%0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e58660 %T Psychological Factors Influencing Appropriate Reliance on AI-enabled Clinical Decision Support Systems: Experimental Web-Based Study Among Dermatologists %A Küper,Alisa %A Lodde,Georg Christian %A Livingstone,Elisabeth %A Schadendorf,Dirk %A Krämer,Nicole %+ , Social Psychology: Media and Communication, University of Duisburg-Essen, Bismarckstraße 120, Duisburg, 47057, Germany, 49 203 379 6027, alisa.kueper@uni-due.de %K AI reliance %K psychological factors %K clinical decision support systems %K medical decision-making %K artificial intelligence %K AI %D 2025 %7 4.4.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: Artificial intelligence (AI)–enabled decision support systems are critical tools in medical practice; however, their reliability is not absolute, necessitating human oversight for final decision-making. Human reliance on such systems can vary, influenced by factors such as individual psychological factors and physician experience. Objective: This study aimed to explore the psychological factors influencing subjective trust and reliance on medical AI’s advice, specifically examining relative AI reliance and relative self-reliance to assess the appropriateness of reliance. Methods: A survey was conducted with 223 dermatologists, which included lesion image classification tasks and validated questionnaires assessing subjective trust, propensity to trust technology, affinity for technology interaction, control beliefs, need for cognition, as well as queries on medical experience and decision confidence. Results: A 2-tailed t test revealed that participants’ accuracy improved significantly with AI support (t222=−3.3; P<.001; Cohen d=4.5), but only by an average of 1% (1/100). Reliance on AI was stronger for correct advice than for incorrect advice (t222=4.2; P<.001; Cohen d=0.1). Notably, participants demonstrated a mean relative AI reliance of 10.04% (139/1384) and a relative self-reliance of 85.6% (487/569), indicating a high level of self-reliance but a low level of AI reliance. Propensity to trust technology influenced AI reliance, mediated by trust (indirect effect=0.024, 95% CI 0.008-0.042; P<.001), and medical experience negatively predicted AI reliance (indirect effect=–0.001, 95% CI –0.002 to −0.001; P<.001). Conclusions: The findings highlight the need to design AI support systems in a way that assists less experienced users with a high propensity to trust technology to identify potential AI errors, while encouraging experienced physicians to actively engage with system recommendations and potentially reassess initial decisions. %M 40184614 %R 10.2196/58660 %U https://www.jmir.org/2025/1/e58660 %U https://doi.org/10.2196/58660 %U http://www.ncbi.nlm.nih.gov/pubmed/40184614 %0 Journal Article %@ 2817-1705 %I JMIR Publications %V 4 %N %P e59295 %T Studying the Potential Effects of Artificial Intelligence on Physician Autonomy: Scoping Review %A Grosser,John %A Düvel,Juliane %A Hasemann,Lena %A Schneider,Emilia %A Greiner,Wolfgang %+ Department of Health Economics and Health Care Management, School of Public Health, Bielefeld University, Universitätsstraße 25, Bielefeld, 33615, Germany, 49 52110686319, john.grosser@uni-bielefeld.de %K autonomy, professional autonomy %K physician autonomy %K ethics %K artificial intelligence %K clinical decision support systems %K CDSS %K ethics of artificial intelligence %K AI ethics %K AI %K scoping review %K physician %K acceptance %K adoption %D 2025 %7 13.3.2025 %9 Review %J JMIR AI %G English %X Background: Physician autonomy has been found to play a role in physician acceptance and adoption of artificial intelligence (AI) in medicine. However, there is still no consensus in the literature on how to define and assess physician autonomy. Furthermore, there is a lack of research focusing specifically on the potential effects of AI on physician autonomy. Objective: This scoping review addresses the following research questions: (1) How do qualitative studies conceptualize and assess physician autonomy? (2) Which aspects of physician autonomy are addressed by these studies? (3) What are the potential benefits and harms of AI for physician autonomy identified by these studies? Methods: We performed a scoping review of qualitative studies on AI and physician autonomy published before November 6, 2023, by searching MEDLINE and Web of Science. To answer research question 1, we determined whether the included studies explicitly include physician autonomy as a research focus and whether their interview, survey, and focus group questions explicitly name or implicitly include aspects of physician autonomy. To answer research question 2, we extracted the qualitative results of the studies, categorizing them into the 7 components of physician autonomy introduced by Schulz and Harrison. We then inductively formed subcomponents based on the results of the included studies in each component. To answer research question 3, we summarized the potentially harmful and beneficial effects of AI on physician autonomy in each of the inductively formed subcomponents. Results: The search yielded 369 studies after duplicates were removed. Of these, 27 studies remained after titles and abstracts were screened. After full texts were screened, we included a total of 7 qualitative studies. Most studies did not explicitly name physician autonomy as a research focus or explicitly address physician autonomy in their interview, survey, and focus group questions. No studies addressed a complete set of components of physician autonomy; while 3 components were addressed by all included studies, 2 components were addressed by none. We identified a total of 11 subcomponents for the 5 components of physician autonomy that were addressed by at least 1 study. For most of these subcomponents, studies reported both potential harms and potential benefits of AI for physician autonomy. Conclusions: Little research to date has explicitly addressed the potential effects of AI on physician autonomy and existing results on these potential effects are mixed. Further qualitative and quantitative research is needed that focuses explicitly on physician autonomy and addresses all relevant components of physician autonomy. %M 40080059 %R 10.2196/59295 %U https://ai.jmir.org/2025/1/e59295 %U https://doi.org/10.2196/59295 %U http://www.ncbi.nlm.nih.gov/pubmed/40080059 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 14 %N %P e60431 %T Exploring Curriculum Considerations to Prepare Future Radiographers for an AI-Assisted Health Care Environment: Protocol for Scoping Review %A Kammies,Chamandra %A Archer,Elize %A Engel-Hills,Penelope %A Volschenk,Mariette %+ Department of Medical Imaging and Radiation Sciences, Faculty of Health Sciences, University of Johannesburg, 37 Nind Street, Doornfontein, Johannesburg, 2094, South Africa, 27 0115596813, chamandrak@uj.ac.za %K artificial intelligence %K machine learning %K radiography %K education %K scoping review %D 2025 %7 6.3.2025 %9 Protocol %J JMIR Res Protoc %G English %X Background: The use of artificial intelligence (AI) technologies in radiography practice is increasing. As this advanced technology becomes more embedded in radiography systems and clinical practice, the role of radiographers will evolve. In the context of these anticipated changes, it may be reasonable to expect modifications to the competencies and educational requirements of current and future practitioners to ensure successful AI adoption. Objective: The aim of this scoping review is to explore and synthesize the literature on the adjustments needed in the radiography curriculum to prepare radiography students for the demands of AI-assisted health care environments. Methods: Using the Joanna Briggs Institute methodology, an initial search was run in Scopus to determine whether the search strategy that was developed with a library specialist would capture the relevant literature by screening the title and abstract of the first 50 articles. Additional search terms identified in the articles were added to the search strategy. Next, EBSCOhost, PubMed, and Web of Science databases were searched. In total, 2 reviewers will independently review the title, abstract, and full-text articles according to the predefined inclusion and exclusion criteria, with conflicts resolved by a third reviewer. Results: The search results will be reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. The final scoping review will present the data analysis as findings in tabular form and through narrative descriptions. The final database searches were completed in October 2024 and yielded 2224 records. Title and abstract screening of 1930 articles is underway after removing 294 duplicates. The scoping review is expected to be finalized by the end of March 2025. Conclusions: A scoping review aims to systematically map the evidence on the adjustments needed in the radiography curriculum to prepare radiography students for the integration of AI technologies in the health care environment. It is relevant to map the evidence because increased integration of AI-based technologies in clinical practice has been noted and changes in practice must be underpinned by appropriate education and training. The findings in this study will provide a better understanding of how the radiography curriculum should adapt to meet the educational needs of current and future radiographers to ensure competent and safe practice in response to AI technologies. Trial Registration: Open Science Framework 3nx2a; https://osf.io/3nx2a International Registered Report Identifier (IRRID): PRR1-10.2196/60431 %M 40053777 %R 10.2196/60431 %U https://www.researchprotocols.org/2025/1/e60431 %U https://doi.org/10.2196/60431 %U http://www.ncbi.nlm.nih.gov/pubmed/40053777 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 9 %N %P e58426 %T Artificial Intelligence–Powered Training Database for Clinical Thinking: App Development Study %A Wang,Heng %A Zheng,Danni %A Wang,Mengying %A Ji,Hong %A Han,Jiangli %A Wang,Yan %A Shen,Ning %A Qiao,Jie %K artificial intelligence %K clinical thinking ability %K virtual medical records %K distance education %K medical education %K online learning %D 2025 %7 3.1.2025 %9 %J JMIR Form Res %G English %X Background: With the development of artificial intelligence (AI), medicine has entered the era of intelligent medicine, and various aspects, such as medical education and talent cultivation, are also being redefined. The cultivation of clinical thinking abilities poses a formidable challenge even for seasoned clinical educators, as offline training modalities often fall short in bridging the divide between current practice and the desired ideal. Consequently, there arises an imperative need for the expeditious development of a web-based database, tailored to empower physicians in their quest to learn and hone their clinical reasoning skills. Objective: This study aimed to introduce an app named “XueYiKu,” which includes consultations, physical examinations, auxiliary examinations, and diagnosis, incorporating AI and actual complete hospital medical records to build an online-learning platform using human-computer interaction. Methods: The “XueYiKu” app was designed as a contactless, self-service, trial-and-error system application based on actual complete hospital medical records and natural language processing technology to comprehensively assess the “clinical competence” of residents at different stages. Case extraction was performed at a hospital’s case data center, and the best-matching cases were differentiated through natural language processing, word segmentation, synonym conversion, and sorting. More than 400 teaching cases covering 65 kinds of diseases were released for students to learn, and the subjects covered internal medicine, surgery, gynecology and obstetrics, and pediatrics. The difficulty of learning cases was divided into four levels in ascending order. Moreover, the learning and teaching effects were evaluated using 6 dimensions covering systematicness, agility, logic, knowledge expansion, multidimensional evaluation indicators, and preciseness. Results: From the app’s first launch on the Android platform in May 2019 to the last version updated in May 2023, the total number of teacher and student users was 6209 and 1180, respectively. The top 3 subjects most frequently learned were respirology (n=606, 24.1%), general surgery (n=506, 20.1%), and urinary surgery (n=390, 15.5%). For diseases, pneumonia was the most frequently learned, followed by cholecystolithiasis (n=216, 14.1%), benign prostate hyperplasia (n=196, 12.8%), and bladder tumor (n=193, 12.6%). Among 479 students, roughly a third (n=168, 35.1%) scored in the 60 to 80 range, and half of them scored over 80 points (n=238, 49.7%). The app enabled medical students’ learning to become more active and self-motivated, with a variety of formats, and provided real-time feedback through assessments on the platform. The learning effect was satisfactory overall and provided important precedence for establishing scientific models and methods for assessing clinical thinking skills in the future. Conclusions: The integration of AI and medical education will undoubtedly assist in the restructuring of education processes; promote the evolution of the education ecosystem; and provide new convenient ways for independent learning, interactive communication, and educational resource sharing. %R 10.2196/58426 %U https://formative.jmir.org/2025/1/e58426 %U https://doi.org/10.2196/58426 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 27 %N %P e59435 %T Application of Large Language Models in Medical Training Evaluation—Using ChatGPT as a Standardized Patient: Multimetric Assessment %A Wang,Chenxu %A Li,Shuhan %A Lin,Nuoxi %A Zhang,Xinyu %A Han,Ying %A Wang,Xiandi %A Liu,Di %A Tan,Xiaomei %A Pu,Dan %A Li,Kang %A Qian,Guangwu %A Yin,Rong %+ West China Biomedical Big Data Center, West China Hospital, Sichuan University, No.37, Guoxue Lane, Wuhou District, Chengdu, 610041, China, 86 02881739902, likang@wchscu.cn %K ChatGPT %K artificial intelligence %K standardized patient %K health care %K prompt engineering %K accuracy %K large language models %K performance evaluation %K medical training %K inflammatory bowel disease %D 2025 %7 1.1.2025 %9 Original Paper %J J Med Internet Res %G English %X Background: With the increasing interest in the application of large language models (LLMs) in the medical field, the feasibility of its potential use as a standardized patient in medical assessment is rarely evaluated. Specifically, we delved into the potential of using ChatGPT, a representative LLM, in transforming medical education by serving as a cost-effective alternative to standardized patients, specifically for history-taking tasks. Objective: The study aims to explore ChatGPT’s viability and performance as a standardized patient, using prompt engineering to refine its accuracy and use in medical assessments. Methods: A 2-phase experiment was conducted. The first phase assessed feasibility by simulating conversations about inflammatory bowel disease (IBD) across 3 quality groups (good, medium, and bad). Responses were categorized based on their relevance and accuracy. Each group consisted of 30 runs, with responses scored to determine whether they were related to the inquiries. For the second phase, we evaluated ChatGPT’s performance against specific criteria, focusing on its anthropomorphism, clinical accuracy, and adaptability. Adjustments were made to prompts based on ChatGPT’s response shortcomings, with a comparative analysis of ChatGPT’s performance between original and revised prompts. A total of 300 runs were conducted and compared against standard reference scores. Finally, the generalizability of the revised prompt was tested using other scripts for another 60 runs, together with the exploration of the impact of the used language on the performance of the chatbot. Results: The feasibility test confirmed ChatGPT’s ability to simulate a standardized patient effectively, differentiating among poor, medium, and good medical inquiries with varying degrees of accuracy. Score differences between the poor (74.7, SD 5.44) and medium (82.67, SD 5.30) inquiry groups (P<.001), between the poor and good (85, SD 3.27) inquiry groups (P<.001) were significant at a significance level (α) of .05, while the score differences between the medium and good inquiry groups were not statistically significant (P=.16). The revised prompt significantly improved ChatGPT’s realism, clinical accuracy, and adaptability, leading to a marked reduction in scoring discrepancies. The score accuracy of ChatGPT improved 4.926 times compared to unrevised prompts. The score difference percentage drops from 29.83% to 6.06%, with a drop in SD from 0.55 to 0.068. The performance of the chatbot on a separate script is acceptable with an average score difference percentage of 3.21%. Moreover, the performance differences between test groups using various language combinations were found to be insignificant. Conclusions: ChatGPT, as a representative LLM, is a viable tool for simulating standardized patients in medical assessments, with the potential to enhance medical training. By incorporating proper prompts, ChatGPT’s scoring accuracy and response realism significantly improved, approaching the feasibility of actual clinical use. Also, the influence of the adopted language is nonsignificant on the outcome of the chatbot. %M 39742453 %R 10.2196/59435 %U https://www.jmir.org/2025/1/e59435 %U https://doi.org/10.2196/59435 %U http://www.ncbi.nlm.nih.gov/pubmed/39742453 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 10 %N %P e58165 %T Topics and Trends of Health Informatics Education Research: Scientometric Analysis %A Han,Qing %+ School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, No. 548 Binwen Road, Binjiang District, Hangzhou, 310053, China, 86 0571 86613545, hanqing@zcmu.edu.cn %K health informatics education %K scientometric analysis %K structural topic model %K health informatics %K medical informatics %K medical education %D 2024 %7 11.12.2024 %9 Original Paper %J JMIR Med Educ %G English %X Background: Academic and educational institutions are making significant contributions toward training health informatics professionals. As research in health informatics education (HIE) continues to grow, it is useful to have a clearer understanding of this research field. Objective: This study aims to comprehensively explore the research topics and trends of HIE from 2014 to 2023. Specifically, it aims to explore (1) the trends of annual articles, (2) the prolific countries/regions, institutions, and publication sources, (3) the scientific collaborations of countries/regions and institutions, and (4) the major research themes and their developmental tendencies. Methods: Using publications in Web of Science Core Collection, a scientometric analysis of 575 articles related to the field of HIE was conducted. The structural topic model was used to identify topics discussed in the literature and to reveal the topic structure and evolutionary trends of HIE research. Results: Research interest in HIE has clearly increased from 2014 to 2023, and is continually expanding. The United States was found to be the most prolific country in this field. Harvard University was found to be the leading institution with the highest publication productivity. Journal of Medical Internet Research, Journal of The American Medical Informatics Association, and Applied Clinical Informatics were the top 3 journals with the highest articles in this field. Countries/regions and institutions having higher levels of international collaboration were more impactful. Research on HIE could be modeled into 7 topics related