This paper is in the following e-collection/theme issue:

User-centred Design Case Studies (563) Human Factors and Usability Case Studies (739) Focus Groups and Qualitative Research for Human Factors Research (764) Patient Monitoring and Anesthesia Information Management Systems (9) Design and Usability of Clinical Software and EHRs (242) Nursing in a Hospital Setting (95) Intensive Care Unit (ICU) (101) Usability Evaluation Case Studies (182) Safety and Error Prevention in Health (186)
Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30655, first published .
A Remote Patient-Monitoring System for Intensive Care Medicine: Mixed Methods Human-Centered Design and Usability Evaluation

A Remote Patient-Monitoring System for Intensive Care Medicine: Mixed Methods Human-Centered Design and Usability Evaluation

A Remote Patient-Monitoring System for Intensive Care Medicine: Mixed Methods Human-Centered Design and Usability Evaluation

Authors of this article:

Akira-Sebastian Poncette1, 2 Author Orcid Image ;   Lina Katharina Mosch1, 2 Author Orcid Image ;   Lars Stablo3 Author Orcid Image ;   Claudia Spies2 Author Orcid Image ;   Monique Schieler2 Author Orcid Image ;   Steffen Weber-Carstens2 Author Orcid Image ;   Markus A Feufel3 Author Orcid Image ;   Felix Balzer1 Author Orcid Image
Article Authors Cited by (5) Tweetations (2) Metrics

    Original Paper

    1Institute of Medical Informatics, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

    2Department of Anesthesiology and Intensive Care Medicine, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany

    3Division of Ergonomics, Department of Psychology and Ergonomics (IPA), Technische Universität Berlin, Berlin, Germany

    *these authors contributed equally

    Corresponding Author:

    Felix Balzer, MSc, MD, PhD

    Institute of Medical Informatics

    Charité – Universitätsmedizin Berlin

    Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin

    Charitéplatz 1

    Berlin, 10117

    Germany

    Phone: 49 30450 ext 651166

    Email: felix.balzer@charite.de


    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

    JMIR Hum Factors 2022;9(1):e30655

    doi:10.2196/30655

    Keywords

    digital health; patient monitoring; intensive care medicine; intensive care unit; technological innovation; user-centered design; usability; user experience; implementation science; qualitative research; interview; mixed methods; mobile phone 


    Background

    Continuous monitoring of vital signs is essential for patient safety in the intensive care unit (ICU) and emergency room [1]. It is also becoming increasingly relevant in general wards [2]. In the past decade, particularly in the context of the digital transformation of health care, vital sign monitoring has undergone constant change and is being transformed and augmented by important technological innovations such as less invasive sensors, remote monitoring technology [3-5], and artificial intelligence for clinical decision support [6,7]. Together, these innovations hold great promise for improving patient safety and health care provision [8,9].

    Effective implementation of novel technologies, such as remote patient-monitoring devices, faces a variety of barriers [10-12], including lack of adoption by clinicians, often because of poor usability of the respective technologies [13-15]. In addition to its importance in successful implementation, usability is closely related to the efficacy of the technology [16,17]. A lack of usability may lead to medical errors, thus compromising patient safety [18,19]. Therefore, usability evaluation and identification of specific usability problems are essential in the development of a novel technology and its implementation in the clinical setting. However, to date, usability problems remain prominent in health information technology (IT), suggesting that usability aspects are often neglected in the health IT development process [20-22].

    The human-centered design (HCD) approach is centered on the involvement of end users and their experiences with the product throughout the design and development process [23]. Applying HCD in the early stages of the design of novel digital health technologies can improve usability, staff adoption, effectiveness, and efficiency [24,25]. Several frameworks and guidelines for redesigning health care interfaces in accordance with HCD have been published; however, their adoption in health care has been lagging, and evidence on the impact of this topic on clinical performance outcomes is scarce [26-32].

    Aim

    We aim to evaluate the usability of a remote patient-monitoring system and, specifically, identify usability problems, positive findings, and user ideas. We hypothesize that an HCD approach will help to implement evidence-based design changes that will improve the subjectively perceived usability and objective measures of the effectiveness and efficiency of the technology.


    Ethics Approval and Consent to Participate

    This study was approved by the ethics committee of the Charité–Universitätsmedizin Berlin (EA1/031/18). All participants provided consent before the study.

    Study Design

    Our usability study followed a five-step, mixed methods approach (Figure 1): (1) formative usability test of the implemented patient-monitoring platform interface design A [33], (2) identification and prioritization of usability problems, (3) conceptualization and design of prototype interface design B with informed design changes, (4) formative usability testing of design B, and (5) comparison of design A and design B. For usability testing, we applied simulated use tests with think-aloud protocols and performance measurements (subjectively perceived usability, efficiency, and effectiveness) [30,34]. For step 5, we chose a single-factor 2-group study design, as described by Gravetter and Forzano [35].

    ‎
    Figure 1. The research approach, beginning with usability testing and identification of major problems in design A, followed by prototyping of design B and its usability testing, concluding with a comparison between design A and design B.
    View this figure

    Study Setting and Technical Setup

    This study was conducted in the context of implementing the Vital Sync 2.4 virtual patient-monitoring platform (Medtronic plc) in the Post Anesthesia Care Unit, an ICU primarily for postoperative patients requiring short-term intensive care treatment and monitoring. VitalSync was used to monitor patients in the ICU from portable tablet computers on hospital premises. The primary patient-monitoring system used was the IntelliVue patient monitoring system (MX800 software, version M.00.03; MMS X2 software, version H.15.41-M.00.04) from Koninklijke Philips NV.

