incentive for users to duplicate and scatter information. By default, the majority of a patient’s medical history remains the same over time, so users should not have to redocument that information. Clinicians on different teams or services mostly share the same medical information, so all data should be collaboratively shared across teams and services (while still allowing for disagreement and nuance). In all cases where a clinician must state that information has remained the same, they should be able to attest to the information without redocumenting it. We designed and built a web-based EMR based on these design principles. Results: We built a medical documentation system that does not use notes and instead treats the chart as a single, dynamically updating, and fully collaborative workspace. All information is organized by clinical topic or problem. Version history functionality is used to enable granular tracking of changes over time. Our system is highly customizable to individual workflows and enables each individual user to decide which data should be structured and which should be unstructured, enabling individuals to leverage the advantages of structured templating and clinical decision support as desired without requiring programming knowledge. The system is designed to facilitate real-time, fully collaborative documentation and communication among multiple clinicians. Conclusions: We demonstrated the feasibility of building a non–note-based, fully collaborative EMR system. Our attachment to the note as the only possible atomic unit of unstructured medical data should be reevaluated, and alternative models should be considered. %M 34751651 %R 10.2196/23789 %U https://formative.jmir.org/2021/11/e23789 %U https://doi.org/10.2196/23789 %U http://www.ncbi.nlm.nih.gov/pubmed/34751651 %0 Journal Article %@ 2561-3278 %I JMIR Publications %V 6 %N 4 %P e20652 %T Tracking the Presence of Software as a Medical Device in US Food and Drug Administration Databases: Retrospective Data Analysis %A Ceross,Aaron %A Bergmann,Jeroen %+ Natural Interaction Lab, Department of Engineering Science, University of Oxford, Parks Road, Oxford, OX1 3PJ, United Kingdom, 44 01865 273000, aaron.ceross@eng.ox.ac.uk %K regulation %K software %K medical device %D 2021 %7 3.11.2021 %9 Proposal %J JMIR Biomed Eng %G English %X Background: Software as a medical device (SaMD) has gained the attention of medical device regulatory bodies as the prospects of standalone software for use in diagnositic and therapeutic settings have increased. However, to date, figures related to SaMD have not been made available by regulators, which limits the understanding of how prevalent these devices are and what actions should be taken to regulate them. Objective: The aim of this study is to empirically evaluate the market approvals and clearances related to SaMD and identify adverse incidents related to these devices. Methods: Using databases managed by the US medical device regulator, the US Food and Drug Administration (FDA), we identified the counts of SaMD registered with the FDA since 2016 through the use of product codes, mapped the path SaMD takes toward classification, and recorded adverse events. Results: SaMD does not seem to be registered at a rate dissimilar to that of other medical devices; thus, adverse events for SaMD only comprise a small portion of the total reported number. Conclusions: Although SaMD has been identified in the literature as an area of development, our analysis suggests that this growth has been modest. These devices are overwhelmingly classified as moderate to high risk, and they take a very particular path to that classification. The digital revolution in health care is less pronounced when evidence related to SaMD is considered. In general, the addition of SaMD to the medical device market seems to mimic that of other medical devices. %M 38907384 %R 10.2196/20652 %U https://biomedeng.jmir.org/2021/4/e20652 %U https://doi.org/10.2196/20652 %U http://www.ncbi.nlm.nih.gov/pubmed/38907384 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 8 %N 4 %P e29180 %T Evaluating An Automated Compounding Workflow Software for Safety and Efficiency: Implementation Study %A Meren,Ülle Helena %A Waterson,James %+ Medical Affairs, Medication Management Solutions, Becton Dickinson Ltd, 11F Blue Bay Tower, Business Bay, Dubai, 52279, United Arab Emirates, 971 566035154, james.waterson@bd.com %K compounding %K medication safety %K positive patient identification %K gravimetric %K automation %K closed loop %D 2021 %7 2.11.2021 %9 Original Paper %J JMIR Hum Factors %G English %X Background: The forms of automation available to the oncology pharmacy range from compounding robotic solutions through to combination workflow software, which can scale-up to cover the entire workflow from prescribing to administration. A solution that offers entire workflow management for oncology is desirable because (in terms of cytotoxic delivery of a regimen to a patient) the chain that starts with prescription and the assay of the patient’s laboratory results and ends with administration has multiple potential safety gaps and choke points. Objective: The aim of this study was to show how incremental change to a core compounding workflow software solution has helped an organization meet goals of improved patient safety; increasing the number of oncology treatments; improving documentation; and improving communication between oncologists, pharmacists, and nurses. We also aimed to illustrate how using this technology flow beyond the pharmacy has extended medication safety to the patient’s bedside through the deployment of a connected solution for confirming and documenting right patient–right medication transactions. Methods: A compounding workflow software solution was introduced for both preparation and documentation, with pharmacist verification of the order, gravimetric checks, and step-by-step on-screen instructions displayed in the work area for the technician. The software supported the technician during compounding by proposing the required drug vial size, diluents, and consumables. Out-of-tolerance concentrations were auto-alerted via an integrated gravimetric scale. A patient-medication label was created. Integration was undertaken between a prescribing module and the compounding module to reduce the risk of transcription errors. The deployment of wireless-connected handheld barcode scanners was then made to allow nurses to use the patient-medication label on each compounded product and to scan patient identification bands to ensure right patient–right prescription. Results: Despite an increase in compounding, with a growth of 12% per annum and no increase in pharmacy headcount, we doubled our output to 14,000 medications per annum through the application of the compounding solution. The use of a handheld barcode scanning device for nurses reduced the time for medication administration from ≈6 minutes per item to 41 seconds, with a mean average saving of 5 minutes and 19 seconds per item. When calculated against our throughput of 14,000 items per annum (current production rate via pharmacy), this gives a saving of 3 hours and 24 minutes of nursing time per day, equivalent to 0.425 full-time nurses per annum. Conclusions: The addition of prescribing, compounding, and administration software solutions to our oncology medication chain has increased detection and decreased the risk of error at each stage of the process. The double-checks that the system has built in by virtue of its own systems and through the flow of control of drugs and dosages from physician to pharmacist to nurse allow it to integrate fully with our human systems of risk management. %M 34456182 %R 10.2196/29180 %U https://humanfactors.jmir.org/2021/4/e29180 %U https://doi.org/10.2196/29180 %U http://www.ncbi.nlm.nih.gov/pubmed/34456182 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 8 %N 4 %P e28618 %T A Shared Decision-making Tool for Drug Interactions Between Warfarin and Nonsteroidal Anti-inflammatory Drugs: Design and Usability Study %A Reese,Thomas J %A Del Fiol,Guilherme %A Morgan,Keaton %A Hurwitz,Jason T %A Kawamoto,Kensaku %A Gomez-Lumbreras,Ainhoa %A Brown,Mary L %A Thiess,Henrik %A Vazquez,Sara R %A Nelson,Scott D %A Boyce,Richard %A Malone,Daniel %+ Vanderbilt University, 2525 West End Avenue, Suite 1475, Nashville, TN, 37203, United States, 1 (615) 936 6867, Thomas.Reese@vumc.org %K shared decision-making %K user-centered design %K drug interaction %K clinical decision support %D 2021 %7 26.10.2021 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Exposure to life-threatening drug-drug interactions (DDIs) occurs despite the widespread use of clinical decision support. The DDI between warfarin and nonsteroidal anti-inflammatory drugs is common and potentially life-threatening. Patients can play a substantial role in preventing harm from DDIs; however, the current model for DDI decision-making is clinician centric. Objective: This study aims to design and study the usability of DDInteract, a tool to support shared decision-making (SDM) between a patient and provider for the DDI between warfarin and nonsteroidal anti-inflammatory drugs. Methods: We used an SDM framework and user-centered design methods to guide the design and usability of DDInteract—an SDM electronic health record app to prevent harm from clinically significant DDIs. The design involved iterative prototypes, qualitative feedback from stakeholders, and a heuristic evaluation. The usability evaluation included patients and clinicians. Patients participated in a simulated SDM discussion using clinical vignettes. Clinicians were asked to complete eight tasks using DDInteract and to assess the tool using a survey adapted from the System Usability Scale. Results: The designed DDInteract prototype includes the following features: a patient-specific risk profile, dynamic risk icon array, patient education section, and treatment decision tree. A total of 4 patients and 11 clinicians participated in the usability study. After an SDM session where patients and clinicians review the tool concurrently, patients generally favored pain treatments with less risk of gastrointestinal bleeding. Clinicians successfully completed the tasks with a mean of 144 (SD 74) seconds and rated the usability of DDInteract as 4.32 (SD 0.52) of 5. Conclusions: This study expands the use of SDM to DDIs. The next steps are to determine if DDInteract can improve shared decision-making quality and to implement it across health systems using interoperable technology. %M 34698649 %R 10.2196/28618 %U https://humanfactors.jmir.org/2021/4/e28618 %U https://doi.org/10.2196/28618 %U http://www.ncbi.nlm.nih.gov/pubmed/34698649 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 10 %P e29695 %T A New Tool for Safety Evaluation and a Combination of Measures for Efficacy Assessment of Cotransplanting Human Allogenic Neuronal Stem Cells and Mesenchymal Stem Cells for the Treatment of Parkinson Disease: Protocol for an Interventional Study %A Jamali,Fatima %A Aldughmi,Mayis %A Khasawneh,Mohammad W %A Dahbour,Said %A Salameh,Alaa A %A Awidi,Abdalla %+ Cell Therapy Center, University of Jordan, Queen Rania St, Amman, 11942, Jordan, 962 6 535 5000 ext 23960, ftmjamali@gmail.com %K Parkinson disease %K neurodegenerative disease %K regenerative medicine %K mesenchymal stem cells %K MSCs %K neuronal stem cells %K NSCs %K Unified Parkinson Disease Rating Scale %K UPDRS %K Mobility Lab %K α-synuclein %K PARK-7 %K stem cells %K stem cell therapy %K therapeutics %K Parkinson’s %K neurological diseases %D 2021 %7 22.10.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Parkinson disease (PD) is a neurodegenerative disorder associated with a broad spectrum of motor and nonmotor symptoms. Any proposed cure needs to address the many aspects of the disease. Stem cell therapy may have potential in this regard as indicated in recent preclinical and clinical studies. Objective: This protocol aims to examine the safety and therapeutic benefit of human Wharton jelly-derived mesenchymal stem cells (WJ-MScs) and their derivatives, neuronal stem cells (NSCs) in PD. Methods: This clinical trial is a double-arm, single-blinded, phase I-II interventional study. Participants have been allocated to 1 of 2 groups: one receiving allogeneic WJ-MSCs alone, the other receiving NSCs and WJ-MScs. Participants are being followed-up and assessed over a period of 6 months. To assess safety, an incidence of treatment-emergent adverse events (TEAEs) tool tailored for PD is being used immediately and up to 6 months after treatment. For efficacy assessment, a number of factors are being used, including the gold standard severity test and the Unified Parkinson Disease Rating Scale. In addition, the following standardized assessments for different common symptoms in PD are being included: motor (both subjectively and objectively assessed with wearable sensors), sensory, quality of life and psychological well-being, cognition, and sleep quality. Furthermore, immune-modulatory cytokines and neuronal damage versus regeneration markers in PD, including the neuronal protein linked to PD, α-synuclein, are being monitored. Results: Ten patients have been enrolled in this study and thus participant recruitment has been completed. The study status is active and beyond the recruiting stage. Study chart implementation, data collection, and analysis are ongoing. Conclusions: The combination of NSCs and MSCs in PD may be useful for harnessing the best of the immunomodulation and neural repair characteristics of these cell types. The tailored comprehensive and scaled TEAEs and the variety of evaluation tools used enables a comprehensive assessment of this cellular therapy treatment protocol. A consideration of this expanded tool set is important in the design of future clinical studies for PD. Trial Registration: ClinicalTrials.gov NCT03684122; https://clinicaltrials.gov/ct2/show/NCT03684122 International Registered Report Identifier (IRRID): DERR1-10.2196/29695 %M 34677138 %R 10.2196/29695 %U https://www.researchprotocols.org/2021/10/e29695 %U https://doi.org/10.2196/29695 %U http://www.ncbi.nlm.nih.gov/pubmed/34677138 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 10 %P e31748 %T Impact of a Mobile App on Paramedics’ Perceived and Physiologic Stress Response During Simulated Prehospital Pediatric Cardiopulmonary Resuscitation: Study Nested Within a Multicenter Randomized Controlled Trial %A Lacour,Matthieu %A Bloudeau,Laurie %A Combescure,Christophe %A Haddad,Kevin %A Hugon,Florence %A Suppan,Laurent %A Rodieux,Frédérique %A Lovis,Christian %A Gervaix,Alain %A Ehrler,Frédéric %A Manzano,Sergio %A Siebert,Johan N %A , %+ Department of Pediatric Emergency Medicine, Geneva Children’s Hospital, Geneva University Hospitals, Avenue de la Roseraie, 47, Geneva, 1205, Switzerland, 41 (0)795534072, Johan.Siebert@hcuge.ch %K cardiopulmonary resuscitation %K drugs %K emergency medical services %K medication errors %K mobile health %K mobile apps %K out-of-hospital cardiac arrest %K paramedics %K pediatrics %K State-Trait Anxiety Inventory %K stress %D 2021 %7 7.10.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Out-of-hospital cardiac arrests (OHCAs) are stressful, high-stake events that are associated with low survival rates. Acute stress experienced in this situation is associated with lower cardiopulmonary resuscitation performance in calculating drug dosages by emergency medical services. Children are particularly vulnerable to such errors. To date, no app has been validated to specifically support emergency drug preparation by paramedics through reducing the stress level of this procedure and medication errors. Objective: This study aims to determine the effectiveness of an evidence-based mobile app compared with that of the conventional preparation methods in reducing acute stress in paramedics at the psychological and physiological levels while safely preparing emergency drugs during simulated pediatric OHCA scenarios. Methods: In a parent, multicenter, randomized controlled trial of 14 emergency medical services, perceived and physiologic stress of advanced paramedics with drug preparation autonomy was assessed during a 20-minute, standardized, fully video-recorded, and highly realistic pediatric OHCA scenario in an 18-month-old child. The primary outcome was participants’ self-reported psychological stress perceived during sequential preparations of 4 intravenous emergency drugs (epinephrine, midazolam, 10% dextrose, and sodium bicarbonate) with the support of the PedAMINES (Pediatric Accurate Medication in Emergency Situations) app designed to help pediatric drug preparation (intervention) or conventional methods (control). The State-Trait Anxiety Inventory and Visual Analog Scale questionnaires were used to measure perceived stress. The secondary outcome was physiologic stress, measured by a single continuous measurement of the participants’ heart rate with optical photoplethysmography. Results: From September 3, 2019, to January 21, 2020, 150 advanced paramedics underwent randomization. A total of 74 participants were assigned to the mobile app (intervention group), and 76 did not use the app (control group). A total of 600 drug doses were prepared. Higher State-Trait Anxiety Inventory–perceived stress increase from baseline was observed during the scenario using the conventional methods (mean 35.4, SD 8.2 to mean 49.8, SD 13.2; a 41.3%, 35.0 increase) than when using the app (mean 36.1, SD 8.1 to mean 39.0, SD 8.4; a 12.3%, 29.0 increase). This revealed a 30.1% (95% CI 20.5%-39.8%; P<.001) lower relative change in stress response in participants who used the app. On the Visual Analog Scale questionnaire, participants in the control group reported a higher increase in stress at the peak of the scenario (mean 7.1, SD 1.8 vs mean 6.4, SD 1.9; difference: −0.8, 95% CI −1.3 to −0.2; P=.005). Increase in heart rate during the scenario and over the 4 drugs was not different between the 2 groups. Conclusions: Compared with the conventional method, dedicated mobile apps can reduce acute perceived stress during the preparation of emergency drugs in the prehospital setting during critical situations. These findings can help advance the development and evaluation of mobile apps for OHCA management and should be encouraged. Trial Registration: ClinicalTrials.gov NCT03921346; https://clinicaltrials.gov/ct2/show/NCT03921346 International Registered Report Identifier (IRRID): RR2-10.1186/s13063-019-3726-4 %M 34617916 %R 10.2196/31748 %U https://mhealth.jmir.org/2021/10/e31748 %U https://doi.org/10.2196/31748 %U http://www.ncbi.nlm.nih.gov/pubmed/34617916 %0 Journal Article %@ 2561-9128 %I JMIR Publications %V 4 %N 2 %P e29200 %T Predicting Prolonged Apnea During Nurse-Administered Procedural Sedation: Machine Learning Study %A Conway,Aaron %A Jungquist,Carla R %A Chang,Kristina %A Kamboj,Navpreet %A Sutherland,Joanna %A Mafeld,Sebastian %A Parotto,Matteo %+ Lawrence S. Bloomberg Faculty of Nursing, University of Toronto, Suite 130, 155 College Street, Toronto, ON, M5T 1P8, Canada, 1 (416) 340 4654, aaron.conway@utoronto.ca %K procedural sedation and analgesia %K conscious sedation %K nursing %K informatics %K patient safety %K machine learning %K capnography %K anesthesia %K anaesthesia %K medical informatics %K sleep apnea %K apnea %K apnoea %K sedation %D 2021 %7 5.10.2021 %9 Original Paper %J JMIR Perioper Med %G English %X Background: Capnography is commonly used for nurse-administered procedural sedation. Distinguishing between capnography waveform abnormalities that signal the need for clinical intervention for an event and those that do not indicate the need for intervention is essential for the successful implementation of this technology into practice. It is possible that capnography alarm management may be improved by using machine learning to create a “smart alarm” that can alert clinicians to apneic events that are predicted to be prolonged. Objective: To determine the accuracy of machine learning models for predicting at the 15-second time point if apnea will be prolonged (ie, apnea that persists for >30 seconds). Methods: A secondary analysis of an observational study was conducted. We selected several candidate models to evaluate, including a random forest model, generalized linear model (logistic regression), least absolute shrinkage and selection operator regression, ridge regression, and the XGBoost model. Out-of-sample accuracy of the models was calculated using 10-fold cross-validation. The net benefit decision analytic measure was used to assist with deciding whether using the models in practice would lead to better outcomes on average than using the current default capnography alarm management strategies. The default strategies are the aggressive approach, in which an alarm is triggered after brief periods of apnea (typically 15 seconds) and the conservative approach, in which an alarm is triggered for only prolonged periods of apnea (typically >30 seconds). Results: A total of 384 apneic events longer than 15 seconds were observed in 61 of the 102 patients (59.8%) who participated in the observational study. Nearly half of the apneic events (180/384, 46.9%) were prolonged. The random forest model performed the best in terms of discrimination (area under the receiver operating characteristic curve 0.66) and calibration. The net benefit associated with the random forest model exceeded that associated with the aggressive strategy but was lower than that associated with the conservative strategy. Conclusions: Decision curve analysis indicated that using a random forest model would lead to a better outcome for capnography alarm management than using an aggressive strategy in which alarms are triggered after 15 seconds of apnea. The model would not be superior to the conservative strategy in which alarms are only triggered after 30 seconds. %M 34609322 %R 10.2196/29200 %U https://periop.jmir.org/2021/2/e29200 %U https://doi.org/10.2196/29200 %U http://www.ncbi.nlm.nih.gov/pubmed/34609322 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e27098 %T Machine Learning Analysis of Time-Dependent Features for Predicting Adverse Events During Hemodialysis Therapy: Model Development and Validation Study %A Liu,Yi-Shiuan %A Yang,Chih-Yu %A Chiu,Ping-Fang %A Lin,Hui-Chu %A Lo,Chung-Chuan %A Lai,Alan Szu-Han %A Chang,Chia-Chu %A Lee,Oscar Kuang-Sheng %+ Institute of Clinical Medicine, National Yang Ming Chiao Tung University School of Medicine, 2F, Shou-Ren Bldg, 155, Sec 2, Li-Nong St, Beitou Dist, Taipei, 11221, Taiwan, 886 228712121 ext 7391, oscarlee9203@gmail.com %K hemodialysis %K intradialytic adverse events %K prediction algorithm %K machine learning %D 2021 %7 7.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Hemodialysis (HD) therapy is an indispensable tool used in critical care management. Patients undergoing HD are at risk for intradialytic adverse events, ranging from muscle cramps to cardiac arrest. So far, there is no effective HD device–integrated algorithm to assist medical staff in response to these adverse events a step earlier during HD. Objective: We aimed to develop machine learning algorithms to predict intradialytic adverse events in an unbiased manner. Methods: Three-month dialysis and physiological time-series data were collected from all patients who underwent maintenance HD therapy at a tertiary care referral center. Dialysis data were collected automatically by HD devices, and physiological data were recorded by medical staff. Intradialytic adverse events were documented by medical staff according to patient complaints. Features extracted from the time series data sets by linear and differential analyses were used for machine learning to predict adverse events during HD. Results: Time series dialysis data were collected during the 4-hour HD session in 108 patients who underwent maintenance HD therapy. There were a total of 4221 HD sessions, 406 of which involved at least one intradialytic adverse event. Models were built by classification algorithms and evaluated by four-fold cross-validation. The developed algorithm predicted overall intradialytic adverse events, with an area under the curve (AUC) of 0.83, sensitivity of 0.53, and specificity of 0.96. The algorithm also predicted muscle cramps, with an AUC of 0.85, and blood pressure elevation, with an AUC of 0.93. In addition, the model built based on ultrafiltration-unrelated features predicted all types of adverse events, with an AUC of 0.81, indicating that ultrafiltration-unrelated factors also contribute to the onset of adverse events. Conclusions: Our results demonstrated that algorithms combining linear and differential analyses with two-class classification machine learning can predict intradialytic adverse events in quasi-real time with high AUCs. Such a methodology implemented with local cloud computation and real-time optimization by personalized HD data could warn clinicians to take timely actions in advance. %M 34491204 %R 10.2196/27098 %U https://www.jmir.org/2021/9/e27098 %U https://doi.org/10.2196/27098 %U http://www.ncbi.nlm.nih.gov/pubmed/34491204 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 8 %N 3 %P e28381 %T Robotic Pharmacy Implementation and Outcomes in Saudi Arabia: A 21-Month Usability Study %A Momattin,Hisham %A Arafa,Shokry %A Momattin,Shahad %A Rahal,Rayan %A Waterson,James %+ Mouwasat Medical Services, Mouwasat Hospital, 16 D Street, Dammam, 32263, Saudi Arabia, 966 9200 04477, Hisham.Momattin@mouwasat.com %K patient satisfaction %K automation %K integration %K medication error %K outpatient %K medication management %K usability %K medication dispensing %K robotics %K pharmacy %K medication records %K error %K record %K implementation %K outcome %D 2021 %7 1.9.2021 %9 Original Paper %J JMIR Hum Factors %G English %X Background: We describe the introduction, use, and evaluation of an automation and integration pharmacy development program in a private facility in Saudi Arabia. The project was specifically undertaken to increase throughput, reduce medication dispensing error rates, improve patient satisfaction, and free up pharmacists’ time to allow for increased face-to-face consultations with patients. Objective: We forecasted growth of our outpatient service at 25% per annum over 5- and 10-year horizons and set out to prepare our outpatient pharmacy service to meet this demand. Initial project goals were set as a 50% reduction in the average patient wait time, a 15% increase in patient satisfaction regarding pharmacy wait time and pharmacy services, a 25% increase in pharmacist productivity, and zero dispensing errors. This was expected to be achieved within 10 months of go-live. Realignment of pharmacist activity toward counseling and medication review with patients was a secondary goal, along with the rapid development of a reputation in the served community for patient-centered care. Methods: Preimplementation data for patient wait time for dispensing of prescribed medications as a specific measure of patient satisfaction was gathered as part of wider ongoing data collection in this field. Pharmacist activity and productivity in terms of patient interaction time were gathered. Reported and discovered dispensing errors per 1000 prescriptions were also aggregated. All preimplementation data was gathered over an 11-month period. Results: From go-live, data were gathered on the above metrics in 1-month increments. At the 10-month point, there had been a 53% reduction in the average wait time, a 20% increase in patient satisfaction regarding pharmacy wait time, with a 22% increase in overall patient satisfaction regarding pharmacy services, and a 33% increase in pharmacist productivity. A zero dispensing error rate was reported. Conclusions: The robotic pharmacy solution studied was highly effective, but a robust upstream supply chain is vital to ensure stock levels, particularly when automated filling is planned. The automation solution must also be seamlessly and completely integrated into the facility’s software systems for appointments, medication records, and prescription generation in order to garner its full benefits. Overall patient satisfaction with pharmacy services is strongly influenced by wait time and follow-up studies are required to identify how to use this positive effect and make optimal use of freed-up pharmacist time. The extra time spent by pharmacists with patients and the opportunity for complete overview of the patient’s medication history, which full integration provides, may allow us to address challenging issues such as medication nonadherence. Reduced wait times may also allow for smaller prescription fill volumes, and more frequent outpatient department visits, allowing patients to have increased contact time with pharmacists. %M 34304149 %R 10.2196/28381 %U https://humanfactors.jmir.org/2021/3/e28381 %U https://doi.org/10.2196/28381 %U http://www.ncbi.nlm.nih.gov/pubmed/34304149 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 8 %P e30470 %T Classification of Electronic Health Record–Related Patient Safety Incidents: Development and Validation Study %A Palojoki,Sari %A Saranto,Kaija %A Reponen,Elina %A Skants,Noora %A Vakkuri,Anne %A Vuokko,Riikka %+ Department of Steering of Healthcare and Social Welfare, Ministry of Social Affairs and Health, P.O. Box 33, Helsinki, 00023, Finland, 358 29516001, sari.palojoki@gmail.com %K classification %K electronic health records %K hospitals %K medical informatics %K patient safety %K risk %D 2021 %7 31.8.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: It is assumed that the implementation of health information technology introduces new vulnerabilities within a complex sociotechnical health care system, but no international consensus exists on a standardized format for enhancing the collection, analysis, and interpretation of technology-induced errors. Objective: This study aims to develop a classification for patient safety incident reporting associated with the use of mature electronic health records (EHRs). It also aims to validate the classification by using a data set of incidents during a 6-month period immediately after the implementation of a new EHR system. Methods: The starting point of the classification development was the Finnish Technology-Induced Error Risk Assessment Scale tool, based on research on commonly recognized error types. A multiprofessional research team used iterative tests on consensus building to develop a classification system. The final classification, with preliminary descriptions of classes, was validated by applying it to analyze EHR-related error incidents (n=428) during the implementation phase of a new EHR system and also to evaluate this classification’s characteristics and applicability for reporting incidents. Interrater agreement was applied. Results: The number of EHR-related patient safety incidents during the implementation period (n=501) was five-fold when compared with the preimplementation period (n=82). The literature identified new error types that were added to the emerging classification. Error types were adapted iteratively after several test rounds to develop a classification for reporting patient safety incidents in the clinical use of a high-maturity EHR system. Of the 427 classified patient safety incidents, interface problems accounted for 96 (22.5%) incident reports, usability problems for 73 (17.1%), documentation problems for 60 (14.1%), and clinical workflow problems for 33 (7.7%). Altogether, 20.8% (89/427) of reports were related to medication section problems, and downtime problems were rare (n=8). During the classification work, 14.8% (74/501) of reports of the original sample were rejected because of insufficient information, even though the reports were deemed to be related to EHRs. The interrater agreement during the blinded review was 97.7%. Conclusions: This study presents a new classification for EHR-related patient safety incidents applicable to mature EHRs. The number of EHR-related patient safety incidents during the implementation period may reflect patient safety challenges during the implementation of a new type of high-maturity EHR system. The results indicate that the types of errors previously identified in the literature change with the EHR development cycle. %M 34245558 %R 10.2196/30470 %U https://medinform.jmir.org/2021/8/e30470 %U https://doi.org/10.2196/30470 %U http://www.ncbi.nlm.nih.gov/pubmed/34245558 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 8 %P e23508 %T Development and Validation of Unplanned Extubation Prediction Models Using Intensive Care Unit Data: Retrospective, Comparative, Machine Learning Study %A Hur,Sujeong %A Min,Ji Young %A Yoo,Junsang %A Kim,Kyunga %A Chung,Chi Ryang %A Dykes,Patricia C %A Cha,Won Chul %+ Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea, 82 2 3410 2053, wc.cha@samsung.com %K intensive care unit %K machine learning %K mechanical ventilator %K patient safety %K unplanned extubation %D 2021 %7 11.8.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Patient safety in the intensive care unit (ICU) is one of the most critical issues, and unplanned extubation (UE) is considered the most adverse event for patient safety. Prevention and early detection of such an event is an essential but difficult component of quality care. Objective: This study aimed to develop and validate prediction models for UE in ICU patients using machine learning. Methods: This study was conducted in an academic tertiary hospital in Seoul, Republic of Korea. The hospital had approximately 2000 inpatient beds and 120 ICU beds. As of January 2019, the hospital had approximately 9000 outpatients on a daily basis. The number of annual ICU admissions was approximately 10,000. We conducted a retrospective study between January 1, 2010, and December 31, 2018. A total of 6914 extubation cases were included. We developed a UE prediction model using machine learning algorithms, which included random forest (RF), logistic regression (LR), artificial neural network (ANN), and support vector machine (SVM). For evaluating the model’s performance, we used the area under the receiver operating characteristic curve (AUROC). The sensitivity, specificity, positive predictive value, negative predictive value, and F1 score were also determined for each model. For performance evaluation, we also used a calibration curve, the Brier score, and the integrated calibration index (ICI) to compare different models. The potential clinical usefulness of the best model at the best threshold was assessed through a net benefit approach using a decision curve. Results: Among the 6914 extubation cases, 248 underwent UE. In the UE group, there were more males than females, higher use of physical restraints, and fewer surgeries. The incidence of UE was higher during the night shift as compared to the planned extubation group. The rate of reintubation within 24 hours and hospital mortality were higher in the UE group. The UE prediction algorithm was developed, and the AUROC for RF was 0.787, for LR was 0.762, for ANN was 0.763, and for SVM was 0.740. Conclusions: We successfully developed and validated machine learning–based prediction models to predict UE in ICU patients using electronic health record data. The best AUROC was 0.787 and the sensitivity was 0.949, which was obtained using the RF algorithm. The RF model was well-calibrated, and the Brier score and ICI were 0.129 and 0.048, respectively. The proposed prediction model uses widely available variables to limit the additional workload on the clinician. Further, this evaluation suggests that the model holds potential for clinical usefulness. %M 34382940 %R 10.2196/23508 %U https://www.jmir.org/2021/8/e23508 %U https://doi.org/10.2196/23508 %U http://www.ncbi.nlm.nih.gov/pubmed/34382940 %0 Journal Article %@ 2563-6316 %I JMIR Publications %V 2 %N 3 %P e27017 %T Finding Potential Adverse Events in the Unstructured Text of Electronic Health Care Records: Development of the Shakespeare Method %A Bright,Roselie A %A Rankin,Summer K %A Dowdy,Katherine %A Blok,Sergey V %A Bright,Susan J %A Palmer,Lee Anne M %+ Booz Allen Hamilton, 8283 Greensboro Dr, McLean, VA, 22102, United States, 1 808 594 5975, rankin_summer@bah.com %K epidemiology %K electronic health record %K electronic health care record %K big data %K patient harm %K patient safety %K public health %K product surveillance, postmarketing %K natural language processing %K proof-of-concept study %K critical care %D 2021 %7 11.8.2021 %9 Original Paper %J JMIRx Med %G English %X Background: Big data tools provide opportunities to monitor adverse events (patient harm associated with medical care) (AEs) in the unstructured text of electronic health care records (EHRs). Writers may explicitly state an apparent association between treatment and adverse outcome (“attributed”) or state the simple treatment and outcome without an association (“unattributed”). Many methods for finding AEs in text rely on predefining possible AEs before searching for prespecified words and phrases or manual labeling (standardization) by investigators. We developed a method to identify possible AEs, even if unknown or unattributed, without any prespecifications or standardization of notes. Our method was inspired by word-frequency analysis methods used to uncover the true authorship of disputed works credited to William Shakespeare. We chose two use cases, “transfusion” and “time-based.” Transfusion was chosen because new transfusion AE types were becoming recognized during the study data period; therefore, we anticipated an opportunity to find unattributed potential AEs (PAEs) in the notes. With the time-based case, we wanted to simulate near real-time surveillance. We chose time periods in the hope of detecting PAEs due to contaminated heparin from mid-2007 to mid-2008 that were announced in early 2008. We hypothesized that the prevalence of contaminated heparin may have been widespread enough to manifest in EHRs through symptoms related to heparin AEs, independent of clinicians’ documentation of attributed AEs. Objective: We aimed to develop a new method to identify attributed and unattributed PAEs using the unstructured text of EHRs. Methods: We used EHRs for adult critical care admissions at a major teaching hospital (2001-2012). For each case, we formed a group of interest and a comparison group. We concatenated the text notes for each admission into one document sorted by date, and deleted replicate sentences and lists. We identified statistically significant words in the group of interest versus the comparison group. Documents in the group of interest were filtered to those words, followed by topic modeling on the filtered documents to produce topics. For each topic, the three documents with the maximum topic scores were manually reviewed to identify PAEs. Results: Topics centered around medical conditions that were unique to or more common in the group of interest, including PAEs. In each use case, most PAEs were unattributed in the notes. Among the transfusion PAEs was unattributed evidence of transfusion-associated cardiac overload and transfusion-related acute lung injury. Some of the PAEs from mid-2007 to mid-2008 were increased unattributed events consistent with AEs related to heparin contamination. Conclusions: The Shakespeare method could be a useful supplement to AE reporting and surveillance of structured EHR data. Future improvements should include automation of the manual review process. %M 37725533 %R 10.2196/27017 %U https://med.jmirx.org/2021/3/e27017 %U https://doi.org/10.2196/27017 %U http://www.ncbi.nlm.nih.gov/pubmed/37725533 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 7 %P e19064 %T Using Text Mining Techniques to Identify Health Care Providers With Patient Safety Problems: Exploratory Study %A Hendrickx,Iris %A Voets,Tim %A van Dyk,Pieter %A Kool,Rudolf B %+ Centre for Language Studies, Centre for Language and Speech Technology, Faculty of Arts, Radboud University, Erasmusplein 1, Nijmegen, 6525HT, Netherlands, 31 2436 15575, i.hendrickx@let.ru.nl %K text mining %K risk management %K health care quality improvement %D 2021 %7 27.7.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Regulatory bodies such as health care inspectorates can identify potential patient safety problems in health care providers by analyzing patient complaints. However, it is challenging to analyze the large number of complaints. Text mining techniques may help identify signals of problems with patient safety at health care providers. Objective: The aim of this study was to explore whether employing text mining techniques on patient complaint databases can help identify potential problems with patient safety at health care providers and automatically predict the severity of patient complaints. Methods: We performed an exploratory study on the complaints database of the Dutch Health and Youth Care Inspectorate with more than 22,000 written complaints. Severe complaints are defined as those cases where the inspectorate contact point experts deemed it worthy of a triage by the inspectorate, or complaints that led to direct action by the inspectorate. We investigated a range of supervised machine learning techniques to assign a severity label to complaints that can be used to prioritize which incoming complaints need the most attention. We studied several features based on the complaints’ written content, including sentiment analysis, to decide which were helpful for severity prediction. Finally, we showcased how we could combine these severity predictions and automatic keyword analysis on the complaints database and listed health care providers and their organization-specific complaints to determine the average severity of complaints per organization. Results: A straightforward text classification approach using a bag-of-words feature representation worked best for the severity prediction of complaints. We obtained an accuracy of 87%-93% (2658-2990 of 3319 complaints) on the held-out test set and an F1 score of 45%-51% on the severe complaints. The skewed class distribution led to only reasonable recall (47%-54%) and precision (44%-49%) scores. The use of sentiment analysis for severity prediction was not helpful. By combining the predicted severity outcomes with an automatic keyword analysis, we identified several health care providers that could have patient safety problems. Conclusions: Text mining techniques for analyzing complaints by civilians can support inspectorates. They can automatically predict the severity of the complaints, or they can be used for keyword analysis. This can help the inspectorate detect potential patient safety problems, or support prioritizing follow-up supervision activities by sorting complaints based on the severity per organization or per sector. %M 34313604 %R 10.2196/19064 %U https://www.jmir.org/2021/7/e19064 %U https://doi.org/10.2196/19064 %U http://www.ncbi.nlm.nih.gov/pubmed/34313604 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 7 %P e29986 %T Ambulatory Risk Models for the Long-Term Prevention of Sepsis: Retrospective Study %A Lee,Jewel Y %A Molani,Sevda %A Fang,Chen %A Jade,Kathleen %A O'Mahony,D Shane %A Kornilov,Sergey A %A Mico,Lindsay T %A Hadlock,Jennifer J %+ Institute for Systems Biology, 401 Terry Ave N, Seattle, WA, 98109, United States, jhadlock@isbscience.org %K sepsis %K machine learning %K electronic health records %K risk prediction %K clinical decision making %K prevention %K risk factors %D 2021 %7 8.7.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Sepsis is a life-threatening condition that can rapidly lead to organ damage and death. Existing risk scores predict outcomes for patients who have already become acutely ill. Objective: We aimed to develop a model for identifying patients at risk of getting sepsis within 2 years in order to support the reduction of sepsis morbidity and mortality. Methods: Machine learning was applied to 2,683,049 electronic health records (EHRs) with over 64 million encounters across five states to develop models for predicting a patient’s risk of getting sepsis within 2 years. Features were selected to be easily obtainable from a patient’s chart in real time during ambulatory encounters. Results: The models showed consistent prediction scores, with the highest area under the receiver operating characteristic curve of 0.82 and a positive likelihood ratio of 2.9 achieved with gradient boosting on all features combined. Predictive features included age, sex, ethnicity, average ambulatory heart rate, standard deviation of BMI, and the number of prior medical conditions and procedures. The findings identified both known and potential new risk factors for long-term sepsis. Model variations also illustrated trade-offs between incrementally higher accuracy, implementability, and interpretability. Conclusions: Accurate implementable models were developed to predict the 2-year risk of sepsis, using EHR data that is easy to obtain from ambulatory encounters. These results help advance the understanding of sepsis and provide a foundation for future trials of risk-informed preventive care. %M 34086596 %R 10.2196/29986 %U https://medinform.jmir.org/2021/7/e29986 %U https://doi.org/10.2196/29986 %U http://www.ncbi.nlm.nih.gov/pubmed/34086596 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 6 %P e26946 %T Design and Implementation of a Real-time Monitoring Platform for Optimal Sepsis Care in an Emergency Department: Observational Cohort Study %A Lee,Andy Hung-Yi %A Aaronson,Emily %A Hibbert,Kathryn A %A Flynn,Micah H %A Rutkey,Hayley %A Mort,Elizabeth %A Sonis,Jonathan D %A Safavi,Kyan C %+ Department of Emergency Medicine, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, United States, 1 617 724 4100, alee85@partners.org %K electronic monitoring platform %K sepsis %K quality improvement %D 2021 %7 24.6.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Sepsis is the leading cause of death in US hospitals. Compliance with bundled care, specifically serial lactates, blood cultures, and antibiotics, improves outcomes but is often delayed or missed altogether in a busy practice environment. Objective: This study aims to design, implement, and validate a novel monitoring and alerting platform that provides real-time feedback to frontline emergency department (ED) providers regarding adherence to bundled care. Methods: This single-center, prospective, observational study was conducted in three phases: the design and technical development phase to build an initial version of the platform; the pilot phase to test and refine the platform in the clinical setting; and the postpilot rollout phase to fully implement the study intervention. Results: During the design and technical development, study team members and stakeholders identified the criteria for patient inclusion, selected bundle measures from the Center for Medicare and Medicaid Sepsis Core Measure for alerting, and defined alert thresholds, message content, delivery mechanisms, and recipients. Additional refinements were made based on 70 provider survey results during the pilot phase, including removing alerts for vasopressor initiation and modifying text in the pages to facilitate patient identification. During the 48 days of the postpilot rollout phase, 15,770 ED encounters were tracked and 711 patient encounters were included in the active monitoring cohort. In total, 634 pages were sent at a rate of 0.98 per attending physician shift. Overall, 38.3% (272/711) patients had at least one page. The missing bundle elements that triggered alerts included: antibiotics 41.6% (136/327), repeat lactate 32.4% (106/327), blood cultures 20.8% (68/327), and initial lactate 5.2% (17/327). Of the missing Sepsis Core Measures elements for which a page was sent, 38.2% (125/327) were successfully completed on time. Conclusions: A real-time sepsis care monitoring and alerting platform was created for the ED environment. The high proportion of patients with at least one alert suggested the significant potential for such a platform to improve care, whereas the overall number of alerts per clinician suggested a low risk of alarm fatigue. The study intervention warrants a more rigorous evaluation to ensure that the added alerts lead to better outcomes for patients with sepsis. %M 34185009 %R 10.2196/26946 %U https://www.jmir.org/2021/6/e26946/ %U https://doi.org/10.2196/26946 %U http://www.ncbi.nlm.nih.gov/pubmed/34185009 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 6 %P e24642 %T Monitoring Diagnostic Safety Risks in Emergency Departments: Protocol for a Machine Learning Study %A Enayati,Moein %A Sir,Mustafa %A Zhang,Xingyu %A Parker,Sarah J %A Duffy,Elizabeth %A Singh,Hardeep %A Mahajan,Prashant %A Pasupathy,Kalyan S %+ Health Care Delivery Research, Kern Center for the Science of Health Care Delivery, Mayo Clinic, Harwick Building, Second Floor, 200 First St SW, HA2-43, Rochester, MN, 55905, United States, 1 (507) 293 2512, Pasupathy.Kalyan@mayo.edu %K diagnostic error %K emergency department %K machine learning %K electronic health records %K electronic triggers %D 2021 %7 14.6.