to the following areas: clinical (130/575, 22.6%), mobile application (123/575, 21.4%), consumer (99/575, 17.2%), teaching (61/575, 10.6%), public health (56/575, 9.7%), discipline (55/575, 9.6%), and nursing (51/575, 8.9%). The results clearly indicate the unique foci for each year, depicting the process of development for health informatics research. Conclusions: This is believed to be the first scientometric analysis exploring the research topics and trends in HIE. This study provides useful insights and implications, and the findings could be used as a guide for HIE contributors. %M 39661981 %R 10.2196/58165 %U https://mededu.jmir.org/2024/1/e58165 %U https://doi.org/10.2196/58165 %U http://www.ncbi.nlm.nih.gov/pubmed/39661981 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 10 %N %P e60031 %T Practical Recommendations for Navigating Digital Tools in Hospitals: Qualitative Interview Study %A Wosny,Marie %A Strasser,Livia Maria %A Kraehenmann,Simone %A Hastings,Janna %+ School of Medicine, University of St Gallen (HSG), St Jakob-Strasse 21, St.Gallen, 9000, Switzerland, 41 712 243 249, mariejohanna.wosny@unisg.ch %K health care %K hospital %K information system %K information technology %K technology implementation %K training %K medical education %K digital literacy %K curriculum development %K health care workforce development %K mobile phone %D 2024 %7 27.11.2024 %9 Original Paper %J JMIR Med Educ %G English %X Background: The digitalization of health care organizations is an integral part of a clinician’s daily life, making it vital for health care professionals (HCPs) to understand and effectively use digital tools in hospital settings. However, clinicians often express a lack of preparedness for their digital work environments. Particularly, new clinical end users, encompassing medical and nursing students, seasoned professionals transitioning to new health care environments, and experienced practitioners encountering new health care technologies, face critically intense learning periods, often with a lack of adequate time for learning digital tools, resulting in difficulties in integrating and adopting these digital tools into clinical practice. Objective: This study aims to comprehensively collect advice from experienced HCPs in Switzerland to guide new clinical end users on how to initiate their engagement with health ITs within hospital settings. Methods: We conducted qualitative interviews with 52 HCPs across Switzerland, representing 24 medical specialties from 14 hospitals. The interviews were transcribed verbatim and analyzed through inductive thematic analysis. Codes were developed iteratively, and themes and aggregated dimensions were refined through collaborative discussions. Results: Ten themes emerged from the interview data, namely (1) digital tool understanding, (2) peer-based learning strategies, (3) experimental learning approaches, (4) knowledge exchange and support, (5) training approaches, (6) proactive innovation, (7) an adaptive technology mindset, (8) critical thinking approaches, (9) dealing with emotions, and (10) empathy and human factors. Consequently, we devised 10 recommendations with specific advice to new clinical end users on how to approach new health care technologies, encompassing the following: take time to get to know and understand the tools you are working with; proactively ask experienced colleagues; simply try it out and practice; know where to get help and information; take sufficient training; embrace curiosity and pursue innovation; maintain an open and adaptable mindset; keep thinking critically and use your knowledge base; overcome your fears, and never lose the human and patient focus. Conclusions: Our study emphasized the importance of comprehensive training and learning approaches for health care technologies based on the advice and recommendations of experienced HCPs based in Swiss hospitals. Moreover, these recommendations have implications for medical educators and clinical instructors, providing advice on effective methods to instruct and support new end users, enabling them to use novel technologies proficiently. Therefore, we advocate for new clinical end users, health care institutions and clinical instructors, academic institutions and medical educators, and regulatory bodies to prioritize effective training and cultivating technological readiness to optimize IT use in health care. %M 39602211 %R 10.2196/60031 %U https://mededu.jmir.org/2024/1/e60031 %U https://doi.org/10.2196/60031 %U http://www.ncbi.nlm.nih.gov/pubmed/39602211 %0 Journal Article %@ 2369-2529 %I JMIR Publications %V 11 %N %P e56432 %T Capabilities for Using Telemonitoring in Physiotherapy Treatment: Exploratory Qualitative Study %A van Westerhuis,Charlotte %A Sanders,Astrid F %A Aarden,Jesse J %A Major,Mel E %A de Leeuwerk,Marijke E %A Florisson,Nadine %A Wijbenga,Miriam H %A van der Schaaf,Marike %A van der Leeden,Marike %A van Egmond,Maarten A %K telemedicine %K telemonitoring %K technology %K physical therapy modalities %K education %K physiotherapist %K physiotherapy %K telehealth %D 2024 %7 24.10.2024 %9 %J JMIR Rehabil Assist Technol %G English %X Background: Telemonitoring (TM), as part of telehealth, allows physiotherapists to monitor and coach their patients using remotely collected data. The use of TM requires a different approach compared with face-to-face treatment. Although a telehealth capability framework exists for health care professionals, it remains unclear what specific capabilities are required to use TM during physiotherapy treatments. Objective: This study aims to identify the capabilities required to use TM in physiotherapy treatment. Methods: An exploratory qualitative study was conducted following a constructivist semistructured grounded theory approach. Three heterogeneous focus groups were conducted with 15 lecturers of the School of Physiotherapy (Bachelor of Science Physiotherapy program) from the Amsterdam University of Applied Sciences. Focus group discussions were audiotaped and transcribed verbatim. Capabilities for using TM in physiotherapy treatment were identified during an iterative process of data collection and analysis, based on an existing framework with 4 different domains. Team discussions supported further conceptualization of the findings. Results: Sixteen capabilities for the use of TM in physiotherapy treatment were found addressing 3 different domains. Four capabilities were identified in the “digital health technologies, systems, and policies” domain, 7 capabilities in the “clinical practice and application” domain, and 5 capabilities in the “data analysis and knowledge creation” domain. No capabilities were identified in the “system and technology implementation” domain. Conclusions: The use of TM in physiotherapy treatment requires specific skills from physiotherapists. To best use TM in physiotherapy treatment, it is important to integrate these capabilities into the education of current and future physiotherapists. %R 10.2196/56432 %U https://rehab.jmir.org/2024/1/e56432 %U https://doi.org/10.2196/56432 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 10 %N %P e53462 %T Design, Implementation, and Analysis of an Assessment and Accreditation Model to Evaluate a Digital Competence Framework for Health Professionals: Mixed Methods Study %A Saigí-Rubió,Francesc %A Romeu,Teresa %A Hernández Encuentra,Eulàlia %A Guitert,Montse %A Andrés,Erik %A Reixach,Elisenda %+ Faculty of Health Sciences, Universitat Oberta de Catalunya, Rambla del Poblenou, 156, Barcelona, 08018, Spain, 34 933 263 622, fsaigi@uoc.edu %K eHealth literacy %K eHealth competencies %K digital health %K competencies %K eHealth %K health literacy %K digital technology %K health care professionals %K health care workers %D 2024 %7 17.10.2024 %9 Original Paper %J JMIR Med Educ %G English %X Background: Although digital health is essential for improving health care, its adoption remains slow due to the lack of literacy in this area. Therefore, it is crucial for health professionals to acquire digital skills and for a digital competence assessment and accreditation model to be implemented to make advances in this field. Objective: This study had two objectives: (1) to create a specific map of digital competences for health professionals and (2) to define and test a digital competence assessment and accreditation model for health professionals. Methods: We took an iterative mixed methods approach, which included a review of the gray literature and consultation with local experts. We used the arithmetic mean and SD in descriptive statistics, P values in hypothesis testing and subgroup comparisons, the greatest lower bound in test diagnosis, and the discrimination index in study instrument analysis. Results: The assessment model designed in accordance with the competence content defined in the map of digital competences and based on scenarios had excellent internal consistency overall (greatest lower bound=0.91). Although most study participants (110/122, 90.2%) reported an intermediate self-perceived digital competence level, we found that the vast majority would not attain a level-2 Accreditation of Competence in Information and Communication Technologies. Conclusions: Knowing the digital competence level of health professionals based on a defined competence framework should enable such professionals to be trained and updated to meet real needs in their specific professional contexts and, consequently, take full advantage of the potential of digital technologies. These results have informed the Health Plan for Catalonia 2021-2025, thus laying the foundations for creating and offering specific training to assess and certify the digital competence of such professionals. %M 39418092 %R 10.2196/53462 %U https://mededu.jmir.org/2024/1/e53462 %U https://doi.org/10.2196/53462 %U http://www.ncbi.nlm.nih.gov/pubmed/39418092 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 10 %N %P e54427 %T Impact of Health Informatics Analyst Education on Job Role, Career Transition, and Skill Development: Survey Study %A Lee,Kye Hwa %A Lee,Jae Ho %A Lee,Yura %A Lee,Hyunna %A Lee,Ji Sung %A Jang,Hye Jeon %A Lee,Kun Hee %A Han,Jeong Hyun %A Jang,SuJung %K health informatics %K health informatics training %K informatics training %K professional development %K training program %K digital health technology %K informatics workforce %K informatics competencies %K competencies %K job skills %K continuing education %K data science %D 2024 %7 25.9.2024 %9 %J JMIR Med Educ %G English %X Background: Professionals with expertise in health informatics play a crucial role in the digital health sector. Despite efforts to train experts in this field, the specific impact of such training, especially for individuals from diverse academic backgrounds, remains undetermined. Objective: This study therefore aims to evaluate the effectiveness of an intensive health informatics training program on graduates with respect to their job roles, transitions, and competencies and to provide insights for curriculum design and future research. Methods: A survey was conducted among 206 students who completed the Advanced Health Informatics Analyst program between 2018 and 2022. The questionnaire comprised four categories: (1) general information about the respondent, (2) changes before and after program completion, (3) the impact of the program on professional practice, and (4) continuing education requirements. Results: The study received 161 (78.2%) responses from the 206 students. Graduates of the program had diverse academic backgrounds and consequently undertook various informatics tasks after their training. Most graduates (117/161, 72.7%) are now involved in tasks such as data preprocessing, visualizing results for better understanding, and report writing for data processing and analysis. Program participation significantly improved job performance (P=.03), especially for those with a master’s degree or higher (odds ratio 2.74, 95% CI 1.08‐6.95) and those from regions other than Seoul or Gyeonggi-do (odds ratio 10.95, 95% CI 1.08‐6.95). A substantial number of respondents indicated that the training had a substantial influence on their career transitions, primarily by providing a better understanding of job roles and generating intrinsic interest in the field. Conclusions: The integrated practical education program was effective in addressing the diverse needs of trainees from various fields, enhancing their capabilities, and preparing them for the evolving industry demands. This study emphasizes the value of providing specialized training in health informatics for graduates regardless of their discipline. %R 10.2196/54427 %U https://mededu.jmir.org/2024/1/e54427 %U https://doi.org/10.2196/54427 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e58578 %T Trial Factors Associated With Completion of Clinical Trials Evaluating AI: Retrospective Case-Control Study %A Chen,David %A Cao,Christian %A Kloosterman,Robert %A Parsa,Rod %A Raman,Srinivas %+ Department of Radiation Oncology, University of Toronto, 610 University Avenue, Toronto, ON, M5G 2M9, Canada, 1 416 946 4501 ext 2320, srinivas.raman@uhn.ca %K artificial intelligence %K clinical trial %K completion %K AI %K cross-sectional study %K application %K intervention %K trial design %K logistic regression %K Europe %K clinical %K trials testing %K health care %K informatics %K health information %D 2024 %7 23.9.2024 %9 Original Paper %J J Med Internet Res %G English %X Background: Evaluation of artificial intelligence (AI) tools in clinical trials remains the gold standard for translation into clinical settings. However, design factors associated with successful trial completion and the common reasons for trial failure are unknown. Objective: This study aims to compare trial design factors of complete and incomplete clinical trials testing AI tools. We conducted a case-control study of complete (n=485) and incomplete (n=51) clinical trials that evaluated AI as an intervention of ClinicalTrials.gov. Methods: Trial design factors, including area of clinical application, intended use population, and intended role of AI, were extracted. Trials that did not evaluate AI as an intervention and active trials were excluded. The assessed trial design factors related to AI interventions included the domain of clinical application related to organ systems; intended use population for patients or health care providers; and the role of AI for different applications in patient-facing clinical workflows, such as diagnosis, screening, and treatment. In addition, we also assessed general trial design factors including study type, allocation, intervention model, masking, age, sex, funder, continent, length of time, sample size, number of enrollment sites, and study start year. The main outcome was the completion of the clinical trial. Odds ratio (OR) and 95% CI values were calculated for all trial design factors using propensity-matched, multivariable logistic regression. Results: We queried ClinicalTrials.gov on December 23, 2023, using AI keywords to identify complete and incomplete trials testing AI technologies as a primary intervention, yielding 485 complete and 51 incomplete trials for inclusion in this study. Our nested propensity-matched, case-control results suggest that trials conducted in Europe were significantly associated with trial completion when compared with North American trials (OR 2.85, 95% CI 1.14-7.10; P=.03), and the trial sample size was positively associated with trial completion (OR 1.00, 95% CI 1.00-1.00; P=.02). Conclusions: Our case-control study is one of the first to identify trial design factors associated with completion of AI trials and catalog study-reported reasons for AI trial failure. We observed that trial design factors positively associated with trial completion include trials conducted in Europe and sample size. Given the promising clinical use of AI tools in health care, our results suggest that future translational research should prioritize addressing the design factors of AI clinical trials associated with trial incompletion and common reasons for study failure. %M 39312296 %R 10.2196/58578 %U https://www.jmir.org/2024/1/e58578 %U https://doi.org/10.2196/58578 %U http://www.ncbi.nlm.nih.gov/pubmed/39312296 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e57406 %T How Patient-Generated Data Enhance Patient-Provider Communication in Chronic Care: Field Study in Design Science Research %A Staehelin,Dario %A Dolata,Mateusz %A Stöckli,Livia %A Schwabe,Gerhard %+ Department of Informatics, University of Zurich, Binzmühlestrasse 14, Zurich, 8050, Switzerland, 41 763103137, dario.staehelin@ost.ch %K patient-provider communication %K patient-generated data %K field study %K chronic care %K design science research %K patient-centered care %K integrated care %K patient-provider collaboration %K mobile phone %D 2024 %7 10.9.2024 %9 Original Paper %J JMIR Med Inform %G English %X Background: Modern approaches such as patient-centered care ask health care providers (eg, nurses, physicians, and dietitians) to activate and include patients to participate in their health care. Mobile health (mHealth) is integral in this endeavor to be more patient centric. However, structural and regulatory barriers have hindered its adoption. Existing mHealth apps often fail to activate and engage patients sufficiently. Moreover, such systems seldom integrate well with health care providers’ workflow. Objective: This study investigated how patient-provider communication behaviors change when introducing patient-generated data into patient-provider communication. Methods: We adopted the design science approach to design PatientHub, an integrated digital health system that engages patients and providers in patient-centered care for weight management. PatientHub was developed in 4 iterations and was evaluated in a 3-week field study with 27 patients and 6 physicians. We analyzed 54 video recordings of PatientHub-supported consultations and interviews with patients and physicians. Results: PatientHub introduces patient-generated data into patient-provider communication. We observed 3 emerging behaviors when introducing patient-generated data into consultations. We named these behaviors emotion labeling, expectation decelerating, and decision ping-pong. Our findings show how these behaviors enhance patient-provider communication and facilitate patient-centered care. Introducing patient-generated data leads to behaviors that make consultations more personal, actionable, trustworthy, and equal. Conclusions: The results of this study indicate that patient-generated data facilitate patient-centered care by activating and engaging patients and providers. We propose 3 design principles for patient-centered communication. Patient-centered communication informs the design of future mHealth systems and offers insights into the inner workings of mHealth-supported patient-provider communication in chronic care. %M 39255481 %R 10.2196/57406 %U https://medinform.jmir.org/2024/1/e57406 %U https://doi.org/10.2196/57406 %U http://www.ncbi.nlm.nih.gov/pubmed/39255481 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 11 %N %P e51972 %T Patients’ Expectations of Doctors’ Clinical Competencies in the Digital Health Care Era: Qualitative Semistructured Interview Study Among Patients %A Zainal,Humairah %A Hui,Xin Xiao %A Thumboo,Julian %A Fong,Warren %A Yong,Fong Kok %+ Health Services Research Unit, Singapore General Hospital, 10 Hospital Boulevard, Singapore, 168582, Singapore, 65 6908 8949, humairah.zainal@sgh.com.sg %K digital health %K clinical competence %K patient engagement %K qualitative research %K Singapore %K mobile phone %D 2024 %7 27.8.2024 %9 Original Paper %J JMIR Hum Factors %G English %X Background: