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Design and Usability of Clinical Software and EHRs (242) Clinical Information and Decision Making (1315) Decision Support for Health Professionals (1129) Cardiology (126) Coronary Heart Disease (72) Computer-Aided Diagnosis (95)

Published on in Vol 11 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/55802, first published .
Clinical Decision Support Requirements for Ventricular Tachycardia Diagnosis Within the Frameworks of Knowledge and Practice: Survey Study

Clinical Decision Support Requirements for Ventricular Tachycardia Diagnosis Within the Frameworks of Knowledge and Practice: Survey Study

Clinical Decision Support Requirements for Ventricular Tachycardia Diagnosis Within the Frameworks of Knowledge and Practice: Survey Study

Authors of this article:

Zhao Hu1 Author Orcid Image ;   Min Wang2 Author Orcid Image ;   Si Zheng2 Author Orcid Image ;   Xiaowei Xu2 Author Orcid Image ;   Zhuxin Zhang1 Author Orcid Image ;   Qiaoyue Ge3 Author Orcid Image ;   Jiao Li2 Author Orcid Image ;   Yan Yao1 Author Orcid Image
Article Authors Cited by Tweetations (3) Metrics

    Original Paper

    1Arrhythmia Center, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College/National Center for Cardiovascular Diseases, Beijing, China

    2Institute of Medical Information, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China

    3West China School of Public Health, West China Fourth Hospital, Sichuan University, Chengdu, China

    *these authors contributed equally

    Corresponding Author:

    Yan Yao, PhD, MD

    Arrhythmia Center

    Fuwai Hospital

    Chinese Academy of Medical Sciences & Peking Union Medical College/National Center for Cardiovascular Diseases

    Beilishi Road 167

    Beijing, 100037

    China

    Phone: 86 10 88322401

    Email: ianyao@263.net.cn


    Background: Ventricular tachycardia (VT) diagnosis is challenging due to the similarity between VT and some forms of supraventricular tachycardia, complexity of clinical manifestations, heterogeneity of underlying diseases, and potential for life-threatening hemodynamic instability. Clinical decision support systems (CDSSs) have emerged as promising tools to augment the diagnostic capabilities of cardiologists. However, a requirements analysis is acknowledged to be vital for the success of a CDSS, especially for complex clinical tasks such as VT diagnosis.

    Objective: The aims of this study were to analyze the requirements for a VT diagnosis CDSS within the frameworks of knowledge and practice and to determine the clinical decision support (CDS) needs.

    Methods: Our multidisciplinary team first conducted semistructured interviews with seven cardiologists related to the clinical challenges of VT and expected decision support. A questionnaire was designed by the multidisciplinary team based on the results of interviews. The questionnaire was divided into four sections: demographic information, knowledge assessment, practice assessment, and CDS needs. The practice section consisted of two simulated cases for a total score of 10 marks. Online questionnaires were disseminated to registered cardiologists across China from December 2022 to February 2023. The scores for the practice section were summarized as continuous variables, using the mean, median, and range. The knowledge and CDS needs sections were assessed using a 4-point Likert scale without a neutral option. Kruskal-Wallis tests were performed to investigate the relationship between scores and practice years or specialty.

    Results: Of the 687 cardiologists who completed the questionnaire, 567 responses were eligible for further analysis. The results of the knowledge assessment showed that 383 cardiologists (68%) lacked knowledge in diagnostic evaluation. The overall average score of the practice assessment was 6.11 (SD 0.55); the etiological diagnosis section had the highest overall scores (mean 6.74, SD 1.75), whereas the diagnostic evaluation section had the lowest scores (mean 5.78, SD 1.19). A majority of cardiologists (344/567, 60.7%) reported the need for a CDSS. There was a significant difference in practice competency scores between general cardiologists and arrhythmia specialists (P=.02).

    Conclusions: There was a notable deficiency in the knowledge and practice of VT among Chinese cardiologists. Specific knowledge and practice support requirements were identified, which provide a foundation for further development and optimization of a CDSS. Moreover, it is important to consider clinicians’ specialization levels and years of practice for effective and personalized support.

    JMIR Hum Factors 2024;11:e55802

    doi:10.2196/55802

    Keywords

    clinical decision support system; requirements analysis; ventricular tachycardia; knowledge; clinical practice; questionnaires 


    Sudden cardiac death (SCD) remains a significant public health issue, accounting for 50% of all cardiovascular deaths. The estimated annual incidences of SCD are 60 [1], 40.7 [2,3], and 36.8 [4] per 100,000 people in the United States, China, and Europe, respectively. Ventricular tachycardia (VT) is a major cause or precursor of SCD [5], which can be the initial or sole manifestation of diverse heart diseases [6,7]. VT diagnosis is challenging due to its similarity with some forms of supraventricular tachycardia, the complexity of clinical manifestations, heterogeneity of underlying diseases, and potential for life-threatening hemodynamic instability [6,8]. Diagnostic accuracy and timing are critical for patients with VT, as the stage of diagnosis determines the selection of treatment [9]. However, studies have revealed a substantial prevalence of misdiagnoses of VT [10-13], focusing on differential diagnosis between VT and supraventricular tachycardia. Although diagnostic error has been a challenge along the development of medicine, measuring diagnostic error can be difficult due to detection and reporting biases, with scarce reports indicating error rates of approximately 10%-15% [14]. We could not find additional estimates for the actual diagnostic error of VT; however, it is commonly acknowledged to represent a substantial challenge considering the complexity of the condition [9,15].

    Diagnosis represents a complex cognitive process comprising a variety of different problem-solving tasks that are related to the clinical reasoning process, such as taking a medical history, forming a differential diagnosis, ordering examinations, and interpreting clinical findings [16]. The diagnostic process requires not only the retention of knowledge but also the judicious application of that knowledge at opportune moments, namely in clinical practice. A proper diagnosis of VT demands a great volume of knowledge. First, the clinician must be able to identify VT among the spectrum of wide QRS tachycardias by inspecting a list of electrocardiogram (ECG) features and comparing the findings to various diagnostic criteria or algorithms [17,18]. Once VT is identified by ECG interpretation, the next step is to diagnose the underlying diseases from a vast disease spectrum. This is a particularly challenging task, as any disease involving the myocardium can cause VT, such as coronary artery disease (CAD), all types of cardiomyopathies, myocarditis, inherited arrhythmia syndromes, autoimmune or inflammatory diseases, and others [7,9]. Moreover, translating the enormous body of knowledge into proper practice can be difficult [19], which is exacerbated by the fact that VT can cause stress to clinicians due to the probability of hemodynamic instability.