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Diagnostic decision making, especially in emergency departments, is a highly complex cognitive process that involves uncertainty and susceptibility to errors. A combination of factors, including patient factors (eg, history, behaviors, complexity, and comorbidity), provider-care team factors (eg, cognitive load and information gathering and synthesis), and system factors (eg, health information technology, crowding, shift-based work, and interruptions) may contribute to diagnostic errors. Using electronic triggers to identify records of patients with certain patterns of care, such as escalation of care, has been useful to screen for diagnostic errors. Once errors are identified, sophisticated data analytics and machine learning techniques can be applied to existing electronic health record (EHR) data sets to shed light on potential risk factors influencing diagnostic decision making. Objective: This study aims to identify variables associated with diagnostic errors in emergency departments using large-scale EHR data and machine learning techniques. Methods: This study plans to use trigger algorithms within EHR data repositories to generate a large data set of records that are labeled trigger-positive or trigger-negative, depending on whether they meet certain criteria. Samples from both data sets will be validated using medical record reviews, upon which we expect to find a higher number of diagnostic safety events in the trigger-positive subset. Machine learning will be used to evaluate relationships between certain patient factors, provider-care team factors, and system-level risk factors and diagnostic safety signals in the statistically matched groups of trigger-positive and trigger-negative charts. Results: This federally funded study was approved by the institutional review board of 2 academic medical centers with affiliated community hospitals. Trigger queries are being developed at both organizations, and sample cohorts will be labeled using the triggers. Machine learning techniques such as association rule mining, chi-square automated interaction detection, and classification and regression trees will be used to discover important variables that could be incorporated within future clinical decision support systems to help identify and reduce risks that contribute to diagnostic errors. Conclusions: The use of large EHR data sets and machine learning to investigate risk factors (related to the patient, provider-care team, and system-level) in the diagnostic process may help create future mechanisms for monitoring diagnostic safety. International Registered Report Identifier (IRRID): DERR1-10.2196/24642 %M 34125077 %R 10.2196/24642 %U https://www.researchprotocols.org/2021/6/e24642 %U https://doi.org/10.2196/24642 %U http://www.ncbi.nlm.nih.gov/pubmed/34125077 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 6 %P e28616 %T Distributed Ledger Infrastructure to Verify Adverse Event Reporting (DeLIVER): Proposal for a Proof-of-Concept Study %A Milne-Ives,Madison %A Lam,Ching %A Rehman,Najib %A Sharif,Raja %A Meinert,Edward %+ Centre for Health Technology, University of Plymouth, 6 Kirkby Place, Room 2, Plymouth, PL4 6DN, United Kingdom, 44 1752600600, edward.meinert@plymouth.ac.uk %K adverse drug reaction reporting systems %K drug-related side effects and adverse reactions %K blockchain %K mobile applications %K distributed ledger technology %D 2021 %7 10.6.2021 %9 Proposal %J JMIR Res Protoc %G English %X Background: Adverse drug event reporting is critical for ensuring patient safety; however, numbers of reports have been declining. There is a need for a more user-friendly reporting system and for a means of verifying reports that have been filed. Objective: This project has 2 main objectives: (1) to identify the perceived benefits and barriers in the current reporting of adverse events by patients and health care providers and (2) to develop a distributed ledger infrastructure and user interface to collect and collate adverse event reports to create a comprehensive and interoperable database. Methods: A review of the literature will be conducted to identify the strengths and limitations of the current UK adverse event reporting system (the Yellow Card System). If insufficient information is found in this review, a survey will be created to collect data from system users. The results of these investigations will be incorporated into the development of a mobile and web app for adverse event reporting. A digital infrastructure will be built using distributed ledger technology to provide a means of linking reports with existing pharmaceutical tracking systems. Results: The key outputs of this project will be the development of a digital infrastructure, including a backend distributed ledger system and an app-based user interface. Conclusions: This infrastructure is expected to improve the accuracy and efficiency of adverse event reporting systems by enabling the monitoring of specific medicines or medical devices over their life course while protecting patients’ personal health data. International Registered Report Identifier (IRRID): PRR1-10.2196/28616 %M 34110292 %R 10.2196/28616 %U https://www.researchprotocols.org/2021/6/e28616 %U https://doi.org/10.2196/28616 %U http://www.ncbi.nlm.nih.gov/pubmed/34110292 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 6 %P e25674 %T The Presence of Fungal and Parasitic Infections in Substances of Human Origin and Their Transmission via Transfusions and Transplantations: Protocol for Two Systematic Reviews %A Dinas,Petros C %A Domanovic,Dragoslav %A Koutedakis,Yiannis %A Hadjichristodoulou,Christos %A Stefanidis,Ioannis %A Papadopoulou,Kalliope %A Dimas,Konstantinos %A Perivoliotis,Konstantinos %A Tepetes,Konstantinos %A Flouris,Andreas D %+ FAME Laboratory, Department of Physical Education and Sport Science, University of Thessaly, Karies, Trikala, 42100, Greece, 30 6974010118, petros.cd@gmail.com %K fungal and parasitic infections %K donor-derived substances %K transplantation %K transfusion %D 2021 %7 10.6.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: The European Union Directives stipulate mandatory tests for the presence of any infections in donors and donations of substances of human origin (SoHO). In some circumstances, other pathogens, including fungi and parasites, may also pose a threat to the microbial safety of SoHO. Objective: The aim of the two systematic reviews is to identify, collect, and evaluate scientific evidence for the presence of fungal and parasitic infections in donors and donations of SoHO, and their transmission via transfusion and transplantation. Methods: An algorithmic search, one each for fungal and parasitic disease, was applied to 6 scientific databases (PubMed, EMBASE, Web of Science, Scopus, Cochrane Library [trials], and CINAHL). Additionally, manual and algorithmic searches were employed in 15 gray literature databases and 22 scientific organization websites. The criteria for eligibility included peer-reviewed publications and peer-reviewed abstract publications from conference proceedings examining the prevalence, incidence, odds ratios, risk ratios, and risk differences for the presence of fungi and parasites in donors and SoHO donations, and their transmission to recipients. Only studies that scrutinized the donors and donations of human blood, blood components, tissues, cells, and organs were considered eligible. Data extraction from eligible publications will be performed independently by two reviewers. Data synthesis will include a qualitative description of the studies lacking evidence suitable for a meta-analysis and a random or fixed-effect meta-analysis model for quantitative data synthesis. Results: This is an ongoing study. The systematic reviews are funded by the European Centre for Disease Prevention and Control, and the results are expected to be presented by the end of 2021. Conclusions: The systematic reviews will provide the basis for developing a risk assessment for fungal and parasitic disease transmission via SoHO. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42020160090; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020160090 ; PROSPERO International Prospective Register of Systematic Reviews CRD42020160110; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020160110 International Registered Report Identifier (IRRID): DERR1-10.2196/25674 %M 34110295 %R 10.2196/25674 %U https://www.researchprotocols.org/2021/6/e25674 %U https://doi.org/10.2196/25674 %U http://www.ncbi.nlm.nih.gov/pubmed/34110295 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 5 %P e26494 %T Patient Monitoring Alarms in an Intensive Care Unit: Observational Study With Do-It-Yourself Instructions %A Poncette,Akira-Sebastian %A Wunderlich,Maximilian Markus %A Spies,Claudia %A Heeren,Patrick %A Vorderwülbecke,Gerald %A Salgado,Eduardo %A Kastrup,Marc %A Feufel,Markus A %A Balzer,Felix %+ Institute of Medical Informatics, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany, 49 30450 ext 651166, felix.balzer@charite.de %K digital health %K patient monitoring %K intensive care unit %K technological innovation %K data science %K alarm fatigue %K alarm management %K patient safety %K ICU %K alarm system %K alarm system quality %K medical devices %K clinical alarms %D 2021 %7 28.5.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: As one of the most essential technical components of the intensive care unit (ICU), continuous monitoring of patients’ vital parameters has significantly improved patient safety by alerting staff through an alarm when a parameter deviates from the normal range. However, the vast number of alarms regularly overwhelms staff and may induce alarm fatigue, a condition recently exacerbated by COVID-19 and potentially endangering patients. Objective: This study focused on providing a complete and repeatable analysis of the alarm data of an ICU’s patient monitoring system. We aimed to develop do-it-yourself (DIY) instructions for technically versed ICU staff to analyze their monitoring data themselves, which is an essential element for developing efficient and effective alarm optimization strategies. Methods: This observational study was conducted using alarm log data extracted from the patient monitoring system of a 21-bed surgical ICU in 2019. DIY instructions were iteratively developed in informal interdisciplinary team meetings. The data analysis was grounded in a framework consisting of 5 dimensions, each with specific metrics: alarm load (eg, alarms per bed per day, alarm flood conditions, alarm per device and per criticality), avoidable alarms, (eg, the number of technical alarms), responsiveness and alarm handling (eg alarm duration), sensing (eg, usage of the alarm pause function), and exposure (eg, alarms per room type). Results were visualized using the R package ggplot2 to provide detailed insights into the ICU’s alarm situation. Results: We developed 6 DIY instructions that should be followed iteratively step by step. Alarm load metrics should be (re)defined before alarm log data are collected and analyzed. Intuitive visualizations of the alarm metrics should be created next and presented to staff in order to help identify patterns in the alarm data for designing and implementing effective alarm management interventions. We provide the script we used for the data preparation and an R-Markdown file to create comprehensive alarm reports. The alarm load in the respective ICU was quantified by 152.5 (SD 42.2) alarms per bed per day on average and alarm flood conditions with, on average, 69.55 (SD 31.12) per day that both occurred mostly in the morning shifts. Most alarms were issued by the ventilator, invasive blood pressure device, and electrocardiogram (ie, high and low blood pressure, high respiratory rate, low heart rate). The exposure to alarms per bed per day was higher in single rooms (26%, mean 172.9/137.2 alarms per day per bed). Conclusions: Analyzing ICU alarm log data provides valuable insights into the current alarm situation. Our results call for alarm management interventions that effectively reduce the number of alarms in order to ensure patient safety and ICU staff’s work satisfaction. We hope our DIY instructions encourage others to follow suit in analyzing and publishing their ICU alarm data. %M 34047701 %R 10.2196/26494 %U https://www.jmir.org/2021/5/e26494 %U https://doi.org/10.2196/26494 %U http://www.ncbi.nlm.nih.gov/pubmed/34047701 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 5 %P e25281 %T Improving the Usability and Safety of Digital Health Systems: The Role of Predictive Human-Computer Interaction Modeling %A Paton,Chris %A Kushniruk,Andre W %A Borycki,Elizabeth M %A English,Mike %A Warren,Jim %+ Nuffield Department of Medicine, University of Oxford, Peter Medawar Building, South Parks Road, Oxford, OX1 3SY, United Kingdom, 44 7552 698058, chris.paton@ndm.ox.ac.uk %K digital health %K human-centered design %K usability %K human-computer interaction %K predictive modeling %D 2021 %7 27.5.2021 %9 Viewpoint %J J Med Internet Res %G English %X In this paper, we describe techniques for predictive modeling of human-computer interaction (HCI) and discuss how they could be used in the development and evaluation of user interfaces for digital health systems such as electronic health record systems. Predictive HCI modeling has the potential to improve the generalizability of usability evaluations of digital health interventions beyond specific contexts, especially when integrated with models of distributed cognition and higher-level sociotechnical frameworks. Evidence generated from building and testing HCI models of the user interface (UI) components for different types of digital health interventions could be valuable for informing evidence-based UI design guidelines to support the development of safer and more effective UIs for digital health interventions. %M 34042590 %R 10.2196/25281 %U https://www.jmir.org/2021/5/e25281 %U https://doi.org/10.2196/25281 %U http://www.ncbi.nlm.nih.gov/pubmed/34042590 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 5 %P e22461 %T Neural Machine Translation–Based Automated Current Procedural Terminology Classification System Using Procedure Text: Development and Validation Study %A Joo,Hyeon %A Burns,Michael %A Kalidaikurichi Lakshmanan,Sai Saradha %A Hu,Yaokun %A Vydiswaran,V G Vinod %+ Department of Learning Health Sciences, University of Michigan, North Campus Research Center, Ann Arbor, MI, 48109, United States, 1 7347649826, thejoo@umich.edu %K CPT classification %K natural language processing %K machine learning %K neural machine translation %D 2021 %7 26.5.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Administrative costs for billing and insurance-related activities in the United States are substantial. One critical cause of the high overhead of administrative costs is medical billing errors. With advanced deep learning techniques, developing advanced models to predict hospital and professional billing codes has become feasible. These models can be used for administrative cost reduction and billing process improvements. Objective: In this study, we aim to develop an automated anesthesiology current procedural terminology (CPT) prediction system that translates manually entered surgical procedure text into standard forms using neural machine translation (NMT) techniques. The standard forms are calculated using similarity scores to predict the most appropriate CPT codes. Although this system aims to enhance medical billing coding accuracy to reduce administrative costs, we compare its performance with that of previously developed machine learning algorithms. Methods: We collected and analyzed all operative procedures performed at Michigan Medicine between January 2017 and June 2019 (2.5 years). The first 2 years of data were used to train and validate the existing models and compare the results from the NMT-based model. Data from 2019 (6-month follow-up period) were then used to measure the accuracy of the CPT code prediction. Three experimental settings were designed with different data types to evaluate the models. Experiment 1 used the surgical procedure text entered manually in the electronic health record. Experiment 2 used preprocessing of the procedure text. Experiment 3 used preprocessing of the combined procedure text and preoperative diagnoses. The NMT-based model was compared with the support vector machine (SVM) and long short-term memory (LSTM) models. Results: The NMT model yielded the highest top-1 accuracy in experiments 1 and 2 at 81.64% and 81.71% compared with the SVM model (81.19% and 81.27%, respectively) and the LSTM model (80.96% and 81.07%, respectively). The SVM model yielded the highest top-1 accuracy of 84.30% in experiment 3, followed by the LSTM model (83.70%) and the NMT model (82.80%). In experiment 3, the addition of preoperative diagnoses showed 3.7%, 3.2%, and 1.3% increases in the SVM, LSTM, and NMT models in top-1 accuracy over those in experiment 2, respectively. For top-3 accuracy, the SVM, LSTM, and NMT models achieved 95.64%, 95.72%, and 95.60% for experiment 1, 95.75%, 95.67%, and 95.69% for experiment 2, and 95.88%, 95.93%, and 95.06% for experiment 3, respectively. Conclusions: This study demonstrates the feasibility of creating an automated anesthesiology CPT classification system based on NMT techniques using surgical procedure text and preoperative diagnosis. Our results show that the performance of the NMT-based CPT prediction system is equivalent to that of the SVM and LSTM prediction models. Importantly, we found that including preoperative diagnoses improved the accuracy of using the procedure text alone. %M 34037526 %R 10.2196/22461 %U https://formative.jmir.org/2021/5/e22461 %U https://doi.org/10.2196/22461 %U http://www.ncbi.nlm.nih.gov/pubmed/34037526 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 5 %P e23479 %T Information Quality Frameworks for Digital Health Technologies: Systematic Review %A Fadahunsi,Kayode Philip %A O'Connor,Siobhan %A Akinlua,James Tosin %A Wark,Petra A %A Gallagher,Joseph %A Carroll,Christopher %A Car,Josip %A Majeed,Azeem %A O'Donoghue,John %+ Department of Public Health and Primary Care, Imperial College London, The Reynolds Building, St. Dunstan’s Road, London, W6 8RP, United Kingdom, 44 07477854209, K.fadahunsi14@imperial.ac.uk %K digital health %K patient safety %K information quality %D 2021 %7 17.5.2021 %9 Review %J J Med Internet Res %G English %X Background: Digital health technologies (DHTs) generate a large volume of information used in health care for administrative, educational, research, and clinical purposes. The clinical use of digital information for diagnostic, therapeutic, and prognostic purposes has multiple patient safety problems, some of which result from poor information quality (IQ). Objective: This systematic review aims to synthesize an IQ framework that could be used to evaluate the extent to which digital health information is fit for clinical purposes. Methods: The review was conducted according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines. We searched Embase, MEDLINE, PubMed, CINAHL, Maternity and Infant Care, PsycINFO, Global Health, ProQuest Dissertations and Theses Global, Scopus, and HMIC (the Health Management Information Consortium) from inception until October 2019. Multidimensional IQ frameworks for assessing DHTs used in the clinical context by health care professionals were included. A thematic synthesis approach was used to synthesize the Clinical Information Quality (CLIQ) framework for digital health. Results: We identified 10 existing IQ frameworks from which we developed the CLIQ framework for digital health with 13 unique dimensions: accessibility, completeness, portability, security, timeliness, accuracy, interpretability, plausibility, provenance, relevance, conformance, consistency, and maintainability, which were categorized into 3 meaningful categories: availability, informativeness, and usability. Conclusions: This systematic review highlights the importance of the IQ of DHTs and its relevance to patient safety. The CLIQ framework for digital health will be useful in evaluating and conceptualizing IQ issues associated with digital health, thus forestalling potential patient safety problems. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42018097142; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=97142 International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2018-024722 %M 33835034 %R 10.2196/23479 %U https://www.jmir.org/2021/5/e23479 %U https://doi.org/10.2196/23479 %U http://www.ncbi.nlm.nih.gov/pubmed/33835034 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 5 %P e27963 %T Presenteeism Among Nurses in Switzerland and Portugal and Its Impact on Patient Safety and Quality of Care: Protocol for a Qualitative Study %A Pereira,Filipa %A Querido,Ana Isabel %A Bieri,Marion %A Verloo,Henk %A Laranjeira,Carlos António %+ School of Health Sciences, HES-SO Valais/Wallis, Chemin de l'Agasse 5, Sion, 1950, Switzerland, 41 786661700, filipa.pereira@hevs.ch %K healthcare %K nurses %K predictors %K presenteeism %K quality of care %K frontline %K managers %K Portugal %K Switzerland %K patient safety %K patients %K safety %K stress %K emotion %K knowledge transfer %K acute care %K long-term care %D 2021 %7 13.5.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: Nurses dispense direct care in a wide variety of settings and are considered the backbone of the health care system. They often work long hours, face emotional stress, and are at a high risk of psychosocial and somatic illnesses. Nurses sometimes fall sick but work regardless, leading to presenteeism and subsequent risks to quality of care and patient safety due to the increased likelihood of patients falling, medication errors, and staff-to-patient disease transmission. Objective: This study aims to understand presenteeism among frontline nurses and nurse managers in acute, primary, and long-term health care settings and to contribute to the development of future interventional studies and recommendations. Methods: A qualitative study based on online focus group discussions will explore the perceptions of, attitudes to, and experiences with presenteeism among frontline nurses and nurse managers. Using a pilot-tested interview guide, 8 focus group discussions will involve nurses working in acute care hospitals, primary care settings, and long-term residential care facilities in Switzerland’s French-speaking region and Portugal’s Center region. The data collected will be examined using a content analysis approach via NVivo 12 QSR International software. Results: The University of Applied Sciences and Arts Western Switzerland’s School of Health Sciences and the Polytechnic of Leiria’s School of Health Sciences in Portugal have both approved funding for the study. The research protocol has been approved by ethics committees in both countries. Study recruitment commenced in February 2021. The results of the data analysis are expected by September 2021. Conclusions: This present study aims to gain more insight into the dilemmas facing nurses as a result of all causes of presenteeism among frontline nurses and nurse managers in different health care settings. The researchers will prepare manuscripts on the study’s findings, publish them in relevant peer-reviewed journals, exhibit them in poster presentations, and give oral presentations at appropriate academic and nonscientific conferences. Regarding further knowledge transfer, researchers will engage with stakeholders to craft messages focused on the needs of nurses and nurse managers and on disseminating our research findings to deal with the issue of nursing presenteeism. International Registered Report Identifier (IRRID): PRR1-10.2196/27963 %M 33983134 %R 10.2196/27963 %U https://www.researchprotocols.org/2021/5/e27963 %U https://doi.org/10.2196/27963 %U http://www.ncbi.nlm.nih.gov/pubmed/33983134 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 4 %P e23961 %T Association of Electronic Health Record Vendors With Hospital Financial and Quality Performance: Retrospective Data Analysis %A Beauvais,Bradley %A Kruse,Clemens Scott %A Fulton,Lawrence %A Shanmugam,Ramalingam %A Ramamonjiarivelo,Zo %A Brooks,Matthew %+ School of Health Administration, College of Health Professions, Texas State University, 601 University Dr, San Marcos, TX, 78666, United States, 1 2103554742, scottkruse@txstate.edu %K electronic health records %K medical informatics %K hospitals %K delivery of health care %K financial management %K quality of health care %K treatment outcome %D 2021 %7 14.4.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Electronic health records (EHRs) are a central feature of care delivery in acute care hospitals; however, the financial and quality outcomes associated with system performance remain unclear. Objective: In this study, we aimed to evaluate the association between the top 3 EHR vendors and measures of hospital financial and quality performance. Methods: This study evaluated 2667 hospitals with Cerner, Epic, or Meditech as their primary EHR and considered their performance with regard to net income, Hospital Value–Based Purchasing Total Performance Score (TPS), and the unweighted subdomains of efficiency and cost reduction; clinical care; patient- and caregiver-centered experience; and patient safety. We hypothesized that there would be a difference among the 3 vendors for each measure. Results: None of the EHR systems were associated with a statistically significant financial relationship in our study. Epic was positively associated with TPS outcomes (R2=23.6%; β=.0159, SE 0.0079; P=.04) and higher patient perceptions of quality (R2=29.3%; β=.0292, SE 0.0099; P=.003) but was negatively associated with patient safety quality scores (R2=24.3%; β=−.0221, SE 0.0102; P=.03). Cerner and Epic were positively associated with improved efficiency (R2=31.9%; Cerner: β=.0330, SE 0.0135, P=.01; Epic: β=.0465, SE 0.0133, P<.001). Finally, all 3 vendors were associated with positive performance in the clinical care domain (Epic: β=.0388, SE 0.0122, P=.002; Cerner: β=.0283, SE 0.0124, P=.02; Meditech: β=.0273, SE 0.0123, P=.03) but with low explanatory power (R2=4.2%). Conclusions: The results of this study provide evidence of a difference in clinical outcome performance among the top 3 EHR vendors and may serve as supportive evidence for health care leaders to target future capital investments to improve health care delivery. %M 33851924 %R 10.2196/23961 %U https://www.jmir.org/2021/4/e23961 %U https://doi.org/10.2196/23961 %U http://www.ncbi.nlm.nih.gov/pubmed/33851924 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 4 %P e16651 %T Applying A/B Testing to Clinical Decision Support: Rapid Randomized Controlled Trials %A Austrian,Jonathan %A Mendoza,Felicia %A Szerencsy,Adam %A Fenelon,Lucille %A Horwitz,Leora I %A Jones,Simon %A Kuznetsova,Masha %A Mann,Devin M %+ Department of Medicine, NYU Grossman School of Medicine, 360 Park Avenue South, New York, NY, United States, 1 646 524 0359, Jonathan.Austrian@nyulangone.org %K AB testing %K randomized controlled trials %K clinical decision support %K clinical informatics %K usability %K alert fatigue %D 2021 %7 9.4.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Clinical decision support (CDS) is a valuable feature of electronic health records (EHRs) designed to improve quality and safety. However, due to the complexities of system design and inconsistent results, CDS tools may inadvertently increase alert fatigue and contribute to physician burnout. A/B testing, or rapid-cycle randomized tests, is a useful method that can be applied to the EHR in order to rapidly understand and iteratively improve design choices embedded within CDS tools. Objective: This paper describes how rapid randomized controlled trials (RCTs) embedded within EHRs can be used to quickly ascertain the superiority of potential CDS design changes to improve their usability, reduce alert fatigue, and promote quality of care. Methods: A multistep process combining tools from user-centered design, A/B testing, and implementation science was used to understand, ideate, prototype, test, analyze, and improve each candidate CDS. CDS engagement metrics (alert views, acceptance rates) were used to evaluate which CDS version is superior. Results: To demonstrate the impact of the process, 2 experiments are highlighted. First, after multiple rounds of usability testing, a revised CDS influenza alert was tested against usual care CDS in a rapid (~6 weeks) RCT. The new alert text resulted in minimal impact on reducing firings per patients per day, but this failure triggered another round of review that identified key technical improvements (ie, removal of dismissal button and firings in procedural areas) that led to a dramatic decrease in firings per patient per day (23.1 to 7.3). In the second experiment, the process was used to test 3 versions (financial, quality, regulatory) of text supporting tobacco cessation alerts as well as 3 supporting images. Based on 3 rounds of RCTs, there was no significant difference in acceptance rates based on the framing of the messages or addition of images. Conclusions: These experiments support the potential for this new process to rapidly develop, deploy, and rigorously evaluate CDS within an EHR. We also identified important considerations in applying these methods. This approach may be an important tool for improving the impact of and experience with CDS. Trial Registration: Flu alert trial: ClinicalTrials.gov NCT03415425; https://clinicaltrials.gov/ct2/show/NCT03415425. Tobacco alert trial: ClinicalTrials.gov NCT03714191; https://clinicaltrials.gov/ct2/show/NCT03714191 %M 33835035 %R 10.2196/16651 %U https://www.jmir.org/2021/4/e16651 %U https://doi.org/10.2196/16651 %U http://www.ncbi.nlm.nih.gov/pubmed/33835035 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 4 %P e24065 %T A Smartphone App Designed to Empower Patients to Contribute Toward Safer Surgical Care: Qualitative Evaluation of Diverse Public and Patient Perceptions Using Focus Groups %A Russ,Stephanie %A Sevdalis,Nick %A Ocloo,Josephine %+ Centre for Implementation Science, King's College London, De Crespigny Park, London, SE58AF, United Kingdom, 44 2078480683, stephanie.russ@kcl.ac.uk %K patient safety %K mobile health %K patient involvement %K perioperative care %K smartphone app %K mobile phone %D 2021 %7 8.4.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: MySurgery is a smartphone app designed to empower patients and their caregivers to contribute toward safer surgical care by following practical advice to help reduce susceptibility to errors and complications. Objective: The aim of this study is to evaluate service users’ perceptions of MySurgery, including its perceived acceptability, the potential barriers and facilitators to accessing and using its content, and ideas about how to facilitate its effective implementation. The secondary aim is to analyze how the intended use of the app might differ for diverse patients, including seldom-heard groups. Methods: We implemented a diversity approach to recruit participants from a range of backgrounds with previous experience of surgery. We aimed to achieve representation from seldom-heard groups, including those from a Black, Asian, and minority ethnic (BAME) background; those with a disability; and those from the lesbian, gay, bisexual, transgender, queer (LGBT+) community. A total of 3 focus groups were conducted across a 2-month period, during which a semistructured protocol was followed to elicit a rich discussion around the app. The focus groups were audio recorded, and thematic analysis was carried out. Results: In total, 22 individuals participated in the focus groups. A total of 50% (n=11) of the participants were from a BAME background, 59% (n=13) had a disability, and 36% (n=8) were from the LGBT+ community. There was a strong degree of support for the MySurgery app. The majority of participants agreed that it was acceptable and appropriate in terms of content and usability, and that it would help to educate patients about how to become involved in improving safety. The checklist-like format was popular. There was rich discussion around the accessibility and inclusivity of MySurgery. Specific user groups were identified who might face barriers in accessing the app or acting on its advice, such as those with visual impairments or learning difficulties and those who preferred to take a more passive role (eg, some individuals because of their cultural background or personality type). The app could be improved by signposting further specialty-specific information and incorporating a calendar and notes section. With regard to implementation, it was agreed that use of the app should be signposted before the preoperative appointment and that training and education should be provided for clinicians to increase awareness and buy-in. Communication about the app should clarify its scientific basis in plain English and should stress that its use is optional. Conclusions: MySurgery was endorsed as a powerful tool for enhancing patient empowerment and facilitating the direct involvement of patients and their caregivers in maintaining patient safety. The diversity approach allowed for a better understanding of the needs of different population groups and highlighted opportunities for increasing accessibility and involvement in the app. %M 33830062 %R 10.2196/24065 %U https://mhealth.jmir.org/2021/4/e24065 %U https://doi.org/10.2196/24065 %U http://www.ncbi.nlm.nih.gov/pubmed/33830062 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 3 %P e24359 %T Applying Clinical Decision Support Design Best Practices With the Practical Robust Implementation and Sustainability Model Versus Reliance on Commercially Available Clinical Decision Support Tools: Randomized Controlled Trial %A Trinkley,Katy E %A Kroehl,Miranda E %A Kahn,Michael G %A Allen,Larry A %A Bennett,Tellen D %A Hale,Gary %A Haugen,Heather %A Heckman,Simeon %A Kao,David P %A Kim,Janet %A Matlock,Daniel M %A Malone,Daniel C %A Page 2nd,Robert L %A Stine,Jessica %A Suresh,Krithika %A Wells,Lauren %A Lin,Chen-Tan %+ Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, 12850 E Montview Blvd, Campus Box C238, Room V20-4125, Aurora, CO, 80045, United States, 1 3037246563, katy.trinkley@cuanschutz.edu %K PRISM %K implementation science %K clinical decision support systems %K RE-AIM %K congestive heart failure %D 2021 %7 22.3.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Limited consideration of clinical decision support (CDS) design best practices, such as a user-centered design, is often cited as a key barrier to CDS adoption and effectiveness. The application of CDS best practices is resource intensive; thus, institutions often rely on commercially available CDS tools that are created to meet the generalized needs of many institutions and are not user centered. Beyond resource availability, insufficient guidance on how to address key aspects of implementation, such as contextual factors, may also limit the application of CDS best practices. An implementation science (IS) framework could provide needed guidance and increase the reproducibility of CDS implementations. Objective: This study aims to compare the effectiveness of an enhanced CDS tool informed by CDS best practices and an IS framework with a generic, commercially available CDS tool. Methods: We conducted an explanatory sequential mixed methods study. An IS-enhanced and commercial CDS alert were compared in a cluster randomized trial across 28 primary care clinics. Both alerts aimed to improve beta-blocker prescribing for heart failure. The enhanced alert was informed by CDS best practices and the Practical, Robust, Implementation, and Sustainability Model (PRISM) IS framework, whereas the commercial alert followed vendor-supplied specifications. Following PRISM, the enhanced alert was informed by iterative, multilevel stakeholder input and the dynamic interactions of the internal and external environment. Outcomes aligned with PRISM’s evaluation measures, including patient reach, clinician adoption, and changes in prescribing behavior. Clinicians exposed to each alert were interviewed to identify design features that might influence adoption. The interviews were analyzed using a thematic approach. Results: Between March 15 and August 23, 2019, the enhanced alert fired for 61 patients (106 alerts, 87 clinicians) and the commercial alert fired for 26 patients (59 alerts, 31 clinicians). The adoption and effectiveness of the enhanced alert were significantly higher than those of the commercial alert (62% vs 29% alerts adopted, P<.001; 14% vs 0% changed prescribing, P=.006). Of the 21 clinicians interviewed, most stated that they preferred the enhanced alert. Conclusions: The results of this study suggest that applying CDS best practices with an IS framework to create CDS tools improves implementation success compared with a commercially available tool. Trial Registration: ClinicalTrials.gov NCT04028557; http://clinicaltrials.gov/ct2/show/NCT04028557 %M 33749610 %R 10.2196/24359 %U https://medinform.jmir.org/2021/3/e24359 %U https://doi.org/10.2196/24359 %U http://www.ncbi.nlm.nih.gov/pubmed/33749610 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 3 %P e15443 %T Implementation of the Operating Room Black Box Research Program at the Ottawa Hospital Through Patient, Clinical, and Organizational Engagement: Case Study %A Boet,Sylvain %A Etherington,Cole %A Lam,Sandy %A Lê,Maxime %A Proulx,Laurie %A Britton,Meghan %A Kenna,Julie %A Przybylak-Brouillard,Antoine %A Grimshaw,Jeremy %A Grantcharov,Teodor %A Singh,Sukhbir %+ Department of Anesthesiology and Pain Medicine, University of Ottawa, 501 Smyth Road, Ottawa, ON, K1H 8L6, Canada, 1 613 798 5555 ext 78187, sboet@toh.ca %K patient safety %K implementation science %K quality improvement %K health personnel %K operating rooms %D 2021 %7 16.3.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: A large proportion of surgical patient harm is preventable; yet, our ability to systematically learn from these incidents and improve clinical practice remains limited. The Operating Room Black Box was developed to address the need for comprehensive assessments of clinical performance in the operating room. It captures synchronized audio, video, patient, and environmental clinical data in real time, which are subsequently analyzed by a combination of expert raters and software-based algorithms. Despite its significant potential to facilitate research and practice improvement, there are many potential implementation challenges at the institutional, clinician, and patient level. This paper summarizes our approach to implementation of the Operating Room Black Box at a large academic Canadian center. Objective: We aimed to contribute to the development of evidence-based best practices for implementing innovative technology in the operating room for direct observation of the clinical performance by using the case of the Operating Room Black Box. Specifically, we outline the systematic approach to the Operating Room Black Box implementation undertaken at our center. Methods: Our implementation approach included seeking support from hospital leadership; building frontline support and a team of champions among patients, nurses, anesthesiologists, and surgeons; accounting for stakeholder perceptions using theory-informed qualitative interviews; engaging patients; and documenting the implementation process, including barriers and facilitators, using the consolidated framework for implementation research. Results: During the 12-month implementation period, we conducted 23 stakeholder engagement activities with over 200 participants. We recruited 10 clinician champions representing nursing, anesthesia, and surgery. We formally interviewed 15 patients and 17 perioperative clinicians and identified key themes to include in an information campaign run as part of the implementation process. Two patient partners were engaged and advised on communications as well as grant and protocol development. Many anticipated and unanticipated challenges were encountered at all levels. Implementation was ultimately successful, with the Operating Room Black Box installed in August 2018, and data collection beginning shortly thereafter. Conclusions: This paper represents the first step toward evidence-guided implementation of technologies for direct observation of performance for research and quality improvement in surgery. With technology increasingly being used in health care settings, the health care community should aim to optimize implementation processes in the best interest of health care professionals and patients. %M 33724199 %R 10.2196/15443 %U https://www.jmir.org/2021/3/e15443 %U https://doi.org/10.2196/15443 %U http://www.ncbi.nlm.nih.gov/pubmed/33724199 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 5 %N 2 %P e19074 %T Assessment of Patients’ Ability to Review Electronic Health Record Information to Identify Potential Errors: Cross-sectional Web-Based Survey %A Freise,Lisa %A Neves,Ana Luisa %A Flott,Kelsey %A Harrison,Paul %A Kelly,John %A Darzi,Ara %A Mayer,Erik K %+ Patient Safety Translational Research Centre, Institute of Global Health Innovation, Imperial College London, St Mary’s Campus Queen, Elizabeth Queen Mother Wing, London, W2 1NY, United Kingdom, 44 (0)20 7589 5111, ana.luisa.neves14@ic.ac.uk %K patient portals %K electronic health records %K patient participation %K medical errors %K patient safety %D 2021 %7 26.2.2021 %9 Original Paper %J JMIR Form Res %G English %X Background: Sharing personal health information positively impacts quality of care across several domains, and particularly, safety and patient-centeredness. Patients may identify and flag up inconsistencies in their electronic health records (EHRs), leading to improved information quality and patient safety. However, in order to identify potential errors, patients need to be able to understand the information contained in their EHRs. Objective: The aim of this study was to assess patients’ perceptions of their ability to understand the information contained in their EHRs and to analyze the main barriers to their understanding. Additionally, the main types of patient-reported errors were characterized. Methods: A cross-sectional web-based survey was undertaken between March 2017 and September 2017. A total of 682 registered users of the Care Information Exchange, a patient portal, with at least one access during the time of the study were invited to complete the survey containing both structured (multiple choice) and unstructured (free text) questions. The survey contained questions on patients’ perceived ability to understand their EHR information and therefore, to identify errors. Free-text questions allowed respondents to expand on the reasoning for their structured responses and provide more detail about their perceptions of EHRs and identifying errors within them. Qualitative data were systematically reviewed by 2 independent researchers using the framework analysis method in order to identify emerging themes. Results: A total of 210 responses were obtained. The majority of the responses (123/210, 58.6%) reported understanding of the information. The main barriers identified were information-related (medical terminology and knowledge and interpretation of test results) and technology-related (user-friendliness of the portal, information display). Inconsistencies relating to incomplete and incorrect information were reported in 12.4% (26/210) of the responses. Conclusions: While the majority of the responses affirmed the understanding of the information contained within the EHRs, both technology and information-based barriers persist. There is a potential to improve the system design to better support opportunities for patients to identify errors. This is with the aim of improving the accuracy, quality, and timeliness of the information held in the EHRs and a mechanism to further engage patients in their health care. %M 33635277 %R 10.2196/19074 %U https://formative.jmir.org/2021/2/e19074 %U https://doi.org/10.2196/19074 %U http://www.ncbi.nlm.nih.gov/pubmed/33635277 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 2 %P e22744 %T Opening a “Can of Worms” to Explore the Public's Hopes and Fears About Health Care Data Sharing: Qualitative Study %A Lounsbury,Olivia %A Roberts,Lily %A Goodman,Jonathan R %A Batey,Philippa %A Naar,Lenny %A Flott,Kelsey M %A Lawrence-Jones,Anna %A Ghafur,Saira %A Darzi,Ara %A Neves,Ana Luisa %+ Patient Safety Translational Research Centre, Institute of Global Health Innovation, Imperial College London, St Mary’s Campus, Queen Elizabeth Queen Mother Wing, London, W2 1NY, United Kingdom, 44 (0)20 7589 5111, ana.luisa.neves14@imperial.ac.uk %K electronic health records %K patient participation %K data sharing %K patient safety %K data security %D 2021 %7 22.2.