Digital technologies have impacted health care delivery globally, and are increasingly being deployed in clinical practice. However, there is limited research on patients’ expectations of doctors’ clinical competencies when using digital health care technologies (DHTs) in medical care. Understanding these expectations can reveal competency gaps, enhance patient confidence, and contribute to digital innovation initiatives. Objective: This study explores patients’ perceptions of doctors’ use of DHTs in clinical care. Using Singapore as a case study, it examines patients’ expectations regarding doctors’ communication, diagnosis, and treatment skills when using telemedicine, health apps, wearable devices, electronic health records, and artificial intelligence. Methods: Findings were drawn from individual semistructured interviews with patients from outpatient clinics. Participants were recruited using purposive sampling. Data were analyzed qualitatively using thematic analysis. Results: Twenty-five participants from different backgrounds and with various chronic conditions participated in the study. They expected doctors to be adept in handling medical data from apps and wearable devices. For telemedicine, participants expected a level of assessment of their medical conditions akin to in-person consultations. In addition, they valued doctors recognizing when a physical examination was necessary. Interestingly, eye contact was appreciated but deemed nonessential by participants across all age bands when electronic health records were used, as they valued the doctor’s efficiency more than eye contact. Nonetheless, participants emphasized the need for empathy throughout the clinical encounter regardless of DHT use. Furthermore, younger participants had a greater expectation for DHT use among doctors compared to older ones, who preferred DHTs as a complement rather than a replacement for clinical skills. The former expected doctors to be knowledgeable about the algorithms, principles, and purposes of DHTs such as artificial intelligence technologies to better assist them in diagnosis and treatment. Conclusions: By identifying patients’ expectations of doctors amid increasing health care digitalization, this study highlights that while basic clinical skills remain crucial in the digital age, the role of clinicians needs to evolve with the introduction of DHTs. It has also provided insights into how DHTs can be integrated effectively into clinical settings, aligning with patients’ expectations and preferences. Overall, the findings offer a framework for high-income countries to harness DHTs in enhancing health care delivery in the digital era. %M 39190915 %R 10.2196/51972 %U https://humanfactors.jmir.org/2024/1/e51972 %U https://doi.org/10.2196/51972 %U http://www.ncbi.nlm.nih.gov/pubmed/39190915 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 12 %N %P e50307 %T Characteristics of Existing Online Patient Navigation Interventions: Scoping Review %A Marsh,Meghan %A Shah,Syeda Rafia %A Munce,Sarah E P %A Perrier,Laure %A Lee,Tin-Suet Joan %A Colella,Tracey J F %A Kokorelias,Kristina Marie %+ Section of Geriatrics, Sinai Health and University Health Network, 600 University Avenue, Geriatrics Department, Toronto, ON, M5G1X5, Canada, 1 4165864800 ext 4573, kristina.kokorelias@sinaihealth.ca %K online %K patient navigation %K peer navigation %K patient navigation interventions %K online patient navigation interventions %K scoping review %K patient portals %K social care services %K online medical tools %K eHealth %K telehealth %K personal support %K social care %K patient navigation intervention %D 2024 %7 19.8.2024 %9 Review %J JMIR Med Inform %G English %X Background: Patient navigation interventions (PNIs) can provide personalized support and promote appropriate coordination or continuation of health and social care services. Online PNIs have demonstrated excellent potential for improving patient knowledge, transition readiness, self-efficacy, and use of services. However, the characteristics (ie, intervention type, mode of delivery, duration, frequency, outcomes and outcome measures, underlying theories or mechanisms of change of the intervention, and impact) of existing online PNIs to support the health and social needs of individuals with illness remain unclear. Objective: This scoping review of the existing literature aims to identify the characteristics of existing online PNIs reported in the literature. Methods: A scoping review based on the guidelines outlined in the Joanna Briggs Institute framework was conducted. A search for peer-reviewed literature published between 1989 and 2022 on online PNIs was conducted using MEDLINE, CINAHL, Embase, PsycInfo, and Cochrane Library databases. Two independent reviewers conducted 2 levels of screening. Data abstraction was conducted to outline key study characteristics (eg, study design, population, and intervention characteristics). The data were analyzed using descriptive statistics and qualitative content analysis. Results: A total of 100 studies met the inclusion criteria. Our findings indicate that a variety of study designs are used to describe and evaluate online PNIs, with literature being published between 2003 and 2022 in Western countries. Of these studies, 39 (39%) studies were randomized controlled trials. In addition, we noticed an increase in reported online PNIs since 2019. The majority of studies involved White females with a diagnosis of cancer and a lack of participants aged 70 years or older was observed. Most online PNIs provide support through navigation, self-management and lifestyle changes, counseling, coaching, education, or a combination of support. Variation was noted in terms of mode of delivery, duration, and frequency. Only a small number of studies described theoretical frameworks or change mechanisms to guide intervention. Conclusions: To our knowledge, this is the first review to comprehensively synthesize the existing literature on online PNIs, by focusing on the characteristics of interventions and studies in this area. Inconsistency in reporting the country of publication, population characteristics, duration and frequency of interventions, and a lack of the use of underlying theories and working mechanisms to inform intervention development, provide guidance for the reporting of future online PNIs. %M 39159443 %R 10.2196/50307 %U https://medinform.jmir.org/2024/1/e50307 %U https://doi.org/10.2196/50307 %U http://www.ncbi.nlm.nih.gov/pubmed/39159443 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 26 %N %P e59066 %T A Quarter-Century of Online Informatics Education: Learners Served and Lessons Learned %A Hersh,William %+ Department of Medical Informatics & Clinical Epidemiology, School of Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, Biomedical Information Communication Center, Portland, OR, 97239, United States, 1 5034944563, hersh@ohsu.edu %K distance education %K online learning %K asynchronous education %K biomedical and health informatics %K learning %K biomedical %K health informatics %K education %K educational %K educational technology %K online program %K online course %K teaching %K students %D 2024 %7 6.8.2024 %9 Viewpoint %J J Med Internet Res %G English %X The value and methods of online learning have changed tremendously over the last 25 years. The goal of this paper is to review a quarter-century of experience with online learning by the author in the field of biomedical and health informatics, describing the learners served and the lessons learned. The author details the history of the decision to pursue online education in informatics, describing the approaches taken as educational technology evolved over time. A large number of learners have been served, and the online learning approach has been well-received, with many lessons learned to optimize the educational experience. Online education in biomedical and health informatics has provided a scalable and exemplary approach to learning in this field. %M 39106486 %R 10.2196/59066 %U https://www.jmir.org/2024/1/e59066 %U https://doi.org/10.2196/59066 %U http://www.ncbi.nlm.nih.gov/pubmed/39106486 %0 Journal Article %@ 2291-9694 %I %V 12 %N %P e55959 %T The Information and Communication Technology Maturity Assessment at Primary Health Care Services Across 9 Provinces in Indonesia: Evaluation Study %A Aisyah,Dewi Nur %A Setiawan,Agus Heri %A Lokopessy,Alfiano Fawwaz %A Faradiba,Nadia %A Setiaji,Setiaji %A Manikam,Logan %A Kozlakidis,Zisis %K public health centers %K Puskesmas %K digital maturity %K infrastructure %K primary health care %K district health office %K primary care clinics %K Asia %K Asian %K Indonesia %K ICT %K information and communication technologies %K information and communication technology %K maturity %K adoption %K readiness %K implementation %K eHealth %K telehealth %K telemedicine %K cross sectional %K survey %K surveys %K questionnaire %K questionnaires %K primary care %D 2024 %7 18.7.2024 %9 %J JMIR Med Inform %G English %X Background: Indonesia has rapidly embraced digital health, particularly during the COVID-19 pandemic, with over 15 million daily health application users. To advance its digital health vision, the government is prioritizing the development of health data and application systems into an integrated health care technology ecosystem. This initiative involves all levels of health care, from primary to tertiary, across all provinces. In particular, it aims to enhance primary health care services (as the main interface with the general population) and contribute to Indonesia’s digital health transformation. Objective: This study assesses the information and communication technology (ICT) maturity in Indonesian health care services to advance digital health initiatives. ICT maturity assessment tools, specifically designed for middle-income countries, were used to evaluate digital health capabilities in 9 provinces across 5 Indonesian islands. Methods: A cross-sectional survey was conducted from February to March 2022, in 9 provinces across Indonesia, representing the country’s diverse conditions on its major islands. Respondents included staff from public health centers (Puskesmas), primary care clinics (Klinik Pratama), and district health offices (Dinas Kesehatan Kabupaten/Kota). The survey used adapted ICT maturity assessment questionnaires, covering human resources, software and system, hardware, and infrastructure. It was administered electronically and involved 121 public health centers, 49 primary care clinics, and 67 IT staff from district health offices. Focus group discussions were held to delve deeper into the assessment results and gain more descriptive insights. Results: In this study, 237 participants represented 3 distinct categories: 121 public health centers, 67 district health offices, and 49 primary clinics. These instances were selected from a sample of 9 of the 34 provinces in Indonesia. Collected data from interviews and focus group discussions were transformed into scores on a scale of 1 to 5, with 1 indicating low ICT readiness and 5 indicating high ICT readiness. On average, the breakdown of ICT maturity scores was as follows: 2.71 for human resources’ capability in ICT use and system management, 2.83 for software and information systems, 2.59 for hardware, and 2.84 for infrastructure, resulting in an overall average score of 2.74. According to the ICT maturity level pyramid, the ICT maturity of health care providers in Indonesia fell between the basic and good levels. The need to pursue best practices also emerged strongly. Further analysis of the ICT maturity scores, when examined by province, revealed regional variations. Conclusions: The maturity of ICT use is influenced by several critical components. Enhancing human resources, ensuring infrastructure, the availability of supportive hardware, and optimizing information systems are imperative to attain ICT maturity in health care services. In the context of ICT maturity assessment, significant score variations were observed across health care levels in the 9 provinces, underscoring the diversity in ICT readiness and the need for regionally customized follow-up actions. %R 10.2196/55959 %U https://medinform.jmir.org/2024/1/e55959 %U https://doi.org/10.2196/55959 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 10 %N %P e54793 %T Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review %A Tolentino,Raymond %A Baradaran,Ashkan %A Gore,Genevieve %A Pluye,Pierre %A Abbasgholizadeh-Rahimi,Samira %+ Department of Family Medicine, McGill University, 5858 Chemin de la Côte-des-Neiges, Montreal, QC, H3S 1Z1, Canada, 1 514 399 9218, samira.rahimi@mcgill.ca %K artificial intelligence %K machine learning %K curriculum %K framework %K medical education %K review %D 2024 %7 18.7.2024 %9 Review %J JMIR Med Educ %G English %X Background: The successful integration of artificial intelligence (AI) into clinical practice is contingent upon physicians’ comprehension of AI principles and its applications. Therefore, it is essential for medical education curricula to incorporate AI topics and concepts, providing future physicians with the foundational knowledge and skills needed. However, there is a knowledge gap in the current understanding and availability of structured AI curriculum frameworks tailored for medical education, which serve as vital guides for instructing and facilitating the learning process. Objective: The overall aim of this study is to synthesize knowledge from the literature on curriculum frameworks and current educational programs that focus on the teaching and learning of AI for medical students, residents, and practicing physicians. Methods: We followed a validated framework and the Joanna Briggs Institute methodological guidance for scoping reviews. An information specialist performed a comprehensive search from 2000 to May 2023 in the following bibliographic databases: MEDLINE (Ovid), Embase (Ovid), CENTRAL (Cochrane Library), CINAHL (EBSCOhost), and Scopus as well as the gray literature. Papers were limited to English and French languages. This review included papers that describe curriculum frameworks for teaching and learning AI in medicine, irrespective of country. All types of papers and study designs were included, except conference abstracts and protocols. Two reviewers independently screened the titles and abstracts, read the full texts, and extracted data using a validated data extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. We adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist for reporting the results. Results: Of the 5104 papers screened, 21 papers relevant to our eligibility criteria were identified. In total, 90% (19/21) of the papers altogether described 30 current or previously offered educational programs, and 10% (2/21) of the papers described elements of a curriculum framework. One framework describes a general approach to integrating AI curricula throughout the medical learning continuum and another describes a core curriculum for AI in ophthalmology. No papers described a theory, pedagogy, or framework that guided the educational programs. Conclusions: This review synthesizes recent advancements in AI curriculum frameworks and educational programs within the domain of medical education. To build on this foundation, future researchers are encouraged to engage in a multidisciplinary approach to curriculum redesign. In addition, it is encouraged to initiate dialogues on the integration of AI into medical curriculum planning and to investigate the development, deployment, and appraisal of these innovative educational programs. International Registered Report Identifier (IRRID): RR2-10.11124/JBIES-22-00374 %M 39023999 %R 10.2196/54793 %U https://mededu.jmir.org/2024/1/e54793 %U https://doi.org/10.2196/54793 %U http://www.ncbi.nlm.nih.gov/pubmed/39023999 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 10 %N %P e46500 %T AI Education for Fourth-Year Medical Students: Two-Year Experience of a Web-Based, Self-Guided Curriculum and Mixed Methods Study %A Abid,Areeba %A Murugan,Avinash %A Banerjee,Imon %A Purkayastha,Saptarshi %A Trivedi,Hari %A Gichoya,Judy %+ Emory University School of Medicine, 2015 Uppergate Dr, Atlanta, GA, 30307, United States, 1 (404) 727 4018, areeba.abid@emory.edu %K medical education %K machine learning %K artificial intelligence %K elective curriculum %K medical student %K student %K students %K elective %K electives %K curricula %K curriculum %K lesson plan %K lesson plans %K educators %K educator %K teacher %K teachers %K teaching %K computer programming %K programming %K coding %K programmer %K programmers %K self guided %K self directed %D 2024 %7 20.2.2024 %9 Original Paper %J JMIR Med Educ %G English %X Background: Artificial intelligence (AI) and machine learning (ML) are poised to have a substantial impact in the health care space. While a plethora of web-based resources exist to teach programming skills and ML model development, there are few introductory curricula specifically tailored to medical students without a background in data science or programming. Programs that do exist are often restricted to a specific specialty. Objective: We hypothesized that a 1-month elective for fourth-year medical students, composed of high-quality existing web-based resources and a project-based structure, would empower students to learn about the impact of AI and ML in their chosen specialty and begin contributing to innovation in their field of interest. This study aims to evaluate the success of this elective in improving self-reported confidence scores in AI and ML. The authors also share our curriculum with other educators who may be interested in its adoption. Methods: This elective was offered in 2 tracks: technical (for students who were already competent programmers) and nontechnical (with no technical prerequisites, focusing on building a conceptual understanding of AI and ML). Students established a conceptual foundation of knowledge using curated web-based resources and relevant research papers, and were then tasked with completing 3 projects in their chosen specialty: a data set analysis, a literature review, and an AI project proposal. The project-based nature of the elective was designed to be self-guided and flexible to each student’s interest area and career goals. Students’ success was measured by self-reported confidence in AI and ML skills in pre and postsurveys. Qualitative feedback on students’ experiences was also collected. Results: This web-based, self-directed elective was offered on a pass-or-fail basis each month to fourth-year students at Emory University School of Medicine beginning in May 2021. As of June 2022, a total of 19 students had successfully completed the elective, representing a wide range of chosen specialties: diagnostic radiology (n=3), general surgery (n=1), internal medicine (n=5), neurology (n=2), obstetrics and gynecology (n=1), ophthalmology (n=1), orthopedic surgery (n=1), otolaryngology (n=2), pathology (n=2), and pediatrics (n=1). Students’ self-reported confidence scores for AI and ML rose by 66% after this 1-month elective. In qualitative surveys, students overwhelmingly reported enthusiasm and satisfaction with the course and commented that the self-direction and flexibility and the project-based design of the course were essential. Conclusions: Course participants were successful in diving deep into applications of AI in their widely-ranging specialties, produced substantial project deliverables, and generally reported satisfaction with their elective experience. The authors are hopeful that a brief, 1-month investment in AI and ML education during medical school will empower this next generation of physicians to pave the way for AI and ML innovation in health care. %M 