    In response to this challenge, the clinical decision support system (CDSS) has emerged as a promising tool to augment the diagnostic capabilities of clinicians. Clinical decision support (CDS) is a process for enhancing health-related decisions with pertinent, organized clinical knowledge and patient information, thus advancing health care delivery [20]. Use of a CDSS can provide clinicians with situation-specific knowledge that aids in making critical clinical decisions such as risk assessment, diagnosis, prognosis, and selection of therapy [21]. A clinical diagnostic decision support system (DDSS) is a computer-based algorithm that assists a clinician with one or more component steps of the diagnostic process [22]. A DDSS is expected to receive relevant patient information and return outputs to assist with the problems the clinician has encountered in the diagnostic process, such as suggesting a likely diagnosis. Some well-known DDSSs such as ISABEL [23] and Dxplain [24] provide a diagnosis list, which can offer a solution to the challenges associated with VT diagnosis. Most CDSSs exhibit efficacy in a laboratory or experimental environment; however, relatively few such systems are being used at present and the rate of use in routine clinical practice is low [20,25-27]. Studies have identified the main barriers to the widespread adoption of CDSSs, including vague requirements, poor integration with the clinical workflow, low user acceptance or trust, and lack of transparency. Among these barriers, comprehensive user requirements engineering should be performed at the very beginning of development, which should be continued iteratively throughout the CDSS design-development-implementation life cycle [25,26,28,29]. To address this gap, several recent studies have aimed at elucidating the clinical requirements for an effective and usable CDSS in the context of specific fields or scenarios [30-34] with a variety of methods, including focus groups [30,35], a workshop [34], expert discussion with a literature review [36,37], semistructured interviews [31,34,35,38], writing user stories [39], and system evaluation [40]. Overall, most studies have adopted a user-centered approach with qualitative analysis.

    To our best knowledge, although an artificial intelligence model was reported for predicting the in-hospital mortality of VT [8], no CDSS has been developed for VT diagnosis. A recent systemic review of cardiovascular CDSSs found that the complexity of the clinical management of cardiovascular disease itself was a barrier during implementation [27], which emphasizes the need for an authentic clinical requirements analysis. Accordingly, the objective of this study was to analyze the requirements for a VT diagnosis CDSS within the frameworks of knowledge, practice, and CDS needs.


    Study Design and Recruitment Process

    Figure 1 shows the overall flow of our study, which consisted of semistructured interviews in the early stages and questionnaires in the later stages. To effectively implement and conduct the questionnaire assessment, we conducted open and explorative interviews about the challenges associated with the management of VT and the expected functions of a CDSS for VT. The interviews were conducted at Fuwai Hospital, the national cardiovascular disease center of China. This hospital actively recruits cardiologists for their fellowships from all regions of China, resulting in a representative sample of interviewees. We sent interview invitations to all 56 cardiologists in the arrythmia center, including cardiologists from the fellowship program or established staff of Fuwai Hospital. Seven cardiologists responded and completed the interview, followed by a brief questionnaire to provide information on demographics and clinical experience (see Multimedia Appendix 1).

    ‎
    Figure 1. Schematic of the overall study workflow and assessment approach. CDS: clinical decision support; ECG: electrocardiogram; MDT: multidisciplinary team; VT: ventricular tachycardia.

    A multidisciplinary team was formed to define the purpose of our study and the design of the questionnaire based on the interview results. The multidisciplinary team comprised three arrhythmia specialists, three experts in medical informatics and CDS, and one clinical statistician. The questionnaire was examined by an additional 20 arrhythmia specialists to ensure its clarity and feasibility. We conducted a nationwide cross-sectional survey with an online questionnaire in mainland China from December 31, 2022, to February 15, 2023. We recruited registered cardiologists using a convenience sampling approach from network groups associated with the Asian Heart Rhythm Association (AHRA) on WeChat, the dominant social media app in China. The AHRA is an academic organization focusing on arrhythmias, whose members are all registered cardiologists. Duplicate submissions were prevented through IP address constraints, and only completed responses were included for analysis.

    Ethical Considerations

    Participants provided online informed consent, which detailed the survey’s background, aim, methods, and confidentiality measures. To protect participants’ privacy, a signature was not required. Instead, participants clicked the “go on” button at the bottom of the informed consent page if they agreed to participate. According to data privacy protocols, no personal information, including the participants’ names or affiliations, was collected. Since patients were not the subject of this study, ethical approval was exempted by the ethics committee of the Institute of Medical Information, Chinese Academy of Medical Sciences/Peking Union Medical College [41]. Each participant received ~US $3 as compensation.

    Questionnaire Design

    Overview

    The questionnaire was divided into four sections (Table 1): demographic information (questions 1-6), knowledge assessment (question 14), practice assessment (questions 7-13), and CDS needs (questions 15-18). A comprehensive version of the questionnaire is provided in Multimedia Appendix 2.

    Table 1. Design of the questionnaire.
    SectionContentRelated questions
    KnowledgeExamination interpretation, etiological diagnosis, diagnostic evaluation, conceptual knowledge14
    PracticeExamination interpretation, etiological diagnosis, diagnostic evaluation7-13
    Clinical decision support needsInterpretable diagnosis, executable processes, knowledge support15-18
    Knowledge Assessment

    Knowledge serves as the theoretical foundation for clinicians to make clinical diagnoses and is thus an essential competency for clinicians. The diagnosis of VT is difficult as it will largely depend on the clinician’s familiarity with the vast knowledge of the field. The European Society of Cardiology (ESC) guideline suggests a protocol for VT diagnosis [15]. The multidisciplinary team abstracted the knowledge points from the ESC guideline for collecting information on the participants’ self-reported knowledge shortcomings.

    Practice Assessment
    Areas of Focus

    To attain a more accurate gauge of the clinical practice competency, we used simulated cases rather than straightforward questions [42], which can help differentiate practice competency from knowledge. To mitigate the risk of low response rates and careless submissions associated with lengthy surveys [43], we designed two stepwise cases containing seven questions. According to the intention, the questions about clinical practice were divided into three parts: examination interpretation, etiological diagnosis, and diagnostic evaluation. Multiple-choice options were available for all the questions. We standardized the total score for each section to 10 points according to the weighting.