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Evidence suggests that health care data sharing may strengthen care coordination, improve quality and safety, and reduce costs. However, to achieve efficient and meaningful adoption of health care data-sharing initiatives, it is necessary to engage all stakeholders, from health care professionals to patients. Although previous work has assessed health care professionals’ perceptions of data sharing, perspectives of the general public and particularly of seldom heard groups have yet to be fully assessed. Objective: This study aims to explore the views of the public, particularly their hopes and concerns, around health care data sharing. Methods: An original, immersive public engagement interactive experience was developed—The Can of Worms installation—in which participants were prompted to reflect about data sharing through listening to individual stories around health care data sharing. A multidisciplinary team with expertise in research, public involvement, and human-centered design developed this concept. The installation took place in three separate events between November 2018 and November 2019. A combination of convenience and snowball sampling was used in this study. Participants were asked to fill self-administered feedback cards and to describe their hopes and fears about the meaningful use of data in health care. The transcripts were compiled verbatim and systematically reviewed by four independent reviewers using the thematic analysis method to identify emerging themes. Results: Our approach exemplifies the potential of using interdisciplinary expertise in research, public involvement, and human-centered design to tell stories, collect perspectives, and spark conversations around complex topics in participatory digital medicine. A total of 352 qualitative feedback cards were collected, each reflecting participants’ hopes and fears for health care data sharing. Thematic analyses identified six themes under hopes: enablement of personal access and ownership, increased interoperability and collaboration, generation of evidence for better and safer care, improved timeliness and efficiency, delivery of more personalized care, and equality. The five main fears identified included inadequate security and exploitation, data inaccuracy, distrust, discrimination and inequality, and less patient-centered care. Conclusions: This study sheds new light on the main hopes and fears of the public regarding health care data sharing. Importantly, our results highlight novel concerns from the public, particularly in terms of the impact on health disparities, both at international and local levels, and on delivering patient-centered care. Incorporating the knowledge generated and focusing on co-designing solutions to tackle these concerns is critical to engage the public as active contributors and to fully leverage the potential of health care data use. %M 33616532 %R 10.2196/22744 %U https://www.jmir.org/2021/2/e22744 %U https://doi.org/10.2196/22744 %U http://www.ncbi.nlm.nih.gov/pubmed/33616532 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 10 %N 2 %P e25284 %T Using Flow Disruptions to Examine System Safety in Robotic-Assisted Surgery: Protocol for a Stepped Wedge Crossover Design %A Alfred,Myrtede C %A Cohen,Tara N %A Cohen,Kate A %A Kanji,Falisha F %A Choi,Eunice %A Del Gaizo,John %A Nemeth,Lynne S %A Alekseyenko,Alexander V %A Shouhed,Daniel %A Savage,Stephen J %A Anger,Jennifer T %A Catchpole,Ken %+ Medical University of South Carolina, Department of Anesthesia and Perioperative Medicine, 167 Ashley Avenue, Suite 300 (MSC 912), Charleston, SC, 29425, United States, 1 8438761838, alfredm@musc.edu %K robotic surgical procedures %K patient safety %K ergonomics %K crossover design %D 2021 %7 9.2.2021 %9 Protocol %J JMIR Res Protoc %G English %X Background: The integration of high technology into health care systems is intended to provide new treatment options and improve the quality, safety, and efficiency of care. Robotic-assisted surgery is an example of high technology integration in health care, which has become ubiquitous in many surgical disciplines. Objective: This study aims to understand and measure current robotic-assisted surgery processes in a systematic, quantitative, and replicable manner to identify latent systemic threats and opportunities for improvement based on our observations and to implement and evaluate interventions. This 5-year study will follow a human factors engineering approach to improve the safety and efficiency of robotic-assisted surgery across 4 US hospitals. Methods: The study uses a stepped wedge crossover design with 3 interventions, introduced in different sequences at each of the hospitals over four 8-month phases. Robotic-assisted surgery procedures will be observed in the following specialties: urogynecology, gynecology, urology, bariatrics, general, and colorectal. We will use the data collected from observations, surveys, and interviews to inform interventions focused on teamwork, task design, and workplace design. We intend to evaluate attitudes toward each intervention, safety culture, subjective workload for each case, effectiveness of each intervention (including through direct observation of a sample of surgeries in each observational phase), operating room duration, length of stay, and patient safety incident reports. Analytic methods will include statistical data analysis, point process analysis, and thematic content analysis. Results: The study was funded in September 2018 and approved by the institutional review board of each institution in May and June of 2019 (CSMC and MDRH: Pro00056245; VCMC: STUDY 270; MUSC: Pro00088741). After refining the 3 interventions in phase 1, data collection for phase 2 (baseline data) began in November 2019 and was scheduled to continue through June 2020. However, data collection was suspended in March 2020 due to the COVID-19 pandemic. We collected a total of 65 observations across the 4 sites before the pandemic. Data collection for phase 2 was resumed in October 2020 at 2 of the 4 sites. Conclusions: This will be the largest direct observational study of surgery ever conducted with data collected on 680 robotic surgery procedures at 4 different institutions. The proposed interventions will be evaluated using individual-level (workload and attitude), process-level (perioperative duration and flow disruption), and organizational-level (safety culture and complications) measures. An implementation science framework is also used to investigate the causes of success or failure of each intervention at each site and understand the potential spread of the interventions. International Registered Report Identifier (IRRID): DERR1-10.2196/25284 %M 33560239 %R 10.2196/25284 %U https://www.researchprotocols.org/2021/2/e25284 %U https://doi.org/10.2196/25284 %U http://www.ncbi.nlm.nih.gov/pubmed/33560239 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 8 %N 1 %P e21884 %T Perceptual Gaps Between Clinicians and Technologists on Health Information Technology-Related Errors in Hospitals: Observational Study %A Ndabu,Theophile %A Mulgund,Pavankumar %A Sharman,Raj %A Singh,Ranjit %+ Department of Management Science and Systems, School of Management, State University of New York at Buffalo, 203 Alfiero Center, Buffalo, NY, 14260, United States, 1 7166453271, tnndabu@buffalo.edu %K patient safety %K medical errors %K health information technology %K sociotechnical framework %K patient harm %D 2021 %7 5.2.2021 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Health information technology (HIT) has been widely adopted in hospital settings, contributing to improved patient safety. However, many types of medical errors attributable to information technology (IT) have negatively impacted patient safety. The continued occurrence of many errors is a reminder that HIT software testing and validation is not adequate in ensuring errorless software functioning within the health care organization. Objective: This pilot study aims to classify technology-related medical errors in a hospital setting using an expanded version of the sociotechnical framework to understand the significant differences in the perceptions of clinical and technology stakeholders regarding the potential causes of these errors. The paper also provides some recommendations to prevent future errors. Methods: Medical errors were collected from previous studies identified in leading health databases. From the main list, we selected errors that occurred in hospital settings. Semistructured interviews with 5 medical and 6 IT professionals were conducted to map the events on different dimensions of the expanded sociotechnical framework. Results: Of the 2319 identified publications, 36 were included in the review. Of the 67 errors collected, 12 occurred in hospital settings. The classification showed the “gulf” that exists between IT and medical professionals in their perspectives on the underlying causes of medical errors. IT experts consider technology as the source of most errors and suggest solutions that are mostly technical. However, clinicians assigned the source of errors within the people, process, and contextual dimensions. For example, for the error “Copied and pasted charting in the wrong window: Before, you could not easily get into someone else’s chart accidentally...because you would have to pull the chart and open it,” medical experts highlighted contextual issues, including the number of patients a health care provider sees in a short time frame, unfamiliarity with a new electronic medical record system, nurse transitions around the time of error, and confusion due to patients having the same name. They emphasized process controls, including failure modes, as a potential fix. Technology experts, in contrast, discussed the lack of notification, poor user interface, and lack of end-user training as critical factors for this error. Conclusions: Knowledge of the dimensions of the sociotechnical framework and their interplay with other dimensions can guide the choice of ways to address medical errors. These findings lead us to conclude that designers need not only a high degree of HIT know-how but also a strong understanding of the medical processes and contextual factors. Although software development teams have historically included clinicians as business analysts or subject matter experts to bridge the gap, development teams will be better served by more immersive exposure to clinical environments, leading to better software design and implementation, and ultimately to enhanced patient safety. %M 33544089 %R 10.2196/21884 %U http://humanfactors.jmir.org/2021/1/e21884/ %U https://doi.org/10.2196/21884 %U http://www.ncbi.nlm.nih.gov/pubmed/33544089 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 2 %P e24785 %T Telehealth in the COVID-19 Era: A Balancing Act to Avoid Harm %A Reeves,J Jeffery %A Ayers,John W %A Longhurst,Christopher A %+ Department of Surgery, University of California San Diego, 9300 Campus Point Drive, MC7400, La Jolla, CA, 92037-7400, United States, 1 505 515 9844, jreeves@ucsd.edu %K telehealth %K patient safety %K COVID-19 %K coronavirus %K informatics %K safety %K harm %K risk %K access %K efficiency %K virtual care %D 2021 %7 1.2.2021 %9 Viewpoint %J J Med Internet Res %G English %X The telehealth revolution in response to COVID-19 has increased essential health care access during an unprecedented public health crisis. However, virtual patient care can also limit the patient-provider relationship, quality of examination, efficiency of health care delivery, and overall quality of care. As we witness the most rapidly adopted medical trend in modern history, clinicians are beginning to comprehend the many possibilities of telehealth, but its limitations also need to be understood. As outcomes are studied and federal regulations reconsidered, it is important to be precise in the virtual patient encounter approach. Herein, we offer some simple guidelines that could assist health care providers and clinic schedulers in determining the appropriateness of a telehealth visit by considering visit types, patient characteristics, and chief complaint or disease states. %M 33477104 %R 10.2196/24785 %U https://www.jmir.org/2021/2/e24785 %U https://doi.org/10.2196/24785 %U http://www.ncbi.nlm.nih.gov/pubmed/33477104 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 1 %P e23454 %T Assessing the International Transferability of a Machine Learning Model for Detecting Medication Error in the General Internal Medicine Clinic: Multicenter Preliminary Validation Study %A Chin,Yen Po Harvey %A Song,Wenyu %A Lien,Chia En %A Yoon,Chang Ho %A Wang,Wei-Chen %A Liu,Jennifer %A Nguyen,Phung Anh %A Feng,Yi Ting %A Zhou,Li %A Li,Yu Chuan Jack %A Bates,David Westfall %+ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No 172-1, Sec 2 Keelung Rd, Taipei City, 110, Taiwan, 886 2 6638 2736, jack@tmu.edu.tw %K electronic health records %K patient safety %K clinical decision support %K medication alert systems %K machine learning %D 2021 %7 27.1.2021 %9 Original Paper %J JMIR Med Inform %G English %X Background: Although most current medication error prevention systems are rule-based, these systems may result in alert fatigue because of poor accuracy. Previously, we had developed a machine learning (ML) model based on Taiwan’s local databases (TLD) to address this issue. However, the international transferability of this model is unclear. Objective: This study examines the international transferability of a machine learning model for detecting medication errors and whether the federated learning approach could further improve the accuracy of the model. Methods: The study cohort included 667,572 outpatient prescriptions from 2 large US academic medical centers. Our ML model was applied to build the original model (O model), the local model (L model), and the hybrid model (H model). The O model was built using the data of 1.34 billion outpatient prescriptions from TLD. A validation set with 8.98% (60,000/667,572) of the prescriptions was first randomly sampled, and the remaining 91.02% (607,572/667,572) of the prescriptions served as the local training set for the L model. With a federated learning approach, the H model used the association values with a higher frequency of co-occurrence among the O and L models. A testing set with 600 prescriptions was classified as substantiated and unsubstantiated by 2 independent physician reviewers and was then used to assess model performance. Results: The interrater agreement was significant in terms of classifying prescriptions as substantiated and unsubstantiated (κ=0.91; 95% CI 0.88 to 0.95). With thresholds ranging from 0.5 to 1.5, the alert accuracy ranged from 75%-78% for the O model, 76%-78% for the L model, and 79%-85% for the H model. Conclusions: Our ML model has good international transferability among US hospital data. Using the federated learning approach with local hospital data could further improve the accuracy of the model. %M 33502331 %R 10.2196/23454 %U http://medinform.jmir.org/2021/1/e23454/ %U https://doi.org/10.2196/23454 %U http://www.ncbi.nlm.nih.gov/pubmed/33502331 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 12 %P e22031 %T The Generalizability of a Medication Administration Discrepancy Detection System: Quantitative Comparative Analysis %A Kirkendall,Eric %A Huth,Hannah %A Rauenbuehler,Benjamin %A Moses,Adam %A Melton,Kristin %A Ni,Yizhao %+ Center for Healthcare Innovation, Wake Forest School of Medicine, 486 North Patterson Avenue, Office 512, Winston Salem, NC, 27101, United States, 1 (336) 716 0462, ekirkend@wakehealth.edu %K medication administration %K error %K automated algorithm %K generalizability %K quantitative comparative analysis %K discrepancy %K detection %K quantitative analysis %K portability %K performance algorithm %K electronic health record %D 2020 %7 2.12.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: As a result of the overwhelming proportion of medication errors occurring each year, there has been an increased focus on developing medication error prevention strategies. Recent advances in electronic health record (EHR) technologies allow institutions the opportunity to identify medication administration error events in real time through computerized algorithms. MED.Safe, a software package comprising medication discrepancy detection algorithms, was developed to meet this need by performing an automated comparison of medication orders to medication administration records (MARs). In order to demonstrate generalizability in other care settings, software such as this must be tested and validated in settings distinct from the development site. Objective: The purpose of this study is to determine the portability and generalizability of the MED.Safe software at a second site by assessing the performance and fit of the algorithms through comparison of discrepancy rates and other metrics across institutions. Methods: The MED.Safe software package was executed on medication use data from the implementation site to generate prescribing ratios and discrepancy rates. A retrospective analysis of medication prescribing and documentation patterns was then performed on the results and compared to those from the development site to determine the algorithmic performance and fit. Variance in performance from the development site was further explored and characterized. Results: Compared to the development site, the implementation site had lower audit/order ratios and higher MAR/(order + audit) ratios. The discrepancy rates on the implementation site were consistently higher than those from the development site. Three drivers for the higher discrepancy rates were alternative clinical workflow using orders with dosing ranges; a data extract, transfer, and load issue causing modified order data to overwrite original order values in the EHRs; and delayed EHR documentation of verbal orders. Opportunities for improvement were identified and applied using a software update, which decreased false-positive discrepancies and improved overall fit. Conclusions: The execution of MED.Safe at a second site was feasible and effective in the detection of medication administration discrepancies. A comparison of medication ordering, administration, and discrepancy rates identified areas where MED.Safe could be improved through customization. One modification of MED.Safe through deployment of a software update improved the overall algorithmic fit at the implementation site. More flexible customizations to accommodate different clinical practice patterns could improve MED.Safe’s fit at new sites. %M 33263548 %R 10.2196/22031 %U https://medinform.jmir.org/2020/12/e22031 %U https://doi.org/10.2196/22031 %U http://www.ncbi.nlm.nih.gov/pubmed/33263548 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 11 %P e23353 %T Universal Patient Identifier and Interoperability for Detection of Serious Drug Interactions: Retrospective Study %A Sragow,Howard Michael %A Bidell,Eileen %A Mager,Douglas %A Grannis,Shaun %+ Express Scripts Inc, 100 Parsons Pond Drive, Franklin Lakes, NJ, 07417, United States, 1 201 269 4342, howard_sragow@express-scripts.com %K patient identification %K pharmacy benefit manager %K interoperability %K adverse drug event %K identity management %K identifier %K pharmacy %K pharmaceuticals %K drug %D 2020 %7 20.11.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: The United States, unlike other high-income countries, currently has no national unique patient identifier to facilitate health information exchange. Because of security and privacy concerns, Congress, in 1998, prevented the government from promulgating a unique patient identifier. The Health and Human Services funding bill that was enacted in 2019 requires that Health and Human Services report their recommendations on patient identification to Congress. While there are anecdotes of incomplete health care data due to patient misidentification, to date there have been insufficient large-scale analyses measuring improvements to patient care that a unique patient identifier might provide. This lack of measurement has made it difficult for policymakers to balance security and privacy concerns against the value of potential improvements. Objective: We sought to determine the frequency of serious drug-drug interaction alerts discovered because a pharmacy benefits manager uses a universal patient identifier and estimate undiscovered serious drug-drug interactions because pharmacy benefit managers do not yet fully share patient records. Methods: We conducted a retrospective study of serious drug-drug interaction alerts provided from September 1, 2016 to August 31, 2019 to retail pharmacies by a national pharmacy benefit manager that uses a unique patient identifier. We compared each alert to the contributing prescription and determined whether the unique patient identifier was necessary in order to identify the crossover alert. We classified each alert’s disposition as override, abandonment, or replacement. Using the crossover alert rate and sample population size, we inferred a rate of missing serious drug-drug interaction alerts for the United States. We performed logistic regression in order to identify factors correlated with crossover and alert outcomes. Results: Among a population of 49.7 million patients, 242,646 serious drug-drug interaction alerts occurred in 3 years. Of these, 2388 (1.0%) crossed insurance and were discovered because the pharmacy benefit manager used a unique patient identifier. We estimate that up to 10% of serious drug-drug alerts in the United States go undetected by pharmacy benefit managers because of unexchanged information or pharmacy benefit managers that do not use a unique patient identifier. These information gaps may contribute, annually, to up to 6000 patients in the United States receiving a contraindicated medication. Conclusions: Comprehensive patient identification across disparate data sources can help protect patients from serious drug-drug interactions. To better safeguard patients, providers should (1) adopt a comprehensive patient identification strategy and (2) share patient prescription history to improve clinical decision support. %M 33216009 %R 10.2196/23353 %U http://medinform.jmir.org/2020/11/e23353/ %U https://doi.org/10.2196/23353 %U http://www.ncbi.nlm.nih.gov/pubmed/33216009 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 11 %P e23351 %T Alert Override Patterns With a Medication Clinical Decision Support System in an Academic Emergency Department: Retrospective Descriptive Study %A Yoo,Junsang %A Lee,Jeonghoon %A Rhee,Poong-Lyul %A Chang,Dong Kyung %A Kang,Mira %A Choi,Jong Soo %A Bates,David W %A Cha,Won Chul %+ Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea, 82 10 5386 6597, docchaster@gmail.com %K medical order entry systems %K decision support systems %K clinical %K alert fatigue %K health personnel %K clinical decision support system %K alert %K emergency department %K medication %D 2020 %7 4.11.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Physicians’ alert overriding behavior is considered to be the most important factor leading to failure of computerized provider order entry (CPOE) combined with a clinical decision support system (CDSS) in achieving its potential adverse drug events prevention effect. Previous studies on this subject have focused on specific diseases or alert types for well-defined targets and particular settings. The emergency department is an optimal environment to examine physicians’ alert overriding behaviors from a broad perspective because patients have a wider range of severity, and many receive interdisciplinary care in this environment. However, less than one-tenth of related studies have targeted this physician behavior in an emergency department setting. Objective: The aim of this study was to describe alert override patterns with a commercial medication CDSS in an academic emergency department. Methods: This study was conducted at a tertiary urban academic hospital in the emergency department with an annual census of 80,000 visits. We analyzed data on the patients who visited the emergency department for 18 months and the medical staff who treated them, including the prescription and CPOE alert log. We also performed descriptive analysis and logistic regression for assessing the risk factors for alert overrides. Results: During the study period, 611 physicians cared for 71,546 patients with 101,186 visits. The emergency department physicians encountered 13.75 alerts during every 100 orders entered. Of the total 102,887 alerts, almost two-thirds (65,616, 63.77%) were overridden. Univariate and multivariate logistic regression analyses identified 21 statistically significant risk factors for emergency department physicians’ alert override behavior. Conclusions: In this retrospective study, we described the alert override patterns with a medication CDSS in an academic emergency department. We found relatively low overrides and assessed their contributing factors, including physicians’ designation and specialty, patients’ severity and chief complaints, and alert and medication type. %M 33146626 %R 10.2196/23351 %U https://medinform.jmir.org/2020/11/e23351 %U https://doi.org/10.2196/23351 %U http://www.ncbi.nlm.nih.gov/pubmed/33146626 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 10 %P e22013 %T Reducing Alert Fatigue by Sharing Low-Level Alerts With Patients and Enhancing Collaborative Decision Making Using Blockchain Technology: Scoping Review and Proposed Framework (MedAlert) %A Wan,Paul Kengfai %A Satybaldy,Abylay %A Huang,Lizhen %A Holtskog,Halvor %A Nowostawski,Mariusz %+ Department of Manufacturing and Civil Engineering, Norwegian University of Science and Technology, Teknologiveien 22, Gjøvik, 2815, Norway, 47 93984604, paul.k.wan@ntnu.no %K blockchain %K health care %K alert fatigue %K clinical decision support %K smart contracts %K information sharing %D 2020 %7 28.10.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Clinical decision support (CDS) is a tool that helps clinicians in decision making by generating clinical alerts to supplement their previous knowledge and experience. However, CDS generates a high volume of irrelevant alerts, resulting in alert fatigue among clinicians. Alert fatigue is the mental state of alerts consuming too much time and mental energy, which often results in relevant alerts being overridden unjustifiably, along with clinically irrelevant ones. Consequently, clinicians become less responsive to important alerts, which opens the door to medication errors. Objective: This study aims to explore how a blockchain-based solution can reduce alert fatigue through collaborative alert sharing in the health sector, thus improving overall health care quality for both patients and clinicians. Methods: We have designed a 4-step approach to answer this research question. First, we identified five potential challenges based on the published literature through a scoping review. Second, a framework is designed to reduce alert fatigue by addressing the identified challenges with different digital components. Third, an evaluation is made by comparing MedAlert with other proposed solutions. Finally, the limitations and future work are also discussed. Results: Of the 341 academic papers collected, 8 were selected and analyzed. MedAlert securely distributes low-level (nonlife-threatening) clinical alerts to patients, enabling a collaborative clinical decision. Among the solutions in our framework, Hyperledger (private permissioned blockchain) and BankID (federated digital identity management) have been selected to overcome challenges such as data integrity, user identity, and privacy issues. Conclusions: MedAlert can reduce alert fatigue by attracting the attention of patients and clinicians, instead of solely reducing the total number of alerts. MedAlert offers other advantages, such as ensuring a higher degree of patient privacy and faster transaction times compared with other frameworks. This framework may not be suitable for elderly patients who are not technology savvy or in-patients. Future work in validating this framework based on real health care scenarios is needed to provide the performance evaluations of MedAlert and thus gain support for the better development of this idea. %M 33112253 %R 10.2196/22013 %U http://www.jmir.org/2020/10/e22013/ %U https://doi.org/10.2196/22013 %U http://www.ncbi.nlm.nih.gov/pubmed/33112253 %0 Journal Article %@ 2291-9279 %I JMIR Publications %V 8 %N 4 %P e18258 %T Web-Based Virtual Learning Environment for Medicine Administration in Pediatrics and Neonatology: Content Evaluation %A Pereira,Alayne Larissa Martins %A Leon,Casandra Genoveva Rosales Martins Ponce %A Ribeiro,Laiane Medeiros %A Brasil,Guilherme Da Costa %A Carneiro,Karen Karoline Gouveia %A Vieira,Géssica Borges %A Barbalho,Yuri Gustavo De Sousa %A Silva,Izabel Cristina Rodrigues Da %A Funghetto,Silvana Schwerz %+ Universidade de Brasília, Centro Metropolitano, Ceilândia Sul, Brasília Distrito Federal, Brazil, 55 061 996118662, alayne_larissa@hotmail.com %K nursing education %K health education %K educational technology %K patient safety. %D 2020 %7 21.10.2020 %9 Original Paper %J JMIR Serious Games %G English %X Background: Worldwide, patient safety has been a widely discussed topic and has currently become one of the greatest challenges for health institutions. This concern is heightened when referring to children. Objective: The goal of this study was to develop a virtual learning environment for medication administration, as a tool to facilitate the training process of undergraduate nursing students. Methods: Descriptive research and methodological development with a quantitative and qualitative approach were used with stages of design-based research as methodological strategies. For the development of the virtual environment, 5 themes were selected: rights of medication administration, medication administration steps, medication administration routes, medication calculation, and nonpharmacological actions for pain relief. After development, 2 groups—expert judges in the field of pediatrics and neonatology for environment validation and undergraduate nursing students for the assessment—were used to assess the virtual learning environment. For the validation of the virtual learning environment by expert judges, the content validity index was used, and for the evaluation of the students, the percentage of agreement was calculated. Results: The study included 13 experts who positively validated the virtual environment with a content validity index of 0.97, and 26 students who considered the content suitable for nursing students, although some adjustments are necessary. Conclusions: The results show the benefit of the virtual learning environment to the training of nursing students and professional nurses who work in health care. It is an effective educational tool for teaching medication administration in pediatrics and neonatology and converges with the conjectures of active methodologies. %M 33084579 %R 10.2196/18258 %U http://games.jmir.org/2020/4/e18258/ %U https://doi.org/10.2196/18258 %U http://www.ncbi.nlm.nih.gov/pubmed/33084579 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 10 %P e17003 %T Impact of Electronic Health Record Interface Design on Unsafe Prescribing of Ciclosporin, Tacrolimus, and Diltiazem: Cohort Study in English National Health Service Primary Care %A MacKenna,Brian %A Bacon,Sebastian %A Walker,Alex J %A Curtis,Helen J %A Croker,Richard %A Goldacre,Ben %+ The DataLab, Nuffield Department of Primary Care Health Sciences, University of Oxford, Radcliffe Observatory Quarter, Woodstock Road, Oxford, OX2 6GG, United Kingdom, 44 1865289313, ben.goldacre@phc.ox.ac.uk %K prescribing %K primary care %K electronic health records %K clinical software %K branded prescribing %K diltiazem %K tacrolimus %K ciclosporin %D 2020 %7 16.10.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: In England, national safety guidance recommends that ciclosporin, tacrolimus, and diltiazem are prescribed by brand name due to their narrow therapeutic windows and, in the case of tacrolimus, to reduce the chance of organ transplantation rejection. Various small studies have shown that changes to electronic health record (EHR) system interfaces can affect prescribing choices. Objective: Our objectives were to assess variation by EHR systems in breach of safety guidance around prescribing of ciclosporin, tacrolimus, and diltiazem, and to conduct user-interface research into the causes of such breaches. Methods: We carried out a retrospective cohort study using prescribing data in English primary care. Participants were English general practices and their respective EHR systems. The main outcome measures were (1) the variation in ratio of safety breaches to adherent prescribing in all practices and (2) the description of observations of EHR system usage. Results: A total of 2,575,411 prescriptions were issued in 2018 for ciclosporin, tacrolimus, and diltiazem (over 60 mg); of these, 316,119 prescriptions breached NHS guidance (12.27%). Breaches were most common among users of the EMIS EHR system (breaches in 18.81% of ciclosporin and tacrolimus prescriptions and in 17.99% of diltiazem prescriptions), but breaches were observed in all EHR systems. Conclusions: Design choices in EHR systems strongly influence safe prescribing of ciclosporin, tacrolimus, and diltiazem, and breaches are prevalent in general practices in England. We recommend that all EHR vendors review their systems to increase safe prescribing of these medicines in line with national guidance. Almost all clinical practice is now mediated through an EHR system; further quantitative research into the effect of EHR system design on clinical practice is long overdue. %M 33064085 %R 10.2196/17003 %U https://www.jmir.org/2020/10/e17003 %U https://doi.org/10.2196/17003 %U http://www.ncbi.nlm.nih.gov/pubmed/33064085 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e19225 %T Checklists for Complications During Systemic Cancer Treatment Shared by Patients, Friends, and Health Care Professionals: Prospective Interventional Cohort Study %A Jones,Helen V %A Smith,Harry %A Cooksley,Tim %A Jones,Philippa %A Woolley,Toby %A Gwyn Murdoch,Derick %A Thomas,Dafydd %A Foster,Betty %A Wakefield,Valerie %A Innominato,Pasquale %A Mullard,Anna %A Ghosal,Niladri %A Subbe,Christian %+ School of Medical Sciences, Bangor University, Brigantia Building, Bangor, LL57 2AS, United Kingdom, 44 +447771922890, csubbe@hotmail.com %K cancer %K patient safety %K checklist %K quality of life %K anxiety %K depression %K health economics %K mHealth %K smartphone %K redundancy %D 2020 %7 25.9.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Advances in cancer management have been associated with an increased incidence of emergency presentations with disease- or treatment-related complications. Objective: This study aimed to measure the ability of patients and members of their social network to complete checklists for complications of systemic treatment for cancer and examine the impact on patient-centered and health-economic outcomes. Methods: A prospective interventional cohort study was performed to assess the impact of a smartphone app used by patients undergoing systemic cancer therapy and members of their network to monitor for common complications. The app was used by patients, a nominated “safety buddy,” and acute oncology services. The control group was made up of patients from the same institution. Measures were based on process (completion of checklists over 60 days), patient experience outcomes (Hospital Anxiety and Depression Scale and the General version of the Functional Assessment of Cancer Therapy at baseline, 1 month, and 2 months) and health-economic outcomes (usage of appointments in primary care and elective and unscheduled hospital admissions). Results: At the conclusion of the study, 50 patients had completed 2882 checklists, and their 50 “safety buddies” had completed 318 checklists. Near daily usage was maintained over the 60-day study period. When compared to a cohort of 50 patients with matching disease profiles from the same institution, patients in the intervention group had comparable changes in Hospital Anxiety and Depression Scale and General version of the Functional Assessment of Cancer Therapy. Patients in the Intervention Group required a third (32 vs 97 nights) of the hospital days with overnight stay compared to patients in the Control Group, though the difference was not significant. The question, “I feel safer with the checklist,” received a mean score of 4.27 (SD 0.87) on a Likert scale (1-5) for patients and 4.55 (SD 0.65) for family and friends. Conclusions: Patients undergoing treatment for cancer and their close contacts can complete checklists for common complications of systemic treatments and take an active role in systems supporting their own safety. A larger sample size will be needed to assess the impact on clinical outcomes and health economics. %M 32975526 %R 10.2196/19225 %U http://mhealth.jmir.org/2020/9/e19225/ %U https://doi.org/10.2196/19225 %U http://www.ncbi.nlm.nih.gov/pubmed/32975526 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 9 %P e14346 %T Efficiency of Text Message Contact on Medical Safety in Outpatient Surgery: Retrospective Study %A Peuchot,Jeremy %A Allard,Etienne %A Dureuil,Bertrand %A Veber,Benoit %A Compère,Vincent %+ Department of Anesthesiology and Critical Care, Rouen University Hospital, 1 rue de Germont, Rouen, France, 33 02 32 88 82 83, Vincent.compere@chu-rouen.fr %K outpatient surgery %K short message service (SMS) %K patient information %K organizational %K cost %K unanticipated admission %K preoperative instructions %D 2020 %7 10.9.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Establishing pre- and postoperative contact with patients is part of successful medical management in outpatient surgery. In France, this is mostly done via telephone. Automated information with short message service (SMS) reminders might be an interesting alternative to increase the rate of compliance with preoperative instructions, but no study has shown the safety of this approach. Objective: The objective of this study was to evaluate the impact of pre- and postoperative automated information with SMS reminders on medical safety in outpatient surgery. Methods: We conducted a retrospective, single-center, nonrandomized, controlled study with a before-after design. All adult patients who had outpatient surgery between September 2016 and December 2017 in our university hospital center were included. Before April 2017, patients were contacted by telephone by an outpatient surgery nurse. After April 2017, patients were contacted by SMS reminder. All patients were contacted the day before and the day after surgery. Patients contacted by SMS reminder were also contacted on day 7 after surgery. The primary end point was the conversion rate to full-time hospitalization. Secondary end points were hospitalization causes (anesthetic, surgical, organizational) and hospitalization costs. Results: A total of 4388 patients were included, 2160 before and 2228 after the introduction of SMS reminders. The conversion rate to full-time hospitalization was 34/4388 (0.77%) with a difference between SMS group (8/2228, 0.36%) and telephone group (26/2160, 1.20%). The cost of SMS reminders was estimated as half that of telephone calls. Conclusions: In this work, we report a decrease in the rate of conversion to full-time hospitalization with the use of pre- and postoperative SMS reminders. This new approach could represent a safe and cost-effective method in an outpatient surgery setting. %M 32909948 %R 10.2196/14346 %U https://mhealth.jmir.org/2020/9/e14346 %U https://doi.org/10.2196/14346 %U http://www.ncbi.nlm.nih.gov/pubmed/32909948 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 9 %P e19774 %T Integrating and Evaluating the Data Quality and Utility of Smart Pump Information in Detecting Medication Administration Errors: Evaluation Study %A Ni,Yizhao %A Lingren,Todd %A Huth,Hannah %A Timmons,Kristen %A Melton,Krisin %A Kirkendall,Eric %+ Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, MLC 7024, Cincinnati, OH, 45229, United States, 1 5138034269, yizhao.ni@cchmc.org %K medication administration errors %K smart infusion pumps %K electronic health records %K concordance %D 2020 %7 2.9.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: At present, electronic health records (EHRs) are the central focus of clinical informatics given their role as the primary source of clinical data. Despite their granularity, the EHR data heavily rely on manual input and are prone to human errors. Many other sources of data exist in the clinical setting, including digital medical devices such as smart infusion pumps. When incorporated with prescribing data from EHRs, smart pump records (SPRs) are capable of shedding light on actions that take place during the medication use process. However, harmoniz-ing the 2 sources is hindered by multiple technical challenges, and the data quality and utility of SPRs have not been fully realized. Objective: This study aims to evaluate the quality and utility of SPRs incorporated with EHR data in detecting medication administration errors. Our overarching hypothesis is that SPRs would contribute unique information in the med-ication use process, enabling more comprehensive detection of discrepancies and potential errors in medication administration. Methods: We evaluated the medication use process of 9 high-risk medications for patients admitted to the neonatal inten-sive care unit during a 1-year period. An automated algorithm was developed to align SPRs with their medica-tion orders in the EHRs using patient ID, medication name, and timestamp. The aligned data were manually re-viewed by a clinical research coordinator and 2 pediatric physicians to identify discrepancies in medication ad-ministration. The data quality of SPRs was assessed with the proportion of information that was linked to valid EHR orders. To evaluate their utility, we compared the frequency and severity of discrepancies captured by the SPR and EHR data, respectively. A novel concordance assessment was also developed to understand the detec-tion power and capabilities of SPR and EHR data. Results: Approximately 70% of the SPRs contained valid patient IDs and medication names, making them feasible for data integration. After combining the 2 sources, the investigative team reviewed 2307 medication orders with 10,575 medication administration records (MARs) and 23,397 SPRs. A total of 321 MAR and 682 SPR dis-crepancies were identified, with vasopressors showing the highest discrepancy rates, followed by narcotics and total parenteral nutrition. Compared with EHR MARs, substantial dosing discrepancies were more commonly detectable using the SPRs. The concordance analysis showed little overlap between MAR and SPR discrepan-cies, with most discrepancies captured by the SPR data. Conclusions: We integrated smart infusion pump information with EHR data to analyze the most error-prone phases of the medication lifecycle. The findings suggested that SPRs could be a more reliable data source for medication error detection. Ultimately, it is imperative to integrate SPR information with EHR data to fully detect and mitigate medication administration errors in the clinical setting. %M 32876578 %R 10.2196/19774 %U https://medinform.jmir.org/2020/9/e19774 %U https://doi.org/10.2196/19774 %U http://www.ncbi.nlm.nih.gov/pubmed/32876578 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 7 %N 3 %P e18103 %T Opportunities and Recommendations for Improving Medication Safety: Understanding the Medication Management System in Primary Care Through an Abstraction Hierarchy %A Baumgartner,Andrew %A Kunkes,Taylor %A Clark,Collin M %A Brady,Laura A %A Monte,Scott V %A Singh,Ranjit %A Wahler Jr,Robert G %A Chen,Huei-Yen Winnie %+ Department of Family Medicine, University at Buffalo, State University of New York, 77 Goodell St, Buffalo, NY, 14203, United States, 1 716 816 7275, adbaumga@buffalo.edu %K patient safety %K polypharmacy %K potentially inappropriate medications %K primary care %D 2020 %7 13.8.2020 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Despite making great strides in improving the treatment of diseases, the minimization of unintended harm by medication therapy continues to be a major hurdle facing the health care system. Medication error and prescription of potentially inappropriate medications (PIMs) represent a prevalent source of harm to patients and are associated with increased rates of adverse events, hospitalizations, and increased health care costs. Attempts to improve medication management systems in primary care have had mixed results. Implementation of new interventions is difficult because of complex contextual factors within the health care system. Abstraction hierarchy (AH), the first step in cognitive work analysis (CWA), is used by human factors practitioners to describe complex sociotechnical systems. Although initially intended for the nuclear power domain and interface design, AH has been used successfully to aid the redesign of numerous health care systems such as the design of decision support tools, mobile patient monitoring apps, and a telephone triage system. Objective: This paper aims to refine our understanding of the primary care office in relation to a patient’s medication through the development of an AH. Emphasis was placed on the elements related to medication safety to provide guidance for the design of a safer medication management system in primary care. Methods: The AH development was guided by the methodology used by seminal CWA literature. It was initially developed by 2 authors and later fine-tuned by an expert panel of clinicians, social scientists, and a human factors engineer. It was subsequently refined until an agreement was reached. A means-ends analysis was performed and described for the nodes of interest. The model represents the primary care office space through functional purposes, values and priorities, function-related purposes, object-related processes, and physical objects. Results: This model depicts the medication management system at various levels of abstraction. The resulting components must be balanced and coordinated to provide medical treatment with limited health care resources. Understanding the physical and informational constraints on activities that occur in a primary care office depicted in the AH defines areas in which medication safety can be improved. Conclusions: Numerous means-ends relationships were identified and analyzed. These can be further evaluated depending on the specific needs of the user. Recommendations for optimizing a medication management system in a primary care facility were made. Individual practices can use AH for clinical redesign to improve prescribing and deprescribing practices. %M 32788157 %R 10.2196/18103 %U http://humanfactors.jmir.org/2020/3/e18103/ %U https://doi.org/10.2196/18103 %U http://www.ncbi.nlm.nih.gov/pubmed/32788157 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 7 %N 3 %P e20364 %T Twelve-Month Review of Infusion Pump Near-Miss Medication and Dose Selection Errors and User-Initiated “Good Save” Corrections: Retrospective Study %A Waterson,James %A Al-Jaber,Rania %A Kassab,Tarek %A Al-Jazairi,Abdulrazaq S %+ Medication Management Solutions, Becton, Dickinson & Company, LLC, 11 Floor, Blue Bay Tower, Business Bay, Dubai, 1197, United Arab Emirates, 971 566035154, redheroes67@icloud.com %K medication library %K smart infusion pumps %K near-miss error %K medication safety %K lookalike-soundalike %D 2020 %7 11.8.2020 %9 Original Paper %J JMIR Hum Factors %G English %X Background: There is a paucity of quantitative evidence in the current literature on the incidence of wrong medication and wrong dose administration of intravenous medications by clinicians. The difficulties of obtaining reliable data are related to the fact that at this stage of the medication administration chain, detection of errors is extremely difficult. Smart pump medication library logs and their reporting software record medication and dose selections made by users, as well as cancellations of selections and the time between these actions. Analysis of these data adds quantitative data to the detection of these kinds of errors. Objective: We aimed to establish, in a reproducible and reliable study, baseline data to show how metrics in the set-up and programming phase of intravenous medication administration can be produced from medication library near-miss error reports from infusion pumps. Methods: We performed a 12-month retrospective review of medication library reports from infusion pumps from across a facility to obtain metrics on the set-up phase of intravenous medication administration. Cancelled infusions and resolutions of all infusion alerts by users were analyzed. Decision times of clinicians were calculated from the time-date stamps of the pumps’ logs. Results: Incorrect medication selections represented 3.45% (10,017/290,807) of all medication library alerts and 22.40% (10,017/44,721) of all cancelled infusions. Of these cancelled medications, all high-risk medications, oncology medications, and all intravenous medications delivered to pediatric patients and neonates required a two-nurse check according to the local policy. Wrong dose selection was responsible for 2.93% (8533/290,807) of all alarms and 19.08% (8533/44,721) of infusion cancellations. Average error recognition to cancellation and correction times were 27.00 s (SD 22.25) for medication error correction and 26.52 s (SD 24.71) for dose correction. The mean character count of medications corrected from initial lookalike-soundalike selection errors was 13.04, with a heavier distribution toward higher character counts. The position of the word/phrase error was spread among name beginning (6991/10,017, 69.79%), middle (2144/10,017, 21.40%), and end (882/10,017, 8.80%). Conclusions: The study identified a high number of lookalike-soundalike near miss errors, with cancellation of one medication being rapidly followed by the programming of a second. This phenomenon was largely centered on initial misreadings of the beginning of the medication name, with some incidences of misreading in the middle and end portions of medication nomenclature. The value of an infusion pump showing the entire medication name complete with TALLman lettering on the interface matching that of medication labeling is supported by these findings. The study provides a quantitative appraisal of an area that has been resistant to study and measurement, which is the number of intravenous medication administration errors of wrong medication and wrong dose that occur in clinical settings. %M 32667895 %R 10.2196/20364 %U http://humanfactors.jmir.org/2020/3/e20364/ %U https://doi.org/10.2196/20364 %U http://www.ncbi.nlm.nih.gov/pubmed/32667895 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 7 %P e18758 %T Barriers and Facilitators to Implementation of Medication Decision Support Systems in Electronic Medical Records: Mixed Methods Approach Based on Structural Equation Modeling and Qualitative Analysis %A Jung,Se Young %A Hwang,Hee %A Lee,Keehyuck %A Lee,Ho-Young %A Kim,Eunhye %A Kim,Miyoung %A Cho,In Young %+ Office of eHealth Research and Businesses, Seoul National University Bundang Hospital, Dolma-ro 172, Bundang-gu, Seongnam, 13605, Republic of Korea, 82 317878992, chrisruga@naver.com %K clinical decision support system %K electronic health record %K medication safety %K Computerized Provider Order Entry (CPOE) %D 2020 %7 22.7.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Adverse drug events (ADEs) resulting from medication error are some of the most common causes of iatrogenic injuries in hospitals. With the appropriate use of medication, ADEs can be prevented and ameliorated. Efforts to reduce medication errors and prevent ADEs have been made by implementing a medication decision support system (MDSS) in electronic health records (EHRs). However, physicians tend to override most MDSS alerts. Objective: In order to improve MDSS functionality, we must understand what factors users consider essential for the successful implementation of an MDSS into their clinical setting. This study followed the implementation process for an MDSS within a comprehensive EHR system and analyzed the relevant barriers and facilitators. Methods: A mixed research methodology was adopted. Data from a structured survey and 15 in-depth interviews were integrated. Structural equation modeling was conducted for quantitative analysis of factors related to user adoption of MDSS. Qualitative analysis based on semistructured interviews with physicians was conducted to collect various opinions on MDSS implementation. Results: Quantitative analysis revealed that physicians’ expectations regarding ease of use and performance improvement are crucial. Qualitative analysis identified four significant barriers to MDSS implementation: alert fatigue, lack of accuracy, poor user interface design, and lack of customizability. Conclusions: This study revealed barriers and facilitators to the implementation of MDSS. The findings can be applied to upgrade MDSS in the future. %M 32706717 %R 10.2196/18758 %U https://medinform.jmir.org/2020/7/e18758 %U https://doi.org/10.2196/18758 %U http://www.ncbi.nlm.nih.gov/pubmed/32706717 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 7 %P e15653 %T Appropriateness of Overridden Alerts in Computerized Physician Order Entry: Systematic Review %A Poly,Tahmina Nasrin %A Islam,Md.Mohaimenul %A Yang,Hsuan-Chia %A Li,Yu-Chuan (Jack) %+ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, No. 250 Wuxing Street, Taipei, 110, Taiwan, 886 2 27361661 ext 7600, jaak88@gmail.com %K clinical decision system %K computerized physician order entry %K alert fatigue %K override %K patient safety %D 2020 %7 20.7.2020 %9 Review %J JMIR Med Inform %G English %X Background: The clinical decision support system (CDSS) has become an indispensable tool for reducing medication errors and adverse drug events. However, numerous studies have reported that CDSS alerts are often overridden. The increase in override rates has raised questions about the appropriateness of CDSS application along with concerns about patient safety and quality of care. Objective: The aim of this study was to conduct a systematic review to examine the override rate, the reasons for the alert override at the time of prescribing, and evaluate the appropriateness of overrides. Methods: We searched electronic databases, including Google Scholar, PubMed, Embase, Scopus, and Web of Science, without language restrictions between January 1, 2000 and March 31, 2019. Two authors independently extracted data and crosschecked the extraction to avoid errors. The quality of the included studies was examined following Cochrane guidelines. Results: We included 23 articles in our systematic review. The range of average override alerts was 46.2%-96.2%. An average of 29.4%-100% of the overrides alerts were classified as appropriate, and the rate of appropriateness varied according to the alert type (drug-allergy interaction 63.4%-100%, drug-drug interaction 0%-95%, dose 43.9%-88.8%, geriatric 14.3%-57%, renal 27%-87.5%). The interrater reliability for the assessment of override alerts appropriateness was excellent (kappa=0.79-0.97). The most common reasons given for the override were “will monitor” and “patients have tolerated before.” Conclusions: The findings of our study show that alert override rates are high, and certain categories of overrides such as drug-drug interaction, renal, and geriatric were classified as inappropriate. Nevertheless, large proportions of drug duplication, drug-allergy, and formulary alerts were appropriate, suggesting that these groups of alerts can be primary targets to revise and update the system for reducing alert fatigue. Future efforts should also focus on optimizing alert types, providing clear information, and explaining the rationale of the alert so that essential alerts are not inappropriately overridden. %M 32706721 %R 10.2196/15653 %U https://medinform.jmir.org/2020/7/e15653 %U https://doi.org/10.2196/15653 %U http://www.ncbi.nlm.nih.gov/pubmed/32706721 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 7 %P e16021 %T Effectiveness and Safety of Using Chatbots to Improve Mental Health: Systematic Review and Meta-Analysis %A Abd-Alrazaq,Alaa Ali %A Rababeh,Asma %A Alajlani,Mohannad %A Bewick,Bridgette M %A Househ,Mowafa %+ College of Science and Engineering, Hamad Bin Khalifa University, Liberal Arts and Sciences Building, Education City, Ar Rayyan, Doha, Qatar, 974 55708549, mhouseh@hbku.edu.qa %K chatbots %K conversational agents %K mental health %K mental disorders %K depression %K anxiety %K effectiveness %K safety %D 2020 %7 13.7.