    Between May 2018 and June 2019, the VitalSync monitoring system was installed for 5 of the 10 ICU beds. Two sensors (for pulse oximetry and capnography) recorded peripheral capillary oxygen saturation, pulse rate, end-tidal carbon dioxide, and respiratory rate at a frequency of 1 Hz. The VitalSync user interface (UI) was displayed on a monitor at the central station and on six tablet computers (2 standard iPads, 2 iPad minis, and 2 Microsoft Surfaces). The UI of the system was structured where the home screen gave an overview of patients admitted to the system, displayed in tiles (Figure 2). Displayed were numerical values for the monitoring parameters, the patient’s name and bed location, and specific information on alarms if any. Clicking on a patient tile took the user to a screen with details about the selected patient (eg, graphical curves for end-tidal carbon dioxide values) and other functions (eg, displaying patient reports, linking, or unlinking devices). There was also the option of clicking on each parameter to see a trend analysis of that value. To link a patient to the system, the Admit Patient screen was accessed, and the patient ID was entered, after which the bed location and monitoring device could be selected to complete the admission process (Figure 3) [36-38]. Further technical description and details regarding the use of the software can be found elsewhere [10].

    ‎
    Figure 2. Home screen of the implemented patient-monitoring platform (design A). etCO2: end-tidal carbon dioxide; PR: pulse rate; RR: respiratory rate; SPO2: peripheral capillary oxygen saturation [36-38].
    View this figure
    ‎
    Figure 3. Admit Patient screen of the implemented patient-monitoring platform (design A) [36-38].
    View this figure

    Research Team

    Following the principles of HCD [39], our research team members have multidisciplinary skills and perspectives. Specifically, the team included a physician with a background in anesthesiology, intensive care medicine, geriatrics, and digital health (ASP); a senior medical student with a focus on digital health (LM); a senior human factors student with a background in engineering (LS); a professor of ergonomics with a PhD in human factors and industrial and organizational psychology (MF); the anesthesiology department’s head of staff (CS); and a professor of medical data science, who is also a consultant anesthesiologist and computer scientist (FB).

    Data Collection

    Data collection took place from August 23, 2019, to March 10, 2020. Our data comprised think-aloud transcripts of the first block of usability tests (ie, design A), researcher notes (including click patterns), and posttest questionnaires from the two blocks of usability tests (ie, design A and design B). We conducted 10 usability tests with ICU staff—5 (50%) tests each for design A (August and November 2019) and design B (February and March 2020). For recruitment, we contacted potential participants via email. We aimed to represent all professions working with the remote patient-monitoring system, namely anesthesiologists (3/10, 30%), ICU nurses (5/10, 50%), and respiratory therapists (2/10, 20%). Participation was voluntary, and no incentives were offered.

    Usability testing of design A and design B was performed on an iPad mini 4 (model A1550). For testing sessions with design A, 5 patients in the ICU were connected to the system. This allowed real-time monitoring of the patients’ vital signs on the iPad used by the participants. Testing of design B differed from testing of design A in that no patients were connected to actual sensors, and only one of the researchers was present during the testing sessions.

    The testing sessions were conducted in German. Participants were asked about their profession and the number of years of professional experience in intensive care medicine. They were then given 4 tasks to complete while verbalizing their thoughts [40]. We provided the participants with the following use context: “A new patient was admitted to the unit and was connected to the etCO2 and SpO2 sensors (Mrs. Schmitt, born 01/01/1950, Patient-ID 12345, bed site 02).”

    In accordance with the requirements for formative usability testing [41], participants were selected to complete the following key tasks during the simulated use test:

    1. “Please add Mrs. Schmitt to the patients you want to monitor in Vital Sync™.”
    2. “You would like to see the trend of Mrs. Schmitt’s oxygen saturation for the last two hours. How do you proceed?”
    3. “You have identified that Mrs. Schmitt is actually not in bed 2 but in bed 6. You want to adjust this information in Vital Sync™. How do you proceed?”
    4. “Mrs. Schmitt has been discharged. Please disconnect Mrs. Schmitt’s devices and delete her entry from Vital Sync™.”

    Audio recordings of the simulated use tests were transcribed verbatim. A researcher who had not performed the transcription reviewed the transcripts. Immediately after the simulated use tests of both designs A and B, participants were asked to complete a posttest questionnaire, including the System Usability Scale (SUS) [42,43].

    Data Analysis

    Qualitative Analysis and Identification of Usability Problems

    To analyze data from the think-aloud transcripts of design A testing sessions, we adapted a deductive analytic approach [44]. A coding scheme introduced by Kushniruk and Patel [44] was refined to the topic of study (patient monitoring in ICUs; Multimedia Appendix 1). Using the qualitative data analysis software MAXQDA 2018 (VERBI GmbH), think-aloud transcripts were coded according to the developed scheme. Coded segments (ie, usability problems) were specified into the subcodes, which were further summarized and listed (eg, meaning of labels unclear).

    To decide which problems to eliminate first in the subsequent design iteration, summarized usability problems were ranked in terms of severity and frequency [45,46]. To assess problem severity, impact scores were assigned to each usability problem by 2 physicians who were experienced in intensive care medicine. The following scores were available for selection:

    • The solution to this problem is subtle and possible enhancement or suggestion (score 1)
    • The problem has a minor effect on usability (score 2)
    • The problem creates significant delay and frustration (score 3)
    • The problem prevents task completion (score 4)

    Subsequently, the probability of occurrence was calculated by dividing the number of participants who encountered a particular problem by the total number of participants. To categorize problem frequency, each usability problem was assigned to one of four frequency levels: frequency ≤10% (level 1), frequency 11% to 50% (level 2), frequency 51% to 89% (level 3), and frequency ≥90% (level 4). Finally, criticality was calculated by adding the impact score and frequency levels [45] (eg, when a usability problem was rated as creating significant delays [impact score 3], which was experienced by 80% of participants [level 3], resulting in a criticality score of 6).