    Examination Interpretation

    Accurate interpretation of an examination is the basis for a correct etiological diagnosis. ECG is the first-line examination modality for arrhythmias, as nearly all arrhythmia episodes are detected by ECG. Therefore, for this section, we focused on the identification of VT and sites of origin of VT on ECG [15].

    Etiological Diagnosis

    A correct etiological diagnosis of VT is necessary for appropriate treatment. The main strategy is to identify or exclude structural heart diseases, including CAD, myocarditis, and cardiomyopathies [44]. In this section, we assessed the correctness of a diagnosis of arrhythmogenic right ventricular cardiomyopathy (ARVC) and acute myocarditis as the two cases.

    Diagnostic Evaluation

    Diagnostic evaluation is a process of collecting clinical information to confirm or exclude a suspected diagnosis. A diagnostic evaluation protocol for VT is recommended in the ESC guideline [15] with the goal of reducing the rate of diagnostic errors. Based on the cases with an etiological diagnosis, we assessed the competency of the participants to arrange further diagnostic evaluations.

    CDS Needs

    According to the ESC guideline [15] and universal CDSS functionality [25], the multidisciplinary team summarized the results of the interviews to produce a list of functions required for CDS, which could be divided into executable processes, interpretable diagnosis, and knowledge support. We employed this list to poll the functionalities required by the cardiologists for a VT CDSS.

    Quality Control of Responses

    To ensure the validity and reliability of our survey responses, we used two strategies to filter out potentially low-quality submissions. First, participants who completed the questionnaire in under 2 minutes were excluded. This threshold was determined through a pretest evaluation coupled with multidisciplinary team discussions. Second, responses were considered to be invalid if participants selected all the available options for questions 7, 8, 9, 11, 12, or 13. This exclusion criterion was established based on the consensus opinion of the multidisciplinary team, who deemed such selections to be unreasonable.

    Statistical Analysis

    We only included valid questionnaire responses in the statistical analysis. All data in the demographic section were categorical. Comparisons were performed using mean, median, range, and percentage. The scores in the practice section are expressed as continuous variables, using the mean, median, and range. The knowledge and CDS sections were phrased as single-choice questions asking clinicians about their subjective views on given statements using a 4-point Likert scale without a neutral option. The internal consistency of the questionnaire was assessed using the Cronbach α value.

    In addition, we grouped participants separately by practice years and specialty for further subgroup analyses. The Kruskal-Wallis test was performed to investigate the relationship between practice scores and practice years or specialty. All analyses were conducted in R version 4.0.3 [45]. We analyzed most of the data descriptively using graphics produced by the R package ggplot2.


    Sociodemographic Characteristics of Participants

    A total of 687 questionnaires were completed. After applying our quality control measures, 567 responses were considered valid, yielding a validity rate of 82.53%. Among the invalid questionnaires, 104 responses were excluded due to a completion time of less than 2 minutes and 16 were excluded for selecting all options in questions 7, 8, 9, 11, 12, or 13. Descriptive statistics regarding the sociodemographic characteristics of participants are presented in Table 2. Of the enrolled participants, 54.50% were men; 93.47% were general cardiologists and the others were cardiac arrhythmia specialists. More than half of the participants were from tertiary A hospitals. Only a small percentage of cardiologists had ever used a CDSS, and the majority reported needing a CDSS to assist them in the management of VT (Table 2).

    Table 2. Demographic characteristics of the survey participants (N=567).
    CharacteristicsParticipants, n (%)
    Gender

    		Woman258 (45.5)

    		Man309 (54.50)
    Age (years)

    		≤3089 (15.7)

    		31-35152 (26.81)

    		36-40129 (22.75)

    		41-4592 (16.23)

    		46-5060 (10.58)

    		≥5145 (7.94)
    Department

    		Cardiology530 (93.47)

    		Cardiac arrhythmia specialty39 (6.88)
    Professional title

    		Resident physician120 (21.16)

    		Attending237 (41.8)

    		Associate chief145 (25.57)

    		Chief65 (11.46)
    Years of practice

    		<10247 (43.54)

    		10-20213 (37.57)

    		>20107 (18.87)
    Hospital tier

    		Tertiary A414 (73.02)

    		Not tertiary A153 (26.98)
    Ever used a CDSSa?

    		Yes72 (12.70)

    		No495 (87.30)
    Is there a need for a CDSS?

    		Yes523 (92.24)

    		No44 (7.76)

    aCDSS: clinical decision support system.

    Semistructured Interviews

    Textbox 1 summarizes the results of the semistructured interviews, in which we focused on the challenges of VT management and CDSS needs. The responses of the seven cardiologists were focused, with each noting that etiological diagnosis and interpretation of ECG results were their main challenges. The most important demand was the provision of quick and concise recommendations on diagnosis and treatment. The interviewees also expected the CDSS to provide clinical pathways.

    Textbox 1. Results of the interviews.
    • Challenges in the management of ventricular tachycardia (VT)
    1. Etiological diagnosis
    2. Wide QRS tachycardia diagnosis on electrocardiogram (ECG)
    3. Determination of the location of VT origin on ECG
    4. Mechanisms of VT
    5. Drug treatment options
    6. Options for the treatment of polymorphic VT
    • Clinical decision support system needs
    1. Rapid and concise recommendations for diagnosis and treatment
    2. Diagnostic and therapeutic pathways for different etiologies
    3. Aids in the identification of wide QRS
    4. Adjunctive etiological diagnosis
    5. Diagnostic supplements for related diseases

    Knowledge

    Figure 2 shows that there was an overall lack of knowledge with respect to diagnostic evaluation, with 383 of the 567 (68.0%) cardiologists indicating full need of assistant knowledge in diagnostic evaluation. This was followed by examination interpretation, where 305 of the 567 (53.8%) cardiologists were in full need of knowledge regarding the interpretation of ECG, cardiac ultrasound, and other cardiac examinations. The need for conceptual knowledge was relatively lower, even though it still reached nearly 60%.

    ‎
    Figure 2. Knowledge assessment.