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: The global shortage of mental health workers has prompted the utilization of technological advancements, such as chatbots, to meet the needs of people with mental health conditions. Chatbots are systems that are able to converse and interact with human users using spoken, written, and visual language. While numerous studies have assessed the effectiveness and safety of using chatbots in mental health, no reviews have pooled the results of those studies. Objective: This study aimed to assess the effectiveness and safety of using chatbots to improve mental health through summarizing and pooling the results of previous studies. Methods: A systematic review was carried out to achieve this objective. The search sources were 7 bibliographic databases (eg, MEDLINE, EMBASE, PsycINFO), the search engine “Google Scholar,” and backward and forward reference list checking of the included studies and relevant reviews. Two reviewers independently selected the studies, extracted data from the included studies, and assessed the risk of bias. Data extracted from studies were synthesized using narrative and statistical methods, as appropriate. Results: Of 1048 citations retrieved, we identified 12 studies examining the effect of using chatbots on 8 outcomes. Weak evidence demonstrated that chatbots were effective in improving depression, distress, stress, and acrophobia. In contrast, according to similar evidence, there was no statistically significant effect of using chatbots on subjective psychological wellbeing. Results were conflicting regarding the effect of chatbots on the severity of anxiety and positive and negative affect. Only two studies assessed the safety of chatbots and concluded that they are safe in mental health, as no adverse events or harms were reported. Conclusions: Chatbots have the potential to improve mental health. However, the evidence in this review was not sufficient to definitely conclude this due to lack of evidence that their effect is clinically important, a lack of studies assessing each outcome, high risk of bias in those studies, and conflicting results for some outcomes. Further studies are required to draw solid conclusions about the effectiveness and safety of chatbots. Trial Registration: PROSPERO International Prospective Register of Systematic Reviews CRD42019141219; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019141219 %M 32673216 %R 10.2196/16021 %U http://www.jmir.org/2020/7/e16021/ %U https://doi.org/10.2196/16021 %U http://www.ncbi.nlm.nih.gov/pubmed/32673216 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 7 %N 2 %P e16036 %T Comparison of the Effects of Automated and Manual Record Keeping on Anesthetists’ Monitoring Performance: Randomized Controlled Simulation Study %A Tse,Man-Kei %A Li,Simon Y W %A Chiu,Tsz Hin %A Lau,Chung Wai %A Lam,Ka Man %A Cheng,Chun Pong Benny %+ Department of Applied Psychology, Lingnan University, WYL-306, WYL Building, 8 Castle Peak Road, Tuen Mun, Hong Kong, , China (Hong Kong), 852 26167129, simonli2@ln.edu.hk %K anesthesia information management system %K automated record keeping %K vigilance %K situation awareness %K mental workload %D 2020 %7 16.6.2020 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Anesthesia information management systems (AIMSs) automatically import real-time vital signs from physiological monitors to anesthetic records, replacing part of anesthetists’ traditional manual record keeping. However, only a handful of studies have examined the effects of AIMSs on anesthetists’ monitoring performance. Objective: This study aimed to compare the effects of AIMS use and manual record keeping on anesthetists’ monitoring performance, using a full-scale high-fidelity simulation. Methods: This simulation study was a randomized controlled trial with a parallel group design that compared the effects of two record-keeping methods (AIMS vs manual) on anesthetists’ monitoring performance. Twenty anesthetists at a tertiary hospital in Hong Kong were randomly assigned to either the AIMS or manual condition, and they participated in a 45-minute scenario in a high-fidelity simulation environment. Participants took over a case involving general anesthesia for below-knee amputation surgery and performed record keeping. The three primary outcomes were participants’ (1) vigilance detection accuracy (%), (2) situation awareness accuracy (%), and (3) subjective mental workload (0-100). Results: With regard to the primary outcomes, there was no significant difference in participants’ vigilance detection accuracy (AIMS, 56.7% vs manual, 56.7%; P=.50), and subjective mental workload was significantly lower in the AIMS condition than in the manual condition (AIMS, 34.2 vs manual, 46.7; P=.02). However, the result for situation awareness accuracy was inconclusive as the study did not have enough power to detect a difference between the two conditions. Conclusions: Our findings suggest that it is promising for AIMS use to become a mainstay of anesthesia record keeping. AIMSs are effective in reducing anesthetists’ workload and improving the quality of their anesthetic record keeping, without compromising vigilance. %M 32543440 %R 10.2196/16036 %U http://humanfactors.jmir.org/2020/2/e16036/ %U https://doi.org/10.2196/16036 %U http://www.ncbi.nlm.nih.gov/pubmed/32543440 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 4 %N 5 %P e16137 %T A Patient Safety Educational Tool for Patients With Chronic Kidney Disease: Development and Usability Study %A Bowman,Cassandra %A Lunyera,Joseph %A Alkon,Aviel %A Boulware,L Ebony %A St Clair Russell,Jennifer %A Riley,Jennie %A Fink,Jeffrey C %A Diamantidis,Clarissa %+ Division of General Internal Medicine, Duke University School of Medicine, 200 Morris Street, Durham, NC, 27701, United States, 1 919 668 1261, clarissa.diamantidis@duke.edu %K patient safety %K chronic kidney disease %K patient education %K mhealth %D 2020 %7 28.5.2020 %9 Original Paper %J JMIR Form Res %G English %X Background: Chronic kidney disease (CKD) is a health condition that threatens patient safety; however, few interventions provide patient-centered education about kidney-specific safety hazards. Objective: We sought to develop and test the usability of a mobile tablet–based educational tool designed to promote patient awareness of relevant safety topics in CKD. Methods: We used plain language principles to develop content for the educational tool, targeting four patient-actionable safety objectives that are relevant for individuals with CKD. These four objectives included avoidance of nonsteroidal anti-inflammatory drugs (NSAIDs); hypoglycemia awareness (among individuals with diabetes); temporary cessation of certain medications during acute volume depletion to prevent acute kidney injury (ie, “sick day protocol”); and contrast dye risk awareness. Our teaching strategies optimized human-computer interaction and content retention using audio, animation, and clinical vignettes to reinforce themes. For example, using a vignette of a patient with CKD with pain and pictures of common NSAIDs, participants were asked “Which of the following pain medicines are safe for Mr. Smith to take for his belly pain?” Assessment methods consisted of preknowledge and postknowledge surveys, with provision of correct responses and explanations. Usability testing of the tablet-based tool was performed among 12 patients with any stage of CKD, and program tasks were rated upon completion as no error, noncritical error (self-corrected), or critical error (needing assistance). Results: The 12 participants in this usability study were predominantly 65 years of age or older (n=7, 58%) and female (n=7, 58%); all participants owned a mobile device and used it daily. Among the 725 total tasks that the participants completed, there were 31 noncritical errors (4.3%) and 15 critical errors (2.1%); 1 participant accounted for 30 of the total errors. Of the 12 participants, 10 (83%) easily completed 90% or more of their tasks. Most participants rated the use of the tablet as very easy (n=7, 58%), the activity length as “just right” (rather than too long or too short) (n=10, 83%), and the use of clinical vignettes as helpful (n=10, 83%); all participants stated that they would recommend this activity to others. The median rating of the activity was 8 on a scale of 1 to 10 (where 10 is best). We incorporated all participant recommendations into the final version of the educational tool. Conclusions: A tablet-based patient safety educational tool is acceptable and usable by individuals with CKD. Future studies leveraging iterations of this educational tool will explore its impact on health outcomes in this high-risk population. %M 32463366 %R 10.2196/16137 %U http://formative.jmir.org/2020/5/e16137/ %U https://doi.org/10.2196/16137 %U http://www.ncbi.nlm.nih.gov/pubmed/32463366 %0 Journal Article %@ 2562-7600 %I JMIR Publications %V 3 %N 1 %P e18914 %T Desirable Features of an Interdisciplinary Handoff %A Sule,Anupam Ashutosh %A Caputo,Dean %A Gohal,Jaskaren %A Dascenzo,Doug %+ Department of Internal Medicine, St Joseph Mercy Oakland, 44405 Woodward Ave, Administration Ste, Pontiac, MI, 48341, United States, 1 2488586281, anupamsule@gmail.com %K handoff %K transition %K sign-out %K electronic %K interdisciplinary %K interprofessional %K communication %K patient safety %D 2020 %7 22.5.2020 %9 Viewpoint %J JMIR Nursing %G English %X Failure of communication of critical information during handoffs is one of the leading causes of medical errors, resulting in serious, yet preventable, adverse events in hospitals across the United States. Recent studies have shown that a majority of these errors occur during patient handoffs, with notable communication gaps in interdisciplinary handoffs. We suggest some features that would improve the handoff usability and effectiveness for interdisciplinary medical and nursing teams while potentially improving patient safety. %M 34345786 %R 10.2196/18914 %U https://nursing.jmir.org/2020/1/e18914/ %U https://doi.org/10.2196/18914 %U http://www.ncbi.nlm.nih.gov/pubmed/34345786 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 5 %P e15407 %T Detecting False Alarms by Analyzing Alarm-Context Information: Algorithm Development and Validation %A Fernandes,Chrystinne %A Miles,Simon %A Lucena,Carlos José Pereira %+ Department of Informatics, Pontifical Catholic University of Rio de Janeiro (PUC-Rio), RDC Bldg, 4th Fl, 225 Marquês de São Vicente St, Rio de Janeiro, 22451-900, Brazil, 55 21 3527 1510, chrystinne@gmail.com %K alarm fatigue %K alarm safety %K false alarms %K eHealth systems %K remote patient monitoring %K notification %K reasoning %K sensors %D 2020 %7 20.5.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Although alarm safety is a critical issue that needs to be addressed to improve patient care, hospitals have not given serious consideration about how their staff should be using, setting, and responding to clinical alarms. Studies have indicated that 80%-99% of alarms in hospital units are false or clinically insignificant and do not represent real danger for patients, leading caregivers to miss relevant alarms that might indicate significant harmful events. The lack of use of any intelligent filter to detect recurrent, irrelevant, and/or false alarms before alerting health providers can culminate in a complex and overwhelming scenario of sensory overload for the medical team, known as alarm fatigue. Objective: This paper’s main goal is to propose a solution to mitigate alarm fatigue by using an automatic reasoning mechanism to decide how to calculate false alarm probability (FAP) for alarms and whether to include an indication of the FAP (ie, FAP_LABEL) with a notification to be visualized by health care team members designed to help them prioritize which alerts they should respond to next. Methods: We present a new approach to cope with the alarm fatigue problem that uses an automatic reasoner to decide how to notify caregivers with an indication of FAP. Our reasoning algorithm calculates FAP for alerts triggered by sensors and multiparametric monitors based on statistical analysis of false alarm indicators (FAIs) in a simulated environment of an intensive care unit (ICU), where a large number of warnings can lead to alarm fatigue. Results: The main contributions described are as follows: (1) a list of FAIs we defined that can be utilized and possibly extended by other researchers, (2) a novel approach to assess the probability of a false alarm using statistical analysis of multiple inputs representing alarm-context information, and (3) a reasoning algorithm that uses alarm-context information to detect false alarms in order to decide whether to notify caregivers with an indication of FAP (ie, FAP_LABEL) to avoid alarm fatigue. Conclusions: Experiments were conducted to demonstrate that by providing an intelligent notification system, we could decide how to identify false alarms by analyzing alarm-context information. The reasoner entity we described in this paper was able to attribute FAP values to alarms based on FAIs and to notify caregivers with a FAP_LABEL indication without compromising patient safety. %M 32432551 %R 10.2196/15407 %U http://medinform.jmir.org/2020/5/e15407/ %U https://doi.org/10.2196/15407 %U http://www.ncbi.nlm.nih.gov/pubmed/32432551 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 4 %P e16970 %T Predicting Inpatient Falls Using Natural Language Processing of Nursing Records Obtained From Japanese Electronic Medical Records: Case-Control Study %A Nakatani,Hayao %A Nakao,Masatoshi %A Uchiyama,Hidefumi %A Toyoshiba,Hiroyoshi %A Ochiai,Chikayuki %+ Pharmaceutical Research Department, Global Pharmaceutical R&D Division, Neopharma Japan Co Ltd, Iidabashi Grand Bloom 4F, 2-10-2 Fujimi, Chiyoda-ku, Tokyo, 102-0071, Japan, 81 90 3896 2658, huchiyam@gmail.com %K fall %K risk factor %K prediction %K nursing record %K natural language processing %K machine learning %D 2020 %7 22.4.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Falls in hospitals are the most common risk factor that affects the safety of inpatients and can result in severe harm. Therefore, preventing falls is one of the most important areas of risk management for health care organizations. However, existing methods for predicting falls are laborious and costly. Objective: The objective of this study is to verify whether hospital inpatient falls can be predicted through the analysis of a single input—unstructured nursing records obtained from Japanese electronic medical records (EMRs)—using a natural language processing (NLP) algorithm and machine learning. Methods: The nursing records of 335 fallers and 408 nonfallers for a 12-month period were extracted from the EMRs of an acute care hospital and randomly divided into a learning data set and test data set. The former data set was subjected to NLP and machine learning to extract morphemes that contributed to separating fallers from nonfallers to construct a model for predicting falls. Then, the latter data set was used to determine the predictive value of the model using receiver operating characteristic (ROC) analysis. Results: The prediction of falls using the test data set showed high accuracy, with an area under the ROC curve, sensitivity, specificity, and odds ratio of mean 0.834 (SD 0.005), mean 0.769 (SD 0.013), mean 0.785 (SD 0.020), and mean 12.27 (SD 1.11) for five independent experiments, respectively. The morphemes incorporated into the final model included many words closely related to known risk factors for falls, such as the use of psychotropic drugs, state of consciousness, and mobility, thereby demonstrating that an NLP algorithm combined with machine learning can effectively extract risk factors for falls from nursing records. Conclusions: We successfully established that falls among hospital inpatients can be predicted by analyzing nursing records using an NLP algorithm and machine learning. Therefore, it may be possible to develop a fall risk monitoring system that analyzes nursing records daily and alerts health care professionals when the fall risk of an inpatient is increased. %M 32319959 %R 10.2196/16970 %U http://medinform.jmir.org/2020/4/e16970/ %U https://doi.org/10.2196/16970 %U http://www.ncbi.nlm.nih.gov/pubmed/32319959 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 4 %P e15304 %T Using Nonexpert Online Reports to Enhance Expert Knowledge About Causes of Death in Dental Offices Reported in Scientific Publications: Qualitative and Quantitative Content Analysis and Search Engine Analysis %A Gaiser,Meike %A Kirsch,Joachim %A Mutzbauer,Till Sebastian %+ Maxillofacial Surgery and Dental Anesthesiology, Mutzbauer & Partner, Tiefenhoefe 11, Zürich, 8001, Switzerland, 41 44211 ext 1465, tsmutzbauer@gmail.com %K dental death %K dental practice %K dental sedation %K risk %K internet search engine %D 2020 %7 17.4.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Fatalities rarely occur in dental offices. Implications for clinicians may be deduced from scientific publications and internet reports about deaths in dental offices. Objective: Data involving deaths in dental facilities were analyzed using Google as well as the PubMed database. By comparing both sources, we examined how internet data may enhance knowledge about deaths in dental offices obtained from scientific medical publications, which causes of death are published online, and how associated life-threatening emergencies may be prevented. Methods: To retrieve relevant information, we searched Google for country-specific incidents of death in dental practices using the following keywords: “death at the dentist,” “death in dental practice,” and “dying at the dentist.” For PubMed searches, the following keywords were used: “dentistry and mortality,” “death and dental treatment,” “dentistry and fatal outcome,” and “death and dentistry.” Deaths associated with dental treatment in a dental facility, attributable causes of death, and documented ages of the deceased were included in our analysis. Deaths occurring in maxillofacial surgery or pre-existing diseases involved in the death (eg, cancer and abscesses) were excluded. A total of 128 cases from online publications and 71 cases from PubMed publications that met the inclusion criteria were analyzed using chi-square statistics after exclusion of duplicates. Results: The comparison between the fatalities from internet (n=117) and PubMed (n=71) publications revealed that more casualties affecting minors appeared online than in PubMed literature (online 68/117, 58.1%; PubMed 20/71, 28%; P<.001). In PubMed articles, 10 fatalities in patients older than 70 years of age were described, while online sources published 5 fatalities (P=.02). Most deaths, both from internet publications and PubMed literature, could be assigned to the category anesthesia, medication, or sedation (online 80/117, 68.4%; PubMed 25/71, 35%; P<.001). Deaths assigned to the categories infection and cardiovascular system appeared more often in the PubMed literature (infection: online 10/117, 8.5%; PubMed 15/71, 21%; P=.01; cardiovascular system: online 5/117, 4.3%; PubMed 15/71, 21%; P<.001). Furthermore, sedative drugs were involved in a larger proportion of fatal incidents listed online compared to in PubMed (online 41/117, 35.0%; PubMed: 14/71, 20%, P=.03). In the United States, more deaths occurred under sedation (44/96, 46%) compared to those in the other countries (Germany and Austria 1/17, 6%, P=.002; United Kingdom 1/14, 7%, P=.006). Conclusions: Online and PubMed databases may increase awareness of life-threatening risks for patients during dental treatment. Negative aspects of anesthesia and sedation, as well as the number of deaths of young patients, were underestimated when reviewing PubMed literature only. Medical history of patients, medication dosages, and vital function monitoring are significant issues for practitioners. A high-impact finding from online reports was the underestimation of risks when performing sedation and even general anesthesia. Detailed knowledge of the definition and understanding of deep sedation and general anesthesia by dentists is of major concern. By avoiding potentially hazardous procedures, such as sedation-aided treatments performed solely by dentists, the risk of treatment-induced life-threatening emergencies may be reduced. %M 32038029 %R 10.2196/15304 %U http://www.jmir.org/2020/4/e15304/ %U https://doi.org/10.2196/15304 %U http://www.ncbi.nlm.nih.gov/pubmed/32038029 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 7 %N 2 %P e17131 %T Teamwork and Safety Attitudes in Complex Aortic Surgery at a Dutch Hospital: Cross-Sectional Survey Study %A Hilt,Alexander D %A Kaptein,Ad A %A Schalij,Martin J %A van Schaik,Jan %+ Department of Vascular Surgery, Leiden University Medical Center, Albinusdreef 2, Leiden, 2333 ZA, Netherlands, 31 715299407, J.van_Schaik@lumc.nl %K human factors %K organizational culture %K SAQ %K SAQ-NL %K safety assessment %K vascular surgery %D 2020 %7 8.4.2020 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Improving teamwork in surgery is a complex goal and difficult to achieve. Human factors questionnaires, such as the Safety Attitudes Questionnaire (SAQ), can help us understand medical teamwork and may assist in achieving this goal. Objective: This paper aimed to assess local team and safety culture in a cardiovascular surgery setting to understand how purposeful teamwork improvements can be reached. Methods: Two cardiovascular surgical teams performing complex aortic treatments were assessed: an endovascular-treatment team (ETT) and an open-treatment team (OTT). Both teams answered an online version of the SAQ Dutch Edition (SAQ-NL) consisting of 30 questions related to six different domains of safety: teamwork climate, safety climate, job satisfaction, stress recognition, perceptions of management, and working conditions. In addition, one open-ended question was posed to gain more insight into the completed questionnaires. Results: The SAQ-NL was completed by all 23 ETT members and all 13 OTT members. Team composition was comparable for both teams: 57% and 62% males, respectively, and 48% and 54% physicians, respectively. All participants worked for 10 years or more in health care. SAQ-NL mean scores were comparable between both teams, with important differences found between the physicians and nonphysicians of the ETT. Nonphysicians were less positive about the safety climate, job satisfaction, and working climate domains than were the physicians (P<.05). Additional education on performed procedures, more conjoined team training, as well as a hybrid operating room were suggested by participants as important areas of improvement. Conclusions: Nonphysicians of a local team performing complex endovascular aortic aneurysm surgery perceived safety climate, job satisfaction, and working conditions less positively than did physicians from the same team. Open-ended questions suggested that this is related to a lack of adequate conjoined training, lack of adequate education, and lack of an adequate operating room. With added open-ended questions, the SAQ-NL appears to be an assessment tool that allows for developing strategies that are instrumental in improving quality of care. %M 32267238 %R 10.2196/17131 %U https://humanfactors.jmir.org/2020/2/e17131 %U https://doi.org/10.2196/17131 %U http://www.ncbi.nlm.nih.gov/pubmed/32267238 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 3 %P e16252 %T Use of Notification and Communication Technology (Call Light Systems) in Nursing Homes: Observational Study %A Ali,Haneen %A Li,Huiyang %+ Binghamton University, 4400 Vestal Pkwy E, Binghamton, NY, 13902, United States, 1 734 546 1910, hli@binghamton.edu %K communication systems %K nursing home %K response time %K safety %K quality of health care %K observational study %D 2020 %7 27.3.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: The call light system is one of the major communication technologies that link nursing home staff to the needs of residents. By providing residents the ability to request assistance, the system becomes an indispensable resource for patient-focused health care. However, little is known about how call light systems are being used in nursing homes and how the system contributes to safety and quality of care for seniors. Objective: This study aimed to understand the experiences of nursing home staff who use call light systems and to uncover usability issues and challenges associated with the implemented systems. Methods: A mix of 150 hours of hypothetico-deductive (unstructured) task analysis and 90 hours of standard procedure (structured) task analysis was conducted in 4 different nursing homes. The data collected included insights into the nursing home’s work system and the process of locating and responding to call lights. Results: The data showed that the highest alarm rate is before and after mealtimes. The staff exceeded the administration’s expectations of time to respond 50% of the time. In addition, the staff canceled 10.0% (20/201) of call lights and did not immediately assist residents because of high workload. Furthermore, the staff forgot to come back to assist residents over 3% of the time. Usability issues such as broken parts, lack of feedback, lack of prioritization, and low or no discriminability also contributed to the long response time. More than 8% of the time, residents notified the staff about call lights after they waited for a long time, and eventually, these residents were left unattended. Conclusions: Nursing homes that are still using old call light systems risk the continuation of usability issues that can affect the performance of the staff and contribute to declining staff and resident outcomes. By incorporating feedback from nurses, nursing home management will better understand the influence that the perceptions and usability of technology have on the quality of health care for their residents. In this study, it has been observed that the call light system is perceived to be an important factor affecting the outcomes of the care process and satisfaction of both residents and staff as well as the staff’s performance. It is important to recognize that communication and notification technology contributes to the challenges the staff faced during their work, making their working conditions more difficult and challenging. %M 32217497 %R 10.2196/16252 %U http://www.jmir.org/2020/3/e16252/ %U https://doi.org/10.2196/16252 %U http://www.ncbi.nlm.nih.gov/pubmed/32217497 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 3 %P e16073 %T Detecting Potential Medication Selection Errors During Outpatient Pharmacy Processing of Electronic Prescriptions With the RxNorm Application Programming Interface: Retrospective Observational Cohort Study %A Lester,Corey A %A Tu,Liyun %A Ding,Yuting %A Flynn,Allen J %+ Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, 428 Church Street, Ann Arbor, MI, 48109, United States, 1 734 647 8849, lesterca@umich.edu %K patient safety %K RxNorm %K electronic prescription %K pharmacy %K pharmacists %K automation %D 2020 %7 11.3.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Medication errors are pervasive. Electronic prescriptions (e-prescriptions) convey secure and computer-readable prescriptions from clinics to outpatient pharmacies for dispensing. Once received, pharmacy staff perform a transcription task to select the medications needed to process e-prescriptions within their dispensing software. Later, pharmacists manually double-check medications selected to fulfill e-prescriptions before dispensing to the patient. Although pharmacist double-checks are mostly effective for catching medication selection mistakes, the cognitive process of medication selection in the computer is still prone to error because of heavy workload, inattention, and fatigue. Leveraging health information technology to identify and recover from medication selection errors can improve patient safety. Objective: This study aimed to determine the performance of an automated double-check of pharmacy prescription records to identify potential medication selection errors made in outpatient pharmacies with the RxNorm application programming interface (API). Methods: We conducted a retrospective observational analysis of 537,710 pairs of e-prescription and dispensing records from a mail-order pharmacy for the period January 2017 to October 2018. National Drug Codes (NDCs) for each pair were obtained from the National Library of Medicine’s (NLM’s) RxNorm API. The API returned RxNorm concept unique identifier (RxCUI) semantic clinical drug (SCD) identifiers associated with every NDC. The SCD identifiers returned for the e-prescription NDC were matched against the corresponding SCD identifiers from the pharmacy dispensing record NDC. An error matrix was created based on the hand-labeling of mismatched SCD pairs. Performance metrics were calculated for the e-prescription-to-dispensing record matching algorithm for both total pairs and unique pairs of NDCs in these data. Results: We analyzed 527,881 e-prescription and pharmacy dispensing record pairs. Four clinically significant cases of mismatched RxCUI identifiers were detected (ie, three different ingredient selections and one different strength selection). A total of 546 less significant cases of mismatched RxCUIs were found. Nearly all of the NDC pairs had matching RxCUIs (28,787/28,817, 99.90%-525,270/527,009, 99.67%). The RxNorm API had a sensitivity of 1, a false-positive rate of 0.00104 to 0.00312, specificity of 0.99896 to 0.99688, precision of 0.00727 to 0.04255, and F1 score of 0.01444 to 0.08163. We found 872 pairs of records without an RxCUI. Conclusions: The NLM’s RxNorm API can perform an independent and automatic double-check of correct medication selection to verify e-prescription processing at outpatient pharmacies. RxNorm has near-comprehensive coverage of prescribed medications and can be used to recover from medication selection errors. In the future, tools such as this may be able to perform automated verification of medication selection accurately enough to free pharmacists from having to perform manual double-checks of the medications selected within pharmacy dispensing software to fulfill e-prescriptions. %M 32044760 %R 10.2196/16073 %U http://medinform.jmir.org/2020/3/e16073/ %U https://doi.org/10.2196/16073 %U http://www.ncbi.nlm.nih.gov/pubmed/32044760 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 3 %P e14130 %T Use of an Electronic Clinical Decision Support System in Primary Care to Assess Inappropriate Polypharmacy in Young Seniors With Multimorbidity: Observational, Descriptive, Cross-Sectional Study %A Rogero-Blanco,Eloisa %A Lopez-Rodriguez,Juan A %A Sanz-Cuesta,Teresa %A Aza-Pascual-Salcedo,Mercedes %A Bujalance-Zafra,M Jose %A Cura-Gonzalez,Isabel %A , %+ General Ricardos Primary Health Care Centre, Calle General Ricardos, Madrid, 28019, Spain, 34 685197913, juanantonio.lopez@salud.madrid.org %K potentially inappropriate medication list %K polypharmacy %K multimorbidity %K clinical decision support systems %K primary care %D 2020 %7 3.3.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Multimorbidity is a global health problem that is usually associated with polypharmacy, which increases the risk of potentially inappropriate prescribing (PIP). PIP entails higher hospitalization rates and mortality and increased usage of services provided by the health system. Tools exist to improve prescription practices and decrease PIP, including screening tools and explicit criteria that can be applied in an automated manner. Objective: This study aimed to describe the prevalence of PIP in primary care consultations among patients aged 65-75 years with multimorbidity and polypharmacy, detected by an electronic clinical decision support system (ECDSS) following the 2015 American Geriatrics Society Beers Criteria, the European Screening Tool of Older Person’s Prescription (STOPP), and the Screening Tool to Alert doctors to Right Treatment (START). Methods: This was an observational, descriptive, cross-sectional study. The sample included 593 community-dwelling adults aged 65-75 years (henceforth called young seniors), with multimorbidity (≥3 diseases) and polypharmacy (≥5 medications), who had visited their primary care doctor at least once over the last year at 1 of the 38 health care centers participating in the Multimorbidity and Polypharmacy in Primary Care (Multi-PAP) trial. Sociodemographic data, clinical and pharmacological treatment variables, and PIP, as detected by 1 ECDSS, were recorded. A multivariate logistic regression model with robust estimators was built to assess the factors affecting PIP according to the STOPP criteria. Results: PIP was detected in 57.0% (338/593; 95% CI 53-61) and 72.8% (432/593; 95% CI 69.3-76.4) of the patients according to the STOPP criteria and the Beers Criteria, respectively, whereas 42.8% (254/593; 95% CI 38.9-46.8) of the patients partially met the START criteria. The most frequently detected PIPs were benzodiazepines (BZD) intake for more than 4 weeks (217/593, 36.6%) using the STOPP version 2 and the prolonged use of proton pump inhibitors (269/593, 45.4%) using the 2015 Beers Criteria. Being a woman (odds ratio [OR] 1.43, 95% CI 1.01-2.01; P=.04), taking a greater number of medicines (OR 1.25, 95% CI 1.14-1.37; P<.04), working in the primary sector (OR 1.91, 95% CI 1.25-2.93; P=.003), and being prescribed drugs for the central nervous system (OR 3.75, 95% CI 2.45-5.76; P<.001) were related to a higher frequency of PIP. Conclusions: There is a high prevalence of PIP in primary care as detected by an ECDSS in community-dwelling young seniors with comorbidity and polypharmacy. The specific PIP criteria defined by this study are consistent with the current literature. This ECDSS can be useful for supervising prescriptions in primary health care consultations. %M 32149715 %R 10.2196/14130 %U https://medinform.jmir.org/2020/3/e14130 %U https://doi.org/10.2196/14130 %U http://www.ncbi.nlm.nih.gov/pubmed/32149715 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 2 %P e14379 %T Expedited Safety Reporting Through an Alert System for Clinical Trial Management at an Academic Medical Center: Retrospective Design Study %A Park,Yu Rang %A Koo,HaYeong %A Yoon,Young-Kwang %A Park,Sumi %A Lim,Young-Suk %A Baek,Seunghee %A Kim,Hae Reong %A Kim,Tae Won %+ Clinical Research Center, Asan Institute of Life Sciences, Asan Medical Center, 88, Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, Republic of Korea, 82 2 3010 3910, twkimmd@amc.seoul.kr %K clinical trial %K adverse event %K early detection %K patient safety %D 2020 %7 27.2.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Early detection or notification of adverse event (AE) occurrences during clinical trials is essential to ensure patient safety. Clinical trials take advantage of innovative strategies, clinical designs, and state-of-the-art technologies to evaluate efficacy and safety, however, early awareness of AE occurrences by investigators still needs to be systematically improved. Objective: This study aimed to build a system to promptly inform investigators when clinical trial participants make unscheduled visits to the emergency room or other departments within the hospital. Methods: We developed the Adverse Event Awareness System (AEAS), which promptly informs investigators and study coordinators of AE occurrences by automatically sending text messages when study participants make unscheduled visits to the emergency department or other clinics at our center. We established the AEAS in July 2015 in the clinical trial management system. We compared the AE reporting timeline data of 305 AE occurrences from 74 clinical trials between the preinitiative period (December 2014-June 2015) and the postinitiative period (July 2015-June 2016) in terms of three AE awareness performance indicators: onset to awareness, awareness to reporting, and onset to reporting. Results: A total of 305 initial AE reports from 74 clinical trials were included. All three AE awareness performance indicators were significantly lower in the postinitiative period. Specifically, the onset-to-reporting times were significantly shorter in the postinitiative period (median 1 day [IQR 0-1], mean rank 140.04 [SD 75.35]) than in the preinitiative period (median 1 day [IQR 0-4], mean rank 173.82 [SD 91.07], P≤.001). In the phase subgroup analysis, the awareness-to-reporting and onset-to-reporting indicators of phase 1 studies were significantly lower in the postinitiative than in the preinitiative period (preinitiative: median 1 day, mean rank of awareness to reporting 47.94, vs postinitiative: median 0 days, mean rank of awareness to reporting 35.75, P=.01; and preinitiative: median 1 day, mean rank of onset to reporting 47.4, vs postinitiative: median 1 day, mean rank of onset to reporting 35.99, P=.03). The risk-level subgroup analysis found that the onset-to-reporting time for low- and high-risk studies significantly decreased postinitiative (preinitiative: median 4 days, mean rank of low-risk studies 18.73, vs postinitiative: median 1 day, mean rank of low-risk studies 11.76, P=.02; and preinitiative: median 1 day, mean rank of high-risk studies 117.36, vs postinitiative: median 1 day, mean rank of high-risk studies 97.27, P=.01). In particular, onset to reporting was reduced more in the low-risk trial than in the high-risk trial (low-risk: median 4-0 days, vs high-risk: median 1-1 day). Conclusions: We demonstrated that a real-time automatic alert system can effectively improve safety reporting timelines. The improvements were prominent in phase 1 and in low- and high-risk clinical trials. These findings suggest that an information technology-driven automatic alert system effectively improves safety reporting timelines, which may enhance patient safety. %R 10.2196/14379 %U http://medinform.jmir.org/2020/2/e14379/ %U https://doi.org/10.2196/14379 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 2 %P e15823 %T Responses of Conversational Agents to Health and Lifestyle Prompts: Investigation of Appropriateness and Presentation Structures %A Kocaballi,Ahmet Baki %A Quiroz,Juan C %A Rezazadegan,Dana %A Berkovsky,Shlomo %A Magrabi,Farah %A Coiera,Enrico %A Laranjo,Liliana %+ Australian Institute of Health Innovation
, Macquarie University, Level 6, 75 Talavera Road, Sydney, New South Wales, 2109, Australia, 61 0466431900, abakik@gmail.com %K conversational agents %K chatbots %K patient safety %K health literacy %K public health %K design principles %K evaluation %D 2020 %7 10.2.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Conversational agents (CAs) are systems that mimic human conversations using text or spoken language. Their widely used examples include voice-activated systems such as Apple Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana. The use of CAs in health care has been on the rise, but concerns about their potential safety risks often remain understudied. Objective: This study aimed to analyze how commonly available, general-purpose CAs on smartphones and smart speakers respond to health and lifestyle prompts (questions and open-ended statements) by examining their responses in terms of content and structure alike. Methods: We followed a piloted script to present health- and lifestyle-related prompts to 8 CAs. The CAs’ responses were assessed for their appropriateness on the basis of the prompt type: responses to safety-critical prompts were deemed appropriate if they included a referral to a health professional or service, whereas responses to lifestyle prompts were deemed appropriate if they provided relevant information to address the problem prompted. The response structure was also examined according to information sources (Web search–based or precoded), response content style (informative and/or directive), confirmation of prompt recognition, and empathy. Results: The 8 studied CAs provided in total 240 responses to 30 prompts. They collectively responded appropriately to 41% (46/112) of the safety-critical and 39% (37/96) of the lifestyle prompts. The ratio of appropriate responses deteriorated when safety-critical prompts were rephrased or when the agent used a voice-only interface. The appropriate responses included mostly directive content and empathy statements for the safety-critical prompts and a mix of informative and directive content for the lifestyle prompts. Conclusions: Our results suggest that the commonly available, general-purpose CAs on smartphones and smart speakers with unconstrained natural language interfaces are limited in their ability to advise on both the safety-critical health prompts and lifestyle prompts. Our study also identified some response structures the CAs employed to present their appropriate responses. Further investigation is needed to establish guidelines for designing suitable response structures for different prompt types. %R 10.2196/15823 %U https://www.jmir.org/2020/2/e15823 %U https://doi.org/10.2196/15823 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 8 %N 1 %P e12859 %T A Smartphone App Designed to Empower Patients to Contribute Toward Safer Surgical Care: Community-Based Evaluation Using a Participatory Approach %A Russ,Stephanie %A Latif,Zahira %A Hazell,Ahmarah Leah %A Ogunmuyiwa,Helen %A Tapper,Josephine %A Wachuku-King,Sylvia %A Sevdalis,Nick %A Ocloo,Josephine %+ King's College London, Institution of Psychiatry, De Crespigny Park, Denmark Hill, London, SE5 8AF, United Kingdom, 44 207 848 0683, stephanie.russ@kcl.ac.uk %K patient safety %K surgery %K smartphone %K mobile phone %K patient empowerment %D 2020 %7 20.1.2020 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: MySurgery is a smartphone app designed to increase patient and carer involvement in behaviors that contribute toward safety in surgical care. Objective: This study presents a pilot evaluation of MySurgery in which we evaluated surgical patients’ perceptions of the app in terms of its content, usability, and potential impacts on communication and safety. Methods: A participatory action research (PAR) approach was used to formulate a research steering group consisting of 5 public representatives and 4 researchers with equal decision-making input. Surgical patients were recruited from the community using multiple approaches, including Web based (eg, social media, recruitment websites, and charitable or voluntary organizations) and face to face (via community centers). Participants referred to MySurgery before, during, and after their surgery and provided feedback via an embedded questionnaire and using reflective notes. Results: A diverse mix of 42 patients took part with good representation from 2 “seldom heard” groups: those with a disability and those from a black, Asian, or minority ethnic group. Most were very supportive of MySurgery, particularly those with previous experience of surgery and those who felt comfortable to be involved in conversations and decisions around their care. The app showed particular potential to empower patients to become involved in their care conversations and safety-related behaviors. Perceptions did not differ according to age, ethnicity, or length of hospital stay. Suggestions for improving the app included how to make it more accessible to certain groups, for example, those with a disability. Conclusions: MySurgery is a novel technology-driven approach for empowering patients to play a role in improving surgical safety that seems feasible for use within the United Kingdom’s National Health Service. Adopting a PAR approach and the use of a diversity strategy considerably enhanced the research process in terms of gaining diverse participant recruitment and patient and public involvement. Further testing with stakeholder groups will follow. %M 31958067 %R 10.2196/12859 %U https://mhealth.jmir.org/2020/1/e12859 %U https://doi.org/10.2196/12859 %U http://www.ncbi.nlm.nih.gov/pubmed/31958067 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 12 %P e16689 %T Quality and Safety in eHealth: The Need to Build the Evidence Base %A Borycki,Elizabeth %+ School of Health Information Science, University of Victoria, 3800 Finnerty Road (Ring Road), Room A202, Victoria, BC, V8P 5C2, Canada, 1 2504725432, emb@uvic.ca %K patient safety %K technology-induced error %K health technology %D 2019 %7 19.12.2019 %9 Viewpoint %J J Med Internet Res %G English %X Research in the area of health technology safety has demonstrated that technology may both improve patient safety and introduce new types of technology-induced errors. Thus, there is a need to publish safety science literature to develop an evidence-based research base, on which we can continually develop new, safe technologies and improve patient safety. The aim of this viewpoint is to argue for the need to advance evidence-based research in health informatics, so that new technologies can be designed, developed, and implemented for their safety prior to their use in health care. This viewpoint offers a historical perspective on the development of health informatics and safety literature in the area of health technology. I argue for the need to conduct safety studies of technologies used by health professionals and consumers to develop an evidence base in this area. Ongoing research is necessary to improve the quality and safety of health technologies. Over the past several decades, we have seen health informatics emerge as a discipline, with growing research in the field examining the design, development, and implementation of different health technologies and new challenges such as those associated with the quality and safety of technology use. Future research will need to focus on how we can continually extend safety science in this area. There is a need to integrate evidence-based research into the design, development, and implementation of health technologies to improve their safety and reduce technology-induced errors. %M 31855183 %R 10.2196/16689 %U http://www.jmir.org/2019/12/e16689/ %U https://doi.org/10.2196/16689 %U http://www.ncbi.nlm.nih.gov/pubmed/31855183 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 8 %N 11 %P e16047 %T Improving Team-Based Decision Making Using Data Analytics and Informatics: Protocol for a Collaborative Decision Support Design %A Roosan,Don %A Law,Anandi V %A Karim,Mazharul %A Roosan,Moom %+ Western University of Health Sciences, College of Pharmacy, 309 E 2nd St, Pomona, CA, 91766, United States, 1 9094698778, droosan@westernu.edu %K informatics %K health care team %K data science %K decision support techniques %K decision-making, computer-assisted %K data display %K diagnosis, computer-assisted %D 2019 %7 27.11.2019 %9 Protocol %J JMIR Res Protoc %G English %X Background: According to the September 2015 Institute of Medicine report, Improving Diagnosis in Health Care, each of us is likely to experience one diagnostic error in our lifetime, often with devastating consequences. Traditionally, diagnostic decision making has been the sole responsibility of an individual clinician. However, diagnosis involves an interaction among interprofessional team members with different training, skills, cultures, knowledge, and backgrounds. Moreover, diagnostic error is prevalent in the interruption-prone environment, such as the emergency department, where the loss of information may hinder a correct diagnosis. Objective: The overall purpose of this protocol is to improve team-based diagnostic decision making by focusing on data analytics and informatics tools that improve collective information management. Methods: To achieve this goal, we will identify the factors contributing to failures in team-based diagnostic decision making (aim 1), understand the barriers of using current health information technology tools for team collaboration (aim 2), and develop and evaluate a collaborative decision-making prototype that can improve team-based diagnostic decision making (aim 3). Results: Between 2019 to 2020, we are collecting data for this study. The results are anticipated to be published between 2020 and 2021. Conclusions: The results from this study can shed light on improving diagnostic decision making by incorporating diagnostics rationale from team members. We believe a positive direction to move forward in solving diagnostic errors is by incorporating all team members, and using informatics. International Registered Report Identifier (IRRID): DERR1-10.2196/16047 %M 31774412 %R 10.2196/16047 %U http://www.researchprotocols.org/2019/11/e16047/ %U https://doi.org/10.2196/16047 %U http://www.ncbi.nlm.nih.gov/pubmed/31774412 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 11 %P e15406 %T Artificial Intelligence Technologies for Coping with Alarm Fatigue in Hospital Environments Because of Sensory Overload: Algorithm Development and Validation %A Fernandes,Chrystinne Oliveira %A Miles,Simon %A Lucena,Carlos José Pereira De %A Cowan,Donald %+ Department of Informatics, Pontifical Catholic University of Rio de Janeiro, Rio Datacenter, 4th Fl, 225 Marquês de São Vicente St, Rio de Janeiro, 22451-900, Brazil, 55 21 3527 1510, chrystinne@gmail.com %K alert fatigue health personnel %K health information systems %K patient monitoring %K alert systems %K artificial intelligence %D 2019 %7 26.11.