    Analysis of Effectiveness, Efficiency, and Subjective Usability

    The task completion rate [47,48] was measured to evaluate the effectiveness of design A and design B. Normative path deviation [49] was assessed based on participants’ click patterns to account for efficiency. The sequence of steps users took when interacting with the interface to complete a task was compared with an optimal sequence of goal-directed steps defined by the researchers for each task. The difference between the normative path and observed path for each user and each task was calculated using the Levenshtein algorithm [33,49]. The SUS was used to assess the perceived usability of design A and design B [42,43,50].

    Prototype Design

    Design solutions were conceptualized by ASP and LS for all identified usability problems. This resulted in a list of ranked usability problems with the suggested design solutions. The identified usability problems from design A were revised by building design B, a clickable prototype, using Axure RP 9. A feedback loop was used to develop design B: one researcher (LS) built the prototype, and another researcher (ASP) reviewed the design and provided feedback from an intensivist’s perspective.

    Statistical Analysis

    To assess the level of improvement between design A and design B, we hypothesized that the task completion rate for design B would be higher than that of design A, design B would lead to lower normative path deviation values than design A, and the SUS scores for design B would be higher than that of design A.

    We used the N-1 chi-square test to compare the task completion rates of both designs [45]. To compare the normative path deviations for both designs, we used a Poisson regression drawing upon a generalized linear mixed-effects model with participants as random effects, as introduced by Schmettow et al [33]. A 2-sample t test was conducted to compare the SUS scores between design A and design B, as recommended by Sauro and Lewis [45]. We tested for normality using the Shapiro–Wilk test [51] and homoscedasticity (homogeneity of variance) using the Levene test [52].


    Overview and Sample

    Measured by task completion rate, normative path deviation, and SUS score, design B was found to be significantly improved compared with design A. We first elaborate on the results of the qualitative analyses and then report the quantitative results.

    The sample comprised a total of 10 ICU staff, aged 25 to 39 years, with work experience ranging from 1 to 20 years, who were divided into groups (5, 50% each) for the evaluation of the 2 designs.

    Qualitative Results

    Summary

    The coding of the transcripts revealed three main codes: usability problems, user ideas and requirements, and positive findings. The codes are visualized with a sunburst diagram (Figure 4; see Multimedia Appendix 1 for the adapted coding scheme by Kushniruk and Patel [44]). Items from the transcripts of the think-aloud protocols were mapped to the subcodes derived by Kushniruk and Patel [44] for the main categories—usability problems and positive findings. For usability problems, the items were assigned to the subcodes of usefulness of the system, response time, responsiveness, meaning of labels, function of UI elements, and navigation; for positive findings, the items were assigned to usefulness, overall ease of use, function of UI elements, layout/screen organization, and color.

    ‎
    Figure 4. Results of qualitative analysis of the think-aloud transcript. Three main codes were identified (inner ring) and subcoded (middle ring). The outer ring represents further information derived from the concrete items that were assigned to the subcodes (ie, specific user ideas or positive findings). UI: user interface.
    View this figure
    Usability Problems

    In total, 37 specific usability problems were identified (Multimedia Appendix 2). The number of usability problems related to the respective codes is visualized in Figure 5; most issues were related to labeling (53/88, 60%). The meaning of labels was mostly unclear—that is, participants were not familiar with certain terms (eg, the meaning of exclamation marks, abbreviations such as those for pulse rate [PR] and integrated pulmonary index [IPI], or terms such as polardiagramm). Users were concerned about whether a certain function was useful for the requirements of their clinical work or when a given task could not be accomplished (eg, participants selected the wrong bed site tile and participants were not sure about the correct patient or device ID; 14/88, 16%). There were difficulties in using or understanding the function of UI elements such as buttons (eg, gray circle or telescope symbols; 8/88, 9%). Furthermore, participants seemed to have problems navigating the monitoring system (ie, finding the right click path to admit patients to the platform; 8/88, 9%). Users criticized the responsiveness of the system (ie, the system did not behave as expected; 3/88, 3%) and the response time (ie, they complained about the time it took the device to respond; 2/88, 2%).

    ‎
    Figure 5. Number of occurrences for each subcode of usability problems. Meaning of labels (n=53), usefulness (n=14), function of UI elements (n=8), navigation (n=8), responsiveness (n=3), response time (n=2). UI: user interface.
    View this figure
    User Ideas and Requirements

    Users emphasized that the system’s ease of use was particularly important to ensure its usability in emergency situations. The tool should be customizable to add other relevant vital signs (eg, intracranial pressure) or to display additional patient information. Participants required audible alarm notifications and the ability to share information regarding relevant patient events with colleagues (eg, about critical patient conditions). Vector graphics were suggested to allow zooming in and out of the vital sign curves. Moreover, participants demanded the ability to see curves of different parameters in an overlapping representation to be able to make inferences from one vital parameter to another. To facilitate spatial orientation, it was suggested that the beds be displayed in the UI according to the physical ward floor plan. Other ideas included adding a drag-and-drop function to rearrange multiple beds at once in the UI and integrating a high-frequency recording function to capture critical events.

    Positive Findings

    Participants stated that the system’s scope of functionality was limited compared with other monitoring solutions. However, the reduced complexity was considered helpful in hospital wards with high patient turnover or stressful environments to get a quick overview of the patient’s health condition. The system’s mobility and overall ease of use were perceived as positive. Participants seemed to be familiar with the following basic UI elements: the home button depicted by a house, the editing symbol depicted by a pen, and the alarm symbol depicted by a warning triangle. Simplicity in the design and use of color was also rated as positive.