    Practice

    The overall average score of the practice questions was 6.11 (SD 0.55), the internal consistency of which was confirmed by a Cronbach α of 0.913. The mean scores of the examination interpretation, etiological diagnosis, and diagnostic evaluation were 6.22 (SD 3.94), 6.74 (SD 1.75), and 5.78 (SD 1.19), respectively. As shown in Figure 3, the etiological diagnosis section was associated with the highest overall score and the distribution of scores was also more concentrated than for the other sections, especially when compared with the distribution of the examination interpretation scores that were more dispersed and polarized.

    ‎
    Figure 3. Practice assessment.

    CDS Needs

    The majority of the surveyed cardiologists reported a positive attitude toward CDS needs (Figure 4). There was relatively higher demand expressed for functions related to executable processes and interpretable diagnosis. In particular, the executable processes function was considered to be an essential requirement of a CDSS by 344 of the 567 cardiologists (60.7%). Knowledge support function received the least support but was still close to 70%.

    ‎
    Figure 4. Clinical decision support needs assessment.

    Subgroup Analysis

    We divided all the cardiologists into subgroups based on specialty (Figure 5A) and practice years (Figure 5B). The Kruskal-Wallis test showed a significant difference in practice competency scores between general cardiologists and arrhythmia specialists (P=.02). Subgroup analysis according to years of practice revealed a significant effect of experience on scores. The <10 years group had significantly lower scores compared to those of the 10-20 years and >20 years groups. However, there was no significant difference between those with 10-20 years and >20 years of experience.

    ‎
    Figure 5. Subgroup analyses according to (A) specialty and (B) years of practice.

    Principal Results

    Based on a combination of semistructured interviews and questionnaires, this study conducted a large-scale nationwide survey for cardiologists to understand their knowledge and practice competence about VT diagnosis and their requirements for a related CDSS. The results indicated that knowledge and practice support in examination interpretation, etiological diagnosis, and diagnostic evaluation are considered to be essential for a VT diagnosis CDSS. In addition, the vast majority of the cardiologists gave a positive response with respect to the need for a CDSS.

    CDSS Requirements

    Previous research on CDSS requirements has primarily relied on methods such as interviews [31,34,35,38,39] and group discussions [30,34,35] to elicit users’ subjective needs. Based on recommendations from clinical experts and medical informatics professionals within our research team, it was acknowledged that certain objective requirements might not be articulated by users during interviews. Consequently, a questionnaire was designed to assess and uncover the requirements that might not have been spontaneously expressed during interviews. Previous studies have used questionnaires to investigate the knowledge, attitudes, and practices of health care professionals in various specific tasks [46-53], providing a basis for our questionnaire approach. To objectively reflect cardiologists’ knowledge and practice deficiencies, we opted to not directly inquire about specific knowledge points but instead used two case scenarios to simulate authentic VT diagnostic situations, which is proven to be an appropriate method to assess practice competence [54]. The survey results endorsed the advantages of this mixed methods approach. The difficulties in VT diagnosis mentioned by the cardiologists during interviews primarily focused on distinguishing wide QRS tachycardias on ECG and identifying the etiology of VT, with no mention of diagnostic evaluation. However, results from the practice section of the questionnaire indicated poorer competence in diagnostic evaluation compared to etiological diagnosis, suggesting that the interviewees were not consciously aware of their weaknesses in diagnostic evaluation during interviews. Currently, there is no unified systematic method for conducting a CDS requirements analysis. While our method of integrating interviews and questionnaires provides a comprehensive approach, there is still room for improvement. Use of a simulation game has been suggested as a better means for clinical competence assessment [42]. Future research could consider incorporating cognitive analysis [55] and real-world system usability evaluation [56] to further optimize CDSS requirements analysis.

    The objective results from case simulations also affirmed the cardiologists’ need for decision support (Figure 4). Regarding knowledge requirements, the results from the CDS needs section of the questionnaire indicated that participants had relatively fewer demands for knowledge support compared to direct decision support. Moreover, the cardiologists revealed a preference for automatically prompted relevant knowledge during the diagnostic and therapeutic processes, which can provide more targeted knowledge support (Figure 2). The challenge lies in ensuring that the CDSS accurately identifies the current diagnostic and therapeutic tasks; determines user knowledge gaps; and automatically retrieves, integrates, and presents knowledge support rapidly and accurately [57]. The results of the practice competence highlighted the need for improvement in the interpretation of diagnostic tests, etiological diagnosis, and diagnostic evaluation, suggesting the need for decision support in these three aspects, which were also highlighted as key clinical reasoning [58]. Notably, the accuracy of etiological diagnosis was relatively high, aligning with the lower knowledge demand for an etiological diagnosis (Figure 3). In terms of CDSS needs, the cardiologists favored direct decision support over knowledge support, including explanatory diagnoses and executable evaluation processes, which has also been recognized in recent studies [57,59,60].

    Synthesizing the findings of this study, we propose the following recommendations of specific functions of a CDSS for VT diagnosis under a framework of knowledge and practice. With respect to knowledge support, the CDSS needs to (1) provide foundational knowledge by offering fundamental knowledge for each relevant disease that is available for clinicians to retrieve and browse; (2) contextualize knowledge delivery by providing closely related knowledge at decision points, including, but not limited to, the interpretation of diagnostic tests such as ECGs and echocardiograms, wide QRS complex differentiation, etiological diagnosis of VT, and the issuance of diagnostic test orders; (3) explain the knowledge underlying CDSS results; and (4) provide evidence-based recommendations at decision points with available evidence support. With respect to practice support, the CDDS should (1) assist in ECG interpretation, including distinguishing wide QRS complex tachycardias, identifying useful features for etiological diagnosis during sinus rhythm and VT, and recommending diagnostic test orders; (2) assist in echocardiogram interpretation, including the recognition of common etiologies of VT such as old myocardial infarction, ARVC, myocarditis, and the classification of phenotypes of cardiomyopathies; (3) provide suspected etiological diagnoses based on existing information for patients with VT, including acute coronary syndrome, ischemic cardiomyopathy, ARVC, and acute myocarditis, with specific emphasis on alerting clinicians who may not have considered the possibility of acute coronary syndrome; and (4) supplement diagnostic assessments with additional information, including critical medical history, physical examination, laboratory tests, and other examinations. Particularly, using a comprehensive differential diagnosis list is advocated to mitigate premature closure [14], as substantiated by a recent study [61].