38376896 %R 10.2196/46500 %U https://mededu.jmir.org/2024/1/e46500 %U https://doi.org/10.2196/46500 %U http://www.ncbi.nlm.nih.gov/pubmed/38376896 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 10 %N %P e51308 %T Comprehensiveness, Accuracy, and Readability of Exercise Recommendations Provided by an AI-Based Chatbot: Mixed Methods Study %A Zaleski,Amanda L %A Berkowsky,Rachel %A Craig,Kelly Jean Thomas %A Pescatello,Linda S %+ Clinical Evidence Development, Aetna Medical Affairs, CVS Health Corporation, 151 Farmington Avenue, Hartford, CT, 06156, United States, 1 8605385003, zaleskia@aetna.com %K exercise prescription %K health literacy %K large language model %K patient education %K artificial intelligence %K AI %K chatbot %D 2024 %7 11.1.2024 %9 Original Paper %J JMIR Med Educ %G English %X Background: Regular physical activity is critical for health and disease prevention. Yet, health care providers and patients face barriers to implement evidence-based lifestyle recommendations. The potential to augment care with the increased availability of artificial intelligence (AI) technologies is limitless; however, the suitability of AI-generated exercise recommendations has yet to be explored. Objective: The purpose of this study was to assess the comprehensiveness, accuracy, and readability of individualized exercise recommendations generated by a novel AI chatbot. Methods: A coding scheme was developed to score AI-generated exercise recommendations across ten categories informed by gold-standard exercise recommendations, including (1) health condition–specific benefits of exercise, (2) exercise preparticipation health screening, (3) frequency, (4) intensity, (5) time, (6) type, (7) volume, (8) progression, (9) special considerations, and (10) references to the primary literature. The AI chatbot was prompted to provide individualized exercise recommendations for 26 clinical populations using an open-source application programming interface. Two independent reviewers coded AI-generated content for each category and calculated comprehensiveness (%) and factual accuracy (%) on a scale of 0%-100%. Readability was assessed using the Flesch-Kincaid formula. Qualitative analysis identified and categorized themes from AI-generated output. Results: AI-generated exercise recommendations were 41.2% (107/260) comprehensive and 90.7% (146/161) accurate, with the majority (8/15, 53%) of inaccuracy related to the need for exercise preparticipation medical clearance. Average readability level of AI-generated exercise recommendations was at the college level (mean 13.7, SD 1.7), with an average Flesch reading ease score of 31.1 (SD 7.7). Several recurring themes and observations of AI-generated output included concern for liability and safety, preference for aerobic exercise, and potential bias and direct discrimination against certain age-based populations and individuals with disabilities. Conclusions: There were notable gaps in the comprehensiveness, accuracy, and readability of AI-generated exercise recommendations. Exercise and health care professionals should be aware of these limitations when using and endorsing AI-based technologies as a tool to support lifestyle change involving exercise. %M 38206661 %R 10.2196/51308 %U https://mededu.jmir.org/2024/1/e51308 %U https://doi.org/10.2196/51308 %U http://www.ncbi.nlm.nih.gov/pubmed/38206661 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 9 %N %P e50903 %T Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence %A Jacobs,Sarah Marie %A Lundy,Neva Nicole %A Issenberg,Saul Barry %A Chandran,Latha %+ Department of Medical Education, University of Miami Miller School of Medicine, 1120 NW 14th Street, Miami, FL, 33136, United States, 1 3052436491, bissenbe@miami.edu %K artificial intelligence %K entrustable professional activities %K medical education %K competency-based education %K educational technology %K machine learning %D 2023 %7 19.12.2023 %9 Viewpoint %J JMIR Med Educ %G English %X The proliferation of generative artificial intelligence (AI) and its extensive potential for integration into many aspects of health care signal a transformational shift within the health care environment. In this context, medical education must evolve to ensure that medical trainees are adequately prepared to navigate the rapidly changing health care landscape. Medical education has moved toward a competency-based education paradigm, leading the Association of American Medical Colleges (AAMC) to define a set of Entrustable Professional Activities (EPAs) as its practical operational framework in undergraduate medical education. The AAMC’s 13 core EPAs for entering residencies have been implemented with varying levels of success across medical schools. In this paper, we critically assess the existing core EPAs in the context of rapid AI integration in medicine. We identify EPAs that require refinement, redefinition, or comprehensive change to align with the emerging trends in health care. Moreover, this perspective proposes a set of “emerging” EPAs, informed by the changing landscape and capabilities presented by generative AI technologies. We provide a practical evaluation of the EPAs, alongside actionable recommendations on how medical education, viewed through the lens of the AAMC EPAs, can adapt and remain relevant amid rapid technological advancements. By leveraging the transformative potential of AI, we can reshape medical education to align with an AI-integrated future of medicine. This approach will help equip future health care professionals with technological competence and adaptive skills to meet the dynamic and evolving demands in health care. %M 38052721 %R 10.2196/50903 %U https://mededu.jmir.org/2023/1/e50903 %U https://doi.org/10.2196/50903 %U http://www.ncbi.nlm.nih.gov/pubmed/38052721 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 9 %N %P e53466 %T Developing Medical Education Curriculum Reform Strategies to Address the Impact of Generative AI: Qualitative Study %A Shimizu,Ikuo %A Kasai,Hajime %A Shikino,Kiyoshi %A Araki,Nobuyuki %A Takahashi,Zaiya %A Onodera,Misaki %A Kimura,Yasuhiko %A Tsukamoto,Tomoko %A Yamauchi,Kazuyo %A Asahina,Mayumi %A Ito,Shoichi %A Kawakami,Eiryo %+ Department of Medical Education, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chiba, 2608672, Japan, 81 432262816, qingshuiyufu@gmail.com %K artificial intelligence %K curriculum reform %K generative artificial intelligence %K large language models %K medical education %K qualitative analysis %K strengths-weaknesses-opportunities-threats (SWOT) framework %D 2023 %7 30.11.2023 %9 Original Paper %J JMIR Med Educ %G English %X Background: Generative artificial intelligence (GAI), represented by large language models, have the potential to transform health care and medical education. In particular, GAI’s impact on higher education has the potential to change students’ learning experience as well as faculty’s teaching. However, concerns have been raised about ethical consideration and decreased reliability of the existing examinations. Furthermore, in medical education, curriculum reform is required to adapt to the revolutionary changes brought about by the integration of GAI into medical practice and research. Objective: This study analyzes the impact of GAI on medical education curricula and explores strategies for adaptation. Methods: The study was conducted in the context of faculty development at a medical school in Japan. A workshop involving faculty and students was organized, and participants were divided into groups to address two research questions: (1) How does GAI affect undergraduate medical education curricula? and (2) How should medical school curricula be reformed to address the impact of GAI? The strength, weakness, opportunity, and threat (SWOT) framework was used, and cross-SWOT matrix analysis was used to devise strategies. Further, 4 researchers conducted content analysis on the data generated during the workshop discussions. Results: The data were collected from 8 groups comprising 55 participants. Further, 5 themes about the impact of GAI on medical education curricula emerged: improvement of teaching and learning, improved access to information, inhibition of existing learning processes, problems in GAI, and changes in physicians’ professionality. Positive impacts included enhanced teaching and learning efficiency and improved access to information, whereas negative impacts included concerns about reduced independent thinking and the adaptability of existing assessment methods. Further, GAI was perceived to change the nature of physicians’ expertise. Three themes emerged from the cross-SWOT analysis for curriculum reform: (1) learning about GAI, (2) learning with GAI, and (3) learning aside from GAI. Participants recommended incorporating GAI literacy, ethical considerations, and compliance into the curriculum. Learning with GAI involved improving learning efficiency, supporting information gathering and dissemination, and facilitating patient involvement. Learning aside from GAI emphasized maintaining GAI-free learning processes, fostering higher cognitive domains of learning, and introducing more communication exercises. Conclusions: This study highlights the profound impact of GAI on medical education curricula and provides insights into curriculum reform strategies. Participants recognized the need for GAI literacy, ethical education, and adaptive learning. Further, GAI was recognized as a tool that can enhance efficiency and involve patients in education. The study also suggests that medical education should focus on competencies that GAI hardly replaces, such as clinical experience and communication. Notably, involving both faculty and students in curriculum reform discussions fosters a sense of ownership and ensures broader perspectives are encompassed. %M 38032695 %R 10.2196/53466 %U https://mededu.jmir.org/2023/1/e53466 %U https://doi.org/10.2196/53466 %U http://www.ncbi.nlm.nih.gov/pubmed/38032695 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e51873 %T Usability and Efficacy of Artificial Intelligence Chatbots (ChatGPT) for Health Sciences Students: Protocol for a Crossover Randomized Controlled Trial %A Veras,Mirella %A Dyer,Joseph-Omer %A Rooney,Morgan %A Barros Silva,Paulo Goberlânio %A Rutherford,Derek %A Kairy,Dahlia %+ Health Sciences, Carleton University, 1125 Colonel By Drive, Ottawa, ON, K1S 5B6, Canada, 1 613 520 2600, mirella.veras@carleton.ca %K artificial intelligence %K AI %K health sciences %K usability %K learning outcomes %K perceptions %K OpenAI %K ChatGPT %K education %K randomized controlled trial %K RCT %K crossover RCT %D 2023 %7 24.11.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: The integration of artificial intelligence (AI) into health sciences students’ education holds significant importance. The rapid advancement of AI has opened new horizons in scientific writing and has the potential to reshape human-technology interactions. AI in education may impact critical thinking, leading to unintended consequences that need to be addressed. Understanding the implications of AI adoption in education is essential for ensuring its responsible and effective use, empowering health sciences students to navigate AI-driven technologies’ evolving field with essential knowledge and skills. Objective: This study aims to provide details on the study protocol and the methods used to investigate the usability and efficacy of ChatGPT, a large language model. The primary focus is on assessing its role as a supplementary learning tool for improving learning processes and outcomes among undergraduate health sciences students, with a specific emphasis on chronic diseases. Methods: This single-blinded, crossover, randomized, controlled trial is part of a broader mixed methods study, and the primary emphasis of this paper is on the quantitative component of the overall research. A total of 50 students will be recruited for this study. The alternative hypothesis posits that there will be a significant difference in learning outcomes and technology usability between students using ChatGPT (group A) and those using standard web-based tools (group B) to access resources and complete assignments. Participants will be allocated to sequence AB or BA in a 1:1 ratio using computer-generated randomization. Both arms include students’ participation in a writing assignment intervention, with a washout period of 21 days between interventions. The primary outcome is the measure of the technology usability and effectiveness of ChatGPT, whereas the secondary outcome is the measure of students’ perceptions and experiences with ChatGPT as a learning tool. Outcome data will be collected up to 24 hours after the interventions. Results: This study aims to understand the potential benefits and challenges of incorporating AI as an educational tool, particularly in the context of student learning. The findings are expected to identify critical areas that need attention and help educators develop a deeper understanding of AI’s impact on the educational field. By exploring the differences in the usability and efficacy between ChatGPT and conventional web-based tools, this study seeks to inform educators and students on the responsible integration of AI into academic settings, with a specific focus on health sciences education. Conclusions: By exploring the usability and efficacy of ChatGPT compared with conventional web-based tools, this study seeks to inform educators and students about the responsible integration of AI into academic settings. Trial Registration: ClinicalTrails.gov NCT05963802; https://clinicaltrials.gov/study/NCT05963802 International Registered Report Identifier (IRRID): PRR1-10.2196/51873 %M 37999958 %R 10.2196/51873 %U https://www.researchprotocols.org/2023/1/e51873 %U https://doi.org/10.2196/51873 %U http://www.ncbi.nlm.nih.gov/pubmed/37999958 %0 Journal Article %@ 1929-0748 %I JMIR Publications %V 12 %N %P e49842 %T Applying AI and Guidelines to Assist Medical Students in Recognizing Patients With Heart Failure: Protocol for a Randomized Trial %A Joo,Hyeon %A Mathis,Michael R %A Tam,Marty %A James,Cornelius %A Han,Peijin %A Mangrulkar,Rajesh S %A Friedman,Charles P %A Vydiswaran,VG Vinod %+ Department of Learning Health Sciences, University of Michigan, 1111 East Catherine Street, Ann Arbor, MI, 48109, United States, 1 7349361644, thejoo@umich.edu %K medical education %K clinical decision support systems %K artificial intelligence %K machine learning %K heart failure %K evidence-based medicine %K guidelines %K digital health interventions %D 2023 %7 24.10.2023 %9 Protocol %J JMIR Res Protoc %G English %X Background: The integration of artificial intelligence (AI) into clinical practice is transforming both clinical practice and medical education. AI-based systems aim to improve the efficacy of clinical tasks, enhancing diagnostic accuracy and tailoring treatment delivery. As it becomes increasingly prevalent in health care for high-quality patient care, it is critical for health care providers to use the systems responsibly to mitigate bias, ensure effective outcomes, and provide safe clinical practices. In this study, the clinical task is the identification of heart failure (HF) prior to surgery with the intention of enhancing clinical decision-making skills. HF is a common and severe disease, but detection remains challenging due to its subtle manifestation, often concurrent with other medical conditions, and the absence of a simple and effective diagnostic test. While advanced HF algorithms have been developed, the use of these AI-based systems to enhance clinical decision-making in medical education remains understudied. Objective: This research protocol is to demonstrate our study design, systematic procedures for selecting surgical cases from electronic health records, and interventions. The primary objective of this study is to measure the effectiveness of interventions aimed at improving HF recognition before surgery, the second objective is to evaluate the impact of inaccurate AI recommendations, and the third objective is to explore the relationship between the inclination to accept AI recommendations and their accuracy. Methods: Our study used a 3 × 2 factorial design (intervention type × order of prepost sets) for this randomized trial with medical students. The student participants are asked to complete a 30-minute e-learning module that includes key information about the intervention and a 5-question quiz, and a 60-minute review of 20 surgical cases to determine the presence of HF. To mitigate selection bias in the pre- and posttests, we adopted a feature-based systematic sampling procedure. From a pool of 703 expert-reviewed surgical cases, 20 were selected based on features such as case complexity, model performance, and positive and negative labels. This study comprises three interventions: (1) a direct AI-based recommendation with a predicted HF score, (2) an indirect AI-based recommendation gauged through the area under the curve metric, and (3) an HF guideline-based intervention. Results: As of July 2023, 62 of the enrolled medical students have fulfilled this study’s participation, including the completion of a short quiz and the review of 20 surgical cases. The subject enrollment commenced in August 2022 and will end in December 2023, with the goal of recruiting 75 medical students in years 3 and 4 with clinical experience. Conclusions: We demonstrated a study protocol for the randomized trial, measuring the effectiveness of interventions using AI and HF guidelines among medical students to enhance HF recognition in preoperative care with electronic health record data. International Registered Report Identifier (IRRID): DERR1-10.2196/49842 %M 37874618 %R 10.2196/49842 %U https://www.researchprotocols.org/2023/1/e49842 %U https://doi.org/10.2196/49842 %U http://www.ncbi.nlm.nih.gov/pubmed/37874618 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e48249 %T Radiology Residents’ Perceptions of Artificial Intelligence: Nationwide Cross-Sectional Survey Study %A Chen,Yanhua %A Wu,Ziye %A Wang,Peicheng %A Xie,Linbo %A Yan,Mengsha %A Jiang,Maoqing %A Yang,Zhenghan %A Zheng,Jianjun %A Zhang,Jingfeng %A Zhu,Jiming %+ Vanke School of Public Health, Tsinghua University, Haidian District, Beijing, 100084, China, 86 62782199, jimingzhu@tsinghua.edu.cn %K artificial intelligence %K technology acceptance %K radiology %K residency %K perceptions %K health care services %K resident %K residents %K perception %K adoption %K readiness %K acceptance %K cross sectional %K survey %D 2023 %7 19.10.