    Design Iteration

    The 37 distinct usability problems were ranked in relation to severity and frequency of occurrence (Multimedia Appendix 2). Potential solutions were assigned to the problems and were realized in design B (Figures 6 and 7). In total, 5 design iterations were performed between ASP and LS.

    The main improvements in the prototype version compared with the previous interface were as follows:

    • More responsive design
    • Unknown labels were replaced or removed
    • Unknown UI elements were replaced or removed
    • A dashboard that counted beds, patients, and monitoring systems was added
    • A confirmation dialog before replacing bed numbers was added
    • State-of-the-art dark theme design was adapted from material.io
    ‎
    Figure 6. Redesign of the user interface of the prototype (design B) patient admission screen.
    View this figure
    ‎
    Figure 7. Redesign of the user interface of the prototype (design B) patient tile overview.
    View this figure

    Quantitative Results

    Effectiveness

    The task completion rate was higher for design B (attempts=20; 0/20, 0% failed and 20/20, 100% passed) than for design A (attempts=18; 7/18, 39% failed and 11/18, 61% passed). A 1-tailed N-1 chi-square test suggests that this is a statistically significant difference (χ21=9.3; P=.002).

    Efficiency

    The average normative path deviation of design B (mean 3.2, SD 3.03; 5/10, 50%) was 63.4% lower than that of design A (mean 8.8, SD 5.26; 5/10, 50%; Figure 8). Poisson mixed-effects regression suggests that this reduction in the normative path deviation is statistically significant (βdesign B=−1.04, 95% CI −2.09 to −0.13; exp [βdesign B]=1.13; P<.001).

    ‎
    Figure 8. Scores of normative path deviation for design A and design B. The circle symbolizes outliers. Outliers are defined in the box plots as values that have 1.5 times the distance between Q1 and Q3 (Q1 is the lower line of the box, and Q3 is the upper line of the box).
    View this figure
    Usability

    The average SUS score of design B (mean 89, SD 4.87; 5/10, 50%) was higher than that of design A (mean 68.50, SD 11.26; 5/10, 50%). This difference was statistically significant with a 1-tailed t test (t8=3735; P=.003).


    Principal Findings

    This study evaluated the usability of a remote patient-monitoring system (design A) by identifying the individual usability problems that informed the conceptualization and design of a revised prototype version (design B). Most of the usability problems identified were related to labeling, followed by the perceived lack of usefulness of the monitoring system [10,53,54]. The UI’s navigation was frequently criticized by participants. Further identified usability problems include unclear UI elements, poor responsiveness, and increased response time. The resolution of the usability problems resulted in a significant increase in the perceived usability, efficiency, and effectiveness of the system.

    Usability of Technologies in Intensive Care Medicine

    Over the past 2 decades, the usability of health IT has been investigated in multiple studies applying different methodologies, revealing relatively poor usability and late involvement of end users in the development process [22,55]. This is reflected in our results; based on an HCD approach, we found a relatively high number of easy-to-solve usability problems, the resolution of which led to a significant improvement in the usability of the remote patient-monitoring solution. Most of the usability problems identified were related to labeling, an important issue that is addressed by regulatory requirements [30,56]. The UI’s navigation was frequently criticized by participants. UI navigation problems can affect the overall usability of medical devices, especially in high-stress situations [57-59]. In this regard, simple, intuitive, and role-specific designs are beneficial [60-62], which is also reflected in the user ideas generated by the participants in our study.

    The ICU is an exceptional environment that places diverse demands on health IT to be used there. High stress levels and patients who are unstable and critically ill, with varying care and treatment requirements, are among the conditions that must be considered [63-67]. Multiple digital devices already in place increase the cognitive load on staff as they are required to operate the devices and interpret their output [62,67]. Health care professionals applying physiological monitoring systems underuse the range of features currently available [28]. This might also be because of inadequate digital skills among health professionals and insufficient training of staff in the use of digital technologies [68-72].

    With the increasing complexity and expanding the functionality of digital technologies and their increased use in all clinical settings, usability considerations have become all the more important to realize the full potential of such innovations. Given our findings, we suggest that HCD plays an important role in realizing the potential of IT in health care.

    HCD in the Implementation of Digital Health Technology

    Applying an HCD approach, the inclusion of usability testing and prototyping of a new UI for a remote patient-monitoring system increased usability, according to our findings. HCD encompasses the involvement of end users (ie, health care professionals) in the design and evaluation process, and the required efforts have been shown to be both worthwhile and beneficial in all development phases of a novel digital health technology, enhancing usability and performance [28,59,73]. Research suggests that user knowledge and beliefs about the technology to be implemented are key factors for the successful implementation of the technology [74]. Therefore, HCD should be applied not only during the design and development processes but also during implementation [55]. This could be achieved by establishing innovation and usability laboratories in universities and maximum care hospitals [75]. In the future, HCD is likely to be indispensable for improving both the performance and implementation of IT in health care.

    Despite many publications demonstrating the benefits and relevance of usability testing and HCD in health care, there still seems to be a lack of awareness of its importance and the value of involving key users in the early stages of technology development. The reasons for this may be the perceived costs and frequent lack of incentives to conduct usability evaluations. Moreover, as was the case in our study, the design and implementation of health technologies are often separate processes, making it difficult to apply an HCD approach across all development and implementation phases [22,73]. Further research needs to be conducted to explore how to overcome these barriers to obtain the most out of IT products in health care for both staff and patients.