    Dxplain [24], one of the few DDSSs available for general practice, provides a diagnosis list according to input patient manifestations, which aligns with our proposed structure for VT etiological diagnosis. However, Dxplain lacks knowledge support, examination interpretation, and diagnostic assessment functions, which are highlighted as requirements for a VT CDSS as mentioned above. Another well-known commercial diagnostic support tool, ISABEL, not only serves as a diagnosis reminder but also provides knowledge support (ie, evidence-based knowledge of each disease). However, it does not satisfy the other requirements identified in this study [23,62]. Dr. Mayson [63] is a Chinese commercial CDSS for general practice, which can abstract data from electronic health records to form a diagnosis list as well as provide assistance in diagnostic assessment. Like ISABEL, Dr. Mayson provides a knowledge database for each disease, including clinical practice guidelines. However, the knowledge support is at the disease level rather than the decision level. In addition, this CDSS does not assist with examination interpretation.

    Although our study mainly investigated the specific functionalities for VT diagnosis, the results indicated some general CDSS functionalities, including interpretability of decision-making as well as the overall feasibility of the CDSS workflow. Several reviews [64-66] summarized other universal features worthy of consideration, such as integration with the clinical workflow and electronic health record system, reduction of manual input of patient data, execution users’ desired action, avoidance of unnecessary alerts, documentation of reasons for rejecting recommendations, as well as the “five rights” of CDS (providing the right information to the right people in the right formats through the right channels at the right time) [67].

    We believe that an excellent CDSS should provide tailored assistance for different types of clinicians. Thus, a subgroup analysis was performed according to the clinician characteristics in the practice section (Figure 5). As anticipated, arrhythmia specialists outperformed general cardiologists, which aligns with the findings of previous research [68]. The American College of Cardiology defines different types of cardiovascular specialists that have requirements for different types of support in cardiovascular health care [69]. A CDSS should be tailored to clinicians’ specialization levels to assist in diagnostic and therapeutic practices. For highly specialized clinicians facing a narrow spectrum of diseases, CDSS assistance may be limited, while support for foundational diagnostic and therapeutic aspects outside their specialty may be necessary. Conversely, less specialized clinicians facing a broader spectrum of diseases may need support in staying updated with the latest diagnostic and therapeutic advancements. For instance, for less experienced clinicians facing patients with VT, the CDSS should always indicate the possibility of CAD. For experienced clinicians, as they have already cultivated the mindset to exclude CAD, the CDSS might only provide this alert when they miss the diagnosis of CAD. Furthermore, it is expected that the CDSS could continually adapt to individual needs through observing clinician users’ behaviors. The impact of years of practice on performance seems to be nonlinear. Clinicians practicing for 10-20 years or more demonstrated better performance than those practicing for less than 10 years. However, there was no significant difference between the 10-20 years and >20 years groups, suggesting that clinical skills may grow in the first 10 years of practice but plateau afterward, thereby challenging the CDSS design to provide targeted support for clinicians with different levels of experience in practice. Additionally, for clinicians entering a bottleneck period in competence growth, the CDSS could facilitate education during practice, thereby supporting lifelong learning. Several studies have been performed in this regard in the areas of pharmaceutical skills [70], imaging interpretation [71], geriatric care [72], and periprocedural antithrombotic use [73].

    Most existing CDSSs have been generally designed for health care providers but might not fully consider the diversity of requirements as well as their expertise levels [74]. The genuine needs of health care providers have not been effectively communicated to system developers, resulting in the design of CDSSs that struggle to fulfill their intended role of assistance and workload reduction. Our study centers around the clinical scenario of VT diagnosis, comprehensively exploring support requirements in both knowledge and practice. This investigation can thus provide a foundation for the development of a relevant CDSS. Additionally, we aspire for this study to serve as a reference for clinical needs research, encouraging more health care providers and system developers to scrutinize clinical requirements and establish a groundwork for the development of highly effective CDSSs.

    Limitations

    Although this study used a combination of structured interviews and questionnaires for assessment, inevitably, some subjective factors from the participants may have biased the results. The questionnaire content of this study was carefully designed based on the results of the interviews as well as the experience of the multidisciplinary team; however, the questionnaire content was unable to cover all aspects of knowledge and practice related to VT diagnosis. Although specific functions for a VT diagnosis CDSS were proposed, they have not been evaluated in a real-world setting. As our team is currently developing a VT CDSS with these functions, more rigorous studies will be conducted to support these findings in our future research.

    Conclusions

    This comprehensive analysis of VT CDSS requirements using a mixed methods approach identified specific knowledge and practice support requirements. The derived functions provide a foundation for further development and optimization of a CDSS. Moreover, it is important to tailor the CDSS to clinicians’ specialization levels and years of practice for effective and personalized support.

    Acknowledgments

    This study was supported by National High Level Hospital Clinical Research Funding (grant 2022-GSP-GG-25), the Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (CIFMS) (grant 2021-I2M-1-056), the Key R&D Program of Hunan Province (grant 2021SK2024), the Key Laboratory of Medical Information Intelligent Technology, and the National Key R&D Program of China (grants 2016YFC0901901 and 2017YFC0907503).

    Data Availability

    The data sets generated and/or analyzed during this study are available from the corresponding author on reasonable request.

    Authors' Contributions

    ZH, MW, SZ, XX, JL, and YY designed the study. ZH, MW, ZZ, YY, SZ, XX, JL, and QG designed the questionnaire. ZH and ZZ collected the data. MW, ZH, and QG analyzed the data. ZH and MW drafted the manuscript. SZ, XX, JL, and YY critically revised the manuscript.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Demographic characteristics and clinical experience of the interviewees.

    XLSX File (Microsoft Excel File), 9 KB
    Multimedia Appendix 2

    Complete version of the questionnaire with the participant consent form.