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Artificial intelligence (AI) is transforming various fields, with health care, especially diagnostic specialties such as radiology, being a key but controversial battleground. However, there is limited research systematically examining the response of “human intelligence” to AI. Objective: This study aims to comprehend radiologists’ perceptions regarding AI, including their views on its potential to replace them, its usefulness, and their willingness to accept it. We examine the influence of various factors, encompassing demographic characteristics, working status, psychosocial aspects, personal experience, and contextual factors. Methods: Between December 1, 2020, and April 30, 2021, a cross-sectional survey was completed by 3666 radiology residents in China. We used multivariable logistic regression models to examine factors and associations, reporting odds ratios (ORs) and 95% CIs. Results: In summary, radiology residents generally hold a positive attitude toward AI, with 29.90% (1096/3666) agreeing that AI may reduce the demand for radiologists, 72.80% (2669/3666) believing AI improves disease diagnosis, and 78.18% (2866/3666) feeling that radiologists should embrace AI. Several associated factors, including age, gender, education, region, eye strain, working hours, time spent on medical images, resilience, burnout, AI experience, and perceptions of residency support and stress, significantly influence AI attitudes. For instance, burnout symptoms were associated with greater concerns about AI replacement (OR 1.89; P<.001), less favorable views on AI usefulness (OR 0.77; P=.005), and reduced willingness to use AI (OR 0.71; P<.001). Moreover, after adjusting for all other factors, perceived AI replacement (OR 0.81; P<.001) and AI usefulness (OR 5.97; P<.001) were shown to significantly impact the intention to use AI. Conclusions: This study profiles radiology residents who are accepting of AI. Our comprehensive findings provide insights for a multidimensional approach to help physicians adapt to AI. Targeted policies, such as digital health care initiatives and medical education, can be developed accordingly. %M 37856181 %R 10.2196/48249 %U https://www.jmir.org/2023/1/e48249 %U https://doi.org/10.2196/48249 %U http://www.ncbi.nlm.nih.gov/pubmed/37856181 %0 Journal Article %@ 2292-9495 %I JMIR Publications %V 10 %N %P e49675 %T The Impact of Feedback Modalities and the Influence of Cognitive Load on Interpersonal Communication in Nonclinical Settings: Experimental Study Design %A Rego,Chryselle %A Montague,Enid %+ Jarvis College of Computing and Digital Media, DePaul University, 1 E Jackson Blvd, Chicago, IL, 60604, United States, 1 3126873958, crego@depaul.edu %K physician-patient interaction %K cognitive load %K visual feedback %K haptic feedback %K postsession feedback %D 2023 %7 5.10.2023 %9 Original Paper %J JMIR Hum Factors %G English %X Background: The escalating demands of modern health care systems, combined with the emotional toll of patient care, have led to an alarming increase in physician burnout rates. This burnout, characterized by emotional exhaustion, depersonalization, and reduced personal accomplishment, can hinder doctors’ ability to connect with patients effectively. Moreover, the cognitive load arising from information overload and the need for multitasking can further hinder doctors’ ability to connect with patients effectively. Understanding the complex relationship between physician burnout and cognitive load is crucial for devising targeted interventions that enhance physician well-being and promote effective physician-patient interactions. Implementing strategies to alleviate burnout and cognitive load can lead to improved health care experiences and patient outcomes. Objective: Our study explores the interplay between physician burnout and its potential impact on interpersonal communication, particularly focusing on the role of cognitive load using a pilot study in a nonclinical setting involving nonclinical participants. Methods: This study uses an experimental design to evaluate 3 feedback tools (haptic, visual, and postvisit summary) and measure the cognitive load they impose on nonclinical participants in a nonclinical environment. The NASA Task Load Index, a widely accepted measure of cognitive load, was used to quantify the cognitive load associated with the feedback tools. The study used a within-subject design, meaning participants experienced all 3 feedback methods. A sample of 18 nonclinical participants was selected using counterbalancing techniques. Results: Postsession feedback not only enhancing performance but also mitigating the influence of cognitive load as compared with real-time feedback (haptic+visual). Participants with interview experience showed lower cognitive load levels when exposed to real-time feedback as compared with novice users. In contrast, postsession feedback was more effective for novice users. In addition, cognitive workload emerged as a moderating factor in the relationship between feedback tools and their impact on performance, particularly in terms of speaking balance and pace. This moderating effect suggests that the correlation between feedback tool efficacy and performance varies based on an individual’s cognitive load while using the feedback tool. The comparison of postfeedback with haptic feedback yielded a Z score of −3.245 and a P value of .001, while the comparison with visual feedback resulted in a Z score of −2.940 and a P value of .003. These outcomes underscore a significant disparity in the means between postsession feedback and real-time feedback (haptic+visual), with postsession feedback indicating the lowest mean score. Conclusions: Through the examination of various feedback tools, this study yields significant and insightful comparisons regarding their usability and appropriateness in nonclinical settings. To enhance the applicability of these findings to clinical environments, further research encompassing diverse participant cohorts and clinical scenarios is warranted. %M 37796596 %R 10.2196/49675 %U https://humanfactors.jmir.org/2023/1/e49675 %U https://doi.org/10.2196/49675 %U http://www.ncbi.nlm.nih.gov/pubmed/37796596 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e47260 %T Artificial Intelligence–Based Consumer Health Informatics Application: Scoping Review %A Asan,Onur %A Choi,Euiji %A Wang,Xiaomei %+ School of Systems and Enterprises, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, United States, 1 4145264330, oasan@stevens.edu %K consumer informatics %K artificial intelligence %K mobile health %K mHealth %K patient outcomes %K personalized health care %K machine learning %K digital health %K mobile phone %D 2023 %7 30.8.2023 %9 Review %J J Med Internet Res %G English %X Background: There is no doubt that the recent surge in artificial intelligence (AI) research will change the trajectory of next-generation health care, making it more approachable and accessible to patients. Therefore, it is critical to research patient perceptions and outcomes because this trend will allow patients to be the primary consumers of health technology and decision makers for their own health. Objective: This study aimed to review and analyze papers on AI-based consumer health informatics (CHI) for successful future patient-centered care. Methods: We searched for all peer-reviewed papers in PubMed published in English before July 2022. Research on an AI-based CHI tool or system that reports patient outcomes or perceptions was identified for the scoping review. Results: We identified 20 papers that met our inclusion criteria. The eligible studies were summarized and discussed with respect to the role of the AI-based CHI system, patient outcomes, and patient perceptions. The AI-based CHI systems identified included systems in mobile health (13/20, 65%), robotics (5/20, 25%), and telemedicine (2/20, 10%). All the systems aimed to provide patients with personalized health care. Patient outcomes and perceptions across various clinical disciplines were discussed, demonstrating the potential of an AI-based CHI system to benefit patients. Conclusions: This scoping review showed the trend in AI-based CHI systems and their impact on patient outcomes as well as patients’ perceptions of these systems. Future studies should also explore how clinicians and health care professionals perceive these consumer-based systems and integrate them into the overall workflow. %M 37647122 %R 10.2196/47260 %U https://www.jmir.org/2023/1/e47260 %U https://doi.org/10.2196/47260 %U http://www.ncbi.nlm.nih.gov/pubmed/37647122 %0 Journal Article %@ 1929-073X %I JMIR Publications %V 12 %N %P e42831 %T Information and Communication Technology Medicine: Integrative Specialty for the Future of Medicine %A Jongen,Peter Joseph %+ MS4 Research Institute, Ubbergseweg 34, Nijmegen, 6522 KJ, Netherlands, 31 24 3239146, ms4ri@kpnmail.nl %K information and communication technology %K ICT %K integrative %K transdisciplinary %K eHealth %K internet %K medical informatics %K application %K artificial intelligence %K digital medicine %K technologies %D 2023 %7 13.7.2023 %9 Viewpoint %J Interact J Med Res %G English %X The impact of information and communication technology (ICT) on medicine is unprecedented and ever-increasing. This has made it more and more difficult for doctors to keep pace with ICT developments and to adequately match the input of ICT experts. As a result, medical disciplines may not be able to take full advantage of growing possibilities. In this personal viewpoint paper, I argue for the establishment of a novel medical specialty, ICT medicine. ICT medicine is needed to optimally face the challenges of ICT-based developments, including artificial intelligence (AI), and to ensure their efficient and beneficial use. ICT medicine is rooted in both medicine and ICT, and in contrast to existing medical specialties it is integrative in nature, as long-standing structural collaborations with ICT and other stakeholders cross the boundaries between disciplines. Thus, new concepts and theories may evolve that are better suited to addressing ICT-related issues in medicine. ICT doctors will be instrumental in the conception, development, implementation, and evaluation of digital tools, systems, and services. They provide a bridge between ICT professionals and clinical users and educate doctors in digital applications and services. Notably, ICT doctors may have a pivotal role in the validation, verification, and evaluation of AI models. ICT medicine institutes offer a home to these new professionals, enhancing their independence within health care organizations and in relation to ICT companies. Importantly, in an era of growing technicalization and use of AI algorithms, ICT doctors may safeguard the human factor in medicine. And, from a societal perspective, they may promote digital inclusion and the continuing high quality of digital services and provide leadership in the future digitalization of medicine. %M 37440294 %R 10.2196/42831 %U https://www.i-jmr.org/2023/1/e42831 %U https://doi.org/10.2196/42831 %U http://www.ncbi.nlm.nih.gov/pubmed/37440294 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 9 %N %P e46344 %T Data Science as a Core Competency in Undergraduate Medical Education in the Age of Artificial Intelligence in Health Care %A Seth,Puneet %A Hueppchen,Nancy %A Miller,Steven D %A Rudzicz,Frank %A Ding,Jerry %A Parakh,Kapil %A Record,Janet D %+ Department of Family Medicine, McMaster University, 100 Main Street West, 6th Floor, Hamilton, ON, L8P 1H6, Canada, 1 4166715114, sethp1@mcmaster.ca %K data science %K medical education %K machine learning %K health data %K artificial intelligence %K AI %K application %K health care delivery %K health care %K develop %K medical educators %K physician %K education %K training %K barriers %K optimize %K integration %K competency %D 2023 %7 11.7.2023 %9 Viewpoint %J JMIR Med Educ %G English %X The increasingly sophisticated and rapidly evolving application of artificial intelligence in medicine is transforming how health care is delivered, highlighting a need for current and future physicians to develop basic competency in the data science that underlies this topic. Medical educators must consider how to incorporate central concepts in data science into their core curricula to train physicians of the future. Similar to how the advent of diagnostic imaging required the physician to understand, interpret, and explain the relevant results to patients, physicians of the future should be able to explain to patients the benefits and limitations of management plans guided by artificial intelligence. We outline major content domains and associated learning outcomes in data science applicable to medical student curricula, suggest ways to incorporate these themes into existing curricula, and note potential implementation barriers and solutions to optimize the integration of this content. %M 37432728 %R 10.2196/46344 %U https://mededu.jmir.org/2023/1/e46344 %U https://doi.org/10.2196/46344 %U http://www.ncbi.nlm.nih.gov/pubmed/37432728 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 9 %N %P e43231 %T Selected Skill Sets as Building Blocks for High School-to-Medical School Bridge: Longitudinal Study Among Undergraduate Medical Students %A Alsuwaidi,Laila %A Otaki,Farah %A Hassan Khamis,Amar %A AlGurg,Reem %A Lakhtakia,Ritu %+ College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Building 14, Dubai Healthcare City, Dubai, PO box 505055, United Arab Emirates, 971 43838708, laila.alsuwaidi@mbru.ac.ae %K transition %K undergraduate %K medical %K education %K academic performance %K self-regulated learning %D 2023 %7 4.7.2023 %9 Original Paper %J JMIR Med Educ %G English %X Background: The high school–to–medical school education transition is a significant milestone in the students’ academic journey, which is characterized by multiple stressors. Although this crucial transition has been repetitively explored, the concept of proactively intervening to support this transition is still novel. Objective: In this study, we investigated the efficacy of a web-based multidimensional resilience building intervention in developing selected soft skills that are believed to drive the learner’s success in any learning setting. The association between the students' academic performance over time and their proficiency in selected modules addressing skill sets, including Time Management, Memory and Study, Listening and Taking Notes, and College Transition, was also assessed to test the impact of the intervention on the students’ learning. Methods: A longitudinal study was conducted on 1 cohort of students of a Bachelor of Medicine, Bachelor of Surgery program (MBBS). The medical students were offered a learning intervention around 4 skill sets during the first year of the 6-year program. Quantitative analyses were conducted using deidentified data, relating to the students' proficiency in the 4 skill sets and to the students’ academic performance: grade point average (GPA). Descriptive analyses constituted computing an overall score of skill sets’ proficiency (of all 4 selected skill sets). The mean and SD (and percentage of the mean) were also calculated for each skill set component, independently, and for the overall score of skill sets’ proficiency. Bivariate Pearson correlations were used to assess the extent to which the academic performance of the students can be explained by the corresponding students’ level of proficiency in each skill set component and by all 4 sets together. Results: Out of the 63 admitted students, 28 participated in the offered intervention. The means and SDs of the annual GPA of the students for years 1 and 2 (GPA range 1-4) were 2.83 (SD 0.74) and 2.83 (SD 0.99), respectively. The mean and SD of the cumulative GPA toward the end of year 2 was 2.92 (SD 0.70). Correlation analysis showed that the overall score of skill sets proficiency was significantly associated with the annual GPA of year 1 (r=0.44; P=.02) but was not associated with their annual GPA of year 2. The cumulative GPA (toward the end of year 2) appeared to be significantly associated with the overall score (r=0.438; P=.02). Conclusions: Developing purposefully selected skill sets among medical students holds the potential of facilitating the high school–to–medical school education transition and is likely to improve their academic performance. As the medical student progresses, the acquired skills need to be continuously reinforced and effectively built upon. %M 37402145 %R 10.2196/43231 %U https://mededu.jmir.org/2023/1/e43231 %U https://doi.org/10.2196/43231 %U http://www.ncbi.nlm.nih.gov/pubmed/37402145 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e45400 %T Capacity Building, Knowledge Enhancement, and Consultative Processes for Development of a Digital Tool (Ni-kshay SETU) to Support the Management of Patients with Tuberculosis: Exploratory Qualitative Study %A Shah,Harsh %A Patel,Jay %A Yasobant,Sandul %A Saxena,Deepak %A Saha,Somen %A Sinha,Anish %A Bhavsar,Priya %A Patel,Yogesh %A Modi,Bhavesh %A Nimavat,Pankaj %A Kapadiya,Dixit %A Fancy,Manish %+ Department of Public Health Science, Indian Institute of Public Health Gandhinagar, NH-147, Palaj Village, opp. New Air Force Station HQ, Gandhinagar, 382042, India, 91 9925220545, hdshah@iiphg.org %K capacity building %K Ni-kshay SETU %K National Tuberculosis Elimination Program %K digital health %K India %K tuberculosis %D 2023 %7 19.6.2023 %9 Original Paper %J J Med Internet Res %G English %X Background: Achieving the target for eliminating tuberculosis (TB) in India by 2025, 5 years ahead of the global target, critically depends on strengthening the capacity of human resources as one of the key components of the health system. Due to the rapid updates of standards and protocols, the human resources for TB health care suffer from a lack of understanding of recent updates and acquiring necessary knowledge. Objective: Despite an increasing focus on the digital revolution in health care, there is no such platform available to deliver the key updates in national TB control programs with easy access. Thus, the aim of this study was to explore the development and evolution of a mobile health tool for capacity building of the Indian health system’s workforce to better manage patients with TB. Methods: This study involved two phases. The first phase was based on a qualitative investigation, including personal interviews to understand the basic requirements of staff working in the management of patients with TB, followed by participatory consultative meetings with stakeholders to validate and develop the content for the mobile health app. Qualitative information was collected from the Purbi Singhbhum and Ranchi districts of Jharkhand and Gandhinagar, and from the Surat districts of Gujarat State. In the second phase, a participatory design process was undertaken as part of the content creation and validation exercises. Results: The first phase collected information from 126 health care staff, with a mean age of 38.4 (SD 8.9) years and average work experience of 8.9 years. The assessment revealed that more than two-thirds of participants needed further training and lacked knowledge of the most current updates to TB program guidelines. The consultative process determined the need for a digital solution in easily accessible formats and ready reckoner content to deliver practical solutions to address operational issues for implementation of the program. Ultimately, the digital platform named Ni-kshay SETU (Support to End Tuberculosis) was developed to support the knowledge enhancement of health care workers. Conclusions: The development of staff capacity is vital to the success or failure of any program or intervention. Having up-to-date information provides confidence to health care staff when interacting with patients in the community and aids in making quick judgments when handling case scenarios. Ni-kshay SETU represents a novel digital capacity-building platform for enhancing human resource skills in achieving the goal of TB elimination. %M 37335610 %R 10.2196/45400 %U https://www.jmir.org/2023/1/e45400 %U https://doi.org/10.2196/45400 %U http://www.ncbi.nlm.nih.gov/pubmed/37335610 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e43311 %T Artificial Intelligence Supporting the Training of Communication Skills in the Education of Health Care Professions: Scoping Review %A Stamer,Tjorven %A Steinhäuser,Jost %A Flägel,Kristina %+ Institute of Family Medicine, University Hospital Schleswig-Holstein Luebeck Campus, Ratzeburger Allee 160, Luebeck, 23562, Germany, 49 451 3101 8013, t.stamer@uni-luebeck.de %K communication %K education %K artificial intelligence %K machine learning %K health care %K skill %K use %K academic %K students %K training %K cost %K cost-effective %K health care professional %D 2023 %7 19.6.2023 %9 Review %J J Med Internet Res %G English %X