    Limitations

    In this study, we showcase an HCD approach to improve the usability of a remote patient-monitoring system in a hospital setting. However, from a scientific perspective, there are several limitations to the scope of the study and the interpretation of results. Owing to the qualitative research design, it is not possible to quantify or generalize the usability problems identified to other health technologies and settings. In addition, translation of our results to other hospital settings or countries is limited because of the single-center design of this study and the relatively small sample size. It was not possible to draw samples randomly, which needs to be considered as a potential source of bias when interpreting the results. The comparison between design A, which was a working medical product installed in the ICU, and design B, a prototype mock-up, may be potentially unfair with a number of confounders in the 2 arms. Nonetheless, given the observed effects of meaningful labeling and easy-to-understand UIs on efficiency and effectiveness, our results help to underline the importance and potential of HCD for realizing the potential of IT in health care. Follow-up studies should be envisioned in collaboration with medical device manufacturers using design B.

    We did not perform a usability test of all features of the remote patient-monitoring device, which comprises more than just the remote monitoring device UI (eg, sensors, bedside monitors, or cables are also part of it). We focused on tablet use for this study as it distinguishes remote patient monitoring from regular patient monitoring, and the tablet is the touchpoint with which the user interacts most frequently. Thus, we restricted the study scope to the UI of the tablet version of the remote monitoring system; that is, the smartphone and desktop UI versions were not investigated. We only tested the German version of the UI, which limits certain findings (eg, regarding the labeling) to German-speaking regions.

    We were not able to refer to a standardized checklist or protocol for reporting the results of this study. The development of such a checklist or protocol could be an interesting area for further research, as it could improve the quality and reproducibility of usability study reports.

    Conclusions

    Applying an HCD approach with usability testing and conceptualized design of a revised prototype version significantly improved the usability of the remote patient-monitoring system for the end points of perceived ease of use, efficiency, and effectiveness. Technical devices should be designed to support efficient and effective work processes, especially in the sensitive working environment of the ICU, with usability being an essential facilitator of maximum performance, successful implementation, and ultimately patient safety. Our results suggest that HCD methods and principles can help realize the goals and potential of IT in health care. However, currently, HCD methods are often not applied early enough in the development process of digital health technologies for ICUs. Further research should explore how to increase early product evaluations in hospitals with end users to take better advantage of their input, not only for the development of user-friendly IT solutions but also for their successful implementation in clinical settings.

    Acknowledgments

    The authors express their gratitude to the intensive care unit staff for their participation in this study. The authors would like to thank the German Research Foundation (Deutsche Forschungsgemeinschaft), the Open Access publication fund of the Charité–Universitätsmedizin Berlin, and the Einstein Center Digital Future. ASP is a participant in the Charité Digital Clinician Scientist Program funded by the Charité–Universitätsmedizin Berlin and the Berlin Institute of Health. Finally, the authors would like to thank Medtronic for providing the aforementioned devices free of charge.

    Authors' Contributions

    CS had the idea for shared decision allocation and initiated the implementation of remote patient-monitoring in the intensive care unit. The study was conceived by ASP, CS, FB, and LKM. LS, ASP, and LKM conducted data acquisition and analysis. ASP and LKM wrote the manuscript, supported by LS. MAF supported the design of the study, conception of methodology, and interpretation of results. FB supervised all parts of the study. All authors critically reviewed and approved the manuscript. The paper was extracted from the MS thesis of LS.

    Conflicts of Interest

    CS and FB report funding from Medtronic. FB also reports grants from German Federal Ministry of Education and Research, grants from German Federal Ministry of Health, grants from Berlin Institute of Health, personal fees from Elsevier Publishing, grants from Hans Böckler Foundation, other from Robert Koch Institute, grants from Einstein Foundation, and grants from Berlin University Alliance outside the submitted work.

    ‎
    Multimedia Appendix 1

    Coding scheme adapted from Kushniruk and Patel [44].

    PNG File , 55 KB
    ‎
    Multimedia Appendix 2

    Usability ranking.