    DOCX File , 979 KB
    1. Stecker EC, Reinier K, Marijon E, Narayanan K, Teodorescu C, Uy-Evanado A, et al. Public health burden of sudden cardiac death in the United States. Circ Arrhythm Electrophysiol. Apr 2014;7(2):212-217. [FREE Full text] [CrossRef] [Medline]
    2. Feng X, Hai J, Ma Y, Wang Z, Tse H. Sudden cardiac death in mainland China: a systematic analysis. Circ Arrhythm Electrophysiol. Nov 2018;11(11):e006684. [CrossRef] [Medline]
    3. Hua W, Zhang L, Wu Y, Liu X, Guo D, Zhou H, et al. Incidence of sudden cardiac death in China: analysis of 4 regional populations. J Am Coll Cardiol. Sep 15, 2009;54(12):1110-1118. [FREE Full text] [CrossRef] [Medline]
    4. Empana J, Lerner I, Valentin E, Folke F, Böttiger B, Gislason G, et al. ESCAPE-NET Investigators. Incidence of sudden cardiac death in the European Union. J Am Coll Cardiol. May 10, 2022;79(18):1818-1827. [FREE Full text] [CrossRef] [Medline]
    5. Guandalini GS, Liang JJ, Marchlinski FE. Ventricular tachycardia ablation: past, present, and future perspectives. JACC Clin Electrophysiol. Dec 2019;5(12):1363-1383. [FREE Full text] [CrossRef] [Medline]
    6. Libby P, Bonow RO, Mann DL, Tomaselli GF, Bhatt DL, Solomon SD, et al. Braunwald's Heart Disease: A Textbook of Cardiovascular Medicine, 12th edition. Amsterdam. Elsevier; 2021.
    7. Roberts-Thomson KC, Lau DH, Sanders P. The diagnosis and management of ventricular arrhythmias. Nat Rev Cardiol. Jun 22, 2011;8(6):311-321. [CrossRef] [Medline]
    8. Li L, Ding L, Zhang Z, Zhou L, Zhang Z, Xiong Y, et al. Development and validation of machine learning-based models to predict in-hospital mortality in life-threatening ventricular arrhythmias: retrospective cohort study. J Med Internet Res. Nov 15, 2023;25:e47664. [FREE Full text] [CrossRef] [Medline]
    9. Hayashi M, Shimizu W, Albert CM. The spectrum of epidemiology underlying sudden cardiac death. Circ Res. Jun 05, 2015;116(12):1887-1906. [FREE Full text] [CrossRef] [Medline]
    10. Dancy M, Camm A, Ward D. Misdiagnosis of chronic recurrent ventricular tachycardia. Lancet. Aug 10, 1985;2(8450):320-323. [CrossRef] [Medline]
    11. Spice C, Stewart K. Misdiagnosis of ventricular tachycardia. Lancet. 1999;354(9196):2165. [CrossRef] [Medline]
    12. Stewart K. Misdiagnosis of ventricular tachycardia. Lancet. Nov 08, 1986;2(8515):1103. [CrossRef] [Medline]
    13. Morady F, Baerman JM, DiCarlo LA, DeBuitleir M, Krol RB, Wahr DW. A prevalent misconception regarding wide-complex tachycardias. JAMA. Nov 15, 1985;254(19):2790-2792. [Medline]
    14. Graber ML, Franklin N, Gordon R. Diagnostic error in internal medicine. Arch Intern Med. Jul 11, 2005;165(13):1493-1499. [CrossRef] [Medline]
    15. Zeppenfeld K, Tfelt-Hansen J, de Riva M, Winkel BG, Behr ER, Blom NA, et al. ESC Scientific Document Group. 2022 ESC Guidelines for the management of patients with ventricular arrhythmias and the prevention of sudden cardiac death. Eur Heart J. Oct 21, 2022;43(40):3997-4126. [CrossRef] [Medline]
    16. Denekamp Y, Peleg M. TiMeDDx--a multi-phase anchor-based diagnostic decision-support model. J Biomed Inform. Feb 2010;43(1):111-124. [FREE Full text] [CrossRef] [Medline]
    17. Abedin Z. Differential diagnosis of wide QRS tachycardia: a review. J Arrhythm. Oct 09, 2021;37(5):1162-1172. [FREE Full text] [CrossRef] [Medline]
    18. Katritsis DG, Brugada J. Differential diagnosis of wide QRS tachycardias. Arrhythm Electrophysiol Rev. Nov 05, 2020;9(3):155-160. [FREE Full text] [CrossRef] [Medline]
    19. Ray S, Jeremy R, Nishimura R, Simpson IA. Identifying excellence in contemporary cardiology practice: transparency, professionalism and the role of the professional society. Heart. Aug 23, 2013;99(16):1144-1145. [CrossRef] [Medline]
    20. Osheroff J, Teich J, Levick D, Saldana L, Velasco F, Sittig D, et al. Improving Outcomes With Clinical Decision Support: An Implementer's Guide. 2nd ed. Milton Park. Routledge; 2012.
    21. Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. A roadmap for national action on clinical decision support. J Am Med Inform Assoc. 2007;14(2):141-145. [FREE Full text] [CrossRef] [Medline]
    22. Miller RA. Diagnostic decision support systems. In: Berner ES, editor. Clinical Decision Support Systems: Theory and Practice. Cham. Springer International Publishing; 2016;181-208.
    23. ISABEL. URL: https://www.isabelhealthcare.com/ [accessed 2024-01-30]
    24. DxPlain. mghls. URL: http://www.mghlcs.org/projects/dxplain [accessed 2024-01-30]
    25. Greenes RA, Bates DW, Kawamoto K, Middleton B, Osheroff J, Shahar Y. Clinical decision support models and frameworks: seeking to address research issues underlying implementation successes and failures. J Biomed Inform. Feb 2018;78:134-143. [FREE Full text] [CrossRef] [Medline]
    26. Khairat S, Marc D, Crosby W, Al Sanousi A. Reasons for physicians not adopting clinical decision support systems: critical analysis. JMIR Med Inform. Apr 18, 2018;6(2):e24. [FREE Full text] [CrossRef] [Medline]
    27. Lu Y, Melnick ER, Krumholz HM. Clinical decision support in cardiovascular medicine. BMJ. May 25, 2022;377:e059818. [CrossRef] [Medline]
    28. Timpka T, Johansson M. The need for requirements engineering in the development of clinical decision-support systems: a qualitative study. Methods Inf Med. May 1994;33(2):227-233. [Medline]
    29. Alvarez R. Discourse analysis of requirements and knowledge elicitation interviews. 2002. Presented at: 35th Annual Hawaii International Conference on System Sciences; January 7-10, 2002; Big Island, HI. [CrossRef]