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Informed estimates claim that 80% to 99% of alarms set off in hospital units are false or clinically insignificant, representing a cacophony of sounds that do not present a real danger to patients. These false alarms can lead to an alert overload that causes a health care provider to miss important events that could be harmful or even life-threatening. As health care units become more dependent on monitoring devices for patient care purposes, the alarm fatigue issue has to be addressed as a major concern for the health care team as well as to enhance patient safety. Objective: The main goal of this paper was to propose a feasible solution for the alarm fatigue problem by using an automatic reasoning mechanism to decide how to notify members of the health care team. The aim was to reduce the number of notifications sent by determining whether or not to group a set of alarms that occur over a short period of time to deliver them together, without compromising patient safety. Methods: This paper describes: (1) a model for supporting reasoning algorithms that decide how to notify caregivers to avoid alarm fatigue; (2) an architecture for health systems that support patient monitoring and notification capabilities; and (3) a reasoning algorithm that specifies how to notify caregivers by deciding whether to aggregate a group of alarms to avoid alarm fatigue. Results: Experiments were used to demonstrate that providing a reasoning system can reduce the notifications received by the caregivers by up to 99.3% (582/586) of the total alarms generated. Our experiments were evaluated through the use of a dataset comprising patient monitoring data and vital signs recorded during 32 surgical cases where patients underwent anesthesia at the Royal Adelaide Hospital. We present the results of our algorithm by using graphs we generated using the R language, where we show whether the algorithm decided to deliver an alarm immediately or after a delay. Conclusions: The experimental results strongly suggest that this reasoning algorithm is a useful strategy for avoiding alarm fatigue. Although we evaluated our algorithm in an experimental environment, we tried to reproduce the context of a clinical environment by using real-world patient data. Our future work is to reproduce the evaluation study based on more realistic clinical conditions by increasing the number of patients, monitoring parameters, and types of alarm. %M 31769762 %R 10.2196/15406 %U http://www.jmir.org/2019/11/e15406/ %U https://doi.org/10.2196/15406 %U http://www.ncbi.nlm.nih.gov/pubmed/31769762 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 11 %P e15940 %T Improving Medication Information Presentation Through Interactive Visualization in Mobile Apps: Human Factors Design %A Roosan,Don %A Li,Yan %A Law,Anandi %A Truong,Huy %A Karim,Mazharul %A Chok,Jay %A Roosan,Moom %+ Western University of Health Sciences, College of Pharmacy, 309 E 2nd St, Pomona, CA, , United States, 1 909 469 8778, droosan@westernu.edu %K visual perception %K adverse drug event %K human factors design %K mobile health %D 2019 %7 25.11.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Despite the detailed patient package inserts (PPIs) with prescription drugs that communicate crucial information about safety, there is a critical gap between patient understanding and the knowledge presented. As a result, patients may suffer from adverse events. We propose using human factors design methodologies such as hierarchical task analysis (HTA) and interactive visualization to bridge this gap. We hypothesize that an innovative mobile app employing human factors design with an interactive visualization can deliver PPI information aligned with patients’ information processing heuristics. Such an app may help patients gain an improved overall knowledge of medications. Objective: The objective of this study was to explore the feasibility of designing an interactive visualization-based mobile app using an HTA approach through a mobile prototype. Methods: Two pharmacists constructed the HTA for the drug risperidone. Later, the specific requirements of the design were translated using infographics. We transferred the wireframes of the prototype into an interactive user interface. Finally, a usability evaluation of the mobile health app was conducted. Results: A mobile app prototype using HTA and infographics was successfully created. We reiterated the design based on the specific recommendations from the usability evaluations. Conclusions: Using HTA methodology, we successfully created a mobile prototype for delivering PPI on the drug risperidone to patients. The hierarchical goals and subgoals were translated into a mobile prototype. %M 31763991 %R 10.2196/15940 %U http://mhealth.jmir.org/2019/11/e15940/ %U https://doi.org/10.2196/15940 %U http://www.ncbi.nlm.nih.gov/pubmed/31763991 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 11 %P e15301 %T Mind the App: Considerations for the Future of Mobile Health in Canada %A Zawati,Ma'n H %A Lang,Michael %+ Centre of Genomics and Policy, McGill University, 740, Avenue Dr Penfield, Suite 5203, Montreal, QC, H3A 0G1, Canada, 1 5146686599, man.zawati@mcgill.ca %K smartphone %K mobile phone %K regulation %K patients %K physicians %D 2019 %7 4.11.2019 %9 Viewpoint %J JMIR Mhealth Uhealth %G English %X Over the past decade, smartphone technology has become increasingly sophisticated and ubiquitous. Modern smartphones, now owned by more than three quarters of Canadians and 94% of millennials, perform an array of functions that are potentially useful in the health care context, such as tracking fitness data, enabling health record sharing, and providing user-friendly platforms for disease management. Approximately half of smartphone users have downloaded at least one health app, and clinicians are increasingly using them in their practice. However, despite widespread use, there is little evidence that supports their safety and efficacy. Few apps have been independently evaluated and many lack basic patient protections such as privacy policies. In this context, the demand for the regulation of mobile health apps has increased. Against this backdrop, regulators, including Health Canada, have begun to propose regulating the use of smartphones in health care. In this viewpoint, we respond to Health Canada’s recent proposal to regulate smartphone use in Canada according to a risk-based model. We argue that although Health Canada’s recent proposed approach is promising, it may require complementary regulation and oversight. %M 31682580 %R 10.2196/15301 %U https://mhealth.jmir.org/2019/11/e15301 %U https://doi.org/10.2196/15301 %U http://www.ncbi.nlm.nih.gov/pubmed/31682580 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 10 %P e14907 %T Utilization, Safety, and Technical Performance of a Telemedicine System for Prehospital Emergency Care: Observational Study %A Felzen,Marc %A Beckers,Stefan Kurt %A Kork,Felix %A Hirsch,Frederik %A Bergrath,Sebastian %A Sommer,Anja %A Brokmann,Jörg Christian %A Czaplik,Michael %A Rossaint,Rolf %+ Department of Anesthesiology, Medical Faculty, RWTH Aachen University, Pauwelsstr 30, Aachen, 52074, Germany, 49 2418088179, mfelzen@ukaachen.de %K emergency medicine %K ambulances %K telemedicine %K quality of care %K eHealth %D 2019 %7 8.10.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: As a consequence of increasing emergency medical service (EMS) missions requiring an EMS physician on site, we had implemented a unique prehospital telemedical emergency service as a new structural component to the conventional physician-based EMS in Germany. Objective: We sought to assess the utilization, safety, and technical performance of this telemedical emergency service. Methods: We conducted a retrospective analysis of all primary emergency missions with telemedical consultation of an EMS physician in the City of Aachen (250,000 inhabitants) during the first 3 operational years of our tele-EMS system. Main outcome measures were the number of teleconsultations, number of complications, and number of transmission malfunctions during teleconsultations. Results: The data of 6265 patients were analyzed. The number of teleconsultations increased during the run-in period of four quarters toward full routine operation from 152 to 420 missions per quarter. When fully operational, around the clock, and providing teleconsultations to 11 mobile ambulances, the number of teleconsultations further increased by 25.9 per quarter (95% CI 9.1-42.6; P=.009). Only 6 of 6265 patients (0.10%; 95% CI 0.04%-0.21%) experienced adverse events, all of them not inherent in the system of teleconsultations. Technical malfunctions of single transmission components occurred from as low as 0.3% (95% CI 0.2%-0.5%) during two-way voice communications to as high as 1.9% (95% CI 1.6%-2.3%) during real-time vital data transmissions. Complete system failures occurred in only 0.3% (95% CI 0.2%-0.6%) of all teleconsultations. Conclusions: The Aachen prehospital EMS is a frequently used, safe, and technically reliable system to provide medical care for emergency patients without an EMS physician physically present. Noninferiority of the tele-EMS physician compared with an on-site EMS physician needs to be demonstrated in a randomized trial. %M 31596244 %R 10.2196/14907 %U https://www.jmir.org/2019/10/e14907 %U https://doi.org/10.2196/14907 %U http://www.ncbi.nlm.nih.gov/pubmed/31596244 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 6 %N 3 %P e14819 %T Postinjury Complications: Retrospective Study of Causative Factors
 %A Warnack,Elizabeth %A Pachter,Hersch Leon %A Choi,Beatrix %A DiMaggio,Charles %A Frangos,Spiros %A Klein,Michael %A Bukur,Marko %+ NYU Langone Health/Bellevue Hospital Center, 462 First Avenue, Fifteenth Floor, New York, NY, 10016, United States, 1 (212) 263 2225, elizabeth.warnack@nyumc.org %K failure to rescue %K error %K harm %K trauma %D 2019 %7 26.9.2019 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Injury care involves the complex interaction of patient, physician, and environment that impacts patient complications, level of harm, and failure to rescue (FTR). FTR represents the likelihood of a hospital to be unable to rescue patients from death after in-hospital complications. Objective: This study aimed to hypothesize that error type and number of errors contribute to increased level of harm and FTR. Methods: Patient information was abstracted from weekly trauma performance improvement (PI) records (from January 1, 2016, to July 19, 2017), where trauma surgeons determined the level of harm and identified the factors associated with complications. Level of harm was determined by definitions set forth by the Agency for Healthcare Research and Quality. Logistic regression was used to determine the impact of individual factors on FTR and level of harm, controlling for age, gender, Charlson score, injury severity score (ISS), error (in diagnosis, technique, or judgment), delay (in diagnosis or intervention), and need for surgery. Results: A total of 2216 trauma patients presented during the study period. Of 2216 patients, 224 (224/2216, 10.10 %) had complications reported at PI meetings; of these, 31 patients (31/224, 13.8 %) had FTR. PI patients were more likely to be older (mean age 51.3 years, SE 1.58, vs 46.5 years, SE 0.51; P=.008) and have higher ISS (median 22 vs 8; P<.001), compared with patients without complications. Physician-attributable errors (odds ratio [OR] 2.82; P=.001), most commonly errors in technique, and nature of injury (OR 1.91; P=.01) were associated with higher levels of harm, whereas delays in diagnosis or intervention were not. Each additional factor involved increased level of harm (OR 2.09; P<.001) and nearly doubled likelihood of FTR (OR 1.95; P=.01). Conclusions: Physician-attributable errors in diagnosis, technique, or judgment are more strongly correlated with harm than delays in diagnosis and intervention. Increasing number of errors identified in patient care correlates with an increasing level of harm and FTR. %M 31573897 %R 10.2196/14819 %U https://humanfactors.jmir.org/2019/3/e14819 %U https://doi.org/10.2196/14819 %U http://www.ncbi.nlm.nih.gov/pubmed/31573897 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 6 %N 3 %P e14123 %T Types and Frequency of Infusion Pump Alarms and Infusion-Interruption to Infusion-Recovery Times for Critical Short Half-Life Infusions: Retrospective Data Analysis %A Waterson,James %A Bedner,Arkadiusz %+ Medication Management Solutions, Becton Dickinson Limited, BD Switzerland Sàrl, Eysins, 1262, Switzerland, 41 215563128, jessjimw@yahoo.com %K medical device %K infusion pump %K alarm fatigue %K critical infusions %K alarm management %K event log %K infusion continuity %K critical care %K critical short half-life infusions %K patient safety %D 2019 %7 12.08.2019 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Alarm fatigue commonly leads to a reduced response to alarms. Appropriate and timely response to intravenous pump alarms is crucial to infusion continuity. The difficulty of filtering out critical short half-life infusion alarms from nonurgent alarms is a key challenge for risk management for clinicians. Critical care areas provide ample opportunities for intravenous medication error with the frequent administration of high-alert, critical short half-life infusions that require rigorous maintenance for continuity of delivery. Most serious medication errors in critical care occur during the execution of treatment, with performance-level failures outweighing rule-based or knowledge-based mistakes. Objective: One objective of this study was to establish baseline data for the types and frequency of alarms that critical care clinicians are exposed to from a variety of infusion devices, including both large volume pumps and syringe drivers. Another objective was to identify the volume of these alarms that specifically relate to critical short half-life infusions and to evaluate user response times to alarms from infusion devices delivering these particular infusions. Methods: The event logs of 1183 infusion pumps used in critical care environments and in general care areas within the European region were mined for a range of alarm states. The study then focused on a selection of infusion alarms from devices delivering critical short half-life infusions that would warrant rapid attention from clinicians in order to avoid potentially harmful prolonged infusion interruption. The reaction time of clinicians to infusion-interruption states and alarms for the selected critical short half-life infusions was then calculated. Results: Initial analysis showed a mean average of 4.50 alarms per infusion in the general critical care pump population as opposed to the whole hospital rate of 1.39. In the pediatric intensive care unit (PICU) group, the alarms per infusion value was significantly above the mean average for all critical care areas, with 8.61 alarms per infusion. Infusion-interruption of critical short half-life infusions was found to be a significant problem in all areas of the general critical care pump population, with a significant number of downstream (ie, vein and access) occlusion events noted. While the mean and median response times to critical short half-life infusion interruptions were generally within the half-lives of the selected medications, there was a high prevalence of outliers in terms of reaction times for all the critical short half-life infusions studied. Conclusions: This study gives an indication of what might be expected in critical care environments in terms of the volume of general infusion alarms and critical short half-life infusion alarms, as well as for clinician reaction times to critical short half-life infusion-interruption events. This study also identifies potentially problematic areas of the hospital for alarm fatigue and for particular issues of infusion and infusion-line management. Application of the proposed protocols can help create benchmarks for pump alarm management and clinician reaction times. These protocols can be applied to studies on the impact of alarm fatigue and for the evaluation of protocols, infusion-monitoring strategies, and infusion pump-based medication safety software aimed at reducing alarm fatigue and ensuring the maintenance of critical short half-life infusions. Given the frequency of infusion alarms seen in this study, the risk of alarm fatigue due to the white noise of pump alarms present in critical care, to which clinicians are constantly exposed, is very high. Furthermore, the added difficulties of maintaining critical short half-life infusions, and other infusions in specialist areas, are made clear by the high ratio of downstream occlusion to infusion starts in the neonatal intensive care unit (NICU). The ability to quantitatively track the volume of alarms and clinician reaction times contributes to a greater understanding of the issues of alarm fatigue in intensive care units. This can be applied to clinical audit, can allow for targeted training to reduce nuisance alarms, and can aid in planning for improvement in the key area of maintenance of steady-state plasma levels of critical short half-life infusions. One clear conclusion is that the medication administration rights should be extended to include right maintenance and ensured delivery continuity of critical short half-life infusions. %M 31407667 %R 10.2196/14123 %U http://humanfactors.jmir.org/2019/3/e14123/ %U https://doi.org/10.2196/14123 %U http://www.ncbi.nlm.nih.gov/pubmed/31407667 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 8 %N 8 %P e13151 %T Evaluation of a Health Information Technology–Enabled Collective Intelligence Platform to Improve Diagnosis in Primary Care and Urgent Care Settings: Protocol for a Pragmatic Randomized Controlled Trial %A Fontil,Valy %A Khoong,Elaine C %A Hoskote,Mekhala %A Radcliffe,Kate %A Ratanawongsa,Neda %A Lyles,Courtney Rees %A Sarkar,Urmimala %+ Division of General Internal Medicine, University of California San Francisco, 1001 Potrero Avenue, Building 10, Ward 13, San Francisco, CA, 94143, United States, 1 415 206 4087, valy.fontil@ucsf.edu %K decision support systems, clinical diagnosis %K medical informatics %D 2019 %7 06.08.2019 %9 Protocol %J JMIR Res Protoc %G English %X Background: Diagnostic error in ambulatory care, a frequent cause of preventable harm, may be mitigated using the collective intelligence of multiple clinicians. The National Academy of Medicine has identified enhanced clinician collaboration and digital tools as a means to improve the diagnostic process. Objective: This study aims to assess the efficacy of a collective intelligence output to improve diagnostic confidence and accuracy in ambulatory care cases (from primary care and urgent care clinic visits) with diagnostic uncertainty. Methods: This is a pragmatic randomized controlled trial of using collective intelligence in cases with diagnostic uncertainty from clinicians at primary care and urgent care clinics in 2 health care systems in San Francisco. Real-life cases, identified for having an element of diagnostic uncertainty, will be entered into a collective intelligence digital platform to acquire collective intelligence from at least 5 clinician contributors on the platform. Cases will be randomized to an intervention group (where clinicians will view the collective intelligence output) or control (where clinicians will not view the collective intelligence output). Clinicians will complete a postvisit questionnaire that assesses their diagnostic confidence for each case; in the intervention cases, clinicians will complete the questionnaire after reviewing the collective intelligence output for the case. Using logistic regression accounting for clinician clustering, we will compare the primary outcome of diagnostic confidence and the secondary outcome of time with diagnosis (the time it takes for a clinician to reach a diagnosis), for intervention versus control cases. We will also assess the usability and satisfaction with the digital tool using measures adapted from the Technology Acceptance Model and Net Promoter Score. Results: We have recruited 32 out of our recruitment goal of 33 participants. This study is funded until May 2020 and is approved by the University of California San Francisco Institutional Review Board until January 2020. We have completed data collection as of June 2019 and will complete our proposed analysis by December 2019. Conclusions: This study will determine if the use of a digital platform for collective intelligence is acceptable, useful, and efficacious in improving diagnostic confidence and accuracy in outpatient cases with diagnostic uncertainty. If shown to be valuable in improving clinicians’ diagnostic process, this type of digital tool may be one of the first innovations used for reducing diagnostic errors in outpatient care. The findings of this study may provide a path forward for improving the diagnostic process. International Registered Report Identifier (IRRID): DERR1-10.2196/13151 %M 31389337 %R 10.2196/13151 %U https://www.researchprotocols.org/2019/8/e13151/ %U https://doi.org/10.2196/13151 %U http://www.ncbi.nlm.nih.gov/pubmed/31389337 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 6 %P e13576 %T Key Issues in the Development of an Evidence-Based Stratified Surgical Patient Safety Improvement Information System: Experience From a Multicenter Surgical Safety Program %A Yu,Xiaochu %A Han,Wei %A Jiang,Jingmei %A Wang,Yipeng %A Xin,Shijie %A Wu,Shizheng %A Sun,Hong %A Wang,Zixing %A Zhao,Yupei %+ Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, No 1 Shuaifuyuan Wangfujing Dongcheng District, Beijing, 100730, China, 86 010 69155789, zhao8028@263.net %K surgery %K patient safety %K information system %K risk factors %K evidence-based practice %D 2019 %7 24.06.2019 %9 Viewpoint %J J Med Internet Res %G English %X Surgery is still far from being completely safe and reliable. Surgical safety has, therefore, been the focus of considerable attention over the last few decades, and there are a growing number of national drives to improve it. There are also a number of large surgical complication reporting systems and system-based interventions, both of which have made remarkable progress in the past two decades. These systems, however, have either mainly focused on reporting complications and played a limited role in guiding practice or have provided nonselective interventions to all patients, perhaps imposing unnecessary burdens on frontline medical staff. We have, therefore, developed an evidence-based stratified surgical safety information system based on a multicenter surgical safety improvement program. This study discusses some critical issues in the process of developing this information system, including (1) decisions about data gathering, (2) establishing and sharing knowledge, (3) developing functions for the system, (4) system implementation, and (5) evaluation and continuous improvement. Using examples drawn from the surgical safety improvement program, we have shown how this type of system can be fitted into day-to-day clinical practice and how it can guide medical practice by incorporating inherent patient-related risk and providing tailored interventions for patients with different levels of risk. We concluded that multidisciplinary collaboration, involving experts in health care (including senior staff in surgery, nursing, and anesthesia), data science, health care management, and health information technology, can help build an evidence-based stratified surgical patient safety improvement system. This can provide an information-intensified surgical safety learning platform and, therefore, benefit surgical patients by delivering tailored interventions and an integrated workflow. %M 31237241 %R 10.2196/13576 %U http://www.jmir.org/2019/6/e13576/ %U https://doi.org/10.2196/13576 %U http://www.ncbi.nlm.nih.gov/pubmed/31237241 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 7 %N 2 %P e11663 %T Exploring the Impact of the Prescription Automatic Screening System in Health Care Services: Quasi-Experiment %A Li,Yan %A Guo,Xitong %A Hsu,Carol %A Liu,Xiaoxiao %A Vogel,Doug %+ eHealth Research Institute, School of Management, Harbin Institute of Technology, 92 West Dazhi Street, Nangang District, Harbin, 15000, China, 86 45186414022, xitongguo@hit.edu.cn %K prescription drug monitoring programs %K hospital information system %K quality of health care %K medical errors %D 2019 %7 14.6.2019 %9 Original Paper %J JMIR Med Inform %G English %X Background: Hospitals have deployed various types of technologies to alleviate the problem of high medical costs. The cost of pharmaceuticals is one of the main drivers of medical costs. The Prescription Automatic Screening System (PASS) aims to monitor physicians’ prescribing behavior, which has the potential to decrease prescription errors and medical treatment costs. However, a substantial number of cases with unsatisfactory results related to the effects of PASS have been noted. Objective: The objectives of this study were to systematically explore the imperative role of PASS on hospitals’ prescription errors and medical treatment costs and examine its contingency factors to clarify the various factors associated with the effective use of PASS. Methods: To systematically examine the various effects of PASS, we adopted a quasi-experiment methodology by using a 2-year observation dataset from 2 hospitals in China. We then analyzed the data related to physicians’ prescriptions both before and after the deployment of PASS and eliminated influences from a variety of perplexing factors by utilizing a control hospital that did not use a PASS system. In total, 754 physicians were included in this experiment comprising 11,054 patients: 400 physicians in the treatment group and 354 physicians in the control group. This study was also preceded by a series of interviews, which were employed to identify moderators. Thereafter, we adopted propensity score matching integrated with difference-in-differences to isolate the effects of PASS. Results: The effects of PASS on prescription errors and medical treatment costs were all significant (error: 95% CI –0.40 to –0.11, P=.001; costs: 95% CI –0.75 to –0.12, P=.007). Pressure from organizational rules and workload decreased the effect of PASS on prescription errors (95% CI 0.18-0.39; P<.001) and medical treatment costs (95% CI 0.07-0.55; P=.01), respectively. We also suspected that other pressures (eg, clinical title and risk categories of illness) also impaired physicians’ attention to alerts from PASS. However, the effects of PASS did not change among physicians with a higher clinical title or when treating diseases demonstrating high risk. This may be attributed to the fact that these physicians will focus more on their patients in these situations, regardless of having access to an intelligent system. Conclusions: Although implementation of PASS decreases prescription errors and medical treatment costs, workload and organizational rules remain problematic, as they tend to impair the positive effects of auxiliary diagnosis systems on performance. This again highlights the importance of considering both technical and organizational issues to obtain the highest level of effectiveness when deploying information technology in hospitals. %M 31199314 %R 10.2196/11663 %U http://medinform.jmir.org/2019/2/e11663/ %U https://doi.org/10.2196/11663 %U http://www.ncbi.nlm.nih.gov/pubmed/31199314 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 6 %P e13614 %T The Use of Smart Devices by Care Providers in Emergency Departments: Cross-Sectional Survey Design %A Alameddine,Mohamad %A Soueidan,Hussein %A Makki,Maha %A Tamim,Hani %A Hitti,Eveline %+ American University of Beirut, Faculty of Medicine, Department of Emergency Medicine, Bliss Street, Beirut, 11-0236, Lebanon, 961 350000, eh16@aub.edu.lb %K health personnel %K smart phones %K emergency departments %K healthcare quality %K policy %D 2019 %7 5.5.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The use of smart devices (SDs) by health care providers in care settings is a common practice nowadays. Such use includes apps related to patient care and often extends to personal calls and applications with frequent prompts and interruptions. These prompts and interruptions enhance the risk of distractions caused by SDs and raise concerns about service quality and patient safety. Such concerns are exacerbated in complex care settings such as the emergency department (ED). Objective: The objective of this study was to measure the frequency and patterns of SD use among health care providers in the ED of a large academic health center in Lebanon. The perceived consequences of care providers using SDs on provider-to-provider communication and the care quality of patients in the ED were assessed. Additionally, factors associated with the use of SDs and the approval for regulating such use were also investigated. Methods: The study was carried out at the ED of an academic health center with the highest volume of patient visits in Lebanon. The data were collected using a cross-sectional electronic survey sent to all ED health care providers (N=236). The target population included core ED faculty members, attending physicians, residents, medical students, and the nursing care providers. The regression model developed in this study was used to find predictors of medical errors in the ED because of the use of SDs. Results: Half of the target population responded to the questionnaire. A total of 83 of 97 respondents (86%) used one or more medical applications on their SDs. 71 out of 87 respondents (82%) believed that using SDs in the ED improved the coordination among the care team, and 71 out of 90 (79%) respondents believed that it was beneficial to patient care. In addition, 37 out of 90 respondents (41%) acknowledged that they were distracted when using their SDs for nonwork purposes. 51 out of 93 respondents (55%) witnessed a colleague committing a near miss or an error owing to the SD-caused distractions. Regression analysis revealed that age (P=.04) and missing information owing to the use of SDs (P=.02) were major predictors of committing an error in the ED. Interestingly, more than 40% of the respondents were significantly addicted to using SDs and more than one-third felt the need to cut down their use. Conclusions: The findings of this study make it imperative to ensure the safety and wellbeing of patients, especially in high intensity, high volume departments like the ED. Irrespective of the positive role SDs play in the health care process, the negative effects of their use mandate proper regulation, in particular, an ethical mandate that takes into consideration the significant consequences that the use of SDs may have on care processes and outcomes. %M 31199328 %R 10.2196/13614 %U https://mhealth.jmir.org/2019/6/e13614/ %U https://doi.org/10.2196/13614 %U http://www.ncbi.nlm.nih.gov/pubmed/31199328 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 7 %N 2 %P e11233 %T Use of Telemedicine to Screen Patients in the Emergency Department: Matched Cohort Study Evaluating Efficiency and Patient Safety of Telemedicine %A Rademacher,Nicholas James %A Cole,Gai %A Psoter,Kevin J %A Kelen,Gabor %A Fan,Jamie Wei Zhi %A Gordon,Dennis %A Razzak,Junaid %+ Department of Emergency Medicine, The Johns Hopkins School of Medicine, 5801 Smith Ave, Ste 3220, Baltimore, MD, 21209, United States, 1 4107356400, junaid.razzak@jhu.edu %K telemedicine %K telehealth %K screening %K triage %K emergency medicine %K emergency health services %K emergency medical service %K left without being seen %K emergency room %K emergency department %K tele-medicine %D 2019 %7 08.05.2019 %9 Original Paper %J JMIR Med Inform %G English %X Background: Early efforts to incorporate telemedicine into Emergency Medicine focused on connecting remote treatment clinics to larger emergency departments (EDs) and providing remote consultation services to EDs with limited resources. Owing to continued ED overcrowding, some EDs have used telemedicine to increase the number of providers during surges of patient visits and offer scheduled “home” face-to-face, on-screen encounters. In this study, we used remote on-screen telemedicine providers in the “screening-in-triage” role. Objective: This study aimed to compare the efficiency and patient safety of in-person screening and telescreening. Methods: This cohort study, matched for days and proximate hours, compared the performance of real-time remote telescreening and in-person screening at a single urban academic ED over 22 weeks in the spring and summer of 2016. The study involved 337 standard screening hours and 315 telescreening hours. The primary outcome measure was patients screened per hour. Additional outcomes were rates of patients who left without being seen, rates of analgesia ordered by the screener, and proportion of patients with chest pain receiving or prescribed a standard set of tests and medications. Results: In-person screeners evaluated 1933 patients over 337 hours (5.7 patients per hour), whereas telescreeners evaluated 1497 patients over 315 hours (4.9 patients per hour; difference=0.8; 95% CI 0.5-1.2). Split analysis revealed that for the final 3 weeks of the evaluation, the patient-per-hour rate differential was neither clinically relevant nor statistically discernable (difference=0.2; 95% CI –0.7 to 1.2). There were fewer patients who left without being seen during in-person screening than during telescreening (2.6% vs 3.8%; difference=–1.2; 95% CI –2.4 to 0.0). However, compared to prior year-, date-, and time-matched data on weekdays from 1 am to 3 am, a period previously void of provider screening, telescreening decreased the rate of patients LWBS from 25.1% to 4.5% (difference=20.7%; 95% CI 10.1-31.2). Analgesia was ordered more frequently by telescreeners than by in-person screeners (51.2% vs 31.6%; difference=19.6%; 95% CI 12.1-27.1). There was no difference in standard care received by patients with chest pain between telescreening and in-person screening (29.4% vs 22.4%; difference=7.0%; 95% CI –3.4 to 17.4). Conclusions: Although the efficiency of telescreening, as measured by the rate of patients seen per hour, was lower early in the study period, telescreening achieved the same level of efficiency as in-person screening by the end of the pilot study. Adding telescreening during 1-3 am on weekdays dramatically decreased the number of patients who left without being seen compared to historic data. Telescreening was an effective and safe way for this ED to expand the hours in which patients were screened by a health care provider in triage. %M 31066698 %R 10.2196/11233 %U http://medinform.jmir.org/2019/2/e11233/ %U https://doi.org/10.2196/11233 %U http://www.ncbi.nlm.nih.gov/pubmed/31066698 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 6 %N 2 %P e10366 %T A Hazard Analysis of Class I Recalls of Infusion Pumps %A Gao,Xuemei %A Wen,Qiang %A Duan,Xiaolian %A Jin,Wei %A Tang,Xiaohong %A Zhong,Ling %A Xia,Shitao %A Feng,Hailing %A Zhong,Daidi %+ Bioengineering College, Chongqing University, Shazheng Street 174, Shapingba, Chongqing, 400044, China, 86 2365102507, daidi.zhong@hotmail.com %K infusion pump %K risk management %K equipment failure %K hazard analysis and critical control points %K man-machine systems %D 2019 %7 03.05.2019 %9 Original Paper %J JMIR Hum Factors %G English %X Background: The adverse event report of medical devices is one of the postmarket surveillance tools used by regulators to monitor device performance, detect potential device-related safety issues, and contribute to benefit-risk assessments of these products. However, with the development of the related technologies and market, the number of adverse events has also been on the rise, which in turn results in the need to develop efficient tools that help to analyze adverse events monitoring data and to identify risk signals. Objective: This study aimed to establish a hazard classification framework of medical devices and to apply it over practical adverse event data on infusion pumps. Subsequently, it aimed to analyze the risks of infusion pumps and to provide a reference for the risk management of this type of device. Methods: The authors define a general hierarchical classification of medical device hazards. This classification is combined with the Trace Intersecting Theory to form a human-machine-environment interaction model. Such a model was applied to the dataset of 2001 to 2017 class I infusion pump recalls extracted from the Food and Drug Administration (FDA) website. This dataset does not include cases involving illegal factors. Results: The proposed model was used for conducting hazard analysis on 70 cases of class I infusion pump recalls by the FDA. According to the analytical results, an important source of product technical risk was that the infusion pumps did not infuse accurate dosage (ie, over- or underdelivery of fluid). In addition, energy hazard and product component failure were identified as the major hazard form associated with infusion pump use and as the main direct cause for adverse events in the studied cases, respectively. Conclusions: The proposed human-machine-environment interaction model, when applied to adverse event data, can help to identify the hazard forms and direct causes of adverse events associated with medical device use. %M 31066695 %R 10.2196/10366 %U http://humanfactors.jmir.org/2019/2/e10366/ %U https://doi.org/10.2196/10366 %U http://www.ncbi.nlm.nih.gov/pubmed/31066695 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 6 %N 2 %P e12553 %T Evaluation of an Anesthesia Dashboard Functional Model Based on a Manufacturer-Independent Communication Standard: Comparative Feasibility Study %A Ohligs,Marian %A Pereira,Carina %A Voigt,Verena %A Koeny,Marcus %A Janß,Armin %A Rossaint,Rolf %A Czaplik,Michael %+ Department of Anesthesiology, Faculty of Medicine, Rheinisch-Westfälische Technische Hochschule Aachen University, Pauwelsstrasse 30, Aachen, 52074, Germany, 49 241 80 ext 83136, mohligs@ukaachen.de %K operating room %K anesthesia %K interconnection %K networking %K human-computer interaction %K process optimization %K intelligent alarms %K decision-support systems %K 11073 SDC %K service-oriented device connectivity %D 2019 %7 01.05.2019 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Current anesthesia workspaces consist of several technical devices, such as patient monitors, anesthesia machines, among others. Commonly, they are produced by different manufacturers; thus, they differ in terms of their modus operandi, user interface, and representation of alarms. Merging the information from these devices using a single joint protocol and displaying it in a single graphical user interface could lead to a general improvement in perioperative management. For this purpose, the recently approved and published Institute of Electrical and Electronics Engineers 11073 service-oriented device connectivity standard was implemented. Objective: This paper aims to develop and then evaluate an anesthesia workstation (ANWS) functional model in terms of usability, fulfillment of clinical requirements, and expected improvements in patient safety. Methods: To compare the self-developed ANWS with the conventional system, a pilot observational study was conducted at the University Hospital Aachen, Germany. A total of 5 anesthesiologists were asked to perform different tasks using the ANWS and then the conventional setup. For evaluation purposes, response times were measured and an interaction-centered usability test with an eye-tracking system was carried out. Finally, the subjects were asked to fill in a questionnaire in order to measure user satisfaction. Results: Response times were significantly higher when using the ANWS, but decreased considerably after one repetition. Furthermore, usability was rated as excellent (≥95) according to the System Usability Scale score, and the majority of clinical requirements were met. Conclusions: In general, the results were highly encouraging, considering that the ANWS was only a functional model, as well as the lack of training of the participants. However, further studies are necessary to improve the universal user interface and the interplay of the various networked devices. %M 31042150 %R 10.2196/12553 %U http://humanfactors.jmir.org/2019/2/e12553/ %U https://doi.org/10.2196/12553 %U http://www.ncbi.nlm.nih.gov/pubmed/31042150 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 4 %P e13447 %T Increasing Completion Rate and Benefits of Checklists: Prospective Evaluation of Surgical Safety Checklists With Smart Glasses %A Boillat,Thomas %A Grantcharov,Peter %A Rivas,Homero %+ Design Lab, College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, PO Box 505055, Dubai,, United Arab Emirates, 971 4 383 8746, homero.rivas@mbru.ac.ae %K smart glasses %K surgical safety checklists %K surgery %K usability %K time-out event %D 2019 %7 29.04.