    XLSX File (Microsoft Excel File), 24 KB
    1. Moreno RP, Rhodes A, Donchin Y, European Society of Intensive Care. Patient safety in intensive care medicine: the Declaration of Vienna. Intensive Care Med 2009;35(10):1667-1672 [FREE Full text] [CrossRef] [Medline]
    2. Hu P, Galvagno Jr SM, Sen A, Dutton R, Jordan S, Floccare D, ONPOINT Group. Identification of dynamic prehospital changes with continuous vital signs acquisition. Air Med J 2014;33(1):27-33. [CrossRef] [Medline]
    3. Balzer F, Habicher M, Sander M, Sterr J, Scholz S, Feldheiser A, et al. Comparison of the non-invasive Nexfin® monitor with conventional methods for the measurement of arterial blood pressure in moderate risk orthopaedic surgery patients. J Int Med Res 2016;44(4):832-843 [FREE Full text] [CrossRef] [Medline]
    4. Shah PS. Wireless monitoring in the ICU on the horizon. Nat Med 2020;26(3):316-317. [CrossRef] [Medline]
    5. Michard F, Bellomo R, Taenzer A. The rise of ward monitoring: opportunities and challenges for critical care specialists. Intensive Care Med 2019;45(5):671-673. [CrossRef] [Medline]
    6. Dziadzko MA, Harrison AM, Tiong IC, Pickering BW, Moreno Franco P, Herasevich V. Testing modes of computerized sepsis alert notification delivery systems. BMC Med Inform Decis Mak 2016;16(1):156 [FREE Full text] [CrossRef] [Medline]
    7. Meyer A, Zverinski D, Pfahringer B, Kempfert J, Kuehne T, Sündermann SH, et al. Machine learning for real-time prediction of complications in critical care: a retrospective study. Lancet Respir Med 2018;6(12):905-914. [CrossRef] [Medline]
    8. Bhatia M, Sood SK. Temporal informative analysis in smart-ICU monitoring: m-healthcare perspective. J Med Syst 2016;40(8):190. [CrossRef] [Medline]
    9. Yamada T, Vacas S, Gricourt Y, Cannesson M. Improving perioperative outcomes through minimally invasive and non-invasive hemodynamic monitoring techniques. Front Med (Lausanne) 2018;5:144 [FREE Full text] [CrossRef] [Medline]
    10. Poncette AS, Meske C, Mosch L, Balzer F. How to overcome barriers for the implementation of new information technologies in intensive care medicine. In: Proceedings of the 21st Human-Computer Interaction International Conference. 2019 Presented at: HCII '19; July 26-31, 2019; Orlando, FL p. 534-546. [CrossRef]
    11. Baig MM, GholamHosseini H, Moqeem AA, Mirza F, Lindén M. A systematic review of wearable patient monitoring systems - current challenges and opportunities for clinical adoption. J Med Syst 2017;41(7):115. [CrossRef] [Medline]
    12. Ross J, Stevenson F, Lau R, Murray E. Factors that influence the implementation of e-health: a systematic review of systematic reviews (an update). Implement Sci 2016;11(1):146 [FREE Full text] [CrossRef] [Medline]
    13. Tscholl DW, Handschin L, Rössler J, Weiss M, Spahn DR, Nöthiger CB. It's not you, it's the design - common problems with patient monitoring reported by anesthesiologists: a mixed qualitative and quantitative study. BMC Anesthesiol 2019;19(1):87 [FREE Full text] [CrossRef] [Medline]
    14. von Dincklage F, Suchodolski K, Lichtner G, Friesdorf W, Podtschaske B, Ragaller M. Investigation of the usability of computerized critical care information systems in Germany. J Intensive Care Med 2019;34(3):227-237. [CrossRef] [Medline]
    15. Wade VA, Eliott JA, Hiller JE. Clinician acceptance is the key factor for sustainable telehealth services. Qual Health Res 2014;24(5):682-694. [CrossRef] [Medline]
    16. Howe JL, Adams KT, Hettinger AZ, Ratwani RM. Electronic health record usability issues and potential contribution to patient harm. JAMA 2018;319(12):1276-1278 [FREE Full text] [CrossRef] [Medline]
    17. Fairbanks RJ, Caplan S. Poor interface design and lack of usability testing facilitate medical error. Jt Comm J Qual Saf 2004;30(10):579-584. [CrossRef] [Medline]
    18. Middleton B, Bloomrosen M, Dente MA, Hashmat B, Koppel R, Overhage JM, American Medical Informatics Association. Enhancing patient safety and quality of care by improving the usability of electronic health record systems: recommendations from AMIA. J Am Med Inform Assoc 2013;20(e1):e2-e8 [FREE Full text] [CrossRef] [Medline]
    19. Kushniruk AW, Triola MM, Borycki EM, Stein B, Kannry JL. Technology induced error and usability: the relationship between usability problems and prescription errors when using a handheld application. Int J Med Inform 2005;74(7-8):519-526. [CrossRef] [Medline]
    20. Bitkina OV, Kim HK, Park J. Usability and user experience of medical devices: an overview of the current state, analysis methodologies, and future challenges. Int J Ind Ergon 2020;76:102932. [CrossRef]
    21. Saeed N, Manzoor M, Khosravi P. An exploration of usability issues in telecare monitoring systems and possible solutions: a systematic literature review. Disabil Rehabil Assist Technol 2020;15(3):271-281. [CrossRef] [Medline]
    22. Yen PY, Bakken S. Review of health information technology usability study methodologies. J Am Med Inform Assoc 2012;19(3):413-422 [FREE Full text] [CrossRef] [Medline]
    23. Vincent CJ, Li Y, Blandford A. Integration of human factors and ergonomics during medical device design and development: it's all about communication. Appl Ergon 2014;45(3):413-419. [CrossRef] [Medline]
    24. Shah SG, Robinson I. Benefits of and barriers to involving users in medical device technology development and evaluation. Int J Technol Assess Health Care 2007;23(1):131-137. [CrossRef] [Medline]
    25. Peischl B, Ferk M, Holzinger A. The fine art of user-centered software development. Software Qual J 2015;23(3):509-536. [CrossRef]