    30. Bright T. Transforming user needs into functional requirements for an antibiotic clinical decision support system: explicating content analysis for system design. Appl Clin Inform. 2013;4(4):618-635. [FREE Full text] [CrossRef] [Medline]
    31. Schüttler C, Hinderer M, Kraus S, Lang A, Prokosch H, Castellanos I. Requirements analysis for a clinical decision support system aiming at improving the artificial nutrition of critically ill patients. Stud Health Technol Inform. 2017;243:137-141. [Medline]
    32. Miller K, Mosby D, Capan M, Kowalski R, Ratwani R, Noaiseh Y, et al. Interface, information, interaction: a narrative review of design and functional requirements for clinical decision support. J Am Med Inform Assoc. May 01, 2018;25(5):585-592. [FREE Full text] [CrossRef] [Medline]
    33. Jones EK, Banks A, Melton GB, Porta CM, Tignanelli CJ. 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. JMIR Hum Factors. Mar 16, 2022;9(1):e29019. [FREE Full text] [CrossRef] [Medline]
    34. Schütze D, Holtz S, Neff MC, Köhler SM, Schaaf J, Frischen LS, et al. Requirements analysis for an AI-based clinical decision support system for general practitioners: a user-centered design process. BMC Med Inform Decis Mak. Jul 31, 2023;23(1):144. [FREE Full text] [CrossRef] [Medline]
    35. Kabukye J, Koch S, Cornet R, Orem J, Hagglund M. User requirements for an electronic medical records system for oncology in developing countries: a case study of Uganda. AMIA Annu Symp Proc. 2017;2017:1004-1013. [FREE Full text] [Medline]
    36. Chaffee B. Developing and assessing requirements for clinical decision support. Am J Health Syst Pharm. Sep 15, 2003;60(18):1875-1879. [CrossRef] [Medline]
    37. Shah H, Allard RD, Enberg R, Krishnan G, Williams P, Nadkarni PM. Requirements for guidelines systems: implementation challenges and lessons from existing software-engineering efforts. BMC Med Inform Decis Mak. Mar 09, 2012;12(1):16. [FREE Full text] [CrossRef] [Medline]
    38. Gulla J, Neri PM, Bates DW, Samal L. User requirements for a chronic kidney disease clinical decision support tool to promote timely referral. Int J Med Inform. May 2017;101:50-57. [FREE Full text] [CrossRef] [Medline]
    39. Kannan V, Basit MA, Bajaj P, Carrington AR, Donahue IB, Flahaven EL, et al. User stories as lightweight requirements for agile clinical decision support development. J Am Med Inform Assoc. Nov 01, 2019;26(11):1344-1354. [FREE Full text] [CrossRef] [Medline]
    40. Balatsoukas P, Williams R, Davies C, Ainsworth J, Buchan I. User interface requirements for web-based integrated care pathways: evidence from the evaluation of an online care pathway investigation tool. J Med Syst. Nov 7, 2015;39(11):183. [CrossRef] [Medline]
    41. Notice on issuing ethical review measures for life sciences and medical research involving humans. National Health Commission, Ministry of Education, Ministry of Science and Technology, Bureau of Traditional Chinese Medicine. URL: https://www.gov.cn/zhengce/zhengceku/2023-02/28/content_5743658.htm [accessed 2024-03-20]
    42. Fonteneau T, Billion E, Abdoul C, Le S, Hadchouel A, Drummond D. Simulation game versus multiple choice questionnaire to assess the clinical competence of medical students: prospective sequential trial. J Med Internet Res. Dec 16, 2020;22(12):e23254. [FREE Full text] [CrossRef] [Medline]
    43. Bradburn NM, Sudman S, Wansink B. Asking Questions: The Definitive Guide to Questionnaire Design--For Market Research, Political Polls, and Social and Health Questionnaires. Hoboken, NJ. John Wiley & Sons; 2004.
    44. Shivkumar K, Do DH. Catheter ablation of ventricular tachycardia: first, treat the underlying disease. J Am Coll Cardiol. Oct 06, 2020;76(14):1657-1659. [FREE Full text] [CrossRef] [Medline]
    45. R: A language and environment for statistical computing (Version 4.0.3). URL: https://www.R-project.org/ [accessed 2023-10-21]
    46. Zhang C, Shen L, Pan M, Zheng Y, Gu Z, Lin H. Perceptions and knowledge gaps on CHADS-VASc score components: a joint survey of Chinese clinicians and clinical pharmacists. Postgrad Med. Jan 08, 2022;134(1):64-77. [CrossRef] [Medline]
    47. Azraii AB, Ramli AS, Ismail Z, Abdul-Razak S, Badlishah-Sham SF, Mohd-Kasim NA, et al. Validity and reliability of an adapted questionnaire measuring knowledge, awareness and practice regarding familial hypercholesterolaemia among primary care physicians in Malaysia. BMC Cardiovasc Disord. Jan 19, 2021;21(1):39. [FREE Full text] [CrossRef] [Medline]
    48. Malhotra S, Masood I, Giglio N, Pruetz JD, Pannaraj PS. Current knowledge of Chagas-related heart disease among pediatric cardiologists in the United States. BMC Cardiovasc Disord. Mar 02, 2021;21(1):116. [CrossRef]
    49. Montagni I, Roussel N, Thiébaut R, Tzourio C. Health care students' knowledge of and attitudes, beliefs, and practices toward the French COVID-19 app: cross-sectional questionnaire study. J Med Internet Res. Mar 03, 2021;23(3):e26399. [FREE Full text] [CrossRef] [Medline]
    50. Sturny L, Regard S, Larribau R, Niquille M, Savoldelli GL, Sarasin F, et al. Differences in basic life support knowledge between junior medical students and lay people: web-based questionnaire study. J Med Internet Res. Feb 23, 2021;23(2):e25125. [FREE Full text] [CrossRef] [Medline]
    51. Sadarmin PP, Wong KCK, Rajappan K, Bashir Y, Betts TR. Questionnaire survey of cardiologists' knowledge, attitudes, and guideline application of implantable cardioverter defibrillator therapy. Pacing Clin Electrophysiol. Jun 27, 2012;35(6):672-680. [CrossRef] [Medline]
    52. Bergman HE, Reeve BB, Moser RP, Scholl S, Klein WM. Development of a comprehensive heart disease knowledge questionnaire. Am J Health Educ. Mar 23, 2011;42(2):74-87. [FREE Full text] [CrossRef] [Medline]