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Studies have demonstrated that surgical safety checklists (SSCs) can significantly reduce surgical complications and mortality rates. Such lists rely on traditional posters or paper, and their contents are generic regarding the type of surgery being performed. SSC completion rates and uniformity of content have been reported as modest and widely variable. Objective: This study aimed to investigate the feasibility and potential of using smart glasses in the operating room to increase the benefits of SSCs by improving usability through contextualized content and, ideally, resulting in improved completion rates. Methods: We prospectively evaluated and compared 80 preoperative time-out events with SSCs at a major academic medical center between June 2016 and February 2017. Participants were assigned to either a conventional checklist approach (poster, memory, or both) or a smart glasses app running on Google Glass. Results: Four different surgeons conducted 41 checklists using conventional methods (ie, memory or poster) and 39 using the smart glasses app. The average checklist completion rate using conventional methods was 76%. Smart glasses allowed a completion rate of up to 100% with a decrease in average checklist duration of 18%. Conclusions: Compared with alternatives such as posters, paper, and memory, smart glasses checklists are easier to use and follow. The glasses allowed surgeons to use contextualized time-out checklists, which increased the completion rate to 100% and reduced the checklist execution time and time required to prepare the equipment during surgical cases. %M 31033451 %R 10.2196/13447 %U http://mhealth.jmir.org/2019/4/e13447/ %U https://doi.org/10.2196/13447 %U http://www.ncbi.nlm.nih.gov/pubmed/31033451 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 7 %N 4 %P e11472 %T A Facial Recognition Mobile App for Patient Safety and Biometric Identification: Design, Development, and Validation %A Jeon,Byoungjun %A Jeong,Boseong %A Jee,Seunghoon %A Huang,Yan %A Kim,Youngmin %A Park,Gee Ho %A Kim,Jungah %A Wufuer,Maierdanjiang %A Jin,Xian %A Kim,Sang Wha %A Choi,Tae Hyun %+ Institute of Human Environment Interface Biology, Department of Plastic and Reconstructive Surgery, Seoul National University College of Medicine, 6th floor, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea, 82 220721978, psthchoi@snu.ac.kr %K facial recognition %K patient identification systems %K biometric identification %K patient safety %K smartphone %K mobile applications %D 2019 %7 08.04.2019 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Patient verification by unique identification is an important procedure in health care settings. Risks to patient safety occur throughout health care settings by failure to correctly identify patients, resulting in the incorrect patient, incorrect site procedure, incorrect medication, and other errors. To avoid medical malpractice, radio-frequency identification (RFID), fingerprint scanners, iris scanners, and other technologies have been implemented in care settings. The drawbacks of these technologies include the possibility to lose the RFID bracelet, infection transmission, and impracticality when the patient is unconscious. Objective: The purpose of this study was to develop a mobile health app for patient identification to overcome the limitations of current patient identification alternatives. The development of this app is expected to provide an easy-to-use alternative method for patient identification. Methods: We have developed a facial recognition mobile app for improved patient verification. As an evaluation purpose, a total of 62 pediatric patients, including both outpatient and inpatient, were registered for the facial recognition test and tracked throughout the facilities for patient verification purpose. Results: The app was developed to contain 5 main parts: registration, medical records, examinations, prescriptions, and appointments. Among 62 patients, 30 were outpatients visiting plastic surgery department and 32 were inpatients reserved for surgery. Whether patients were under anesthesia or unconscious, facial recognition verified all patients with 99% accuracy even after a surgery. Conclusions: It is possible to correctly identify both outpatients and inpatients and also reduce the unnecessary cost of patient verification by using the mobile facial recognition app with great accuracy. Our mobile app can provide valuable aid to patient verification, including when the patient is unconscious, as an alternative identification method. %M 30958275 %R 10.2196/11472 %U https://mhealth.jmir.org/2019/4/e11472/ %U https://doi.org/10.2196/11472 %U http://www.ncbi.nlm.nih.gov/pubmed/30958275 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 7 %N 1 %P e11873 %T Transcription Errors of Blood Glucose Values and Insulin Errors in an Intensive Care Unit: Secondary Data Analysis Toward Electronic Medical Record-Glucometer Interoperability %A Sowan,Azizeh Khaled %A Vera,Ana %A Malshe,Ashwin %A Reed,Charles %+ School of Nursing, University of Texas Health at San Antonio, 7703 Floyd Curl Drive - MC 7975, San Antonio, TX, 78229, United States, 1 210 567 5799, sowan@uthscsa.edu %K transcription errors %K blood glucose %K insulin errors %K interoperability %K glucometer %K electronic medical records %K secondary data analysis %K intensive care units %K medication errors %D 2019 %7 25.03.2019 %9 Original Paper %J JMIR Med Inform %G English %X Background: Critically ill patients require constant point-of-care blood glucose testing to guide insulin-related decisions. Transcribing these values from glucometers into a paper log and the electronic medical record is very common yet error-prone in intensive care units, given the lack of connectivity between glucometers and the electronic medical record in many US hospitals. Objective: We examined (1) transcription errors of glucometer blood glucose values documented in the paper log and in the electronic medical record vital signs flow sheet in a surgical trauma intensive care unit, (2) insulin errors resulting from transcription errors, (3) lack of documenting these values in the paper log and the electronic medical record vital signs flow sheet, and (4) average time for docking the glucometer. Methods: This secondary data analysis examined 5049 point-of-care blood glucose tests. We obtained values of blood glucose tests from bidirectional interface software that transfers the meters’ data to the electronic medical record, the paper log, and the vital signs flow sheet. We obtained patient demographic and clinical-related information from the electronic medical record. Results: Of the 5049 blood glucose tests, which were pertinent to 234 patients, the total numbers of undocumented or untranscribed tests were 608 (12.04%) in the paper log, 2064 (40.88%) in the flow sheet, and 239 (4.73%) in both. The numbers of transcription errors for the documented tests were 98 (2.21% of 4441 documented tests) in the paper log, 242 (8.11% of 2985 tests) in the flow sheet, and 43 (1.64% of 2616 tests) in both. The numbers of transcription errors per patient were 0.4 (98 errors/234 patients) in the paper log, 1 (242 errors/234 patients) in the flow sheet, and 0.2 in both (43 errors/234 patients). Transcription errors in the paper log, the flow sheet, and in both resulted in 8, 24, and 2 insulin errors, respectively. As a consequence, patients were given a lower or higher insulin dose than the dose they should have received had there been no errors. Discrepancies in insulin doses were 2 to 8 U lower doses in paper log transcription errors, 10 U lower to 3 U higher doses in flow sheet transcription errors, and 2 U lower in transcription errors in both. Overall, 30 unique insulin errors affected 25 of 234 patients (10.7%). The average time from point-of-care testing to meter docking was 8 hours (median 5.5 hours), with some taking 56 hours (2.3 days) to be uploaded. Conclusions: Given the high dependence on glucometers for point-of-care blood glucose testing in intensive care units, full electronic medical record-glucometer interoperability is required for complete, accurate, and timely documentation of blood glucose values and elimination of transcription errors and the subsequent insulin-related errors in intensive care units. %M 30907735 %R 10.2196/11873 %U http://medinform.jmir.org/2019/1/e11873/ %U https://doi.org/10.2196/11873 %U http://www.ncbi.nlm.nih.gov/pubmed/30907735 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 7 %N 1 %P e11678 %T The Impact of an Electronic Patient Bedside Observation and Handover System on Clinical Practice: Mixed-Methods Evaluation %A Lang,Alexandra %A Simmonds,Mark %A Pinchin,James %A Sharples,Sarah %A Dunn,Lorrayne %A Clarke,Susan %A Bennett,Owen %A Wood,Sally %A Swinscoe,Caron %+ Trent Simulation and Clinical Skills Centre, Nottingham University Hospitals NHS Trust, Queen's Medical Centre, Derby Road, Nottingham, NG7 2UH, United Kingdom, 44 07921912376, alexandra.lang@nottingham.ac.uk %K health information technology %K early warning score %K mobile health %K staff workload %K clinical deterioration %K patient safety %K mixed methods %D 2019 %7 06.03.2019 %9 Original Paper %J JMIR Med Inform %G English %X Background: Patient safety literature has long reported the need for early recognition of deteriorating patients. Early warning scores (EWSs) are commonly implemented as “track and trigger,” or rapid response systems for monitoring and early recognition of acute patient deterioration. This study presents a human factors evaluation of a hospital-wide transformation in practice, engendered by the deployment of an innovative electronic observations (eObs) and handover system. This technology enables real-time information processing at the patient’s bedside, improves visibility of patient data, and streamlines communication within clinical teams. Objective: The aim of this study was to identify improvement and deterioration in workplace efficiency and quality of care resulting from the large-scale imposition of new technology. Methods: A total of 85 hours of direct structured observations of clinical staff were carried out before and after deployment. We conducted 40 interviews with a range of clinicians. A longitudinal analysis of critical care audit and electronically recorded patient safety incident reports was conducted. The study was undertaken in a large secondary-care facility in the United Kingdom. Results: Roll-out of eObs was associated with approximately 10% reduction in total unplanned admissions to critical care units from eObs-equipped wards. Over time, staff appropriated the technology as a tool for communication, workload management, and improving awareness of team capacity. A negative factor was perceived as lack of engagement with the system by senior clinicians. Doctors spent less time in the office (68.7% to 25.6%). More time was spent at the nurses’ station (6.6% to 41.7%). Patient contact time was more than doubled (2.9% to 7.3%). Conclusions: Since deployment, clinicians have more time for patient care because of reduced time spent inputting and accessing data. The formation of a specialist clinical team to lead the roll-out was universally lauded as the reason for success. Staff valued the technology as a tool for managing workload and identified improved situational awareness as a key benefit. For future technology deployments, the staff requested more training preroll-out, in addition to engagement and support from senior clinicians. %M 30839278 %R 10.2196/11678 %U http://medinform.jmir.org/2019/1/e11678/ %U https://doi.org/10.2196/11678 %U http://www.ncbi.nlm.nih.gov/pubmed/30839278 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 3 %P e10736 %T eHealth Apps Replacing or Complementing Health Care Contacts: Scoping Review on Adverse Effects %A Stevens,Wilhelmina Josepha Maria %A van der Sande,Rob %A Beijer,Lilian J %A Gerritsen,Maarten GM %A Assendelft,Willem JJ %+ Faculty of Health, Hogeschool van Arnhem en Nijmegen University of Applied Sciences, Kapittelweg 33, Nijmegen, 6525 EN, Netherlands, 31 651450260, marjo.stevens@han.nl %K eHealth %K adverse effects %K scoping review %D 2019 %7 01.03.2019 %9 Review %J J Med Internet Res %G English %X Background: The use of eHealth has increased tremendously in recent years. eHealth is generally considered to have a positive effect on health care quality and to be a promising alternative to face-to-face health care contacts. Surprisingly little is known about possible adverse effects of eHealth apps. Objective: We conducted a scoping review on empirical research into adverse effects of eHealth apps that aim to deliver health care at a distance. We investigated whether adverse effects were reported and the nature and quality of research into these possible adverse effects. Methods: For this scoping review, we followed the five steps of Arksey and O’Malley’s scoping review methodology. We searched specifically for studies into eHealth apps that replaced or complemented the face-to-face contact between a health professional and a patient in the context of treatment, health monitoring, or supporting self-management. Studies were included when eHealth and adverse effects were mentioned in the title or abstract and when empirical data on adverse effects were provided. All health conditions, with the exception of mental health conditions, all ages, and all sample sizes were included. We examined the literature published between December 2012 and August 2017 in the following databases: PubMed, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science, and the Cochrane Library. The methodological quality of the studies was assessed using the Critical Appraisal Skills Programme (CASP) checklists. Results: Our search identified 79 papers that were potentially relevant; 11 studies met our inclusion criteria after screening. These studies differed in many ways and the majority were characterized by small research populations and low study quality. Adverse effects are rarely subject to systematic scientific research. So far, information on real adverse effects is mainly limited to incidental reporting or as a bycatch from qualitative pilot studies. Despite the shortage of solid research, we found some indications of possible negative impact on patient-centeredness and efficiency, such as less transparency in the relationship between health professionals and patients and time-consuming work routines. Conclusions: There is a lack of high-quality empirical research on adverse effects of eHealth apps that replace or complement face-to-face care. While the development of eHealth apps is ongoing, the knowledge with regard to possible adverse effects is limited. The available research often focuses on efficacy, added value, implementation issues, use, and satisfaction, whereas adverse effects are underexplored. A better understanding of possible adverse effects could be a starting point in improving the positive impact of eHealth-based health care delivery. %M 30821690 %R 10.2196/10736 %U https://www.jmir.org/2019/3/e10736/ %U https://doi.org/10.2196/10736 %U http://www.ncbi.nlm.nih.gov/pubmed/30821690 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 6 %N 1 %P e10245 %T Improving Provider Adoption With Adaptive Clinical Decision Support Surveillance: An Observational Study %A Khan,Sundas %A Richardson,Safiya %A Liu,Andrew %A Mechery,Vinodh %A McCullagh,Lauren %A Schachter,Andy %A Pardo,Salvatore %A McGinn,Thomas %+ Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, 600 Community Drive, Manhasset, NY, 11030, United States, 1 516 600 1419, skhan31@northwell.edu %K pulmonary embolism %K clinical decision support %K evidence-based medicine %D 2019 %7 20.02.2019 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Successful clinical decision support (CDS) tools can help use evidence-based medicine to effectively improve patient outcomes. However, the impact of these tools has been limited by low provider adoption due to overtriggering, leading to alert fatigue. We developed a tracking mechanism for monitoring trigger (percent of total visits for which the tool triggers) and adoption (percent of completed tools) rates of a complex CDS tool based on the Wells criteria for pulmonary embolism (PE). Objective: We aimed to monitor and evaluate the adoption and trigger rates of the tool and assess whether ongoing tool modifications would improve adoption rates. Methods: As part of a larger clinical trial, a CDS tool was developed using the Wells criteria to calculate pretest probability for PE at 2 tertiary centers’ emergency departments (EDs). The tool had multiple triggers: any order for D-dimer, computed tomography (CT) of the chest with intravenous contrast, CT pulmonary angiography (CTPA), ventilation-perfusion scan, or lower extremity Doppler ultrasound. A tracking dashboard was developed using Tableau to monitor real-time trigger and adoption rates. Based on initial low provider adoption rates of the tool, we conducted small focus groups with key ED providers to elicit barriers to tool use. We identified overtriggering of the tool for non-PE-related evaluations and inability to order CT testing for intermediate-risk patients. Thus, the tool was modified to allow CT testing for the intermediate-risk group and not to trigger for CT chest with intravenous contrast orders. A dialogue box, “Are you considering PE for this patient?” was added before the tool triggered to account for CTPAs ordered for aortic dissection evaluation. Results: In the ED of tertiary center 1, 95,295 patients visited during the academic year. The tool triggered for an average of 509 patients per month (average trigger rate 2036/30,234, 6.73%) before the modifications, reducing to 423 patients per month (average trigger rate 1629/31,361, 5.22%). In the ED of tertiary center 2, 88,956 patients visited during the academic year, with the tool triggering for about 473 patients per month (average trigger rate 1892/29,706, 6.37%) before the modifications and for about 400 per month (average trigger rate 1534/30,006, 5.12%) afterward. The modifications resulted in a significant 4.5- and 3-fold increase in provider adoption rates in tertiary centers 1 and 2, respectively. The modifications increased the average monthly adoption rate from 23.20/360 (6.5%) tools to 81.60/280.20 (29.3%) tools and 46.60/318.80 (14.7%) tools to 111.20/263.40 (42.6%) tools in centers 1 and 2, respectively. Conclusions: Close postimplementation monitoring of CDS tools may help improve provider adoption. Adaptive modifications based on user feedback may increase targeted CDS with lower trigger rates, reducing alert fatigue and increasing provider adoption. Iterative improvements and a postimplementation monitoring dashboard can significantly improve adoption rates. %M 30785410 %R 10.2196/10245 %U http://humanfactors.jmir.org/2019/1/e10245/ %U https://doi.org/10.2196/10245 %U http://www.ncbi.nlm.nih.gov/pubmed/30785410 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 7 %N 1 %P e12650 %T Use of Electronic Health Record Access and Audit Logs to Identify Physician Actions Following Noninterruptive Alert Opening: Descriptive Study %A Amroze,Azraa %A Field,Terry S %A Fouayzi,Hassan %A Sundaresan,Devi %A Burns,Laura %A Garber,Lawrence %A Sadasivam,Rajani S %A Mazor,Kathleen M %A Gurwitz,Jerry H %A Cutrona,Sarah L %+ Meyers Primary Care Institute, 385 Grove Street, Worcester, MA, 01605, United States, 1 774 275 6030, Azraa.Amroze@meyersprimary.org %K electronic health records %K health services research %K health information technology %K health care communication %D 2019 %7 07.02.2019 %9 Original Paper %J JMIR Med Inform %G English %X Background: Electronic health record (EHR) access and audit logs record behaviors of providers as they navigate the EHR. These data can be used to better understand provider responses to EHR–based clinical decision support (CDS), shedding light on whether and why CDS is effective. Objective: This study aimed to determine the feasibility of using EHR access and audit logs to track primary care physicians’ (PCPs’) opening of and response to noninterruptive alerts delivered to EHR InBaskets. Methods: We conducted a descriptive study to assess the use of EHR log data to track provider behavior. We analyzed data recorded following opening of 799 noninterruptive alerts sent to 75 PCPs’ InBaskets through a prior randomized controlled trial. Three types of alerts highlighted new medication concerns for older patients’ posthospital discharge: information only (n=593), medication recommendations (n=37), and test recommendations (n=169). We sought log data to identify the person opening the alert and the timing and type of PCPs’ follow-up EHR actions (immediate vs by the end of the following day). We performed multivariate analyses examining associations between alert type, patient characteristics, provider characteristics, and contextual factors and likelihood of immediate or subsequent PCP action (general, medication-specific, or laboratory-specific actions). We describe challenges and strategies for log data use. Results: We successfully identified the required data in EHR access and audit logs. More than three-quarters of alerts (78.5%, 627/799) were opened by the PCP to whom they were directed, allowing us to assess immediate PCP action; of these, 208 alerts were followed by immediate action. Expanding on our analyses to include alerts opened by staff or covering physicians, we found that an additional 330 of the 799 alerts demonstrated PCP action by the end of the following day. The remaining 261 alerts showed no PCP action. Compared to information-only alerts, the odds ratio (OR) of immediate action was 4.03 (95% CI 1.67-9.72) for medication-recommendation and 2.14 (95% CI 1.38-3.32) for test-recommendation alerts. Compared to information-only alerts, ORs of medication-specific action by end of the following day were significantly greater for medication recommendations (5.59; 95% CI 2.42-12.94) and test recommendations (1.71; 95% CI 1.09-2.68). We found a similar pattern for OR of laboratory-specific action. We encountered 2 main challenges: (1) Capturing a historical snapshot of EHR status (number of InBasket messages at time of alert delivery) required incorporation of data generated many months prior with longitudinal follow-up. (2) Accurately interpreting data elements required iterative work by a physician/data manager team taking action within the EHR and then examining audit logs to identify corresponding documentation. Conclusions: EHR log data could inform future efforts and provide valuable information during development and refinement of CDS interventions. To address challenges, use of these data should be planned before implementing an EHR–based study. %M 30730293 %R 10.2196/12650 %U http://medinform.jmir.org/2019/1/e12650/ %U https://doi.org/10.2196/12650 %U http://www.ncbi.nlm.nih.gov/pubmed/30730293 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 21 %N 1 %P e10008 %T Assessing the Effectiveness of Engaging Patients and Their Families in the Three-Step Fall Prevention Process Across Modalities of an Evidence-Based Fall Prevention Toolkit: An Implementation Science Study %A Duckworth,Megan %A Adelman,Jason %A Belategui,Katherine %A Feliciano,Zinnia %A Jackson,Emily %A Khasnabish,Srijesa %A Lehman,I-Fong Sun %A Lindros,Mary Ellen %A Mortimer,Heather %A Ryan,Kasey %A Scanlan,Maureen %A Berger Spivack,Linda %A Yu,Shao Ping %A Bates,David Westfall %A Dykes,Patricia C %+ Division of General Internal Medicine, Brigham and Women's Hospital, 1620 Tremont Street, 3rd Floor Division of General Internal Medicine, Boston, MA, 02115, United States, 1 6172764527, msd4cs@virginia.edu %K clinical decision support %K fall prevention %K fall prevention toolkit %K health information technology %K implementation science %K patient safety %D 2019 %7 21.01.2019 %9 Original Paper %J J Med Internet Res %G English %X Background: Patient falls are a major problem in hospitals. The development of a Patient-Centered Fall Prevention Toolkit, Fall TIPS (Tailoring Interventions for Patient Safety), reduced falls by 25% in acute care hospitals by leveraging health information technology to complete the 3-step fall prevention process—(1) conduct fall risk assessments; (2) develop tailored fall prevention plans with the evidence-based interventions; and (3) consistently implement the plan. We learned that Fall TIPS was most effective when patients and family were engaged in all 3 steps of the fall prevention process. Over the past decade, our team developed 3 Fall TIPS modalities—the original electronic health record (EHR) version, a laminated paper version that uses color to provide clinical decision support linking patient-specific risk factors to the interventions, and a bedside display version that automatically populates the bedside monitor with the patients’ fall prevention plan based on the clinical documentation in the EHR. However, the relative effectiveness of each Fall TIPS modality for engaging patients and family in the 3-step fall prevention process remains unknown. Objective: This study aims to examine if the Fall TIPS modality impacts patient engagement in the 3-step fall prevention process and thus Fall TIPS efficacy. Methods: To assess patient engagement in the 3-step fall prevention process, we conducted random audits with the question, “Does the patient/family member know their fall prevention plan?” In addition, audits were conducted to measure adherence, defined by the presence of the Fall TIPS poster at the bedside. Champions from 3 hospitals reported data from April to June 2017 on 6 neurology and 7 medical units. Peer-to-peer feedback to reiterate the best practice for patient engagement was central to data collection. Results: Overall, 1209 audits were submitted for the patient engagement measure and 1401 for the presence of the Fall TIPS poster at the bedside. All units reached 80% adherence for both measures. While some units maintained high levels of patient engagement and adherence with the poster protocol, others showed improvement over time, reaching clinically significant adherence (>80%) by the final month of data collection. Conclusions: Each Fall TIPS modality effectively facilitates patient engagement in the 3-step fall prevention process, suggesting all 3 can be used to integrate evidence-based fall prevention practices into the clinical workflow. The 3 Fall TIPS modalities may prove an effective strategy for the spread, allowing diverse institutions to choose the modality that fits with the organizational culture and health information technology infrastructure. %M 30664454 %R 10.2196/10008 %U http://www.jmir.org/2019/1/e10008/ %U https://doi.org/10.2196/10008 %U http://www.ncbi.nlm.nih.gov/pubmed/30664454 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 5 %N 4 %P e12232 %T Usability Testing of a Mobile App to Report Medication Errors Anonymously: Mixed-Methods Approach %A George,Doris %A Hassali,Mohamed Azmi %A HSS,Amar-Singh %+ Social & Administrative Pharmacy, School of Pharmaceutical Sciences, Universiti Sains Malaysia, Minden, Penang, 11800, Malaysia, 60 052085555 ext 5293, doris.moh.gov@gmail.com %K mobile app %K usability %K medication error reporting %K anonymous %D 2018 %7 21.12.2018 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Reporting of medication errors is one of the essential mechanisms to identify risky health care systems and practices that lead to medication errors. Unreported medication errors are a real issue; one of the identified causes is a burdensome medication error reporting system. An anonymous and user-friendly mobile app for reporting medication errors could be an alternative method of reporting medication error in busy health care settings. Objective: The objective of this paper is to report usability testing of the Medication Error Reporting App (MERA), a mobile app for reporting medication errors anonymously. Methods: Quantitative and qualitative methods were employed involving 45 different testers (pharmacists, doctors, and nurses) from a large tertiary hospital in Malaysia. Quantitative data was retrieved using task performance and rating of MERA and qualitative data were retrieved through focus group discussions. Three sessions, with 15 testers each session, were conducted from January to March 2018. Results: The majority of testers were pharmacists (23/45, 51%), female (35/45, 78%), and the mean age was 36 (SD 9) years. A total of 135 complete reports were successfully submitted by the testers (three reports per tester) and 79.2% (107/135) of the reports were correct. There was significant improvement in mean System Usability Scale scores in each session of the development process (P<.001) and mean time to report medication errors using the app was not significantly different between each session (P=.70) with an overall mean time of 6.7 (SD 2.4) minutes. Testers found the app easy to use, but doctors and nurses were unfamiliar with terms used especially medication process at which error occurred and type of error. Although, testers agreed the app can be used in the future for reporting, they were apprehensive about security, validation, and abuse of feedback featured in the app. Conclusions: MERA can be used to report medication errors easily by various health care personnel and it has the capacity to provide feedback on reporting. However, education on medication error reporting should be provided to doctors and nurses in Malaysia and the security of the app needs to be established to boost reporting by this method. %M 30578216 %R 10.2196/12232 %U http://humanfactors.jmir.org/2018/4/e12232/ %U https://doi.org/10.2196/12232 %U http://www.ncbi.nlm.nih.gov/pubmed/30578216 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 5 %N 4 %P e11704 %T Alarm-Related Workload in Default and Modified Alarm Settings and the Relationship Between Alarm Workload, Alarm Response Rate, and Care Provider Experience: Quantification and Comparison Study %A Shanmugham,Manikantan %A Strawderman,Lesley %A Babski-Reeves,Kari %A Bian,Linkan %+ Department of Industrial and Systems Engineering, Mississippi State University, 260 McCain Hall, 479-2 Hardy Road, Box 9542, Mississippi State, MS, 39762, United States, 1 8016739973, ms195@msstate.edu %K clinical alarms %K fatigue %K physiologic monitoring %K nursing %K workload %D 2018 %7 23.10.2018 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Delayed or no response to impending patient safety–related calls, poor care provider experience, low job satisfaction, and adverse events are all unwanted outcomes of alarm fatigue. Nurses often cite increases in alarm-related workload as a reason for alarm fatigue, which is a major contributor to the aforementioned unwanted outcomes. Increased workload affects both the care provider and the patient. No studies to date have evaluated the workload while caring for patients and managing alarms simultaneously and related it to the primary measures of alarm fatigue—alarm response rate and care provider experience. Many studies have assessed the effect of modifying the default alarm setting; however, studies on the perceived workload under different alarm settings are limited. Objective: This study aimed to assess nurses’ or assistants’ perceived workload index of providing care under different clinical alarm settings and establish the relationship between perceived workload, alarm response rate, and care provider experience. Methods: In a clinical simulator, 30 participants responded to alarms that occurred on a physiological monitor under 2 conditions (default and modified) for a given clinical condition. Participants completed a National Aeronautics and Space Administration-Task Load Index questionnaire and rated the demand experienced on a 20-point visual analog scale with low and high ratings. A correlational analysis was performed to assess the relationships between the perceived workload score, alarm response rate, and care provider experience. Results: Participants experienced lower workloads when the clinical alarm threshold limits were modified according to patients’ clinical conditions. The workload index was higher for the default alarm setting (57.60 [SD 2.59]) than for the modified alarm setting (52.39 [SD 2.29]), with a statistically significant difference of 5.21 (95% CI 3.38-7.04), t28=5.838, P<.05. Significant correlations were found between the workload index and alarm response rate. There was a strong negative correlation between alarm response rate and perceived workload, ρ28=−.54, P<.001 with workload explaining 29% of the variation in alarm response rate. There was a moderate negative correlation between the experience reported during patient care and the perceived workload, ρ28=−.49, P<.05. Conclusions: The perceived workload index was comparatively lower with alarm settings modified for individual patient care than in an unmodified default clinical alarm setting. These findings demonstrate that the modification of clinical alarm limits positively affects the number of alarms accurately addressed, care providers’ experience, and overall satisfaction. The findings support the removal of nonessential alarms based on patient conditions, which can help care providers address the remaining alarms accurately and provide better patient care. %M 30355550 %R 10.2196/11704 %U http://humanfactors.jmir.org/2018/4/e11704/ %U https://doi.org/10.2196/11704 %U http://www.ncbi.nlm.nih.gov/pubmed/30355550 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 20 %N 9 %P e11510 %T Patient and Consumer Safety Risks When Using Conversational Assistants for Medical Information: An Observational Study of Siri, Alexa, and Google Assistant %A Bickmore,Timothy W %A Trinh,Ha %A Olafsson,Stefan %A O'Leary,Teresa K %A Asadi,Reza %A Rickles,Nathaniel M %A Cruz,Ricardo %+ College of Computer and Information Science, Northeastern University, 910-177, 360 Huntington Avenue, Boston, MA, 02115, United States, 1 6173735477, bickmore@ccs.neu.edu %K conversational assistant %K conversational interface %K dialogue system %K medical error %K patient safety %D 2018 %7 04.09.2018 %9 Original Paper %J J Med Internet Res %G English %X Background: Conversational assistants, such as Siri, Alexa, and Google Assistant, are ubiquitous and are beginning to be used as portals for medical services. However, the potential safety issues of using conversational assistants for medical information by patients and consumers are not understood. Objective: To determine the prevalence and nature of the harm that could result from patients or consumers using conversational assistants for medical information. Methods: Participants were given medical problems to pose to Siri, Alexa, or Google Assistant, and asked to determine an action to take based on information from the system. Assignment of tasks and systems were randomized across participants, and participants queried the conversational assistants in their own words, making as many attempts as needed until they either reported an action to take or gave up. Participant-reported actions for each medical task were rated for patient harm using an Agency for Healthcare Research and Quality harm scale. Results: Fifty-four subjects completed the study with a mean age of 42 years (SD 18). Twenty-nine (54%) were female, 31 (57%) Caucasian, and 26 (50%) were college educated. Only 8 (15%) reported using a conversational assistant regularly, while 22 (41%) had never used one, and 24 (44%) had tried one “a few times.“ Forty-four (82%) used computers regularly. Subjects were only able to complete 168 (43%) of their 394 tasks. Of these, 49 (29%) reported actions that could have resulted in some degree of patient harm, including 27 (16%) that could have resulted in death. Conclusions: Reliance on conversational assistants for actionable medical information represents a safety risk for patients and consumers. Patients should be cautioned to not use these technologies for answers to medical questions they intend to act on without further consultation from a health care provider. %M 30181110 %R 10.2196/11510 %U http://www.jmir.org/2018/9/e11510/ %U https://doi.org/10.2196/11510 %U http://www.ncbi.nlm.nih.gov/pubmed/30181110 %0 Journal Article %@ 2561-9128 %I JMIR Publications %V 1 %N 2 %P e3 %T A Digital Patient-Led Hospital Checklist for Enhancing Safety in Cataract Surgery: Qualitative Study %A Stolk-Vos,Aline C %A van der Steen,Jolet JE %A Drossaert,Constance HC %A Braakman-Jansen,Annemarie %A Zijlmans,Bart LM %A Kranenburg,Leonieke W %A de Korne,Dirk F %+ Rotterdam Ophthalmic Institute, Ooghuis, 4th Floor, Schiedamse Vest 160, Rotterdam, 3011BH, Netherlands, 31 0104023448, a.stolk@eyehospital.nl %K patient participation %K checklist %K cataract %K surgery %K patient safety %K handheld computers %K health information management %K health communication %K information technology %D 2018 %7 16.07.2018 %9 Original Paper %J JMIR Perioper Med %G English %X Background: Surgery holds high risk for iatrogenic patient harm. Correct and sufficient communication and information during the surgical process is a root solution for preventing patient harm. Information technology may substantially contribute to engaging patients in this process. Objective: To explore the feasibility of a digital patient-led checklist for cataract surgery, we evaluated the experiences of patients and nurses who have used this novel tool with a focus on use, appreciation, and impact. Methods: A multidisciplinary team, including cataract surgeons, nurses, pharmacists and administrative representatives developed a 19-item digital patient-led checklist for cataract patients who underwent surgery in an ambulatory setting. This “EYEpad” checklist was distributed to patients and their companions during their hospital visit via an application on a tablet. It contained necessary information the patient should have received before or during the surgical preparation (8 items), before anesthesia (2 items), and before discharge (9 items). Patients and their companions were invited to actively indicate the information they received, or information discussed with them, by ticking on the EYEpad. Our qualitative research design included semi-structured individual interviews with 17 patients and a focus group involving 6 nurses. The transcripts were analyzed by 2 independent coders using both deductive and inductive coding. Results: All but one of the 17 patients used the EYEpad, occasionally assisted by his or her companion (usually the partner). In several cases, the checklist was completed by the companion. Most patients felt positively about the usability of the EYEpad. Yet, for most of the patients, it was not clear why they received the checklist. Only 4 of them indicated that they understood that the EYEpad was used to determine if there were sufficient and correct information discussed or checked by the nurses. Although most nurses agreed the EYEpad was easy to use and could be a useful tool for improving patient engagement for improving safety, they felt that not all elderly patients were willing or capable of using it and it interfered with the existing surgical process. They also anticipated the need to spend more time explaining the purpose and use of the EYEpad. Conclusions: Our results showed that a digital patient-led checklist is a potentially valid way to increase patient participation in safety improvement efforts, even among elderly patients. It also illustrates the crucial role nurses play in the implementation and diffusion of technological innovations. Increased patient participation will only improve safety when both healthcare workers and patients feel empowered to share responsibility and balance their power. %M 33401370 %R 10.2196/periop.9463 %U http://periop.jmir.org/2018/2/e3/ %U https://doi.org/10.2196/periop.9463 %U http://www.ncbi.nlm.nih.gov/pubmed/33401370 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 6 %P e10446 %T Types and Frequency of Infusion Pump Alarms: Protocol for a Retrospective Data Analysis %A Glover,Kevin R %A Vitoux,Rachel R %A Schuster,Catherine %A Curtin,Christopher R %+ Scientific Affairs, B. Braun Medical Inc, 901 Marcon Blvd., Allentown, PA, 18109, United States, 1 267 251 3917, kevin.Glover@bbraunusa.com %K medical device %K alarms %K infusion pumps %K alarm fatigue %K patient safety %D 2018 %7 14.06.2018 %9 Protocol %J JMIR Res Protoc %G English %X Background: The variety of alarms from all types of medical devices has increased from 6 to 40 in the last three decades, with today’s most critically ill patients experiencing as many as 45 alarms per hour. Alarm fatigue has been identified as a critical safety issue for clinical staff that can lead to potentially dangerous delays or nonresponse to actionable alarms, resulting in serious patient injury and death. To date, most research on medical device alarms has focused on the nonactionable alarms of physiological monitoring devices. While there have been some reports in the literature related to drug library alerts during the infusion pump programing sequence, research related to the types and frequencies of actionable infusion pump alarms remains largely unexplored. Objective: The objectives of this study protocol are to establish baseline data related to the types and frequency of infusion pump alarms from the B. Braun Outlook 400ES Safety Infusion System with the accompanying DoseTrac Infusion Management Software. Methods: The most recent consecutive 60-day period of backup hospital data received between April 2014 and February 2017 from 32 United States-based hospitals will be selected for analysis. Microsoft SQL Server (2012 - 11.0.5343.0 X64) will be used to manage the data with unique code written to sort data and perform descriptive analyses. A validated data management methodology will be utilized to clean and analyze the data. Data management procedures will include blinding, cleaning, and review of existing infusion data within the DoseTrac Infusion Management Software databases at each hospital. Patient-identifying data will be removed prior to merging into a dedicated and secure data repository. This pooled data will then be analyzed. Results: This exploratory study will analyze the aggregate alarm data for each hospital by care area, drug infused, time of day, and day of week, including: overall infusion pump alarm frequency (number of alarms per active infusion), duration of alarms (average, range, median), and type and frequency of alarms distributed by care area. Conclusions: Infusion pump alarm data collected and analyzed in this study will be used to help establish a baseline of infusion pump alarm types and relative frequencies. Understanding the incidences and characteristics of infusion pump alarms will result in more informed quality improvement recommendations to decrease and/or modify infusion pump alarms, and potentially reduce clinical staff alarm fatigue and improve patient safety.  Registered Report Identifier: RR1-10.2196/10446 %M 29903696 %R 10.2196/10446 %U http://www.researchprotocols.org/2018/6/e10446/ %U https://doi.org/10.2196/10446 %U http://www.ncbi.nlm.nih.gov/pubmed/29903696 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 6 %P e152 %T The General Practitioner Prompt Study to Reduce Cardiovascular and Renal Complications in Patients With Type 2 Diabetes and Renal Complications: Protocol and Baseline Characteristics for a Cluster Randomized Controlled Trial %A Willis,Andrew %A Crasto,Winston %A Gray,Laura %A Dallosso,Helen %A Waheed,Ghazala %A Gray,Geri %A Davies,Melanie J %A Khunti,Kamlesh %+ Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, LE169HT, United Kingdom, 44 1162588995 ext 8995, aw187@le.ac.uk %K diabetes mellitus, type 2 %K primary health care %D 2018 %7 08.06.2018 %9 Original Paper %J JMIR Res Protoc %G English %X Background: Adherence to evidence-based cardiovascular risk factor targets in patients with type 2 diabetes and microalbuminuria has shown long-term reduction in mortality and morbidity. Strategies to achieve such adherence have been delivered at individual patient level and are not cost-effective. Health care professional-level intervention has the potential to promote better adherence at lower cost. Objective: The aim of this study was to assess the effectiveness of a multifactorial technology-driven intervention comprising health care professional training, a software prompt installed on practice systems, clinician email support, and enhanced performance and feedback reporting. Methods: A cluster randomized trial will be performed where the primary outcome is the proportion of eligible patients meeting tight cardiovascular risk factor targets, including systolic and diastolic blood pressure (BP; BP<130/80 mm Hg) and total cholesterol (TC; TC<3.5 mmol/L) at 24 months. Secondary outcomes include proportion of patients with glycated hemoglobin (HbA1c) <58 mmol/mol (7.5%), change in medication prescribing, changes in microalbuminuria and renal function (estimated glomerular filtration rate, eGFR), incidence of major adverse CV events and mortality, and coding accuracy. Cost-effectiveness of the intervention will also be assessed. Results: Among 2721 eligible patients, mean age was 62.9 (SD 10.0) years, and duration of diabetes was 10.46 (SD 7.22) years. Mean HbA1c was 59.3 (SD 17.4) mmol/mol; mean systolic and diastolic BP (mm Hg) were 134.3 (SD 14.6) and 76.1 (SD 9.5) mm Hg, respectively; and mean TC was 4.1 (SD 0.98) mmol/L. Overall, 131 out of 2721 (4.81%) patients achieved all 3 “tight” cardiovascular risk factor targets. Cardiovascular risk factor burden increased two-fold in those with eGFR<60 mL/min/1.73 m2 compared with those with eGFR≥60 mL/min/1.73 m2. Prevalence of microalbuminuria was 22.76%. In total, 1076 out of 2721 (39.54%) patients were coded for microalbuminuria or proteinuria on their primary care medical record. Conclusions: The general practitioner prompt study is the largest UK primary care-based, technology-driven, randomized controlled trial to support intensive intervention in high-risk group of multiethnic individuals with type 2 diabetes and microalbuminuria. This paper provides contemporary estimates for prevalent cardiovascular disease and adherence to evidence-based cardiovascular risk factor targets at baseline in a population with type 2 diabetes and microalbuminuria. The main trial results, including cost-effectiveness data, will be submitted for publication in 2018. Trial Registration: International Standard Randomized Controlled Trial Number ISRCTN14918517; http://www.isrctn.com/ISRCTN14918517 (Archived by WebCite at http://www.webcitation.org/6zqm53wNA) Registered Report Identifier: RR1-10.2196/9588 %M 29884609 %R 10.2196/resprot.9588 %U http://www.researchprotocols.org/2018/6/e152/ %U https://doi.org/10.2196/resprot.9588 %U http://www.ncbi.nlm.nih.gov/pubmed/29884609 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 6 %N 2 %P e10264 %T Health Information Technology in Healthcare Quality and Patient Safety: Literature Review %A Feldman,Sue S %A Buchalter,Scott %A Hayes,Leslie W %+ Department of Health Services Administration, The University of Alabama at Birmingham, 1716 9th Avenue, SHPB #590K, Birmingham, AL, 35294, United States, 1 6616188805, suefeldman1009@gmail.com %K Health Information Technology %K Healthcare Quality %K Patient Safety %D 2018 %7 04.06.2018 %9 Review %J JMIR Med Inform %G English %X Background: The area of healthcare quality and patient safety is starting to use health information technology to prevent reportable events, identify them before they become issues, and act on events that are thought to be unavoidable. As healthcare organizations begin to explore the use of health information technology in this realm, it is often unclear where fiscal and human efforts should be focused. Objective: The purpose of this study was to provide a foundation for understanding where to focus health information technology fiscal and human resources as well as expectations for the use of health information technology in healthcare quality and patient safety. Methods: A literature review was conducted to identify peer-reviewed publications reporting on the actual use of health information technology in healthcare quality and patient safety. Inductive thematic analysis with open coding was used to categorize a total of 41 studies. Three pre-set categories were used: prevention, identification, and action. Three additional categories were formed through coding: challenges, outcomes, and location. Results: This study identifies five main categories across seven study settings. A majority of the studies used health IT for identification and prevention of healthcare quality and patient safety issues. In this realm, alerts, clinical decision support, and customized health IT solutions were most often implemented. Implementation, interface design, and culture were most often noted as challenges. Conclusions: This study provides valuable information as organizations determine where they stand to get the most “bang for their buck” relative to health IT for quality and patient safety. Knowing what implementations are being effectivity used by other organizations helps with fiscal and human resource planning as well as managing expectations relative to cost, scope, and outcomes. The findings from this scan of the literature suggest that having organizational champion leaders that can shepherd implementation, impact culture, and bridge knowledge with developers would be a valuable resource allocation to consider. %R 10.2196/10264 %U http://medinform.jmir.org/2018/2/e10264/ %U https://doi.org/10.2196/10264 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 4 %P e109 %T Safer Prescribing and Care for the Elderly (SPACE): Protocol of a Cluster Randomized Controlled Trial in Primary Care %A Wallis,Katharine Ann %A Elley,Carolyn Raina %A Lee,Arier %A Moyes,Simon %A Kerse,Ngaire %+ Department of General Practice and Primary Health Care, University of Auckland, 261 Morrin Road, Auckland, 1142, New Zealand, 64 9 923 9161, k.wallis@auckland.ac.nz %K general practice %K safety %K prescriptions %K multimorbidity %K polypharmacy %K adverse drug events %D 2018 %7 26.04.2018 %9 Protocol %J JMIR Res Protoc %G English %X Background: High-risk prescribing, adverse drug events, and avoidable adverse drug event hospitalizations are common. The single greatest risk factor for high-risk prescribing and adverse drug events is the number of medications a person is taking. More people are living longer and taking more medications for multiple long-term conditions. Most on-going prescribing occurs in primary care. The most effective, cost-effective, and practical approach to safer prescribing in primary care is not yet known. Objective: To test the effect of the Safer Prescribing And Care for the Elderly (SPACE) intervention on high-risk prescribing of nonsteroidal anti-inflammatory and antiplatelet medicines, and related adverse drug event hospitalizations. Methods: This is a protocol of a cluster randomized controlled trial. The clusters will be primary care practices. Data collection and analysis will be at the level of patient. Results: Recruitment started in 2018. Six-month data collection will be in 2018. Conclusions: This study addresses an important translational gap, testing an intervention designed to prompt medicines review and support safer prescribing in routine primary care practice. Trial Registration: Australian New Zealand Clinical Trials Registry: ACTRN12618000034235 http://www.ANZCTR.org.au/ACTRN12618000034235.aspx (Archived with Webcite at http://www.webcitation.org/6yj9RImDf) %M 29699966 %R 10.2196/resprot.9839 %U http://www.researchprotocols.org/2018/4/e109/ %U https://doi.org/10.2196/resprot.9839 %U http://www.ncbi.nlm.nih.gov/pubmed/29699966 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 6 %N 3 %P e74 %T Consumer Mobile Apps for Potential Drug-Drug Interaction Check: Systematic Review and Content Analysis Using the Mobile App Rating Scale (MARS) %A Kim,Ben YB %A Sharafoddini,Anis %A Tran,Nam %A Wen,Emily Y %A Lee,Joon %+ Health Data Science Lab, School of Public Health and Health Systems, University of Waterloo, Lyle Hallman North, 3rd Floor, 200 University Avenue W, Waterloo, ON, N2L 3G1, Canada, 1 519 888 4567 ext 31567, joon.lee@uwaterloo.ca %K drug interactions %K telemedicine %K mobile applications %K smartphone %K consumer health informatics %K consumer health information %D 2018 %7 28.03.2018 %9 Review %J JMIR Mhealth Uhealth %G English %X Background: General consumers can now easily access drug information and quickly check for potential drug-drug interactions (PDDIs) through mobile health (mHealth) apps. With aging population in Canada, more people have chronic diseases and comorbidities leading to increasing numbers of medications. The use of mHealth apps for checking PDDIs can be helpful in ensuring patient safety and empowerment. Objective: The aim of this study was to review the characteristics and quality of publicly available mHealth apps that check for PDDIs. Methods: Apple App Store and Google Play were searched to identify apps with PDDI functionality. The apps’ general and feature characteristics were extracted. The Mobile App Rating Scale (MARS) was used to assess the quality. Results: A total of 23 apps were included for the review—12 from Apple App Store and 11 from Google Play. Only 5 of these were paid apps, with an average price of $7.19 CAD. The mean MARS score was 3.23 out of 5 (interquartile range 1.34). The mean MARS scores for the apps from Google Play and Apple App Store were not statistically different (P=.84). The information dimension was associated with the highest score (3.63), whereas the engagement dimension resulted in the lowest score (2.75). The total number of features per app, average rating, and price were significantly associated with the total MARS score. Conclusions: Some apps provided accurate and comprehensive information about potential adverse drug effects from PDDIs. Given the potentially severe consequences of incorrect drug information, there is a need for oversight to eliminate low quality and potentially harmful apps. Because managing PDDIs is complex in the absence of complete information, secondary features such as medication reminder, refill reminder, medication history tracking, and pill identification could help enhance the effectiveness of PDDI apps. %M 29592848 %R 10.2196/mhealth.8613 %U http://mhealth.jmir.org/2018/3/e74/ %U https://doi.org/10.2196/mhealth.8613 %U http://www.ncbi.nlm.nih.gov/pubmed/29592848 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 7 %N 3 %P e59 %T Improving Transplant Medication Safety Through a Pharmacist-Empowered, Patient-Centered, mHealth-Based Intervention: TRANSAFE Rx Study Protocol %A Fleming,James N %A Treiber,Frank %A McGillicuddy,John %A Gebregziabher,Mulugeta %A Taber,David J %+ Department of Pharmacy, Medical University of South Carolina, 150 Ashley Avenue, Charleston, SC, 29414, United States, 1 843 792 5868, fleminj@musc.edu %K telemedicine %K mhealth %K transplant %K clinical trial %K errors %K adherence %D 2018 %7 02.03.2018 %9 Protocol %J JMIR Res Protoc %G English %X Background: Medication errors, adverse drug events, and nonadherence are the predominant causes of graft loss in kidney transplant recipients and lead to increased healthcare utilization. Research has demonstrated that clinical pharmacists have the unique education and training to identify these events early and develop strategies to mitigate or prevent downstream sequelae. In addition, studies utilizing mHealth interventions have demonstrated success in improving the control of chronic conditions that lead to kidney transplant deterioration. Objective: The goal of the prospective, randomized TRANSAFE Rx study is to measure the clinical and economic effectiveness of a pharmacist-led, mHealth-based intervention, as compared to usual care, in kidney transplant recipients. Methods: TRANSAFE Rx is a 12-month, parallel, two-arm, 1:1 randomized controlled clinical trial involving 136 participants (68 in each arm) and measuring the clinical and economic effectiveness of a pharmacist-led intervention which utilizes an innovative mobile health application to improve medication safety and health outcomes, as compared to usual posttransplant care. Results: The primary outcome measure of this study will be the incidence and severity of MEs and ADRs, which will be identified, categorized, and compared between the intervention and control cohorts. The exploratory outcome measures of this study are to compare the incidence and severity of acute rejections, infections, graft function, graft loss, and death between research cohorts and measure the association between medication safety issues and these events. Additional data that will be gathered includes sociodemographics, health literacy, depression, and support. Conclusions: With this report we describe the study design, methods, and outcome measures that will be utilized in the ongoing TRANSAFE Rx clinical trial. Trial Registration: ClinicalTrials.gov NCT03247322: https://clinicaltrials.gov/ct2/show/NCT03247322 (Archived by WebCite at http://www.webcitation.org/6xcSUnuzW) %M 29500161 %R 10.2196/resprot.9078 %U https://www.researchprotocols.org/2018/3/e59/ %U https://doi.org/10.2196/resprot.9078 %U http://www.ncbi.nlm.nih.gov/pubmed/29500161 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 6 %N 1 %P e13 %T Experiences of Indian Health Workers Using WhatsApp for Improving Aseptic Practices With Newborns: Exploratory Qualitative Study %A Pahwa,Parika %A Lunsford,Sarah %A Livesley,Nigel %+ EnCompass LLC, 5404 Wisconsin Ave, Chevy Chase, MD, 20815, United States, 1 6177849008, ssmith@urc-chs.com %K quality improvement %K mobile apps %K communication %K patient care team %D 2018 %7 01.03.2018 %9 Original Paper %J JMIR Med Inform %G English %X Background: Quality improvement (QI) involves the following 4 steps: (1) forming a team to work on a specific aim, (2) analyzing the reasons for current underperformance, (3) developing changes that could improve care and testing these changes using plan-do-study-act cycles (PDSA), and (4) implementing successful interventions to sustain improvements. Teamwork and group discussion are key for effective QI, but convening in-person meetings with all staff can be challenging due to workload and shift changes. Mobile technologies can support communication within a team when face-to-face meetings are not possible. WhatsApp, a mobile messaging platform, was implemented as a communication tool by a neonatal intensive care unit (NICU) team in an Indian tertiary hospital seeking to reduce nosocomial infections in newborns. Objective: This exploratory qualitative study aimed to examine experiences with WhatsApp as a communication tool among improvement team members and an external coach to improve adherence to aseptic protocols. Methods: Ten QI team members and the external coach were interviewed on communication processes and approaches and thematically analyzed. The WhatsApp transcript for the implementation period was also included in the analysis. Results: WhatsApp was effective for disseminating information, including guidance on QI and clinical practice, and data on performance indicators. It was not effective as a platform for group discussion to generate change ideas or analyze the performance indicator data. The decision of who to include in the WhatsApp group and how members engaged in the group may have reinforced existing hierarchies. Using WhatsApp created a work environment in which members were accessible all the time, breaking down barriers between personal and professional time. The continual influx of messages was distracting to some respondents, and how respondents managed these messages (eg, using the silent function) may have influenced their perceptions of WhatsApp. The coach used WhatsApp to share information, schedule site visits, and prompt action on behalf of the team. Conclusions: WhatsApp is a productive communication tool that can be used by teams and coaches to disseminate information and prompt action to improve the quality of care, but cannot replace in-person meetings. %M 29496651 %R 10.2196/medinform.8154 %U http://medinform.jmir.org/2018/1/e13/ %U https://doi.org/10.2196/medinform.8154 %U http://www.ncbi.nlm.nih.gov/pubmed/29496651 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 1 %P e21 %T Why Clinicians Don’t Report Adverse Drug Events: Qualitative Study %A Hohl,Corinne M %A Small,Serena S %A Peddie,David %A Badke,Katherin %A Bailey,Chantelle %A Balka,Ellen %+ Centre for Clinical Epidemiology and Evaluation, Vancouver Coastal Health Research Institute, 828 West 10th Ave, 7th Fl, Vancouver, BC,, Canada, 1 604 875 4111 ext 63467, chohl@mail.ubc.ca %K adverse events %K pharmacovigilance %K drug safety %K adverse drug reaction %K adverse drug event %K electronic health records %K information and technology %K medication reconciliation %K qualitative research %D 2018 %7 27.02.