    26. Roberts JP, Fisher TR, Trowbridge MJ, Bent C. A design thinking framework for healthcare management and innovation. Healthc (Amst) 2016;4(1):11-14. [CrossRef] [Medline]
    27. Johnson CM, Johnson TR, Zhang J. A user-centered framework for redesigning health care interfaces. J Biomed Inform 2005;38(1):75-87 [FREE Full text] [CrossRef] [Medline]
    28. Fidler R, Bond R, Finlay D, Guldenring D, Gallagher A, Pelter M, et al. Human factors approach to evaluate the user interface of physiologic monitoring. J Electrocardiol 2015;48(6):982-987. [CrossRef] [Medline]
    29. Wiggermann N, Rempel K, Zerhusen RM, Pelo T, Mann N. Human-centered design process for a hospital bed: promoting patient safety and ease of use. Ergon Des 2019;27(2):4-12. [CrossRef]
    30. Applying human factors and usability engineering to medical devices: guidance for industry and Food and Drug Administration staff. U.S. Food and Drug Administration. 2016.   URL: https:/​/www.​fda.gov/​regulatory-information/​search-fda-guidance-documents/​applying-human-factors-and-usability-engineering-medical-devices [accessed 2022-03-03]
    31. Alexander G, Staggers N. A systematic review of the designs of clinical technology: findings and recommendations for future research. ANS Adv Nurs Sci 2009;32(3):252-279 [FREE Full text] [CrossRef] [Medline]
    32. Bazzano AN, Martin J, Hicks E, Faughnan M, Murphy L. Human-centred design in global health: a scoping review of applications and contexts. PLoS One 2017;12(11):e0186744 [FREE Full text] [CrossRef] [Medline]
    33. Schmettow M, Schnittker R, Schraagen JM. An extended protocol for usability validation of medical devices: research design and reference model. J Biomed Inform 2017;69:99-114 [FREE Full text] [CrossRef] [Medline]
    34. Daniels J, Fels S, Kushniruk A, Lim J, Ansermino JM. A framework for evaluating usability of clinical monitoring technology. J Clin Monit Comput 2007;21(5):323-330. [CrossRef] [Medline]
    35. Gravetter FJ, Forzanno LB. Research methods for the behavioral sciences. 4th edition. Belmont, CA: Wadsworth Publishing; 2012.
    36. Quick guide for clinician users: vital SyncTM virtual patient monitoring platform. Medtronic. 2017.   URL: https://tinyurl.com/44tjfek9 [accessed 2022-03-03]
    37. Vital SyncTM virtual patient monitoring platform: user guide. Medtronic. 2017.   URL: https://tinyurl.com/2p8mcp52 [accessed 2022-03-03]
    38. Health informatics and monitoring: vital SyncTM virtual patient monitoring platform. Medtronic.   URL: https://tinyurl.com/5cbzu9ts [accessed 2019-01-15]
    39. Ergonomie der Mensch-System-Interaktion - Methoden zur Gewährleistung der Gebrauchstauglichkeit, die eine benutzer-orientierte Gestaltung unterstützen. Beuth Publishing DIN. 2002.   URL: https://www.beuth.de/de/technische-regel/iso-tr-16982/57203361 [accessed 2022-03-03]
    40. Fonteyn ME, Kuipers B, Grobe SJ. A description of think aloud method and protocol analysis. Qual Health Res 1993;3(4):430-441. [CrossRef]
    41. Wiklund ME, Kendler J, Strochlic AY. Selecting tasks. In: Wilkund ME, Kendler J, Strochlic AY, editors. Usability testing of medical devices. 2nd edition. Boca Raton, FL: CRC Press; 2016:261-316.
    42. Bangor A, Kortum PT, Miller JT. An empirical evaluation of the system usability scale. Int J Hum Comput Interact 2008;24(6):574-594. [CrossRef]
    43. Lewis JR. The system usability scale: past, present, and future. Int J Hum-Comput Interact 2018;34(7):577-590. [CrossRef]
    44. Kushniruk AW, Patel VL. Cognitive and usability engineering methods for the evaluation of clinical information systems. J Biomed Inform 2004;37(1):56-76 [FREE Full text] [CrossRef] [Medline]
    45. Sauro J, Lewis J. Quantifying user research. In: Sauro J, Lewis J, editors. Quantifying the user experience: practical statistics of user research. Cambridge, MA: Morgan Kaufmann; 2016:9-18.
    46. Rubin J, Chisnell D, Spool J. Handbook of usability testing: how to plan, design, and conduct effective tests. 2nd edition. Indianapolis, IN: Wiley; 2008.
    47. Broekhuis M, van Velsen L, Hermens H. Assessing usability of eHealth technology: a comparison of usability benchmarking instruments. Int J Med Inform 2019;128:24-31. [CrossRef] [Medline]
    48. Nielsen J, Budiu R. Success rate: the simplest usability metric. Nielsen Norman Group. 2001.   URL: https://www.nngroup.com/articles/success-rate-the-simplest-usability-metric/ [accessed 2019-11-06]
    49. Schnittker R, Schmettow M, Verhoeven F, Schraagen JM. Combining situated Cognitive Engineering with a novel testing method in a case study comparing two infusion pump interfaces. Appl Ergon 2016;55:16-26. [CrossRef] [Medline]
    50. Albert B, Tullis T. Measuring the user experience: collecting, analyzing, and presenting usability metrics. 2nd edition. Amsterdam, The Netherlands: Morgan Kaufmann; 2013.
    51. Shapiro SS, Wilk MB. An analysis of variance test for normality (complete samples). Biometrika 1965;52(3/4):591-611. [CrossRef]
    52. Levene H. Robust tests for equality of variances. In: Olkin I, editor. Contributions to probability and statistics: essays in honor of Harold hotelling. Redwood City, CA: Stanford University Press; 1960:278-292.
    53. Poncette AS, Mosch L, Spies C, Schmieding M, Schiefenhövel F, Krampe H, et al. Improvements in patient monitoring in the intensive care unit: survey study. J Med Internet Res 2020;22(6):e19091 [FREE Full text] [CrossRef] [Medline]
    54. Poncette AS, Spies C, Mosch L, Schieler M, Weber-Carstens S, Krampe H, et al. Clinical requirements of future patient monitoring in the intensive care unit: qualitative study. JMIR Med Inform 2019;7(2):e13064 [FREE Full text] [CrossRef] [Medline]