Background: Communication is a crucial element of every health care profession, rendering communication skills training in all health care professions as being of great importance. Technological advances such as artificial intelligence (AI) and particularly machine learning (ML) may support this cause: it may provide students with an opportunity for easily accessible and readily available communication training. Objective: This scoping review aimed to summarize the status quo regarding the use of AI or ML in the acquisition of communication skills in academic health care professions. Methods: We conducted a comprehensive literature search across the PubMed, Scopus, Cochrane Library, Web of Science Core Collection, and CINAHL databases to identify articles that covered the use of AI or ML in communication skills training of undergraduate students pursuing health care profession education. Using an inductive approach, the included studies were organized into distinct categories. The specific characteristics of the studies, methods and techniques used by AI or ML applications, and main outcomes of the studies were evaluated. Furthermore, supporting and hindering factors in the use of AI and ML for communication skills training of health care professionals were outlined. Results: The titles and abstracts of 385 studies were identified, of which 29 (7.5%) underwent full-text review. Of the 29 studies, based on the inclusion and exclusion criteria, 12 (3.1%) were included. The studies were organized into 3 distinct categories: studies using AI and ML for text analysis and information extraction, studies using AI and ML and virtual reality, and studies using AI and ML and the simulation of virtual patients, each within the academic training of the communication skills of health care professionals. Within these thematic domains, AI was also used for the provision of feedback. The motivation of the involved agents played a major role in the implementation process. Reported barriers to the use of AI and ML in communication skills training revolved around the lack of authenticity and limited natural flow of language exhibited by the AI- and ML-based virtual patient systems. Furthermore, the use of educational AI- and ML-based systems in communication skills training for health care professionals is currently limited to only a few cases, topics, and clinical domains. Conclusions: The use of AI and ML in communication skills training for health care professionals is clearly a growing and promising field with a potential to render training more cost-effective and less time-consuming. Furthermore, it may serve learners as an individualized and readily available exercise method. However, in most cases, the outlined applications and technical solutions are limited in terms of access, possible scenarios, the natural flow of a conversation, and authenticity. These issues still stand in the way of any widespread implementation ambitions. %M 37335593 %R 10.2196/43311 %U https://www.jmir.org/2023/1/e43311 %U https://doi.org/10.2196/43311 %U http://www.ncbi.nlm.nih.gov/pubmed/37335593 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e43896 %T Expectations of Anesthesiology and Intensive Care Professionals Toward Artificial Intelligence: Observational Study %A Kloka,Jan Andreas %A Holtmann,Sophie C %A Nürenberg-Goloub,Elina %A Piekarski,Florian %A Zacharowski,Kai %A Friedrichson,Benjamin %+ Department of Anaesthesiology, Intensive Care Medicine and Pain Therapy, University Hospital Frankfurt, Goethe University, Theodor-Stern Kai 7, Frankfurt, 60590, Germany, 49 630183876, JanAndreas.Kloka@kgu.de %K anesthesiology %K artificial intelligence %K health care %K intensive care %K medical informatics %K technology acceptance %K Europe-wide survey %D 2023 %7 12.6.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Artificial intelligence (AI) applications offer numerous opportunities to improve health care. To be used in the intensive care unit, AI must meet the needs of staff, and potential barriers must be addressed through joint action by all stakeholders. It is thus critical to assess the needs and concerns of anesthesiologists and intensive care physicians related to AI in health care throughout Europe. Objective: This Europe-wide, cross-sectional observational study investigates how potential users of AI systems in anesthesiology and intensive care assess the opportunities and risks of the new technology. The web-based questionnaire was based on the established analytic model of acceptance of innovations by Rogers to record 5 stages of innovation acceptance. Methods: The questionnaire was sent twice in 2 months (March 11, 2021, and November 5, 2021) through the European Society of Anaesthesiology and Intensive Care (ESAIC) member email distribution list. A total of 9294 ESAIC members were reached, of whom 728 filled out the questionnaire (response rate 728/9294, 8%). Due to missing data, 27 questionnaires were excluded. The analyses were conducted with 701 participants. Results: A total of 701 questionnaires (female: n=299, 42%) were analyzed. Overall, 265 (37.8%) of the participants have been in contact with AI and evaluated the benefits of this technology higher (mean 3.22, SD 0.39) than participants who stated no previous contact (mean 3.01, SD 0.48). Physicians see the most benefits of AI application in early warning systems (335/701, 48% strongly agreed, and 358/701, 51% agreed). Major potential disadvantages were technical problems (236/701, 34% strongly agreed, and 410/701, 58% agreed) and handling difficulties (126/701, 18% strongly agreed, and 462/701, 66% agreed), both of which could be addressed by Europe-wide digitalization and education. In addition, the lack of a secure legal basis for the research and use of medical AI in the European Union leads doctors to expect problems with legal liability (186/701, 27% strongly agreed, and 374/701, 53% agreed) and data protection (148/701, 21% strongly agreed, and 343/701, 49% agreed). Conclusions: Anesthesiologists and intensive care personnel are open to AI applications in their professional field and expect numerous benefits for staff and patients. Regional differences in the digitalization of the private sector are not reflected in the acceptance of AI among health care professionals. Physicians anticipate technical difficulties and lack a stable legal basis for the use of AI. Training for medical staff could increase the benefits of AI in professional medicine. Therefore, we suggest that the development and implementation of AI in health care require a solid technical, legal, and ethical basis, as well as adequate education and training of users. %M 37307038 %R 10.2196/43896 %U https://formative.jmir.org/2023/1/e43896 %U https://doi.org/10.2196/43896 %U http://www.ncbi.nlm.nih.gov/pubmed/37307038 %0 Journal Article %@ 2561-326X %I JMIR Publications %V 7 %N %P e46020 %T Roles and Competencies of Doctors in Artificial Intelligence Implementation: Qualitative Analysis Through Physician Interviews %A Tanaka,Masashi %A Matsumura,Shinji %A Bito,Seiji %+ Department of Clinical Epidemiology, Tokyo Medical Center, National Hospital Organization, 2-5-1,higashigaoka,meguroku, Tokyo, 1520021, Japan, 81 334110111, tanakamasashino@gmail.com %K artificial intelligence %K shared decision-making %K competency %K decision-making, qualitative research %K patient-physician %K medical field %K AI services %K AI technology %D 2023 %7 18.5.2023 %9 Original Paper %J JMIR Form Res %G English %X Background: Artificial intelligence (AI) is a term used to describe the use of computers and technology to emulate human intelligence mechanisms. Although AI is known to affect health services, the impact of information provided by AI on the patient-physician relationship in actual practice is unclear. Objective: The purpose of this study is to investigate the effect of introducing AI functions into the medical field on the role of the physician or physician-patient relationship, as well as potential concerns in the AI era. Methods: We conducted focus group interviews in Tokyo’s suburbs with physicians recruited through snowball sampling. The interviews were conducted in accordance with the questions listed in the interview guide. A verbatim transcript recording of all interviews was qualitatively analyzed using content analysis by all authors. Similarly, extracted code was grouped into subcategories, categories, and then core categories. We continued interviewing, analyzing, and discussing until we reached data saturation. In addition, we shared the results with all interviewees and confirmed the content to ensure the credibility of the analysis results. Results: A total of 9 participants who belonged to various clinical departments in the 3 groups were interviewed. The same interviewers conducted the interview as the moderator each time. The average group interview time for the 3 groups was 102 minutes. Content saturation and theme development were achieved with the 3 groups. We identified three core categories: (1) functions expected to be replaced by AI, (2) functions still expected of human physicians, and (3) concerns about the medical field in the AI era. We also summarized the roles of physicians and patients, as well as the changes in the clinical environment in the age of AI. Some of the current functions of the physician were primarily replaced by AI functions, while others were inherited as the functions of the physician. In addition, “functions extended by AI” obtained by processing massive amounts of data will emerge, and a new role for physicians will be created to deal with them. Accordingly, the importance of physician functions, such as responsibility and commitment based on values, will increase, which will simultaneously increase the expectations of the patients that physicians will perform these functions. Conclusions: We presented our findings on how the medical processes of physicians and patients will change as AI technology is fully implemented. Promoting interdisciplinary discussions on how to overcome the challenges is essential, referring to the discussions being conducted in other fields. %M 37200074 %R 10.2196/46020 %U https://formative.jmir.org/2023/1/e46020 %U https://doi.org/10.2196/46020 %U http://www.ncbi.nlm.nih.gov/pubmed/37200074 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 11 %N %P e42458 %T Barriers and Opportunities for the Use of Digital Tools in Medicines Optimization Across the Interfaces of Care: Stakeholder Interviews in the United Kingdom %A Tolley,Clare %A Seymour,Helen %A Watson,Neil %A Nazar,Hamde %A Heed,Jude %A Belshaw,Dave %+ Pharmacy Department, The Newcastle upon Tyne Hospitals NHS Foundation Trust, Queen Victoria Road,, Newcastle upon Tyne, NE1 4LP, United Kingdom, 44 0191 282 4488, clare.brown@newcastle.ac.uk %K health information exchange %K patient safety %K medicines optimization %K transfer of care %K health informatics %K qualitative %D 2023 %7 10.3.2023 %9 Original Paper %J JMIR Med Inform %G English %X Background: People with long-term conditions frequently transition between care settings that require information about a patient’s medicines to be transferred or translated between systems. This process is currently error prone and associated with unintentional changes to medications and miscommunication, which can lead to serious patient consequences. One study estimated that approximately 250,000 serious medication errors occur in England when a patient transitions from hospital to home. Digital tools can equip health care professionals with the right information at the right time and place to support practice. Objective: This study aimed to answer the following questions: what systems are being used to transfer information across interfaces of care within a region of England? and what are the challenges and potential opportunities for more effective cross-sector working to support medicines optimization? Methods: A team of researchers at Newcastle University conducted a qualitative study by performing in-depth semistructured interviews with 23 key stakeholders in medicines optimization and IT between January and March 2022. The interviews lasted for approximately 1 hour. The interviews and field notes were transcribed and analyzed using the framework approach. The themes were discussed, refined, and applied systematically to the data set. Member checking was also performed. Results: This study revealed themes and subthemes pertaining to 3 key areas: transfer of care issues, challenges of digital tools, and future hopes and opportunities. We identified a major complexity in terms of the number of different medicine management systems used throughout the region. There were also important challenges owing to incomplete patient records. We also highlighted the barriers related to using multiple systems and their subsequent impact on user workflow, a lack of interoperability between systems, gaps in the availability of digital data, and poor IT and change management. Finally, participants described their hopes and opportunities for the future provision of medicines optimization services, and there was a clear need for a patient-centered consolidated integrated health record for use by all health and care professionals across different sectors, bridging those working in primary, secondary, and social care. Conclusions: The effectiveness and utility of shared records depend on the data within; therefore, health care and digital leaders must support and strongly encourage the adoption of established and approved digital information standards. Specific priorities regarding understanding of the vision for pharmacy services and supporting this with appropriate funding arrangements and strategic planning of the workforce were also described. In addition, the following were identified as key enablers to harness the benefits of digital tools to support future medicines optimization: development of minimal system requirements; enhanced IT system management to reduce unnecessary repetition; and importantly, meaningful and continued collaboration with clinical and IT stakeholders to optimize systems and share good practices across care sectors. %M 36897631 %R 10.2196/42458 %U https://medinform.jmir.org/2023/1/e42458 %U https://doi.org/10.2196/42458 %U http://www.ncbi.nlm.nih.gov/pubmed/36897631 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 11 %P e37478 %T Considering Clinician Competencies for the Implementation of Artificial Intelligence–Based Tools in Health Care: Findings From a Scoping Review %A Garvey,Kim V %A Thomas Craig,Kelly Jean %A Russell,Regina %A Novak,Laurie L %A Moore,Don %A Miller,Bonnie M %+ Clinical Evidence Development, Aetna Medical Affairs, CVS Health, 151 Farmington Avenue, RC31, Hartford, CT, 06156, United States, 1 970 261 3366, craigk@aetna.com %K artificial intelligence %K competency %K clinical education %K patient %K digital health %K digital tool %K clinical tool %K health technology %K health care %K educational framework %K decision-making %K clinical decision %K health information %K physician %D 2022 %7 16.11.2022 %9 Review %J JMIR Med Inform %G English %X Background: The use of artificial intelligence (AI)–based tools in the care of individual patients and patient populations is rapidly expanding. Objective: The aim of this paper is to systematically identify research on provider competencies needed for the use of AI in clinical settings. Methods: A scoping review was conducted to identify articles published between January 1, 2009, and May 1, 2020, from MEDLINE, CINAHL, and the Cochrane Library databases, using search queries for terms related to health care professionals (eg, medical, nursing, and pharmacy) and their professional development in all phases of clinical education, AI-based tools in all settings of clinical practice, and professional education domains of competencies and performance. Limits were provided for English language, studies on humans with abstracts, and settings in the United States. Results: The searches identified 3476 records, of which 4 met the inclusion criteria. These studies described the use of AI in clinical practice and measured at least one aspect of clinician competence. While many studies measured the performance of the AI-based tool, only 4 measured clinician performance in terms of the knowledge, skills, or attitudes needed to understand and effectively use the new tools being tested. These 4 articles primarily focused on the ability of AI to enhance patient care and clinical decision-making by improving information flow and display, specifically for physicians. Conclusions: While many research studies were identified that investigate the potential effectiveness of using AI technologies in health care, very few address specific competencies that are needed by clinicians to use them effectively. This highlights a critical gap. %M 36318697 %R 10.2196/37478 %U https://medinform.jmir.org/2022/11/e37478 %U https://doi.org/10.2196/37478 %U http://www.ncbi.nlm.nih.gov/pubmed/36318697 %0 Journal Article %@ 2368-7959 %I JMIR Publications %V 9 %N 10 %P e37939 %T Teaching Telepsychiatry Skills: Building on the Lessons of the COVID-19 Pandemic to Enhance Mental Health Care in the Future %A Smith,Katharine %A Torous,John %A Cipriani,Andrea %+ Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, OX3 7JX, United Kingdom, 44 01865618200, andrea.cipriani@psych.ox.ac.uk %K mHealth %K mental health %K smartphones %K telehealth %K telepsychiatry %K COVID-19 %D 2022 %7 14.10.2022 %9 Editorial %J JMIR Ment Health %G English %X COVID-19 has accelerated the use of telehealth and technology in mental health care, creating new avenues to increase both access to and quality of care. As video visits and synchronous telehealth become more routine, the field is now on the verge of embracing asynchronous telehealth, with the potential to radically transform mental health. However, sustaining the use of basic synchronous telehealth, let alone embracing asynchronous telehealth, requires new and immediate effort. Programs to increase digital literacy and competencies among both clinicians and patients are now critical to ensure all parties have the knowledge, confidence, and ability to equitably benefit from emerging innovations. This editorial outlines the immediate potential as well as concrete steps toward realizing the potential of a new, more personalized, scalable mental health system. %M 35358948 %R 10.2196/37939 %U https://mental.jmir.org/2022/10/e37939 %U https://doi.org/10.2196/37939 %U http://www.ncbi.nlm.nih.gov/pubmed/35358948 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 8 %N 3 %P e39794 %T Informatics in Undergraduate Medical Education: Analysis of Competency Frameworks and Practices Across North America %A Chartash,David %A Rosenman,Marc %A Wang,Karen %A Chen,Elizabeth %+ Center for Medical Informatics, Yale University School of Medicine, 300 George Street Suite 501, New Haven, CT, 06511, United States, 1 203 737 5325, dchartas@ieee.org %K undergraduate medical education %K medical informatics %K curriculum %K medical education %K education %K North America %K framework %K clinical %K informatics %K Canada %K United States %K US %K teaching %K management %K cognitive %D 2022 %7 13.9.2022 %9 Original Paper %J JMIR Med Educ %G English %X Background: With the advent of competency-based medical education, as well as Canadian efforts to include clinical informatics within undergraduate medical education, competency frameworks in the United States have not emphasized the skills associated with clinical informatics pertinent to the broader practice of medicine. Objective: By examining the competency frameworks with which undergraduate medical education in clinical informatics has been developed in Canada and the United States, we