    53. Shah S, Davies M, Quinn T. Management of acute coronary syndromes, a questionnaire survey of the clinical practice of cardiologists and other medical physicians belonging to west midland hospitals. Int J Cardiol. Mar 10, 2005;99(1):71-75. [CrossRef] [Medline]
    54. Wass V, Van der Vleuten C, Shatzer J, Jones R. Assessment of clinical competence. Lancet. Mar 24, 2001;357(9260):945-949. [CrossRef] [Medline]
    55. Patel VL, Arocha JF, Diermeier M, Greenes RA, Shortliffe EH. Methods of cognitive analysis to support the design and evaluation of biomedical systems: the case of clinical practice guidelines. J Biomed Inform. Feb 2001;34(1):52-66. [FREE Full text] [CrossRef] [Medline]
    56. Trinkley KE, Kahn MG, Bennett TD, Glasgow RE, Haugen H, Kao DP, et al. Integrating the practical robust implementation and sustainability model with best practices in clinical decision support design: implementation science approach. J Med Internet Res. Oct 29, 2020;22(10):e19676. [FREE Full text] [CrossRef] [Medline]
    57. Mittermaier M, Raza M, Kvedar JC. Collaborative strategies for deploying AI-based physician decision support systems: challenges and deployment approaches. NPJ Digit Med. Aug 05, 2023;6(1):137. [CrossRef] [Medline]
    58. Brush JE, Sherbino J, Norman GR. Diagnostic reasoning in cardiovascular medicine. BMJ. Jan 05, 2022;376:e064389. [CrossRef] [Medline]
    59. Garg AX, Adhikari NKJ, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. Mar 09, 2005;293(10):1223-1238. [CrossRef] [Medline]
    60. Mastrianni A, Sarcevic A, Hu A, Almengor L, Tempel P, Gao S, et al. Transitioning cognitive aids into decision support platforms: requirements and design guidelines. ACM Trans Comput Hum Interact. Jun 10, 2023;30(3):1-28. [CrossRef] [Medline]
    61. Sibbald M, Monteiro S, Sherbino J, LoGiudice A, Friedman C, Norman G. Should electronic differential diagnosis support be used early or late in the diagnostic process? A multicentre experimental study of Isabel. BMJ Qual Saf. Jun 05, 2022;31(6):426-433. [FREE Full text] [CrossRef] [Medline]
    62. Graber ML, Mathew A. Performance of a web-based clinical diagnosis support system for internists. J Gen Intern Med. Jan 19, 2008;23(Suppl 1):37-40. [FREE Full text] [CrossRef] [Medline]
    63. Dr. Mayson. Humei. URL: http://www.huimei.com/specialist [accessed 2024-01-30]
    64. Camacho J, Zanoletti-Mannello M, Landis-Lewis Z, Kane-Gill SL, Boyce RD. A conceptual framework to study the implementation of clinical decision support systems (BEAR): literature review and concept mapping. J Med Internet Res. Aug 06, 2020;22(8):e18388. [FREE Full text] [CrossRef] [Medline]
    65. Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med. Feb 06, 2020;3(1):17. [CrossRef] [Medline]
    66. Roshanov PS, Fernandes N, Wilczynski JM, Hemens BJ, You JJ, Handler SM, et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ. Feb 14, 2013;346(feb14 1):f657-f657. [FREE Full text] [CrossRef] [Medline]
    67. Campbell R. The five "rights" of clinical decision support. J AHIMA. Oct 2013;84(10):42-7; quiz 48. [Medline]
    68. Harrold LR, Field TS, Gurwitz JH. Knowledge, patterns of care, and outcomes of care for generalists and specialists. J Gen Intern Med. Aug 1999;14(8):499-511. [FREE Full text] [CrossRef] [Medline]
    69. Fuster V, Hirshfeld JW, Brown AS, Brundage BH, Fye W, Lewis RP, et al. Working group 8: Defining the different types of cardiovascular specialists and developing a new model for training general clinical cardiologists. J Am Coll Cardiol. Jul 21, 2004;44(2):267-271. [FREE Full text] [CrossRef] [Medline]
    70. Paydar P, Ebrahimpour S, Zehtab Hashemi H, Mohamadi M, Namazi S. Design, development and evaluation of an application based on clinical decision support systems (CDSS) for over-the-counter (OTC) therapy: an educational interventions in community pharmacists. J Adv Med Educ Prof. Apr 2023;11(2):95-104. [FREE Full text] [CrossRef] [Medline]
    71. Shah C, Davtyan K, Nasrallah I, Bryan RN, Mohan S. Artificial intelligence-powered clinical decision support and simulation platform for radiology trainee education. J Digit Imaging. Feb 24, 2023;36(1):11-16. [FREE Full text] [CrossRef] [Medline]
    72. Litvin CB, Davis KS, Moran WP, Iverson PJ, Zhao Y, Zapka J. The use of clinical decision-support tools to facilitate geriatric education. J Am Geriatr Soc. Jun 2012;60(6):1145-1149. [CrossRef] [Medline]
    73. Strauss AT, James TW, Mathews SC. Fellow education improved through mobile clinical decision support application: a multi-center approach involving peri-procedural antithrombotic use. Gastroenterology. Dec 2018;155(6):2014-2015. [CrossRef] [Medline]
    74. Gong Y, Kang H. Usability and clinical decision support. In: Berner ES, editor. Clinical Decision Support Systems: Theory and Practice. Cham. Springer International Publishing; 2016;69-86.

    ‎
    AHRA: Asian Heart Rhythm Association
    ARVC: arrhythmogenic right ventricular cardiomyopathy
    CAD: coronary artery disease
    CDS: clinical decision support
    CDSS: clinical decision support system
    DDSS: diagnostic decision support system
    ECG: electrocardiogram
    ESC: European Society of Cardiology
    SCD: sudden cardiac death
    VT: ventricular tachycardia

    Edited by A Kushniruk; submitted 25.12.23; peer-reviewed by H Younes; comments to author 25.01.24; revised version received 15.02.24; accepted 02.03.24; published 26.03.24.

    Copyright

    ©Zhao Hu, Min Wang, Si Zheng, Xiaowei Xu, Zhuxin Zhang, Qiaoyue Ge, Jiao Li, Yan Yao. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 26.03.2024.

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