    55. Turner P, Kushniruk A, Nohr C. Are we there yet? Human factors knowledge and health information technology - the challenges of implementation and impact. Yearb Med Inform 2017;26(1):84-91 [FREE Full text] [CrossRef] [Medline]
    56. Labeling: regulatory requirements for medical devices. U.S. Food & Drug Administration. 1997.   URL: https:/​/www.​fda.gov/​files/​medical%20devices/​published/​Labeling---Regulatory-Requirements-for-Medical-Devices-(FDA-89-4203).​pdf [accessed 2022-03-03]
    57. Roman LC, Ancker JS, Johnson SB, Senathirajah Y. Navigation in the electronic health record: a review of the safety and usability literature. J Biomed Inform 2017;67:69-79 [FREE Full text] [CrossRef] [Medline]
    58. Moradian S, Krzyzanowska MK, Maguire R, Morita PP, Kukreti V, Avery J, et al. Usability evaluation of a mobile phone-based system for remote monitoring and management of chemotherapy-related side effects in cancer patients: mixed-methods study. JMIR Cancer 2018;4(2):e10932 [FREE Full text] [CrossRef] [Medline]
    59. Andrade E, Quinlan L, Harte R, Byrne D, Fallon E, Kelly M, et al. Novel interface designs for patient monitoring applications in critical care medicine: human factors review. JMIR Hum Factors 2020;7(3):e15052 [FREE Full text] [CrossRef] [Medline]
    60. Flohr L, Beaudry S, Johnson KT, West N, Burns CM, Ansermino JM, et al. Clinician-driven design of VitalPAD - an intelligent monitoring and communication device to improve patient safety in the intensive care unit. IEEE J Transl Eng Health Med 2018;6:3000114 [FREE Full text] [CrossRef] [Medline]
    61. Faiola A, Srinivas P, Hillier S. Improving patient safety: integrating data visualization and communication into ICU workflow to reduce cognitive load. Proc Int Symp Hum Factors Ergon Healthc 2015;4(1):55-61. [CrossRef]
    62. Faiola A, Srinivas P, Duke J. Supporting clinical cognition: a human-centered approach to a novel ICU information visualization dashboard. AMIA Annu Symp Proc 2015;2015:560-569 [FREE Full text] [Medline]
    63. Chuang CH, Tseng PC, Lin CY, Lin KH, Chen YY. Burnout in the intensive care unit professionals: a systematic review. Medicine (Baltimore) 2016;95(50):e5629 [FREE Full text] [CrossRef] [Medline]
    64. Richardsen AM, Martinussen M, Kaiser S. Stress, human errors and accidents. In: Burke RJ, Richardsen AM, editors. Increasing occupational health and safety workplaces: individual, work and organizational factors. Cheltenham, UK: Edward Elgar Publishing; 2019:48-67.
    65. Kumar A, Pore P, Gupta S, Wani AO. Level of stress and its determinants among intensive care unit staff. Indian J Occup Environ Med 2016;20(3):129-132 [FREE Full text] [CrossRef] [Medline]
    66. Graber ML, Kissam S, Payne VL, Meyer AN, Sorensen A, Lenfestey N, et al. Cognitive interventions to reduce diagnostic error: a narrative review. BMJ Qual Saf 2012;21(7):535-557. [CrossRef] [Medline]
    67. Kahn JM. What we talk about when we talk about intensive care unit strain. Ann Am Thorac Soc 2014;11(2):219-220. [CrossRef] [Medline]
    68. The Topol review: preparing the healthcare workforce to deliver the digital future: an independent report on behalf of the Secretary of State for Health and Social Care. National Health Service. 2019.   URL: https://topol.hee.nhs.uk/wp-content/uploads/HEE-Topol-Review-2019.pdf [accessed 2022-03-03]
    69. Gonzalez Velasco JM. Complex and transdisciplinary strategies for promotion and prevention in digital health: towards the ecology of knowledge. In: Mantel-Teeuwisse A, Khatri B, Uzman N, Mellianti S, editors. FIP Digital health in pharmacy education: developing a digitally enabled pharmaceutical workforce. The Hague, The Netherlands: International Pharmaceutical Federation; 2021:106-108.
    70. Machleid F, Kaczmarczyk R, Johann D, Balčiūnas J, Atienza-Carbonell B, von Maltzahn F, et al. Perceptions of digital health education among European medical students: mixed methods survey. J Med Internet Res 2020;22(8):e19827 [FREE Full text] [CrossRef] [Medline]
    71. Matern U. Design, usability and staff training - what is more important? In: Doffy VG, editor. Advances in human aspects of healthcare. Boca Raton, FL: CRC Press; 2012:426-428.
    72. Poncette AS, Glauert DL, Mosch L, Braune K, Balzer F, Back DA. Undergraduate medical competencies in digital health and curricular module development: mixed methods study. J Med Internet Res 2020;22(10):e22161 [FREE Full text] [CrossRef] [Medline]
    73. Holeman I, Kane D. Human-centered design for global health equity. Inf Technol Dev 2019;26(3):477-505 [FREE Full text] [CrossRef] [Medline]
    74. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci 2009;4:50 [FREE Full text] [CrossRef] [Medline]
    75. Laboratories: human factors engineering: Susan Hallbeck. Mayo Clinic.   URL: https://www.mayo.edu/research/labs/human-factors-engineering/overview [accessed 2020-05-09]

    ‎
    HCD: human-centered design
    ICU: intensive care unit
    IT: information technology
    SUS: System Usability Scale
    UI: user interface

    Edited by G Eysenbach; submitted 25.05.21; peer-reviewed by N West; comments to author 05.07.21; revised version received 13.08.21; accepted 19.09.21; published 11.03.22

    Copyright

    ©Akira-Sebastian Poncette, Lina Katharina Mosch, Lars Stablo, Claudia Spies, Monique Schieler, Steffen Weber-Carstens, Markus A Feufel, Felix Balzer. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 11.03.2022.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Human Factors, is properly cited. The complete bibliographic information, a link to the original publication on https://humanfactors.jmir.org, as well as this copyright and license information must be included.