hypothesized that there is a gap: the lack of a unified competency set and frame for clinical informatics education across North America. Methods: We performed directional competency mapping between Canadian and American graduate clinical informatics competencies and general graduate medical education competencies. Directional competency mapping was performed between Canadian roles and American common program requirements using keyword matching at the subcompetency and enabling competency levels. In addition, for general graduate medical education competencies, the Physician Competency Reference Set developed for the Liaison Committee on Medical Education was used as a direct means of computing the ontological overlap between competency frameworks. Results: Upon mapping Canadian roles to American competencies via both undergraduate and graduate medical education competency frameworks, the difference in focus between the 2 countries can be thematically described as a difference between the concepts of clinical and management reasoning. Conclusions: We suggest that the development or deployment of informatics competencies in undergraduate medical education should focus on 3 items: the teaching of diagnostic reasoning, such that the information tasks that comprise both clinical and management reasoning can be discussed; precision medical education, where informatics can provide for more fine-grained evaluation; and assessment methods to support traditional pedagogical efforts (both at the bedside and beyond). Assessment using cases or structured assessments (eg, Objective Structured Clinical Examinations) would help students draw parallels between clinical informatics and fundamental clinical subjects and would better emphasize the cognitive techniques taught through informatics. %M 36099007 %R 10.2196/39794 %U https://mededu.jmir.org/2022/3/e39794 %U https://doi.org/10.2196/39794 %U http://www.ncbi.nlm.nih.gov/pubmed/36099007 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 8 %P e34304 %T Tempering Expectations on the Medical Artificial Intelligence Revolution: The Medical Trainee Viewpoint %A Hu,Zoe %A Hu,Ricky %A Yau,Olivia %A Teng,Minnie %A Wang,Patrick %A Hu,Grace %A Singla,Rohit %+ School of Medicine, Queen's University, 166 Brock Street, Kingston, ON, K7L5G2, Canada, 1 6132042952, zhu@qmed.ca %K medical education %K artificial intelligence %K health care trainees %K AI %K health care workers %D 2022 %7 15.8.2022 %9 Viewpoint %J JMIR Med Inform %G English %X The rapid development of artificial intelligence (AI) in medicine has resulted in an increased number of applications deployed in clinical trials. AI tools have been developed with goals of improving diagnostic accuracy, workflow efficiency through automation, and discovery of novel features in clinical data. There is subsequent concern on the role of AI in replacing existing tasks traditionally entrusted to physicians. This has implications for medical trainees who may make decisions based on the perception of how disruptive AI may be to their future career. This commentary discusses current barriers to AI adoption to moderate concerns of the role of AI in the clinical setting, particularly as a standalone tool that replaces physicians. Technical limitations of AI include generalizability of performance and deficits in existing infrastructure to accommodate data, both of which are less obvious in pilot studies, where high performance is achieved in a controlled data processing environment. Economic limitations include rigorous regulatory requirements to deploy medical devices safely, particularly if AI is to replace human decision-making. Ethical guidelines are also required in the event of dysfunction to identify responsibility of the developer of the tool, health care authority, and patient. The consequences are apparent when identifying the scope of existing AI tools, most of which aim to be physician assisting rather than a physician replacement. The combination of the limitations will delay the onset of ubiquitous AI tools that perform standalone clinical tasks. The role of the physician likely remains paramount to clinical decision-making in the near future. %M 35969464 %R 10.2196/34304 %U https://medinform.jmir.org/2022/8/e34304 %U https://doi.org/10.2196/34304 %U http://www.ncbi.nlm.nih.gov/pubmed/35969464 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 4 %P e30518 %T The Effectiveness of the Capacity Building and Mentorship Program in Improving Evidence-Based Decision-making in the Amhara Region, Northwest Ethiopia: Difference-in-Differences Study %A Chanyalew,Moges Asressie %A Yitayal,Mezgebu %A Atnafu,Asmamaw %A Mengiste,Shegaw Anagaw %A Tilahun,Binyam %+ Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia, 251 911617734, mogesabu@gmail.com %K capacity building %K mentorship %K mentoring %K mentor %K training %K data use %K information use %K facility head %K department head %K quasi-experiment %K difference-in-differences %K Ethiopia %K Amhara %K weak health information system %K HIS %K health information system %K CBMP %K DID %K decision-making %K Africa %K evidence based %K effectiveness %D 2022 %7 22.4.2022 %9 Original Paper %J JMIR Med Inform %G English %X Background: Weak health information systems (HISs) hobble countries’ abilities to effectively manage and distribute their resources to match the burden of disease. The Capacity Building and Mentorship Program (CBMP) was implemented in select districts of the Amhara region of Ethiopia to improve HIS performance; however, evidence about the effectiveness of the intervention was meager. Objective: This study aimed to determine the effectiveness of routine health information use for evidence-based decision-making among health facility and department heads in the Amhara region, Northwest Ethiopia. Methods: The study was conducted in 10 districts of the Amhara region: five were in the intervention group and five were in the comparison group. We employed a quasi-experimental study design in the form of a pretest-posttest comparison group. Data were collected from June to July 2020 from the heads of departments and facilities in 36 intervention and 43 comparison facilities. The sample size was calculated using the double population formula, and we recruited 172 participants from each group. We applied a difference-in-differences analysis approach to determine the effectiveness of the intervention. Heterogeneity of program effect among subgroups was assessed using a triple differences method (ie, difference-in-difference-in-differences [DIDID] method). Thus, the β coefficients, 95% CIs, and P values were calculated for each parameter, and we determined that the program was effective if the interaction term was significant at P<.05. Results: Data were collected using the endpoint survey from 155 out of 172 (90.1%) participants in the intervention group and 166 out of 172 (96.5%) participants in the comparison group. The average level of information use for the comparison group was 37.3% (95% CI 31.1%-43.6%) at baseline and 43.7% (95% CI 37.9%-49.5%) at study endpoint. The average level of information use for the intervention group was 52.2% (95% CI 46.2%-58.3%) at baseline and 75.8% (95% CI 71.6%-80.0%) at study endpoint. The study indicated that the net program change over time was 17% (95% CI 5%-28%; P=.003). The subgroup analysis also indicated that location showed significant program effect heterogeneity, with a DIDID estimate equal to 0.16 (95% CI 0.026-0.29; P=.02). However, sex, age, educational level, salary, and experience did not show significant heterogeneity in program effect, with DIDID estimates of 0.046 (95% CI –0.089 to 0.182), –0.002 (95% CI –0.015 to 0.009), –0.055 (95% CI –0.190 to 0.079), –1.63 (95% CI –5.22 to 1.95), and –0.006 (95% CI –0.017 to 0.005), respectively. Conclusions: The CBMP was effective at enhancing the capacity of study participants in using the routine HIS for decision-making. We noted that urban facilities had benefited more than their counterparts. The intervention has been shown to produce positive outcomes and should be scaled up to be used in other districts. Moreover, the mentorship modalities for rural facilities should be redesigned to maximize the benefits. Trial Registration: Pan African Clinical Trials Registry PACTR202001559723931; https://tinyurl.com/3j7e5ka5 %M 35451990 %R 10.2196/30518 %U https://medinform.jmir.org/2022/4/e30518 %U https://doi.org/10.2196/30518 %U http://www.ncbi.nlm.nih.gov/pubmed/35451990 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 4 %P e33842 %T Global Scientific Research Landscape on Medical Informatics From 2011 to 2020: Bibliometric Analysis %A He,Xuefei %A Peng,Cheng %A Xu,Yingxin %A Zhang,Ye %A Wang,Zhongqing %+ Information Center, The First Hospital of China Medical University, 155 Nanjingbei Street, Shenyang, 110001, China, 86 15940082159, wangzhongqing@cmu.edu.cn %K medical informatics %K bibliometrics %K VOSviewer %K data visualization %D 2022 %7 21.4.2022 %9 Original Paper %J JMIR Med Inform %G English %X Background: With the emerging information and communication technology, the field of medical informatics has dramatically evolved in health care and medicine. Thus, it is crucial to explore the global scientific research landscape on medical informatics. Objective: This study aims to present a visual form to clarify the overall scientific research trends of medical informatics in the past decade. Methods: A bibliometric analysis of data retrieved and extracted from the Web of Science Core Collection (WoSCC) database was performed to analyze global scientific research trends on medical informatics, including publication year, journals, authors, institutions, countries/regions, references, and keywords, from January 1, 2011, to December 31, 2020. Results: The data set recorded 34,742 articles related to medical informatics from WoSCC between 2011 and 2020. The annual global publications increased by 193.86% from 1987 in 2011 to 5839 in 2020. Journal of Medical Internet Research (3600 publications and 63,932 citations) was the most productive and most highly cited journal in the field of medical informatics. David W Bates (99 publications), Harvard University (1161 publications), and the United States (12,927 publications) were the most productive author, institution, and country, respectively. The co-occurrence cluster analysis of high-frequency author keywords formed 4 clusters: (1) artificial intelligence in health care and medicine; (2) mobile health; (3) implementation and evaluation of electronic health records; (4) medical informatics technology application in public health. COVID-19, which ranked third in 2020, was the emerging theme of medical informatics. Conclusions: We summarize the recent advances in medical informatics in the past decade and shed light on their publication trends, influential journals, global collaboration patterns, basic knowledge, research hotspots, and theme evolution through bibliometric analysis and visualization maps. These findings will accurately and quickly grasp the research trends and provide valuable guidance for future medical informatics research. %M 35451986 %R 10.2196/33842 %U https://medinform.jmir.org/2022/4/e33842 %U https://doi.org/10.2196/33842 %U http://www.ncbi.nlm.nih.gov/pubmed/35451986 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 8 %N 2 %P e28965 %T Current and Future Needs for Human Resources for Ethiopia’s National Health Information System: Survey and Forecasting Study %A Tilahun,Binyam %A Endehabtu,Berhanu F %A Gashu,Kassahun D %A Mekonnen,Zeleke A %A Animut,Netsanet %A Belay,Hiwot %A Denboba,Wubshet %A Alemu,Hibret %A Mohammed,Mesoud %A Abate,Biruk %+ Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, PO Box 196, Gondar, Ethiopia, 251 921013129, berhanufikadie@gmail.com %K forecasting %K human resources %K health information system %K workforce %K Ethiopia %K health informatics %K healthcare professionals %D 2022 %7 12.4.2022 %9 Original Paper %J JMIR Med Educ %G English %X Background: Strengthening the national health information system is one of Ethiopia’s priority transformation agendas. A well-trained and competent workforce is the essential ingredient to a strong health information system. However, this workforce has neither been quantified nor characterized well, and there is no roadmap of required human resources to enhance the national health information system. Objective: We aimed to determine the current state of the health information system workforce and to forecast the human resources needed for the health information system by 2030. Methods: We conducted a survey to estimate the current number of individuals employed in the health information system unit and the turnover rate. Document review and key-informant interviews were used to collect current human resources and available health information system position data from 110 institutions, including the Ministry of Health, federal agencies, regional health bureaus, zonal health departments, district health offices, and health facilities. The Delphi technique was used to forecast human resources required for the health information system in the next ten years: 3 rounds of workshops with experts from the Ministry of Health, universities, agencies, and regional health bureaus were held. In the first expert meeting, we set criteria, which was followed by expert suggestions and feedback. Results: As of April 2020, there were 10,344 health information system professionals working in the governmental health system. Nearly 95% (20/21) of district health offices and 86.7% (26/30) of health centers reported that the current number of health information system positions was inadequate. In the period from June 2015 to June 2019, health information technicians had high turnover (48/244, 19.7%) at all levels of the health system. In the next ten years, we estimate that 50,656 health information system professionals will be needed to effectively implement the Ethiopia's national health information system. Conclusions: Current health information system–related staffing levels were found to be inadequate. To meet the estimated need of 50,656 multidisciplinary health information system professionals by 2030, the Ministry of Health and regional health bureaus, in collaboration with partners and academic institutions, need to work on retaining existing and training additional health information system professionals. %M 35412469 %R 10.2196/28965 %U https://mededu.jmir.org/2022/2/e28965 %U https://doi.org/10.2196/28965 %U http://www.ncbi.nlm.nih.gov/pubmed/35412469 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 8 %N 2 %P e34973 %T Readiness to Embrace Artificial Intelligence Among Medical Doctors and Students: Questionnaire-Based Study %A Boillat,Thomas %A Nawaz,Faisal A %A Rivas,Homero %+ Design Lab, College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Healthcare City 14, Dubai, United Arab Emirates, 971 43838759, Thomas.boillat@mbru.ac.ae %K artificial intelligence in medicine %K health care %K questionnaire %K medical doctors %K medical students %D 2022 %7 12.4.2022 %9 Original Paper %J JMIR Med Educ %G English %X Background: Similar to understanding how blood pressure is measured by a sphygmomanometer, physicians will soon have to understand how an artificial intelligence–based application has come to the conclusion that a patient has hypertension, diabetes, or cancer. Although there are an increasing number of use cases where artificial intelligence is or can be applied to improve medical outcomes, the extent to which medical doctors and students are ready to work and leverage this paradigm is unclear. Objective: This research aims to capture medical students’ and doctors’ level of familiarity toward artificial intelligence in medicine as well as their challenges, barriers, and potential risks linked to the democratization of this new paradigm. Methods: A web-based questionnaire comprising five dimensions—demographics, concepts and definitions, training and education, implementation, and risks—was systematically designed from a literature search. It was completed by 207 participants in total, of which 105 (50.7%) medical doctors and 102 (49.3%) medical students trained in all continents, with most of them in Europe, the Middle East, Asia, and North America. Results: The results revealed no significant difference in the familiarity of artificial intelligence between medical doctors and students (P=.91), except that medical students perceived artificial intelligence in medicine to lead to higher risks for patients and the field of medicine in general (P<.001). We also identified a rather low level of familiarity with artificial intelligence (medical students=2.11/5; medical doctors=2.06/5) as well as a low attendance to education or training. Only 2.9% (3/105) of medical doctors attended a course on artificial intelligence within the previous year, compared with 9.8% (10/102) of medical students. The complexity of the field of medicine was considered one of the biggest challenges (medical doctors=3.5/5; medical students=3.8/5), whereas the reduction of physicians’ skills was the most important risk (medical doctors=3.3; medical students=3.6; P=.03). Conclusions: The question is not whether artificial intelligence will be used in medicine, but when it will become a standard practice for optimizing health care. The low level of familiarity with artificial intelligence identified in this study calls for the implementation of specific education and training in medical schools and hospitals to ensure that medical professionals can leverage this new paradigm and improve health outcomes. %M 35412463 %R 10.2196/34973 %U https://mededu.jmir.org/2022/2/e34973 %U https://doi.org/10.2196/34973 %U http://www.ncbi.nlm.nih.gov/pubmed/35412463 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 10 %N 4 %P e29813 %T The Effect of an Additional Structured Methods Presentation on Decision-Makers’ Reading Time and Opinions on the Helpfulness of the Methods in a Quantitative Report: Nonrandomized Trial %A Koetsenruijter,Jan %A Wronski,Pamela %A Ghosh,Sucheta %A Müller,Wolfgang %A Wensing,Michel %+ Department of General Practice and Health Services Research, University Hospital Heidelberg, Im Neuenheimer Feld 130.3, Heidelberg, 69120, Germany, 49 6221 56 4743, jankoetsenruyter@hotmail.com %K decision-making %K health care reports %K reading behavior %K research methods %K eye-tracking %K perceived importance %K electronic health records %K feasibility %K quantitative methods %D 2022 %7 12.4.2022 %9 Original Paper %J JMIR Med Inform %G English %X Background: Although decision-makers in health care settings need to read and understand the validity of quantitative reports, they do not always carefully read information on research methods. Presenting the methods in a more structured way could improve the time spent reading the methods and increase the perceived relevance of this important report section. Objective: To test the effect of a structured summary of the methods used in a quantitative data report on reading behavior with eye-tracking and measure the effect on the perceived importance of this section. Methods: A nonrandomized pilot trial was performed in a computer laboratory setting with advanced medical students. All participants were asked to read a quantitative data report; an intervention arm was also shown a textbox summarizing key features of the methods used in the report. Three data-collection methods were used to document reading behavior and the views of participants: eye-tracking (during reading), a written questionnaire, and a face-to-face interview. Results: We included 35 participants, 22 in the control arm and 13 in the intervention arm. The overall time spent reading the methods did not differ between