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: Adverse drug events are unintended and harmful events related to medications. Adverse drug events are important for patient care, quality improvement, drug safety research, and postmarketing surveillance, but they are vastly underreported. Objective: Our objectives were to identify barriers to adverse drug event documentation and factors contributing to underreporting. Methods: This qualitative study was conducted in 1 ambulatory center, and the emergency departments and inpatient wards of 3 acute care hospitals in British Columbia between March 2014 and December 2016. We completed workplace observations and focus groups with general practitioners, hospitalists, emergency physicians, and hospital and community pharmacists. We analyzed field notes by coding and iteratively analyzing our data to identify emerging concepts, generate thematic and event summaries, and create workflow diagrams. Clinicians validated emerging concepts by applying them to cases from their clinical practice. Results: We completed 238 hours of observations during which clinicians investigated 65 suspect adverse drug events. The observed events were often complex and diagnosed over time, requiring the input of multiple providers. Providers documented adverse drug events in charts to support continuity of care but never reported them to external agencies. Providers faced time constraints, and reporting would have required duplication of documentation. Conclusions: Existing reporting systems are not suited to capture the complex nature of adverse drug events or adapted to workflow and are simply not used by frontline clinicians. Systems that are integrated into electronic medical records, make use of existing data to avoid duplication of documentation, and generate alerts to improve safety may address the shortcomings of existing systems and generate robust adverse drug event data as a by-product of safer care. %M 29487041 %R 10.2196/publichealth.9282 %U http://publichealth.jmir.org/2018/1/e21/ %U https://doi.org/10.2196/publichealth.9282 %U http://www.ncbi.nlm.nih.gov/pubmed/29487041 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 5 %N 1 %P e4 %T Reducing Misses and Near Misses Related to Multitasking on the Electronic Health Record: Observational Study and Qualitative Analysis %A Ratanawongsa,Neda %A Matta,George Y %A Bohsali,Fuad B %A Chisolm,Margaret S %+ Division of General Internal Medicine, Department of Medicine, University of California, San Francisco, 1001 Potrero Avenue, Box 1364, San Francisco, CA, 94143, United States, 1 416 206 3188, neda.ratanawongsa@ucsf.edu %K electronic health records %K physician-patient relations %K patient safety %D 2018 %7 06.02.2018 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Clinicians’ use of electronic health record (EHR) systems while multitasking may increase the risk of making errors, but silent EHR system use may lower patient satisfaction. Delaying EHR system use until after patient visits may increase clinicians’ EHR workload, stress, and burnout. Objective: We aimed to describe the perspectives of clinicians, educators, administrators, and researchers about misses and near misses that they felt were related to clinician multitasking while using EHR systems. Methods: This observational study was a thematic analysis of perspectives elicited from 63 continuing medical education (CME) participants during 2 workshops and 1 interactive lecture about challenges and strategies for relationship-centered communication during clinician EHR system use. The workshop elicited reflection about memorable times when multitasking EHR use was associated with “misses” (errors that were not caught at the time) or “near misses” (mistakes that were caught before leading to errors). We conducted qualitative analysis using an editing analysis style to identify codes and then select representative themes and quotes. Results: All workshop participants shared stories of misses or near misses in EHR system ordering and documentation or patient-clinician communication, wondering about “misses we don’t even know about.” Risk factors included the computer’s position, EHR system usability, note content and style, information overload, problematic workflows, systems issues, and provider and patient communication behaviors and expectations. Strategies to reduce multitasking EHR system misses included clinician transparency when needing silent EHR system use (eg, for prescribing), narrating EHR system use, patient activation during EHR system use, adapting visit organization and workflow, improving EHR system design, and improving team support and systems. Conclusions: CME participants shared numerous stories of errors and near misses in EHR tasks and communication that they felt related to EHR multitasking. However, they brainstormed diverse strategies for using EHR systems safely while preserving patient relationships. %M 29410388 %R 10.2196/humanfactors.9371 %U http://humanfactors.jmir.org/2018/1/e4/ %U https://doi.org/10.2196/humanfactors.9371 %U http://www.ncbi.nlm.nih.gov/pubmed/29410388 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 6 %N 1 %P e3 %T Quality of Decision Support in Computerized Provider Order Entry: Systematic Literature Review %A Carli,Delphine %A Fahrni,Guillaume %A Bonnabry,Pascal %A Lovis,Christian %+ Division of Pharmacy, University Hospitals of Geneva, Rue Gabrielle-Perret-Gentil 4, Geneva, 1211, Switzerland, 41 786532871, delphine.carli@chuv.ch %K decision support systems, clinical %K medical order entry systems %K system, medication alert %K sensitivity %K specificity %K predictive value of tests %D 2018 %7 24.01.2018 %9 Review %J JMIR Med Inform %G English %X Background: Computerized decision support systems have raised a lot of hopes and expectations in the field of order entry. Although there are numerous studies reporting positive impacts, concerns are increasingly high about alert fatigue and effective impacts of these systems. One of the root causes of fatigue alert reported is the low clinical relevance of these alerts. Objective: The objective of this systematic review was to assess the reported positive predictive value (PPV), as a proxy to clinical relevance, of decision support systems in computerized provider order entry (CPOE). Methods: A systematic search of the scientific literature published between February 2009 and March 2015 on CPOE, clinical decision support systems, and the predictive value associated with alert fatigue was conducted using PubMed database. Inclusion criteria were as follows: English language, full text available (free or pay for access), assessed medication, direct or indirect level of predictive value, sensitivity, or specificity. When possible with the information provided, PPV was calculated or evaluated. Results: Additive queries on PubMed retrieved 928 candidate papers. Of these, 376 were eligible based on abstract. Finally, 26 studies qualified for a full-text review, and 17 provided enough information for the study objectives. An additional 4 papers were added from the references of the reviewed papers. The results demonstrate massive variations in PPVs ranging from 8% to 83% according to the object of the decision support, with most results between 20% and 40%. The best results were observed when patients’ characteristics, such as comorbidity or laboratory test results, were taken into account. There was also an important variation in sensitivity, ranging from 38% to 91%. Conclusions: There is increasing reporting of alerts override in CPOE decision support. Several causes are discussed in the literature, the most important one being the clinical relevance of alerts. In this paper, we tried to assess formally the clinical relevance of alerts, using a near-strong proxy, which is the PPV of alerts, or any way to express it such as the rate of true and false positive alerts. In doing this literature review, three inferences were drawn. First, very few papers report direct or enough indirect elements that support the use or the computation of PPV, which is a gold standard for all diagnostic tools in medicine and should be systematically reported for decision support. Second, the PPV varies a lot according to the typology of decision support, so that overall rates are not useful, but must be reported by the type of alert. Finally, in general, the PPVs are below or near 50%, which can be considered as very low. %M 29367187 %R 10.2196/medinform.7170 %U http://medinform.jmir.org/2018/1/e3/ %U https://doi.org/10.2196/medinform.7170 %U http://www.ncbi.nlm.nih.gov/pubmed/29367187 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 6 %N 1 %P e6 %T A Clinical Decision Support Engine Based on a National Medication Repository for the Detection of Potential Duplicate Medications: Design and Evaluation %A Yang,Cheng-Yi %A Lo,Yu-Sheng %A Chen,Ray-Jade %A Liu,Chien-Tsai %+ Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, 250 Wuxing St, Taipei, 11030, Taiwan, 886 266382736 ext 1509, ctliu@tmu.edu.tw %K duplicate medication %K adverse drug reaction %K clinical decision support system %K PharmaCloud %D 2018 %7 19.1.2018 %9 Original Paper %J JMIR Med Inform %G English %X Background: A computerized physician order entry (CPOE) system combined with a clinical decision support system can reduce duplication of medications and thus adverse drug reactions. However, without infrastructure that supports patients’ integrated medication history across health care facilities nationwide, duplication of medication can still occur. In Taiwan, the National Health Insurance Administration has implemented a national medication repository and Web-based query system known as the PharmaCloud, which allows physicians to access their patients’ medication records prescribed by different health care facilities across Taiwan. Objective: This study aimed to develop a scalable, flexible, and thematic design-based clinical decision support (CDS) engine, which integrates a national medication repository to support CPOE systems in the detection of potential duplication of medication across health care facilities, as well as to analyze its impact on clinical encounters. Methods: A CDS engine was developed that can download patients’ up-to-date medication history from the PharmaCloud and support a CPOE system in the detection of potential duplicate medications. When prescribing a medication order using the CPOE system, a physician receives an alert if there is a potential duplicate medication. To investigate the impact of the CDS engine on clinical encounters in outpatient services, a clinical encounter log was created to collect information about time, prescribed drugs, and physicians’ responses to handling the alerts for each encounter. Results: The CDS engine was installed in a teaching affiliate hospital, and the clinical encounter log collected information for 3 months, during which a total of 178,300 prescriptions were prescribed in the outpatient departments. In all, 43,844/178,300 (24.59%) patients signed the PharmaCloud consent form allowing their physicians to access their medication history in the PharmaCloud. The rate of duplicate medication was 5.83% (1843/31,614) of prescriptions. When prescribing using the CDS engine, the median encounter time was 4.3 (IQR 2.3-7.3) min, longer than that without using the CDS engine (median 3.6, IQR 2.0-6.3 min). From the physicians’ responses, we found that 42.06% (1908/4536) of the potential duplicate medications were recognized by the physicians and the medication orders were canceled. Conclusions: The CDS engine could easily extend functions for detection of adverse drug reactions when more and more electronic health record systems are adopted. Moreover, the CDS engine can retrieve more updated and completed medication histories in the PharmaCloud, so it can have better performance for detection of duplicate medications. Although our CDS engine approach could enhance medication safety, it would make for a longer encounter time. This problem can be mitigated by careful evaluation of adopted solutions for implementation of the CDS engine. The successful key component of a CDS engine is the completeness of the patient’s medication history, thus further research to assess the factors in increasing the PharmaCloud consent rate is required. %R 10.2196/medinform.9064 %U http://medinform.jmir.org/2018/1/e6/ %U https://doi.org/10.2196/medinform.9064 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 6 %N 1 %P e4 %T The Use of Technology in Identifying Hospital Malnutrition: Scoping Review %A Trtovac,Dino %A Lee,Joon %+ Health Data Science Lab, School of Public Health and Health Systems, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada, 1 519 888 4567 ext 31567, joon.lee@uwaterloo.ca %K hospital malnutrition %K technology-driven health care %K malnutrition detection %K nutrition diagnosis %K malnutrition assessment %K food-intake monitoring %K automated data %K malnutrition %K nutritional status %K nutrition assessment %D 2018 %7 19.01.2018 %9 Review %J JMIR Med Inform %G English %X Background: Malnutrition is a condition most commonly arising from the inadequate consumption of nutrients necessary to maintain physiological health and is associated with the development of cardiovascular disease, osteoporosis, and sarcopenia. Malnutrition occurring in the hospital setting is caused by insufficient monitoring, identification, and assessment efforts. Furthermore, the ability of health care workers to identify and recognize malnourished patients is suboptimal. Therefore, interventions focusing on the identification and treatment of malnutrition are valuable, as they reduce the risks and rates of malnutrition within hospitals. Technology may be a particularly useful ally in identifying malnutrition due to scalability, timeliness, and effectiveness. In an effort to explore the issue, this scoping review synthesized the availability of technological tools to detect and identify hospital malnutrition. Objective: Our objective was to conduct a scoping review of the different forms of technology used in addressing malnutrition among adults admitted to hospital to (1) identify the extent of the published literature on this topic, (2) describe key findings, and (3) identify outcomes. Methods: We designed and implemented a search strategy in 3 databases (PubMed, Scopus, and CINAHL). We completed a descriptive numerical summary and analyzed study characteristics. One reviewer independently extracted data from the databases. Results: We retrieved and reviewed a total of 21 articles. We categorized articles by the computerized tool or app type: malnutrition assessment (n=15), food intake monitoring (n=5), or both (n=1). Within those categories, we subcategorized the different technologies as either hardware (n=4), software (n=13), or both (n=4). An additional subcategory under software was cloud-based apps (n=1). Malnutrition in the acute hospital setting was largely an unrecognized problem, owing to insufficient monitoring, identification, and initial assessments of identifying both patients who are already malnourished and those who are at risk of malnourishment. Studies went on to examine the effectiveness of health care workers (nurses and doctors) with a knowledge base focused on clinical care and their ability to accurately and consistently identify malnourished geriatric patients within that setting. Conclusions: Most articles reported effectiveness in accurately increasing malnutrition detection and awareness. Computerized tools and apps may also help reduce health care workers’ workload and time spent assessing patients for malnutrition. Hospitals may also benefit from implementing malnutrition technology through observing decreased length of stay, along with decreased foregone costs related to missing malnutrition diagnoses. It is beneficial to study the impact of these technologies to examine possible areas of improvement. A future systematic review would further contribute to the evidence and effectiveness of the use of technologies in assessing and monitoring hospital malnutrition. %M 29351894 %R 10.2196/medinform.7601 %U https://medinform.jmir.org/2018/1/e4/ %U https://doi.org/10.2196/medinform.7601 %U http://www.ncbi.nlm.nih.gov/pubmed/29351894 %0 Journal Article %@ 2369-2960 %I JMIR Publications %V 4 %N 1 %P e1 %T Social Media Impact of the Food and Drug Administration's Drug Safety Communication Messaging About Zolpidem: Mixed-Methods Analysis %A Sinha,Michael S %A Freifeld,Clark C %A Brownstein,John S %A Donneyong,Macarius M %A Rausch,Paula %A Lappin,Brian M %A Zhou,Esther H %A Dal Pan,Gerald J %A Pawar,Ajinkya M %A Hwang,Thomas J %A Avorn,Jerry %A Kesselheim,Aaron S %+ Program On Regulation, Therapeutics, And Law, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA, 02120, United States, 1 617 278 0930, akesselheim@partners.org %K Food and Drug Administration %K drug safety communications %K surveillance %K epidemiology %K social media %K Twitter %K Facebook %K Google Trends %D 2018 %7 05.01.2018 %9 Original Paper %J JMIR Public Health Surveill %G English %X Background: The Food and Drug Administration (FDA) issues drug safety communications (DSCs) to health care professionals, patients, and the public when safety issues emerge related to FDA-approved drug products. These safety messages are disseminated through social media to ensure broad uptake. Objective: The objective of this study was to assess the social media dissemination of 2 DSCs released in 2013 for the sleep aid zolpidem. Methods: We used the MedWatcher Social program and the DataSift historic query tool to aggregate Twitter and Facebook posts from October 1, 2012 through August 31, 2013, a period beginning approximately 3 months before the first DSC and ending 3 months after the second. Posts were categorized as (1) junk, (2) mention, and (3) adverse event (AE) based on a score between –0.2 (completely unrelated) to 1 (perfectly related). We also looked at Google Trends data and Wikipedia edits for the same time period. Google Trends search volume is scaled on a range of 0 to 100 and includes “Related queries” during the relevant time periods. An interrupted time series (ITS) analysis assessed the impact of DSCs on the counts of posts with specific mention of zolpidem-containing products. Chow tests for known structural breaks were conducted on data from Twitter, Facebook, and Google Trends. Finally, Wikipedia edits were pulled from the website’s editorial history, which lists all revisions to a given page and the editor’s identity. Results: In total, 174,286 Twitter posts and 59,641 Facebook posts met entry criteria. Of those, 16.63% (28,989/174,286) of Twitter posts and 25.91% (15,453/59,641) of Facebook posts were labeled as junk and excluded. AEs and mentions represented 9.21% (16,051/174,286) and 74.16% (129,246/174,286) of Twitter posts and 5.11% (3,050/59,641) and 68.98% (41,138/59,641) of Facebook posts, respectively. Total daily counts of posts about zolpidem-containing products increased on Twitter and Facebook on the day of the first DSC; Google searches increased on the week of the first DSC. ITS analyses demonstrated variability but pointed to an increase in interest around the first DSC. Chow tests were significant (P<.0001) for both DSCs on Facebook and Twitter, but only the first DSC on Google Trends. Wikipedia edits occurred soon after each DSC release, citing news articles rather than the DSC itself and presenting content that needed subsequent revisions for accuracy. Conclusions: Social media offers challenges and opportunities for dissemination of the DSC messages. The FDA could consider strategies for more actively disseminating DSC safety information through social media platforms, particularly when announcements require updating. The FDA may also benefit from directly contributing content to websites like Wikipedia that are frequently accessed for drug-related information. %M 29305342 %R 10.2196/publichealth.7823 %U http://publichealth.jmir.org/2018/1/e1/ %U https://doi.org/10.2196/publichealth.7823 %U http://www.ncbi.nlm.nih.gov/pubmed/29305342 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 11 %P e393 %T Bioimpedance Alerts from Cardiovascular Implantable Electronic Devices: Observational Study of Diagnostic Relevance and Clinical Outcomes %A Smeets,Christophe JP %A Vranken,Julie %A Van der Auwera,Jo %A Verbrugge,Frederik H %A Mullens,Wilfried %A Dupont,Matthias %A Grieten,Lars %A De Cannière,Hélène %A Lanssens,Dorien %A Vandenberk,Thijs %A Storms,Valerie %A Thijs,Inge M %A Vandervoort,Pieter M %+ Department of Cardiology, Ziekenhuis Oost-Limburg, Schiepse Bos 6, Genk,, Belgium, 32 089 32 15 26, christophe.smeets@uhasselt.be %K defibrillators, implantable %K cardiac resynchronization therapy %K telemedicine %K electric impedance %K algorithms %K call centers %D 2017 %7 23.11.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: The use of implantable cardioverter-defibrillators (ICDs) and cardiac resynchronization therapy (CRT) devices is expanding in the treatment of heart failure. Most of the current devices are equipped with remote monitoring functions, including bioimpedance for fluid status monitoring. The question remains whether bioimpedance measurements positively impact clinical outcome. Objective: The aim of this study was to provide a comprehensive overview of the clinical interventions taken based on remote bioimpedance monitoring alerts and their impact on clinical outcome. Methods: This is a single-center observational study of consecutive ICD and CRT patients (n=282) participating in protocol-driven remote follow-up. Bioimpedance alerts were analyzed with subsequently triggered interventions. Results: A total of 55.0% (155/282) of patients had an ICD or CRT device equipped with a remote bioimpedance algorithm. During 34 (SD 12) months of follow-up, 1751 remote monitoring alarm notifications were received (2.2 per patient-year of follow-up), comprising 2096 unique alerts (2.6 per patient-year of follow-up). Since 591 (28.2%) of all incoming alerts were bioimpedance-related, patients with an ICD or CRT including a bioimpedance algorithm had significantly more alerts (3.4 versus 1.8 alerts per patient-year of follow-up, P<.001). Bioimpedance-only alerts resulted in a phone contact in 91.0% (498/547) of cases, which triggered an actual intervention in 15.9% (87/547) of cases, since in 75.1% (411/547) of cases reenforcing heart failure education sufficed. Overall survival was lower in patients with a cardiovascular implantable electronic device with a bioimpedance algorithm; however, this difference was driven by differences in baseline characteristics (adjusted hazard ratio of 2.118, 95% CI 0.845-5.791). No significant differences between both groups were observed in terms of the number of follow-up visits in the outpatient heart failure clinic, the number of hospital admissions with a primary diagnosis of heart failure, or mean length of hospital stay. Conclusions: Bioimpedance-only alerts constituted a substantial amount of incoming alerts when turned on during remote follow-up and triggered an additional intervention in only 16% of cases since in 75% of cases, providing general heart failure education sufficed. The high frequency of heart failure education that was provided could have contributed to fewer heart failure–related hospitalizations despite significant differences in baseline characteristics. %M 29170147 %R 10.2196/jmir.8066 %U http://www.jmir.org/2017/11/e393/ %U https://doi.org/10.2196/jmir.8066 %U http://www.ncbi.nlm.nih.gov/pubmed/29170147 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 5 %N 4 %P e45 %T Predicting Unplanned Transfers to the Intensive Care Unit: A Machine Learning Approach Leveraging Diverse Clinical Elements %A Wellner,Ben %A Grand,Joan %A Canzone,Elizabeth %A Coarr,Matt %A Brady,Patrick W %A Simmons,Jeffrey %A Kirkendall,Eric %A Dean,Nathan %A Kleinman,Monica %A Sylvester,Peter %+ The MITRE Corporation, 202 Burlington Rd, Bedford, MA, 01730, United States, +1 781 271 7191, wellner@mitre.org %K clinical deterioration %K machine learning %K data mining %K electronic health record %K patient acuity %K vital signs %K nursing assessment %K clinical laboratory techniques %D 2017 %7 22.11.2017 %9 Original Paper %J JMIR Med Inform %G English %X Background: Early warning scores aid in the detection of pediatric clinical deteriorations but include limited data inputs, rarely include data trends over time, and have limited validation. Objective: Machine learning methods that make use of large numbers of predictor variables are now commonplace. This work examines how different types of predictor variables derived from the electronic health record affect the performance of predicting unplanned transfers to the intensive care unit (ICU) at three large children’s hospitals. Methods: We trained separate models with data from three different institutions from 2011 through 2013 and evaluated models with 2014 data. Cases consisted of patients who transferred from the floor to the ICU and met one or more of 5 different priori defined criteria for suspected unplanned transfers. Controls were patients who were never transferred to the ICU. Predictor variables for the models were derived from vitals, labs, acuity scores, and nursing assessments. Classification models consisted of L1 and L2 regularized logistic regression and neural network models. We evaluated model performance over prediction horizons ranging from 1 to 16 hours. Results: Across the three institutions, the c-statistic values for our best models were 0.892 (95% CI 0.875-0.904), 0.902 (95% CI 0.880-0.923), and 0.899 (95% CI 0.879-0.919) for the task of identifying unplanned ICU transfer 6 hours before its occurrence and achieved 0.871 (95% CI 0.855-0.888), 0.872 (95% CI 0.850-0.895), and 0.850 (95% CI 0.825-0.875) for a prediction horizon of 16 hours. For our first model at 80% sensitivity, this resulted in a specificity of 80.5% (95% CI 77.4-83.7) and a positive predictive value of 5.2% (95% CI 4.5-6.2). Conclusions: Feature-rich models with many predictor variables allow for patient deterioration to be predicted accurately, even up to 16 hours in advance. %M 29167089 %R 10.2196/medinform.8680 %U http://medinform.jmir.org/2017/4/e45/ %U https://doi.org/10.2196/medinform.8680 %U http://www.ncbi.nlm.nih.gov/pubmed/29167089 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 6 %N 2 %P e20 %T A Strategy to Reduce Critical Cardiorespiratory Alarms due to Intermittent Enteral Feeding of Preterm Neonates in Intensive Care %A Joshi,Rohan %A van Pul,Carola %A Sanders,Anouk %A Weda,Hans %A Bikker,Jan Willem %A Feijs,Loe %A Andriessen,Peter %+ Department of Industrial Design, Eindhoven University of Technology, Laplace, PO Box 513, Eindhoven, 5600MB, Netherlands, 31 617935137, r.joshi@tue.nl %K preterm infants %K enteral feeding %K bradycardia %K hypoxia %K alarms %D 2017 %7 20.10.2017 %9 Original Paper %J Interact J Med Res %G English %X Background: Many preterm infants require enteral feeding as they cannot coordinate sucking, swallowing, and breathing. In enteral feeding, milk feeds are delivered through a small feeding tube passed via the nose or mouth into the stomach. Intermittent milk feeds may either be administered using a syringe to gently push milk into the infant’s stomach (push feed) or milk can be poured into a syringe attached to the tube and allowed to drip in by gravity (gravity feed). This practice of enteral feeding is common in neonatal intensive care units. There is, however, no evidence in the literature to recommend the use of one method of feeding over the other. Objective: The aim of this study was to investigate which of the two methods of feeding is physiologically better tolerated by infants, as measured by the incidence of critical cardiorespiratory alarms during and immediately after feeding. Methods: We conducted a prospectively designed observational study with records of all feeding episodes in infants of gestational age less than 30 weeks at birth and with a minimum enteral intake of 100 mL/kg/day. In total, 2140 enteral feeding episodes were noted from 25 infants over 308 infant-days with records for several characteristics of the infants (eg, gestational age), feeding (eg, the position of infants), and of nursing-care events before feeding (eg, diapering). Logistic regression with mixed effects was used to model cardiorespiratory alarms for the push and gravity methods of feeding. Results: After adjustments were made for all confounding variables, the position of infants was found to be statistically significant in changing the outcome of critical alarms for the two methods of feeding (P=.02). For infants in the lateral position, push feeds led to 40% more instances of one or more critical cardiorespiratory alarms in comparison with the gravity method. Both methods of feeding created a statistically comparable number of alarms for infants in the prone position. Conclusions: This study provides objective data that may assist in optimizing enteral feeding protocols for premature infants. The incidence of critical cardiorespiratory alarms for infants in the lateral position can be lowered by the use of gravity instead of push feeding. No differences were observed between the two types of feeding when infants were in the prone position. %M 29054835 %R 10.2196/ijmr.7756 %U http://www.i-jmr.org/2017/2/e20/ %U https://doi.org/10.2196/ijmr.7756 %U http://www.ncbi.nlm.nih.gov/pubmed/29054835 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 4 %N 4 %P e27 %T Workarounds Emerging From Electronic Health Record System Usage: Consequences for Patient Safety, Effectiveness of Care, and Efficiency of Care %A Blijleven,Vincent %A Koelemeijer,Kitty %A Wetzels,Marijntje %A Jaspers,Monique %+ Center for Marketing & Supply Chain Management, Nyenrode Business University, Straatweg 25, Breukelen, 3621 BG, Netherlands, 31 630023248, vincentblijleven@gmail.com %K electronic health records %K qualitative research %K physicians %K nurses %K patient safety %K quality of health care %K efficiency %K workflow %D 2017 %7 05.10.2017 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Health care providers resort to informal temporary practices known as workarounds for handling exceptions to normal workflow unintendedly imposed by electronic health record systems (EHRs). Although workarounds may seem favorable at first sight, they are generally suboptimal and may jeopardize patient safety, effectiveness of care, and efficiency of care. Objective: Research into the scope and impact of EHR workarounds on patient care processes is scarce. This paper provides insight into the effects of EHR workarounds on organizational workflows and outcomes of care services by identifying EHR workarounds and determining their rationales, scope, and impact on health care providers’ workflows, patient safety, effectiveness of care, and efficiency of care. Knowing the rationale of a workaround provides valuable clues about the source of origin of each workaround and how each workaround could most effectively be resolved. Knowing the scope and impact a workaround has on EHR-related safety, effectiveness, and efficiency provides insight into how to address related concerns. Methods: Direct observations and follow-up semistructured interviews with 31 physicians, 13 nurses, and 3 clerks and qualitative bottom-up coding techniques was used to identify, analyze, and classify EHR workarounds. The research was conducted within 3 specialties and settings at a large university hospital. Rationales were associated with work system components (persons, technology and tools, tasks, organization, and physical environment) of the Systems Engineering Initiative for Patient Safety (SEIPS) framework to reveal their source of origin as well as to determine the scope and the impact of each EHR workaround from a structure-process-outcome perspective. Results: A total of 15 rationales for EHR workarounds were identified of which 5 were associated with persons, 4 with technology and tools, 4 with the organization, and 2 with the tasks. Three of these 15 rationales for EHR workarounds have not been identified in prior research: data migration policy, enforced data entry, and task interference. Conclusions: EHR workaround rationales associated with different SEIPS work system components demand a different approach to be resolved. Persons-related workarounds may most effectively be resolved through personal training, organization-related workarounds through reviewing organizational policy and regulations, tasks-related workarounds through process redesign, and technology- and tools-related workarounds through EHR redesign efforts. Furthermore, insights gained from knowing a workaround’s degree of influence as well as impact on patient safety, effectiveness of care, and efficiency of care can inform design and redesign of EHRs to further align EHR design with work contexts, subsequently leading to better organization and (safe) provision of care. In doing so, a research team in collaboration with all stakeholders could use the SEIPS framework to reflect on the current and potential future configurations of the work system to prevent unfavorable workarounds from occurring and how a redesign of the EHR would impact interactions between the work system components. %M 28982645 %R 10.2196/humanfactors.7978 %U http://humanfactors.jmir.org/2017/4/e27/ %U https://doi.org/10.2196/humanfactors.7978 %U http://www.ncbi.nlm.nih.gov/pubmed/28982645 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 4 %N 3 %P e17 %T A Technological Innovation to Reduce Prescribing Errors Based on Implementation Intentions: The Acceptability and Feasibility of MyPrescribe %A Keyworth,Chris %A Hart,Jo %A Thoong,Hong %A Ferguson,Jane %A Tully,Mary %+ Manchester Centre for Health Psychology, Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Coupland 1 Building, Oxford Road, Manchester, M13 9PL, United Kingdom, 44 161 275 2589, chris.keyworth@manchester.ac.uk %K drug prescribing %K behavior and behavior mechanisms %K clinical competence %K qualitative research %K mobile applications %K pharmacists %K patient safety %K telemedicine %D 2017 %7 01.08.2017 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Although prescribing of medication in hospitals is rarely an error-free process, prescribers receive little feedback on their mistakes and ways to change future practices. Audit and feedback interventions may be an effective approach to modifying the clinical practice of health professionals, but these may pose logistical challenges when used in hospitals. Moreover, such interventions are often labor intensive. Consequently, there is a need to develop effective and innovative interventions to overcome these challenges and to improve the delivery of feedback on prescribing. Implementation intentions, which have been shown to be effective in changing behavior, link critical situations with an appropriate response; however, these have rarely been used in the context of improving prescribing practices. Objective: Semistructured qualitative interviews were conducted to evaluate the acceptability and feasibility of providing feedback on prescribing errors via MyPrescribe, a mobile-compatible website informed by implementation intentions. Methods: Data relating to 200 prescribing errors made by 52 junior doctors were collected by 11 hospital pharmacists. These errors were populated into MyPrescribe, where prescribers were able to construct their own personalized action plans. Qualitative interviews with a subsample of 15 junior doctors were used to explore issues regarding feasibility and acceptability of MyPrescribe and their experiences of using implementation intentions to construct prescribing action plans. Framework analysis was used to identify prominent themes, with findings mapped to the behavioral components of the COM-B model (capability, opportunity, motivation, and behavior) to inform the development of future interventions. Results: MyPrescribe was perceived to be effective in providing opportunities for critical reflection on prescribing errors and to complement existing training (such as junior doctors’ e-portfolio). The participants were able to provide examples of how they would use “If-Then” plans for patient management. Technology, as opposed to other methods of learning (eg, traditional “paper based” learning), was seen as a positive advancement for continued learning. Conclusions: MyPrescribe was perceived as an acceptable and feasible learning tool for changing prescribing practices, with participants suggesting that it would make an important addition to medical prescribers’ training in reflective practice. MyPrescribe is a novel theory-based technological innovation that provides the platform for doctors to create personalized implementation intentions. Applying the COM-B model allows for a more detailed understanding of the perceived mechanisms behind prescribing practices and the ways in which interventions aimed at changing professional practice can be implemented. %M 28765104 %R 10.2196/humanfactors.7153 %U http://humanfactors.jmir.org/2017/3/e17/ %U https://doi.org/10.2196/humanfactors.7153 %U http://www.ncbi.nlm.nih.gov/pubmed/28765104 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 6 %P e203 %T The Second Victim Phenomenon After a Clinical Error: The Design and Evaluation of a Website to Reduce Caregivers’ Emotional Responses After a Clinical Error %A Mira,José Joaquín %A Carrillo,Irene %A Guilabert,Mercedes %A Lorenzo,Susana %A Pérez-Pérez,Pastora %A Silvestre,Carmen %A Ferrús,Lena %A , %+ Alicante-Sant Joan Health District, Universidad Miguel Hernández, Avenue Universidad s/n, Elche (Alicante), 03202, Spain, 34 606433599, jose.mira@umh.es %K patient safety %K professionals %K hospital %K primary care %K second victims %K clinical error %K e-learning %D 2017 %7 08.06.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Adverse events (incidents that harm a patient) can also produce emotional hardship for the professionals involved (second victims). Although a few international pioneering programs exist that aim to facilitate the recovery of the second victim, there are no known initiatives that aim to raise awareness in the professional community about this issue and prevent the situation from worsening. Objective: The aim of this study was to design and evaluate an online program directed at frontline hospital and primary care health professionals that raises awareness and provides information about the second victim phenomenon. Methods: The design of the Mitigating Impact in Second Victims (MISE) online program was based on a literature review, and its contents were selected by a group of 15 experts on patient safety with experience in both clinical and academic settings. The website hosting MISE was subjected to an accreditation process by an external quality agency that specializes in evaluating health websites. The MISE structure and content were evaluated by 26 patient safety managers at hospitals and within primary care in addition to 266 frontline health care professionals who followed the program, taking into account its comprehension, usefulness of the information, and general adequacy. Finally, the amount of knowledge gained from the program was assessed with three objective measures (pre- and posttest design). Results: The website earned Advanced Accreditation for health websites after fulfilling required standards. The comprehension and practical value of the MISE content were positively assessed by 88% (23/26) and 92% (24/26) of patient safety managers, respectively. MISE was positively evaluated by health care professionals, who awarded it 8.8 points out of a maximum 10. Users who finished MISE improved their knowledge on patient safety terminology, prevalence and impact of adverse events and clinical errors, second victim support models, and recommended actions following a severe adverse event (P<.001). Conclusions: The MISE program differs from existing intervention initiatives by its preventive nature in relation to the second victim phenomenon. Its online nature makes it an easily accessible tool for the professional community. This program has shown to increase user’s knowledge on this issue and it helps them correct their approach. Furthermore, it is one of the first initiatives to attempt to bring the second victim phenomenon closer to primary care. %M 28596148 %R 10.2196/jmir.7840 %U http://www.jmir.org/2017/6/e203/ %U https://doi.org/10.2196/jmir.7840 %U http://www.ncbi.nlm.nih.gov/pubmed/28596148 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 5 %N 5 %P e78 %T Mobile App Design, Development, and Publication for Adverse Drug Reaction Assessments of Causality, Severity, and Preventability %A Ithnin,Muslimah %A Mohd Rani,Mohd Dzulkhairi %A Abd Latif,Zuraidah %A Kani,Paveethra %A Syaiful,Asmalita %A Nor Aripin,Khairun Nain %A Tengku Mohd,Tengku Amatullah Madeehah %+ Universiti Sains Islam Malaysia, Tingkat 13, Menara B, Persiaran MPAJ, Jalan Pandan Utama, Pandan Indah, Kuala Lumpur, 55100, Malaysia, 60 3 4289 2400, madeehah@usim.edu.my %K mobile applications %K computer-assisted decision making %K drug monitoring %K pharmacovigilance %K adverse drug reactions %D 2017 %7 30.05.2017 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Adverse drug reactions (ADRs) cause significant morbidity and mortality. Improved assessment of ADRs to identify the causal relationship, the severity, and the preventability will aid ADRs prevention or reduce patient burden. Objective: The aim of this study was to develop mobile apps in assisting clinical decision in ADR assessments of causality, severity, and preventability using validated tools. The usability of the apps was assessed. Methods: We designed mobile apps using validated assessment tools for ADRs. They are the Liverpool ADRs Causality Assessment Tool, Hartwig’s Severity Assessment Scale, and the Modified Schumock and Thronton Preventability Scale. The apps were named “Adverse Drug ReactionCausality,” “Adverse Drug ReactionSeverity,” and “Adverse Drug RxnPreventability.” A survey was conducted using the System Usability Scale (SUS) to assess the usability of the developed apps among health care professionals. Results: These apps are available for download through Google Play Store for free since January 2015. From the survey, the mean SUS score was 70.9 based on 26 responses from the pediatric ward of Hospital Ampang, Malaysia. Conclusions: The developed apps received an overall acceptable usability among health care professionals. The usage of these apps will improve detection, assessment, and avoidance of future ADRs. They will also contribute to future research on ADRs, thus increasing drug safety. %M 28559222 %R 10.2196/mhealth.6261 %U http://mhealth.jmir.org/2017/5/e78/ %U https://doi.org/10.2196/mhealth.6261 %U http://www.ncbi.nlm.nih.gov/pubmed/28559222 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 5 %P e150 %T Using a Medical Intranet of Things System to Prevent Bed Falls in an Acute Care Hospital: A Pilot Study %A Balaguera,Henri U %A Wise,Diana %A Ng,Chun Yin %A Tso,Han-Wen %A Chiang,Wan-Lin %A Hutchinson,Aimee M %A Galvin,Tracy %A Hilborne,Lee %A Hoffman,Cathy %A Huang,Chi-Cheng %A Wang,C Jason %+ Lahey Hospital and Medical Center, 41 Mall Road, Burlington, MA, 01805, United States, 1 781 744 3939, balaguera@gmail.com %K accidental falls %K acute care %K nursing %K patient safety %K patient-centered care %K sensor devices and platforms %K health care technology %K mobile apps %K patient monitoring %K health technology assessment %D 2017 %7 04.05.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: Hospitalized patients in the United States experience falls at a rate of 2.6 to 17.1 per 1000 patient-days, with the majority occurring when a patient is moving to, from, and around the bed. Each fall with injury costs an average of US $14,000. Objective: The aim was to conduct a technology evaluation, including feasibility, usability, and user experience, of a medical sensor-based Intranet of things (IoT) system in facilitating nursing response to bed exits in an acute care hospital. Methods: Patients 18 years and older with a Morse fall score of 45 or greater were recruited from a 35-bed medical-surgical ward in a 317-bed Massachusetts teaching hospital. Eligible patients were recruited between August 4, 2015 and July 31, 2016. Participants received a sensor pad placed between the top of their mattress and bed sheet. The sensor pad was positioned to monitor movement from patients’ shoulders to their thighs. The SensableCare System was evaluated for monitoring patient movement and delivering timely alerts to nursing staff via mobile devices when there appeared to be a bed-exit attempt. Sensor pad data were collected automatically from the system. The primary outcomes included number of falls, time to turn off bed-exit alerts, and the number of attempted bed-exit events. Data on patient falls were collected by clinical research assistants and confirmed with the unit nurse manager. Explanatory variables included room locations (zones 1-3), day of the week, nursing shift, and Morse Fall Scale (ie, positive fall history, positive secondary diagnosis, positive ambulatory aid, weak impaired gait/transfer, positive IV/saline lock, mentally forgets limitations). We also assessed user experience via nurse focus groups. Qualitative data regarding staff interactions with the system were collected during two focus groups with 25 total nurses, each lasting approximately 1.5 hours. Results: A total of 91 patients used the system for 234.0 patient-days and experienced no bed falls during the study period. On average, patients were assisted/returned to bed 46 seconds after the alert system was triggered. Response times were longer during the overnight nursing shift versus day shift (P=.005), but were independent of the patient’s location on the unit. Focus groups revealed that nurses found the system integrated well into the clinical nursing workflow and the alerts were helpful in patient monitoring. Conclusions: A medical IoT system can be integrated into the existing nursing workflow and may reduce patient bed fall risk in acute care hospitals, a high priority but an elusive patient safety challenge. By using an alerting system that sends notifications directly to nurses’ mobile devices, nurses can equally respond to unassisted bed-exit attempts wherever patients are located on the ward. Further study, including a fully powered randomized controlled trial, is needed to assess effectiveness across hospital settings. %M 28473306 %R 10.2196/jmir.7131 %U http://www.jmir.org/2017/5/e150/ %U https://doi.org/10.2196/jmir.7131 %U http://www.ncbi.nlm.nih.gov/pubmed/28473306 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 6 %N 4 %P e74 %T Association Between Workarounds and Medication Administration Errors in Bar Code-Assisted Medication Administration: Protocol of a Multicenter Study %A van der Veen,Willem %A van den Bemt,Patricia MLA %A Bijlsma,Maarten %A de Gier,Han J %A Taxis,Katja %+ Faculty of Science and Engineering, Unit PharmacoTherapy, -Epidemiology & -Economics, University of Groningen, HPC: XB45, A Deusinglaan 1, Groningen, 9713 AV, Netherlands, 31 50 363 75 76, willem.van.der.veen@rug.nl %K BCMA %K bar code-assisted medication administration systems %K workarounds %K medication administration errors %K bar coded medication administration %K medication safety, hospitals %D 2017 %7 28.04.2017 %9 Protocol %J JMIR Res Protoc %G English %X Background: Information technology-based methods such as bar code-assisted medication administration (BCMA) systems have the potential to reduce medication administration errors (MAEs) in hospitalized patients. In practice, however, systems are often not used as intended, leading to workarounds. Workarounds may result in MAEs that may harm patients. Objective: The primary aim is to study the association of workarounds with MAEs in the BCMA process. Second, we will determine the frequency and type of workarounds and MAEs and explore the potential risk factors (determinants) for workarounds. Methods: This is a multicenter prospective study on internal medicine and surgical wards of 4 Dutch hospitals using BCMA systems to administer medication. We will include a total of 6000 individual drug administrations using direct observation to collect data. Results: The project was funded in 2014 and enrollment was completed at the end of 2016. Data analysis is under way and the first results are expected to be submitted for publication at the end of 2017. Conclusions: If an association between workarounds and MAEs is established, this information can be used to reduce the frequency of MAEs. Information on determinants of workarounds can aid in a focused approach to reduce workarounds and thus increase patient safety. Trial Registration: Netherlands Trial Register NTR4355; http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=4355 (Archived by WebCite at http://www.webcitation.org/6pqTLxc6i). %M 28455275 %R 10.2196/resprot.7060 %U http://www.researchprotocols.org/2017/4/e74/ %U https://doi.org/10.2196/resprot.7060 %U http://www.ncbi.nlm.nih.gov/pubmed/28455275 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 4 %N 2 %P e10 %T Personal Communication Device Use by Nurses Providing In-Patient Care: Survey of Prevalence, Patterns, and Distraction Potential %A McBride,Deborah L %A LeVasseur,Sandra A %+ Samuel Merritt University, 1720 S Amphlett Blvd #300, San Mateo, CA, 94402, United States, 1 510 848 1721, dmcbride@samuelmerritt.edu %K distraction %K mobile devices %K nurses %D 2017 %7 13.04.2017 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Coincident with the proliferation of employer-provided mobile communication devices, personal communication devices, including basic and enhanced mobile phones (smartphones) and tablet computers that are owned by the user, have become ubiquitous among registered nurses working in hospitals. While there are numerous benefits of personal communication device use by nurses at work, little is known about the impact of these devices on in-patient care. Objective: Our aim was to examine how hospital-registered nurses use their personal communication devices while doing both work-related and non‒work-related activities and to assess the impact of these devices on in-patient care. Methods: A previously validated survey was emailed to 14,797 members of two national nursing organizations. Participants were asked about personal communication device use and their opinions about the impact of these devices on their own and their colleagues’ work. Results: Of the 1268 respondents (8.57% response rate), only 5.65% (70/1237) never used their personal communication device at work (excluding lunch and breaks). Respondents self-reported using their personal communication devices at work for work-related activities including checking or sending text messages or emails to health care team members (29.02%, 363/1251), as a calculator (25.34%, 316/1247), and to access work-related medical information (20.13%, 251/1247). Fewer nurses reported using their devices for non‒work-related activities including checking or sending text messages or emails to friends and family (18.75%, 235/1253), shopping (5.14%, 64/1244), or playing games (2.73%, 34/1249). A minority of respondents believe that their personal device use at work had a positive effect on their work including reducing stress (29.88%, 369/1235), benefiting patient care (28.74%, 357/1242), improving coordination of patient care among the health care team (25.34%, 315/1243), or increasing unit teamwork (17.70%, 220/1243). A majority (69.06%, 848/1228) of respondents believe that on average personal communication devices have a more negative than positive impact on patient care and 39.07% (481/1231) reported that personal communication devices were always or often a distraction while working. Respondents acknowledged their own device use negatively affected their work performance (7.56%, 94/1243), or caused them to miss important clinical information (3.83%, 47/1225) or make a medical error (0.90%, 11/1218). Respondents reported witnessing another nurse’s use of devices negatively affect their work performance (69.41%, 860/1239), or cause them to miss important clinical information (30.61%, 378/1235) or make a medical error (12.51%, 155/1239). Younger respondents reported greater device use while at work than older respondents and generally had more positive opinions about the impact of personal communication devices on their work. Conclusions: The majority of registered nurses believe that the use of personal communication devices on hospital units raises significant safety issues. The high rate of respondents who saw colleagues distracted by their devices compared to the rate who acknowledged their own distraction may be an indication that nurses are unaware of their own attention deficits while using their devices. There were clear generational differences in personal communication device use at work and opinions about the impact of these devices on patient care. Professional codes of conduct for personal communication device use by hospital nurses need to be developed that maximize the benefits of personal communication device use, while reducing the potential for distraction and adverse outcomes. %M 28408359 %R 10.2196/humanfactors.5110 %U http://humanfactors.jmir.org/2017/2/e10/ %U https://doi.org/10.2196/humanfactors.5110 %U http://www.ncbi.nlm.nih.gov/pubmed/28408359 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 19 %N 2 %P e31 %T A Mobile Device App to Reduce Time to Drug Delivery and Medication Errors During Simulated Pediatric Cardiopulmonary Resuscitation: A Randomized Controlled Trial %A Siebert,Johan N %A Ehrler,Frederic %A Combescure,Christophe %A Lacroix,Laurence %A Haddad,Kevin %A Sanchez,Oliver %A Gervaix,Alain %A Lovis,Christian %A Manzano,Sergio %+ Department of Pediatric Emergency Medicine, Geneva Children's Hospital, University Hospitals of Geneva, Avenue de la Roseraie, 47, Geneva, 1205, Switzerland, 41 795534072, Johan.Siebert@hcuge.ch %K resuscitation %K medication errors %K pharmaceutical preparations %K pediatrics %K biomedical technology %D 2017 %7 01.02.2017 %9 Original Paper %J J Med Internet Res %G English %X Background: During pediatric cardiopulmonary resuscitation (CPR), vasoactive drug preparation for continuous infusion is both complex and time-consuming, placing children at higher risk than adults for medication errors. Following an evidence-based ergonomic-driven approach, we developed a mobile device app called Pediatric Accurate Medication in Emergency Situations (PedAMINES), intended to guide caregivers step-by-step from preparation to delivery of drugs requiring continuous infusion. Objective: The aim of our study was to determine whether the use of PedAMINES reduces drug preparation time (TDP) and time to delivery (TDD; primary outcome), as well as medication errors (secondary outcomes) when compared with conventional preparation methods. Methods: The study was a randomized controlled crossover trial with 2 parallel groups comparing PedAMINES with a conventional and internationally used drugs infusion rate table in the preparation of continuous drug infusion. We used a simulation-based pediatric CPR cardiac arrest scenario with a high-fidelity manikin in the shock room of a tertiary care pediatric emergency department. After epinephrine-induced return of spontaneous circulation, pediatric emergency nurses were first asked to prepare a continuous infusion of dopamine, using either PedAMINES (intervention group) or the infusion table (control group), and second, a continuous infusion of norepinephrine by crossing the procedure. The primary outcome was the elapsed time in seconds, in each allocation group, from the oral prescription by the physician to TDD by the nurse. TDD included TDP. The secondary outcome was the medication dosage error rate during the sequence from drug preparation to drug injection. Results: A total of 20 nurses were randomized into 2 groups. During the first study period, mean TDP while using PedAMINES and conventional preparation methods was 128.1 s (95% CI 102-154) and 308.1 s (95% CI 216-400), respectively (180 s reduction, P=.002). Mean TDD was 214 s (95% CI 171-256) and 391 s (95% CI 298-483), respectively (177.3 s reduction, P=.002). Medication errors were reduced from 70% to 0% (P<.001) by using PedAMINES when compared with conventional methods. Conclusions: In this simulation-based study, PedAMINES dramatically reduced TDP, to delivery and the rate of medication errors. %M 28148473 %R 10.2196/jmir.7005 %U http://www.jmir.org/2017/2/e31/ %U https://doi.org/10.2196/jmir.7005 %U http://www.ncbi.nlm.nih.gov/pubmed/28148473 %0 Journal Article %@ 2369-1999 %I JMIR Publications %V 2 %N 2 %P e16 %T Clinical Trial Electronic Portals for Expedited Safety Reporting: Recommendations from the Clinical Trials Transformation Initiative Investigational New Drug Safety Advancement Project %A Perez,Raymond P %A Finnigan,Shanda %A Patel,Krupa %A Whitney,Shanell %A Forrest,Annemarie %+ Clinical Trials Transformation Initiative, 300 W Morgan St, Suite 800, Durham, NC, United States, 1 919 943 0358, annemarie.forrest@duke.edu %K clinical trials %K investigational new drug application %K risk management %D 2016 %7 15.12.2016 %9 Original Paper %J JMIR Cancer %G English %X Background: Use of electronic clinical trial portals has increased in recent years to assist with sponsor-investigator communication, safety reporting, and clinical trial management. Electronic portals can help reduce time and costs associated with processing paperwork and add security measures; however, there is a lack of information on clinical trial investigative staff’s perceived challenges and benefits of using portals. Objective: The Clinical Trials Transformation Initiative (CTTI) sought to (1) identify challenges to investigator receipt and management of investigational new drug (IND) safety reports at oncologic investigative sites and coordinating centers and (2) facilitate adoption of best practices for communicating and managing IND safety reports using electronic portals. Methods: CTTI, a public-private partnership to improve the conduct of clinical trials, distributed surveys and conducted interviews in an opinion-gathering effort to record investigator and research staff views on electronic portals in the context of the new safety reporting requirements described in the US Food and Drug Administration’s final rule (Code of Federal Regulations Title 21 Section 312). The project focused on receipt, management, and review of safety reports as opposed to the reporting of adverse events. Results: The top challenge investigators and staff identified in using individual sponsor portals was remembering several complex individual passwords to access each site. Also, certain tasks are time-consuming (eg, downloading reports) due to slow sites or difficulties associated with particular operating systems or software. To improve user experiences, respondents suggested that portals function independently of browsers and operating systems, have intuitive interfaces with easy navigation, and incorporate additional features that would allow users to filter, search, and batch safety reports. Conclusions: Results indicate that an ideal system for sharing expedited IND safety information is through a central portal used by all sponsors. Until this is feasible, electronic reporting portals should at least have consistent functionality. CTTI has issued recommendations to improve the quality and use of electronic portals. %M 28410179 %R 10.2196/cancer.6701 %U http://cancer.jmir.org/2016/2/e16/ %U https://doi.org/10.2196/cancer.6701 %U http://www.ncbi.nlm.nih.gov/pubmed/28410179 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 4 %N 4 %P e131 %T Design and Testing of the Safety Agenda Mobile App for Managing Health Care Managers’ Patient Safety Responsibilities %A Mira,José Joaquín %A Carrillo,Irene %A Fernandez,Cesar %A Vicente,Maria Asuncion %A Guilabert,Mercedes %+ Alicante-Sant Joan Health District, Consellería Sanitat, Hospital-Plá Health Center c/ Hermanos López Osaba s/n, Alicante, 03013, Spain, 34 606433599, jose.mira@umh.es %K patient safety %K mobile apps %K administrators %K health service %D 2016 %7 08.12.2016 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: Adverse events are a reality in clinical practice. Reducing the prevalence of preventable adverse events by stemming their causes requires health managers’ engagement. Objective: The objective of our study was to develop an app for mobile phones and tablets that would provide managers with an overview of their responsibilities in matters of patient safety and would help them manage interventions that are expected to be carried out throughout the year. Methods: The Safety Agenda Mobile App (SAMA) was designed based on standardized regulations and reviews of studies about health managers’ roles in patient safety. A total of 7 managers used a beta version of SAMA for 2 months and then they assessed and proposed improvements in its design. Their experience permitted redesigning SAMA, improving functions and navigation. A total of 74 Spanish health managers tried out the revised version of SAMA. After 4 months, their assessment was requested in a voluntary and anonymous manner. Results: SAMA is an iOS app that includes 37 predefined tasks that are the responsibility of health managers. Health managers can adapt these tasks to their schedule, add new ones, and share them with their team. SAMA menus are structured in 4 main areas: information, registry, task list, and settings. Of the 74 users who tested SAMA, 64 (86%) users provided a positive assessment of SAMA characteristics and utility. Over an 11-month period, 238 users downloaded SAMA. This mobile app has obtained the AppSaludable (HealthyApp) Quality Seal. Conclusions: SAMA includes a set of activities that are expected to be carried out by health managers in matters of patient safety and contributes toward improving the awareness of their responsibilities in matters of safety. %M 27932315 %R 10.2196/mhealth.5796 %U http://mhealth.jmir.org/2016/4/e131/ %U https://doi.org/10.2196/mhealth.5796 %U http://www.ncbi.nlm.nih.gov/pubmed/27932315 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 3 %N 2 %P e24 %T Role of Large Clinical Datasets From Physiologic Monitors in Improving the Safety of Clinical Alarm Systems and Methodological Considerations: A Case From Philips Monitors %A Sowan,Azizeh Khaled %A Reed,Charles Calhoun %A Staggers,Nancy %+ School of Nursing, Department of Health Restoration & Care Systems Management, University of Texas Health Science Center at San Antonio, Suite 2.628, 7703 Floyd Curl Dr - MC 7975, San Antonio, TX, 78229-3900, United States, 1 (210) 567 579, sowan@uthscsa.edu %K large clinical data %K audit log %K physiologic monitors %K clinical alarms %K alarm fatigue %K intensive care unit %K nursing %D 2016 %7 30.09.2016 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Large datasets of the audit log of modern physiologic monitoring devices have rarely been used for predictive modeling, capturing unsafe practices, or guiding initiatives on alarm systems safety. Objective: This paper (1) describes a large clinical dataset using the audit log of the physiologic monitors, (2) discusses benefits and challenges of using the audit log in identifying the most important alarm signals and improving the safety of clinical alarm systems, and (3) provides suggestions for presenting alarm data and improving the audit log of the physiologic monitors. Methods: At a 20-bed transplant cardiac intensive care unit, alarm data recorded via the audit log of bedside monitors were retrieved from the server of the central station monitor. Results: Benefits of the audit log are many. They include easily retrievable data at no cost, complete alarm records, easy capture of inconsistent and unsafe practices, and easy identification of bedside monitors missed from a unit change of alarm settings adjustments. Challenges in analyzing the audit log are related to the time-consuming processes of data cleaning and analysis, and limited storage and retrieval capabilities of the monitors. Conclusions: The audit log is a function of current capabilities of the physiologic monitoring systems, monitor’s configuration, and alarm management practices by clinicians. Despite current challenges in data retrieval and analysis, large digitalized clinical datasets hold great promise in performance, safety, and quality improvement. Vendors, clinicians, researchers, and professional organizations should work closely to identify the most useful format and type of clinical data to expand medical devices’ log capacity. %M 27694097 %R 10.2196/humanfactors.6427 %U http://humanfactors.jmir.org/2016/2/e24/ %U https://doi.org/10.2196/humanfactors.6427 %U http://www.ncbi.nlm.nih.gov/pubmed/27694097 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 9 %P e257 %T Design and Testing of BACRA, a Web-Based Tool for Middle Managers at Health Care Facilities to Lead the Search for Solutions to Patient Safety Incidents %A Carrillo,Irene %A Mira,José Joaquín %A Vicente,Maria Asuncion %A Fernandez,Cesar %A Guilabert,Mercedes %A Ferrús,Lena %A Zavala,Elena %A Silvestre,Carmen %A Pérez-Pérez,Pastora %+ Health Psychology Department, Miguel Hernández University, Universidad s/n, Elche, 03202, Spain, 34 966658984, icarrillo@umh.es %K patient safety %K risk management %K root cause analysis %K hospital %K primary care %K frontline health professionals %K middle managers %D 2016 %7 27.09.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Lack of time, lack of familiarity with root cause analysis, or suspicion that the reporting may result in negative consequences hinder involvement in the analysis of safety incidents and the search for preventive actions that can improve patient safety. Objective: The aim was develop a tool that enables hospitals and primary care professionals to immediately analyze the causes of incidents and to propose and implement measures intended to prevent their recurrence. Methods: The design of the Web-based tool (BACRA) considered research on the barriers for reporting, review of incident analysis tools, and the experience of eight managers from the field of patient safety. BACRA’s design was improved in successive versions (BACRA v1.1 and BACRA v1.2) based on feedback from 86 middle managers. BACRA v1.1 was used by 13 frontline professionals to analyze incidents of safety; 59 professionals used BACRA v1.2 and assessed the respective usefulness and ease of use of both versions. Results: BACRA contains seven tabs that guide the user through the process of analyzing a safety incident and proposing preventive actions for similar future incidents. BACRA does not identify the person completing each analysis since the password introduced to hide said analysis only is linked to the information concerning the incident and not to any personal data. The tool was used by 72 professionals from hospitals and primary care centers. BACRA v1.2 was assessed more favorably than BACRA v1.1, both in terms of its usefulness (z=2.2, P=.03) and its ease of use (z=3.0, P=.003). Conclusions: BACRA helps to analyze incidents of safety and to propose preventive actions. BACRA guarantees anonymity of the analysis and reduces the reluctance of professionals to carry out this task. BACRA is useful and easy to use. %M 27678308 %R 10.2196/jmir.5942 %U http://www.jmir.org/2016/9/e257/ %U https://doi.org/10.2196/jmir.5942 %U http://www.ncbi.nlm.nih.gov/pubmed/27678308 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 7 %P e201 %T Investigating the Potential Contribution of Patient Rating Sites to Hospital Supervision: Exploratory Results From an Interview Study in the Netherlands %A Kleefstra,Sophia Martine %A Zandbelt,Linda C %A Borghans,Ine %A de Haes,Hanneke J.C.J.M %A Kool,Rudolf B %+ Dutch Health Care Inspectorate, Department of Risk Detection and Development, Stadsplateau 1, Utrecht, 3521 AZ, Netherlands, 31 881205000, sm.kleefstra@igz.nl %K patient rating sites %K patient satisfaction %K patient experiences %K hospitals %K quality of health care %K supervision %D 2016 %7 20.07.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Over the last decades, the patient perspective on health care quality has been unconditionally integrated into quality management. For several years now, patient rating sites have been rapidly gaining attention. These offer a new approach toward hearing the patient’s perspective on the quality of health care. Objective: The aim of our study was to explore whether and how patient reviews of hospitals, as reported on rating sites, have the potential to contribute to health care inspector’s daily supervision of hospital care. Methods: Given the unexplored nature of the topic, an interview study among hospital inspectors was designed in the Netherlands. We performed 2 rounds of interviews with 10 senior inspectors, addressing their use and their judgment on the relevance of review data from a rating site. Results: All 10 Dutch senior hospital inspectors participated in this research. The inspectors initially showed some reluctance to use the major patient rating site in their daily supervision. This was mainly because of objections such as worries about how representative they are, subjectivity, and doubts about the relevance of patient reviews for supervision. However, confrontation with, and assessment of, negative reviews by the inspectors resulted in 23% of the reviews being deemed relevant for risk identification. Most inspectors were cautiously positive about the contribution of the reviews to their risk identification. Conclusions: Patient rating sites may be of value to the risk-based supervision of hospital care carried out by the Health Care Inspectorate. Health care inspectors do have several objections against the use of patient rating sites for daily supervision. However, when they are presented with texts of negative reviews from a hospital under their supervision, it appears that most inspectors consider it as an additional source of information to detect poor quality of care. Still, it should always be accompanied and verified by other quality and safety indicators. More research on the value and usability of patient rating sites in daily hospital supervision and other health settings is needed. %M 27439392 %R 10.2196/jmir.5552 %U http://www.jmir.org/2016/7/e201/ %U https://doi.org/10.2196/jmir.5552 %U http://www.ncbi.nlm.nih.gov/pubmed/27439392 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 7 %P e180 %T The Effect of a Freely Available Flipped Classroom Course on Health Care Worker Patient Safety Culture: A Prospective Controlled Study %A Ling,Lowell %A Gomersall,Charles David %A Samy,Winnie %A Joynt,Gavin Matthew %A Leung,Czarina CH %A Wong,Wai-Tat %A Lee,Anna %+ The Chinese University of Hong Kong, Department of Anaesthesia and Intensive Care, 4th Floor, Main Clinical Block and Trauma Centre, Prince of Wales Hospital, Shatin,, China (Hong Kong), 852 2632 2735, annalee@cuhk.edu.hk %K patient safety %K critical care %K education, professional %K education, distance %K safety culture %D 2016 %7 05.07.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Patient safety culture is an integral aspect of good standard of care. A good patient safety culture is believed to be a prerequisite for safe medical care. However, there is little evidence on whether general education can enhance patient safety culture. Objective: Our aim was to assess the impact of a standardized patient safety course on health care worker patient safety culture. Methods: Health care workers from Intensive Care Units (ICU) at two hospitals (A and B) in Hong Kong were recruited to compare the changes in safety culture before and after a patient safety course. The BASIC Patient Safety course was administered only to staff from Hospital A ICU. Safety culture was assessed in both units at two time points, one before and one after the course, by using the Hospital Survey on Patient Safety Culture questionnaire. Responses were coded according to the Survey User’s Guide, and positive response percentages for each patient safety domain were compared to the 2012 Agency for Healthcare Research and Quality ICU sample of 36,120 respondents. Results: We distributed 127 questionnaires across the two hospitals with an overall response rate of 74.8% (95 respondents). After the safety course, ICU A significantly improved on teamwork within hospital units (P=.008) and hospital management support for patient safety (P<.001), but decreased in the frequency of reporting mistakes compared to the initial survey (P=.006). Overall, ICU A staff showed significantly greater enhancement in positive responses in five domains than staff from ICU B. Pooled data indicated that patient safety culture was poorer in the two ICUs than the average ICU in the Agency for Healthcare Research and Quality database, both overall and in every individual domain except hospital management support for patient safety and hospital handoffs and transitions. Conclusions: Our study demonstrates that a structured, reproducible short course on patient safety may be associated with an enhancement in several domains in ICU patient safety culture. %M 27381876 %R 10.2196/jmir.5378 %U http://www.jmir.org/2016/7/e180/ %U https://doi.org/10.2196/jmir.5378 %U http://www.ncbi.nlm.nih.gov/pubmed/27381876 %0 Journal Article %@ 2292-9495 %I Gunther Eysenbach %V 3 %N 1 %P e15 %T How Regrouping Alerts in Computerized Physician Order Entry Layout Influences Physicians’ Prescription Behavior: Results of a Crossover Randomized Trial %A Wipfli,Rolf %A Ehrler,Frederic %A Bediang,Georges %A Bétrancourt,Mireille %A Lovis,Christian %+ Division of Medical Information Sciences, Department of Radiology and Medical Informatics, University Hospitals of Geneva, Rue Gabrielle-Perret-Gentil 4, Geneva,, Switzerland, 41 22 372 8697, frederic.ehrler@hcuge.ch %K medical order entry systems %K clinical decision support systems %K adverse drug reaction reporting systems %K User-Computer Interface %K eye tracking %D 2016 %7 02.06.2016 %9 Original Paper %J JMIR Human Factors %G English %X Background: As demonstrated in several publications, low positive predictive value alerts in computerized physician order entry (CPOE) induce fatigue and may interrupt physicians unnecessarily during prescription of medication. Although it is difficult to increase the consideration of medical alerts by physician through an improvement of their predictive value, another approach consists to act on the way they are presented. The interruption management model inspired us to propose an alternative alert display strategy of regrouping the alerts in the screen layout, as a possible solution for reducing the interruption in physicians’ workflow. Objective: In this study, we compared 2 CPOE designs based on a particular alert presentation strategy: one design involved regrouping the alerts in a single place on the screen, and in the other, the alerts were located next to the triggering information. Our objective was to evaluate experimentally whether the new design led to fewer interruptions in workflow and if it affected alert handling. Methods: The 2 CPOE designs were compared in a controlled crossover randomized trial. All interactions with the system and eye movements were stored for quantitative analysis. Results: The study involved a group of 22 users consisting of physicians and medical students who solved medical scenarios containing prescription tasks. Scenario completion time was shorter when the alerts were regrouped (mean 117.29 seconds, SD 36.68) than when disseminated on the screen (mean 145.58 seconds, SD 75.07; P=.045). Eye tracking revealed that physicians fixated longer on alerts in the classic design (mean 119.71 seconds, SD 76.77) than in the centralized alert design (mean 70.58 seconds, SD 33.53; P=.001). Visual switches between prescription and alert areas, indicating interruption, were reduced with centralized alerts (mean 41.29, SD 21.26) compared with the classic design (mean 57.81, SD 35.97; P=.04). Prescription behavior (ie, prescription changes after alerting), however, did not change significantly between the 2 strategies of display. The After-Scenario Questionnaire (ASQ) that was filled out after each scenario showed that overall satisfaction was significantly rated lower when alerts were regrouped (mean 4.37, SD 1.23) than when displayed next to the triggering information (mean 5.32, SD 0.94; P=.02). Conclusions: Centralization of alerts in a table might be a way to motivate physicians to manage alerts more actively, in a meaningful way, rather than just being interrupted by them. Our study could not provide clear recommendations yet, but provides objective data through a cognitive psychological approach. Future tests should work on standardized scenarios that would enable to not only measure physicians’ behavior (visual fixations and handling of alerts) but also validate those actions using clinical criteria. %M 27255612 %R 10.2196/humanfactors.5320 %U http://humanfactors.jmir.org/2016/1/e15/ %U https://doi.org/10.2196/humanfactors.5320 %U http://www.ncbi.nlm.nih.gov/pubmed/27255612 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 18 %N 5 %P e125 %T Patient-Reported Safety Events in Chronic Kidney Disease Recorded With an Interactive Voice-Inquiry Dial-Response System: Monthly Report Analysis %A Fink,Jeffrey C %A Doerfler,Rebecca M %A Yoffe,Marni R %A Diamantidis,Clarissa J %A Blumenthal,Jacob B %A Siddiqui,Tariq %A Gardner,James F %A Snitker,Soren %A Zhan,Min %+ University of Maryland School of Medicine, Department of Medicine, 22 S Greene St, Baltimore, MD, 21201, United States, 1 4107066563, jfink@medicine.umaryland.edu %K patient-reported outcomes %K CKD %K interactive voice-response system %K patient safety %D 2016 %7 26.05.2016 %9 Original Paper %J J Med Internet Res %G English %X Background: Monitoring patient-reported outcomes (PROs) may improve safety of chronic kidney disease (CKD) patients. Objective: Evaluate the performance of an interactive voice-inquiry dial-response system (IVRDS) in detecting CKD-pertinent adverse safety events outside of the clinical environment and compare the incidence of events using the IVDRS to that detected by paper diary. Methods: This was a 6-month study of Stage III-V CKD patients in the Safe Kidney Care (SKC) study. Participants crossed over from a paper diary to the IVDRS for recording patient-reported safety events defined as symptoms or events attributable to medications or care. The IVDRS was adapted from the SKC paper diary to record event frequency and remediation. Participants were auto-called weekly and permitted to self-initiate calls. Monthly reports were reviewed by two physician adjudicators for their clinical significance. Results: 52 participants were followed over a total of 1384 weeks. 28 out of 52 participants (54%) reported events using the IVDRS versus 8 out of 52 (15%) with the paper diary; hypoglycemia was the most common event for both methods. All IVDRS menu options were selected at least once except for confusion and rash. Events were reported on 121 calls, with 8 calls reporting event remediation by ambulance or emergency room (ER) visit. The event rate with the IVDRS and paper diary, with and without hypoglycemia, was 26.7 versus 4.7 and 18.3 versus 0.8 per 100 person weeks, respectively (P=.002 and P<.001). The frequent users (ie, >10 events) largely differed by method, and event rates excluding the most frequent user of each were 16.9 versus 2.5 per 100 person weeks, respectively (P<.001). Adjudicators found approximately half the 80 reports clinically significant, with about a quarter judged as actionable. Hypoglycemia was often associated with additional reports of fatigue and falling. Participants expressed favorable satisfaction with the IVDRS. Conclusions: Use of the IVDRS among CKD patients reveals a high frequency of clinically significant safety events and has the potential to be used as an important supplement to clinical care for improving patient safety. %M 27230267 %R 10.2196/jmir.5203 %U http://www.jmir.org/2016/5/e125/ %U https://doi.org/10.2196/jmir.5203 %U http://www.ncbi.nlm.nih.gov/pubmed/27230267 %0 Journal Article %@ 2291-9694 %I Gunther Eysenbach %V 4 %N 2 %P e13 %T Electronic Health Record-Related Safety Concerns: A Cross-Sectional Survey of Electronic Health Record Users %A Palojoki,Sari %A Pajunen,Tuuli %A Saranto,Kaija %A Lehtonen,Lasse %+ Helsinki and Uusimaa Hospital District, Helsinki University Hospital, Group Administration, P.O. Box 100, Stenbäckinkatu 9, Helsinki, 00029, Finland, 358 504284179, sari.palojoki@hus.fi %K Electronic Health Records %K Health Information Technology %K Patient Safety %K Risk Assessment %K Questionnaire %D 2016 %7 06.05.2016 %9 Original Paper %J JMIR Med Inform %G English %X Background: The rapid expansion in the use of electronic health records (EHR) has increased the number of medical errors originating in health information systems (HIS). The sociotechnical approach helps in understanding risks in the development, implementation, and use of EHR and health information technology (HIT) while accounting for complex interactions of technology within the health care system. Objective: This study addresses two important questions: (1) “which of the common EHR error types are associated with perceived high- and extreme-risk severity ratings among EHR users?”, and (2) “which variables are associated with high- and extreme-risk severity ratings?” Methods: This study was a quantitative, non-experimental, descriptive study of EHR users. We conducted a cross-sectional web-based questionnaire study at the largest hospital district in Finland. Statistical tests included the reliability of the summative scales tested with Cronbach’s alpha. Logistic regression served to assess the association of the independent variables to each of the eight risk factors examined. Results: A total of 2864 eligible respondents provided the final data. Almost half of the respondents reported a high level of risk related to the error type “extended EHR unavailability”. The lowest overall risk level was associated with “selecting incorrectly from a list of items”. In multivariate analyses, profession and clinical unit proved to be the strongest predictors for high perceived risk. Physicians perceived risk levels to be the highest (P<.001 in six of eight error types), while emergency departments, operating rooms, and procedure units were associated with higher perceived risk levels (P<.001 in four of eight error types). Previous participation in eLearning courses on EHR-use was associated with lower risk for some of the risk factors. Conclusions: Based on a large number of Finnish EHR users in hospitals, this study indicates that HIT safety hazards should be taken very seriously, particularly in operating rooms, procedure units, emergency departments, and intensive care units/critical care units. Health care organizations should use proactive and systematic assessments of EHR risks before harmful events occur. An EHR training program should be compulsory for all EHR users in order to address EHR safety concerns resulting from the failure to use HIT appropriately. %M 27154599 %R 10.2196/medinform.5238 %U http://medinform.jmir.org/2016/2/e13/ %U https://doi.org/10.2196/medinform.5238 %U http://www.ncbi.nlm.nih.gov/pubmed/27154599 %0 Journal Article %@ 2292-9495 %I Gunther Eysenbach %V 2 %N 2 %P e15 %T Assessing the Usability of Six Data Entry Mobile Interfaces for Caregivers: A Randomized Trial %A Ehrler,Frederic %A Haller,Guy %A Sarrey,Evelyne %A Walesa,Magali %A Wipfli,Rolf %A Lovis,Christian %+ Division of Medical Information Sciences, Department of medical imaging and medical information sciences, University Hospitals of Geneva, Rue Gabrielle-Perret-Gentil 4, Geneva, 1211, Switzerland, 41 223728697, frederic.ehrler@hcuge.ch %K data collection %K mobile applications %K computers, handheld %K user-computer interface %K vital signs %K patient safety %D 2015 %7 15.12.2015 %9 Original Paper %J JMIR Human Factors %G English %X Background: There is an increased demand in hospitals for tools, such as dedicated mobile device apps, that enable the recording of clinical information in an electronic format at the patient’s bedside. Although the human-machine interface design on mobile devices strongly influences the accuracy and effectiveness of data recording, there is still a lack of evidence as to which interface design offers the best guarantee for ease of use and quality of recording. Therefore, interfaces need to be assessed both for usability and reliability because recording errors can seriously impact the overall level of quality of the data and affect the care provided. Objective: In this randomized crossover trial, we formally compared 6 handheld device interfaces for both speed of data entry and accuracy of recorded information. Three types of numerical data commonly recorded at the patient’s bedside were used to evaluate the interfaces. Methods: In total, 150 health care professionals from the University Hospitals of Geneva volunteered to record a series of randomly generated data on each of the 6 interfaces provided on a smartphone. The interfaces were presented in a randomized order as part of fully automated data entry scenarios. During the data entry process, accuracy and effectiveness were automatically recorded by the software. Results: Various types of errors occurred, which ranged from 0.7% for the most reliable design to 18.5% for the least reliable one. The length of time needed for data recording ranged from 2.81 sec to 14.68 sec, depending on the interface. The numeric keyboard interface delivered the best performance for pulse data entry with a mean time of 3.08 sec (SD 0.06) and an accuracy of 99.3%. Conclusions: Our study highlights the critical impact the choice of an interface can have on the quality of recorded data. Selecting an interface should be driven less by the needs of specific end-user groups or the necessity to facilitate the developer’s task (eg, by opting for default solutions provided by commercial platforms) than by the level of speed and accuracy an interface can provide for recording information. An important effort must be made to properly validate mobile device interfaces intended for use in the clinical setting. In this regard, our study identified the numeric keyboard, among the proposed designs, as the most accurate interface for entering specific numerical values. This is an important step toward providing clearer guidelines on which interface to choose for the appropriate use of handheld device interfaces in the health care setting. %M 27025648 %R 10.2196/humanfactors.4093 %U http://humanfactors.jmir.org/2015/2/e15/ %U https://doi.org/10.2196/humanfactors.4093 %U http://www.ncbi.nlm.nih.gov/pubmed/27025648 %0 Journal Article %@ 2292-9495 %I Gunther Eysenbach %V 2 %N 1 %P e6 %T Nursing Performance and Mobile Phone Use: Are Nurses Aware of Their Performance Decrements? %A McBride,Deborah %A LeVasseur,Sandra A %A Li,Dongmei %+ Samuel Merritt University, 3100 Telegraph Ave, Oakland, CA, 94609, United States, 1 510 848 1721, dmcbride@samuelmerritt.edu %K distraction %K mobile phone %K cellular phone %K Internet %K nurses %K hospital %K non-work related mobile phone use %D 2015 %7 23.04.2015 %9 Original Paper %J JMIR Human Factors %G English %X Background: Prior research has documented the effect of concurrent mobile phone use on medical care. This study examined the extent of hospital registered nurses’ awareness of their mobile-phone-associated performance decrements. Objective: The objective of this study was to compare self-reported performance with reported observed performance of others with respect to mobile phone use by hospital registered nurses. Methods: In March 2014, a previously validated survey was emailed to the 10,978 members of the Academy of Medical Surgical Nurses. The responses were analyzed using a two-proportion z test (alpha=.05, two-tailed) to examine whether self-reported and observed rates of error were significantly different. All possible demographic and employment confounders which could potentially contribute to self-reported and observed performance errors were tested for significance. Results: Of the 950 respondents, 825 (8.68%, 825/950) met the inclusion criteria for analysis. The representativeness of the sample relative to the US nursing workforce was assessed using a two-proportion z test. This indicated that sex and location of primary place of employment (urban/rural) were represented appropriately in the study sample. Respondents in the age groups <40 years old were underrepresented, while age groups >55 years old were overrepresented. Whites, American Indians/Alaskan natives, and Native Hawaiian or Pacific Islanders were underrepresented, while Hispanic and multiple/other ethnicities were overrepresented. It was decided to report the unweighted, rather than the weighted survey data, with the recognition that the results, while valuable, may not be generalizable to the entire US registered nursing workforce. A significant difference was found between registered nurses’ self-reported and observed rates of errors associated with concurrent mobile phone use in following three categories (1) work performance (z=−26.6142, P<.001, Fisher’s exact test), (2) missing important clinical information (z=−13.9882, P=.008, Fisher’s exact test), and (3) making a medical error (z=−9.6798, P<.001, Fisher’s exact test). Respondents reported that personal mobile phone use by nurses at work was a serious distraction; always (13%, 107/825), often (29.6%, 244/825), sometimes (44.6%, 368/825), rarely (8.7%, 72/825), or never (1.2%, 10/825). On balance, 69.5% (573/825) of respondents believed that nurses’ use of personal mobile phones while working had a negative effect on patient care. Since all possible confounders were tested and none were deemed significant, a multivariate analysis was not considered necessary. Conclusions: Many hospitals are drawing up policies that allow workers to decide how to use their devices at work. This study found that nurses express a disproportionately high confidence in their ability to manage the risk associated with the use of mobile phones and may not be able to accurately assess when it is appropriate to use these devices at work. %M 27026182 %R 10.2196/humanfactors.4070 %U http://humanfactors.jmir.org/2015/1/e6/ %U https://doi.org/10.2196/humanfactors.4070 %U http://www.ncbi.nlm.nih.gov/pubmed/27026182 %0 Journal Article %@ 2292-9495 %I Gunther Eysenbach %V 2 %N 1 %P e3 %T Nurses' Perceptions and Practices Toward Clinical Alarms in a Transplant Cardiac Intensive Care Unit: Exploring Key Issues Leading to Alarm Fatigue %A Sowan,Azizeh Khaled %A Tarriela,Albert Fajardo %A Gomez,Tiffany Michelle %A Reed,Charles Calhoun %A Rapp,Kami Marie %+ University of Texas Health Science Center at San Antonio, School of Nursing, Department of Health Restoration and Care Systems Management, School of Nursing, 7703 Floyd Curl Dr. - MC 7975, San Antonio, TX, 78229-3900, United States, 1 210 567 5799, sowan@uthscsa.edu %K clinical alarms %K alarm fatigue %K critical care %K physiologic monitors %K nursing %K survey %D 2015 %7 16.03.2015 %9 Original Paper %J JMIR Human Factors %G English %X Background: Intensive care units (ICUs) are complex work environments where false alarms occur more frequently than on non-critical care units. The Joint Commission National Patient Safety Goal .06.01.01 targeted improving the safety of clinical alarm systems and required health care facilities to establish alarm systems safety as a hospital priority by July 2014. An important initial step toward this requirement is identifying ICU nurses’ perceptions and common clinical practices toward clinical alarms, where little information is available. Objective: Our aim was to determine perceptions and practices of transplant/cardiac ICU (TCICU) nurses toward clinical alarms and benchmark the results against the 2011 Healthcare Technology Foundation’s (HTF) Clinical Alarms Committee Survey. Methods: A quality improvement project was conducted on a 20-bed TCICU with 39 full- and part-time nurses. Nurses were surveyed about their perceptions and attitudes toward and practices on clinical alarms using an adapted HTF clinical alarms survey. Results were compared to the 2011 HTF data. Correlations among variables were examined. Results: All TCICU nurses provided usable responses (N=39, 100%). Almost all nurses (95%-98%) believed that false alarms are frequent, disrupt care, and reduce trust in alarm systems, causing nurses to inappropriately disable them. Unlike the 2011 HTF clinical alarms survey results, a significantly higher percentage of our TCICU nurses believed that existing devices are complex, questioned the ability and adequacy of the new monitoring systems to solve alarm management issues, pointed to the lack of prompt response to alarms, and indicated the lack of clinical policy on alarm management (P<.01). Major themes in the narrative data focused on nurses’ frustration related to the excessive number of alarms and poor usability of the cardiac monitors. A lack of standardized approaches exists in changing patients’ electrodes and individualizing parameters. Around 60% of nurses indicated they received insufficient training on bedside and central cardiac monitors. A correlation also showed the need for training on cardiac monitors, specifically for older nurses (P=.01). Conclusions: False and non-actionable alarms continue to desensitize TCICU nurses, perhaps resulting in missing fatal alarms. Nurses’ attitudes and practices related to clinical alarms are key elements for designing contextually sensitive quality initiatives to fight alarm fatigue. Alarm management in ICUs is a multidimensional complex process involving usability of monitoring devices, and unit, clinicians, training, and policy-related factors. This indicates the need for a multi-method approach to decrease alarm fatigue and improve alarm systems safety. %M 27025940 %R 10.2196/humanfactors.4196 %U http://humanfactors.jmir.org/2015/1/e3/ %U https://doi.org/10.2196/humanfactors.4196 %U http://www.ncbi.nlm.nih.gov/pubmed/27025940