the 2 arms. The intervention arm considered the information in the methods section to be less helpful for decision-making than did the control arm (scores for perceived helpfulness were 4.1 and 2.9, respectively, range 1-10). Participants who read the box more intensively tended to spend more time on the methods as a whole (Pearson correlation 0.81, P=.001). Conclusions: Adding a structured summary of information on research methods attracted attention from most participants, but did not increase the time spent on reading the methods or lead to increased perceptions that the methods section was helpful for decision-making. Participants made use of the summary to quickly judge the methods, but this did not increase the perceived relevance of this section. %M 35412464 %R 10.2196/29813 %U https://medinform.jmir.org/2022/4/e29813 %U https://doi.org/10.2196/29813 %U http://www.ncbi.nlm.nih.gov/pubmed/35412464 %0 Journal Article %@ 2561-9128 %I JMIR Publications %V 5 %N 1 %P e32738 %T The Case for the Anesthesiologist-Informaticist %A Lee,Robert %A Hitt,James %A Hobika,Geoffrey G %A Nader,Nader D %+ Department of Anesthesiology, VA Western New York Healthcare System, 3495 Bailey Ave, Buffalo, NY, 14215, United States, 1 716 834 9200, rlee32@buffalo.edu %K anesthesia %K anesthesiology %K AIMS %K anesthesia information management systems %K clinical informatics %K anesthesia informatics %K perioperative informatics %K health information %K perioperative medicine %K health technology %D 2022 %7 28.2.2022 %9 Viewpoint %J JMIR Perioper Med %G English %X Health care has been transformed by computerization, and the use of electronic health record systems has become widespread. Anesthesia information management systems are commonly used in the operating room to maintain records of anesthetic care delivery. The perioperative environment and the practice of anesthesia generate a large volume of data that may be reused to support clinical decision-making, research, and process improvement. Anesthesiologists trained in clinical informatics, referred to as informaticists or informaticians, may help implement and optimize anesthesia information management systems. They may also participate in clinical research, management of information systems, and quality improvement in the operating room or throughout a health care system. Here, we describe the specialty of clinical informatics, how anesthesiologists may obtain training in clinical informatics, and the considerations particular to the subspecialty of anesthesia informatics. Management of perioperative information systems, implementation of computerized clinical decision support systems in the perioperative environment, the role of virtual visits and remote monitoring, perioperative informatics research, perioperative process improvement, leadership, and change management are described from the perspective of the anesthesiologist-informaticist. %M 35225822 %R 10.2196/32738 %U https://periop.jmir.org/2022/1/e32738 %U https://doi.org/10.2196/32738 %U http://www.ncbi.nlm.nih.gov/pubmed/35225822 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 12 %P e33540 %T How Clinicians Perceive Artificial Intelligence–Assisted Technologies in Diagnostic Decision Making: Mixed Methods Approach %A Hah,Hyeyoung %A Goldin,Deana Shevit %+ Information Systems and Business Analytics, College of Business, Florida International University, 11200 SW 8th Street, Miami, FL, 33199, United States, 1 3053484342, hhah@fiu.edu %K artificial intelligence algorithms %K AI %K diagnostic capability %K virtual care %K multilevel modeling %K human-AI teaming %K natural language understanding %D 2021 %7 16.12.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: With the rapid development of artificial intelligence (AI) and related technologies, AI algorithms are being embedded into various health information technologies that assist clinicians in clinical decision making. Objective: This study aimed to explore how clinicians perceive AI assistance in diagnostic decision making and suggest the paths forward for AI-human teaming for clinical decision making in health care. Methods: This study used a mixed methods approach, utilizing hierarchical linear modeling and sentiment analysis through natural language understanding techniques. Results: A total of 114 clinicians participated in online simulation surveys in 2020 and 2021. These clinicians studied family medicine and used AI algorithms to aid in patient diagnosis. Their overall sentiment toward AI-assisted diagnosis was positive and comparable with diagnoses made without the assistance of AI. However, AI-guided decision making was not congruent with the way clinicians typically made decisions in diagnosing illnesses. In a quantitative survey, clinicians reported perceiving current AI assistance as not likely to enhance diagnostic capability and negatively influenced their overall performance (β=–0.421, P=.02). Instead, clinicians’ diagnostic capabilities tended to be associated with well-known parameters, such as education, age, and daily habit of technology use on social media platforms. Conclusions: This study elucidated clinicians’ current perceptions and sentiments toward AI-enabled diagnosis. Although the sentiment was positive, the current form of AI assistance may not be linked with efficient decision making, as AI algorithms are not well aligned with subjective human reasoning in clinical diagnosis. Developers and policy makers in health could gather behavioral data from clinicians in various disciplines to help align AI algorithms with the unique subjective patterns of reasoning that humans employ in clinical diagnosis. %M 34924356 %R 10.2196/33540 %U https://www.jmir.org/2021/12/e33540 %U https://doi.org/10.2196/33540 %U http://www.ncbi.nlm.nih.gov/pubmed/34924356 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 7 %N 4 %P e31043 %T Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review %A Charow,Rebecca %A Jeyakumar,Tharshini %A Younus,Sarah %A Dolatabadi,Elham %A Salhia,Mohammad %A Al-Mouaswas,Dalia %A Anderson,Melanie %A Balakumar,Sarmini %A Clare,Megan %A Dhalla,Azra %A Gillan,Caitlin %A Haghzare,Shabnam %A Jackson,Ethan %A Lalani,Nadim %A Mattson,Jane %A Peteanu,Wanda %A Tripp,Tim %A Waldorf,Jacqueline %A Williams,Spencer %A Tavares,Walter %A Wiljer,David %+ University Health Network, 190 Elizabeth Street, R. Fraser Elliott Building RFE 3S-441, Toronto, ON, M5G 2C4, Canada, 1 416 340 4800 ext 6322, david.wiljer@uhn.ca %K machine learning %K deep learning %K health care providers %K education %K learning %K patient care %D 2021 %7 13.12.2021 %9 Review %J JMIR Med Educ %G English %X Background: As the adoption of artificial intelligence (AI) in health care increases, it will become increasingly crucial to involve health care professionals (HCPs) in developing, validating, and implementing AI-enabled technologies. However, because of a lack of AI literacy, most HCPs are not adequately prepared for this revolution. This is a significant barrier to adopting and implementing AI that will affect patients. In addition, the limited existing AI education programs face barriers to development and implementation at various levels of medical education. Objective: With a view to informing future AI education programs for HCPs, this scoping review aims to provide an overview of the types of current or past AI education programs that pertains to the programs’ curricular content, modes of delivery, critical implementation factors for education delivery, and outcomes used to assess the programs’ effectiveness. Methods: After the creation of a search strategy and keyword searches, a 2-stage screening process was conducted by 2 independent reviewers to determine study eligibility. When consensus was not reached, the conflict was resolved by consulting a third reviewer. This process consisted of a title and abstract scan and a full-text review. The articles were included if they discussed an actual training program or educational intervention, or a potential training program or educational intervention and the desired content to be covered, focused on AI, and were designed or intended for HCPs (at any stage of their career). Results: Of the 10,094 unique citations scanned, 41 (0.41%) studies relevant to our eligibility criteria were identified. Among the 41 included studies, 10 (24%) described 13 unique programs and 31 (76%) discussed recommended curricular content. The curricular content of the unique programs ranged from AI use, AI interpretation, and cultivating skills to explain results derived from AI algorithms. The curricular topics were categorized into three main domains: cognitive, psychomotor, and affective. Conclusions: This review provides an overview of the current landscape of AI in medical education and highlights the skills and competencies required by HCPs to effectively use AI in enhancing the quality of care and optimizing patient outcomes. Future education efforts should focus on the development of regulatory strategies, a multidisciplinary approach to curriculum redesign, a competency-based curriculum, and patient-clinician interaction. %M 34898458 %R 10.2196/31043 %U https://mededu.jmir.org/2021/4/e31043 %U https://doi.org/10.2196/31043 %U http://www.ncbi.nlm.nih.gov/pubmed/34898458 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 7 %N 4 %P e30613 %T Best Practices for the Implementation and Sustainment of Virtual Health Information System Training: Qualitative Study %A Jeyakumar,Tharshini %A Ambata-Villanueva,Sharon %A McClure,Sarah %A Henderson,Carolyn %A Wiljer,David %+ University Health Network, 190 Elizabeth Street, R. Fraser Elliot Building, RFE 3S-441, Toronto, ON, M5G 2C4, Canada, 1 14163404800, David.Wiljer@uhn.ca %K training %K health care providers %K educational technology %K patient care %K COVID-19 %K development %K practice %K best practice %K pedagogy %K teaching %K implementation %K medical education %K online education %K care delivery %K perception %K effectiveness %D 2021 %7 22.10.2021 %9 Original Paper %J JMIR Med Educ %G English %X Background: The COVID-19 pandemic has necessitated the adoption and implementation of digital technologies to help transform the educational ecosystem and the delivery of care. Objective: We sought to understand instructors’ and learners’ perceptions of the challenges and opportunities faced in implementing health information system virtual training amid the COVID-19 pandemic. Methods: Semistructured interviews were conducted with education specialists and health care staff who provided or had taken part in a virtual instructor-led training at a large Canadian academic health sciences center. Guided by the Technology Acceptance Model and the Community of Inquiry framework, we analyzed interview transcript themes deductively and inductively. Results: Of the 18 individuals participating in the study, 9 were education specialists, 5 were learners, 3 were program coordinators, and 1 was a senior manager at the Centre for Learning, Innovation, and Simulation. We found 3 predominant themes: adopting a learner-centered approach for a meaningful learning experience, embracing the advances in educational technologies to maximize the transfer of learning, and enhancing the virtual user experience. Conclusions: This study adds to the literature on designing and implementing virtual training in health care organizations by highlighting the importance of recognizing learners’ needs and maximizing the transfer of learning. Findings from this study can be used to help inform the design and development of training strategies to support learners across an organization during the current climate and to ensure changes are sustainable. %M 34449402 %R 10.2196/30613 %U https://mededu.jmir.org/2021/4/e30613 %U https://doi.org/10.2196/30613 %U http://www.ncbi.nlm.nih.gov/pubmed/34449402 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e29239 %T Scholarly Productivity Evaluation of KL2 Scholars Using Bibliometrics and Federal Follow-on Funding: Cross-Institution Study %A Qua,Kelli %A Yu,Fei %A Patel,Tanha %A Dave,Gaurav %A Cornelius,Katherine %A Pelfrey,Clara M %+ Health Sciences Library, University of North Carolina at Chapel Hill, 335 S. Columbia Street CB 7585, Chapel Hill, NC, 27599, United States, 1 9199622219, feifei@unc.edu %K bibliometrics %K Clinical and Translational Science Award %K KL2 %K translational research %K career development %D 2021 %7 29.9.2021 %9 Original Paper %J J Med Internet Res %G English %X Background: Evaluating outcomes of the clinical and translational research (CTR) training of a Clinical and Translational Science Award (CTSA) hub (eg, the KL2 program) requires the selection of reliable, accessible, and standardized measures. As measures of scholarly success usually focus on publication output and extramural funding, CTSA hubs have started to use bibliometrics to evaluate the impact of their supported scholarly activities. However, the evaluation of KL2 programs across CTSAs is limited, and the use of bibliometrics and follow-on funding is minimal. Objective: This study seeks to evaluate scholarly productivity, impact, and collaboration using bibliometrics and federal follow-on funding of KL2 scholars from 3 CTSA hubs and to define and assess CTR training success indicators. Methods: The sample included KL2 scholars from 3 CTSA institutions (A-C). Bibliometric data for each scholar in the sample were collected from both SciVal and iCite, including scholarly productivity, citation impact, and research collaboration. Three federal follow-on funding measures (at the 5-year, 8-year, and overall time points) were collected internally and confirmed by examining a federal funding database. Both descriptive and inferential statistical analyses were computed using SPSS to assess the bibliometric and federal follow-on funding results. Results: A total of 143 KL2 scholars were included in the sample with relatively equal groups across the 3 CTSA institutions. The included KL2 scholars produced more publications and citation counts per year on average at the 8-year time point (3.75 publications and 26.44 citation counts) than the 5-year time point (3.4 publications vs 26.16 citation counts). Overall, the KL2 publications from all 3 institutions were cited twice as much as others in their fields based on the relative citation ratio. KL2 scholars published work with researchers from other US institutions over 2 times (5-year time point) or 3.5 times (8-year time point) more than others in their research fields. Within 5 years and 8 years postmatriculation, 44.1% (63/143) and 51.7% (74/143) of KL2 scholars achieved federal funding, respectively. The KL2-scholars of Institution C had a significantly higher citation rate per publication than the other institutions (P<.001). Institution A had a significantly lower rate of nationally field-weighted collaboration than did the other institutions (P<.001). Institution B scholars were more likely to have received federal funding than scholars at Institution A or C (P<.001). Conclusions: Multi-institutional data showed a high level of scholarly productivity, impact, collaboration, and federal follow-on funding achieved by KL2 scholars. This study provides insights on the use of bibliometric and federal follow-on funding data to evaluate CTR training success across institutions. CTSA KL2 programs and other CTR career training programs can benefit from these findings in terms of understanding metrics of career success and using that knowledge to develop highly targeted strategies to support early-stage career development of CTR investigators. %M 34586077 %R 10.2196/29239 %U https://www.jmir.org/2021/9/e29239 %U https://doi.org/10.2196/29239 %U http://www.ncbi.nlm.nih.gov/pubmed/34586077 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 9 %N 9 %P e29374 %T Using a New Model of Electronic Health Record Training to Reduce Physician Burnout: A Plan for Action %A Mohan,Vishnu %A Garrison,Cort %A Gold,Jeffrey A %+ Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, Mail Code BICC, Portland, OR, 97239-3098, United States, 1 5034944469, mohanv@ohsu.edu %K electronic health records %K clinician burnout %K EHR training %K clinician wellness %K after-hours EHR use %K EHR %K patient data %K burnout %K simulation %K efficiency %K optimization %K well-being %D 2021 %7 20.9.2021 %9 Viewpoint %J JMIR Med Inform %G English %X Physician burnout in the United States has been growing at an alarming rate, and health care organizations are beginning to invest significant resources in combating this phenomenon. Although the causes for burnout are multifactorial, a key issue that affects physicians is that they spend a significant proportion of their time interacting with their electronic health record (EHR) system, primarily because of the need to sift through increasing amounts of patient data, coupled with a significant documentation burden. This has led to physicians spending increasing amounts of time with the EHR outside working hours trying to catch up on paperwork (“pajama time”), which is a factor linked to burnout. In this paper, we propose an innovative model of EHR training using high-fidelity EHR simulations designed to facilitate efficient optimization of EHR use by clinicians and emphasize the importance of both lifelong learning and physician well-being. %M 34325400 %R 10.2196/29374 %U https://medinform.jmir.org/2021/9/e29374 %U https://doi.org/10.2196/29374 %U http://www.ncbi.nlm.nih.gov/pubmed/34325400 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 7 %N 3 %P e28275 %T Digital Health Training Programs for Medical Students: Scoping Review %A Tudor Car,Lorainne %A Kyaw,Bhone Myint %A Nannan Panday,Rishi S %A van der Kleij,Rianne %A Chavannes,Niels %A Majeed,Azeem %A Car,Josip %+ Family Medicine and Primary Care, Lee Kong Chian School of Medicine, Nanyang Technological University, 11 Mandalay Road, Singapore, 639798, Singapore, 65 63402480, lorainne.tudor.car@ntu.edu.sg %K digital health %K education %K eHealth %K medical students %K scoping review %K electronic health records %K computer literacy %D 2021 %7 21.7.2021 %9 Review %J JMIR Med Educ %G English %X Background: Medical schools worldwide are accelerating the introduction of digital health courses into their curricula. The COVID-19 pandemic has contributed to this swift and widespread transition to digital health and education. However, the need for digital health competencies goes beyond the COVID-19 pandemic because they are becoming essential for the delivery of effective, efficient, and safe care. Objective: This review aims to collate and analyze studies evaluating digital health education for medical students to inform the development of future courses and identify areas where curricula may need to be strengthened. Methods: We carried out a scoping review by following the guidance of the Joanna Briggs Institute, and the results were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. We searched 6 major bibliographic databases and gray literature sources for articles published between January 2000 and November 2019. Two authors independently screened the retrieved citations and extracted the data from the included studies. Discrepancies were resolved by consensus discussions between the authors. The findings were analyzed using thematic analysis and presented narratively. Results: A total of 34 studies focusing on different digital courses were included in this review. Most of the studies (22/34, 65%) were published between 2010 and 2019 and originated in the United States (20/34, 59%). The reported digital health courses were mostly elective (20/34, 59%), were integrated into the existing curriculum (24/34, 71%), and focused mainly on medical informatics (17/34, 50%). Most of the courses targeted medical students from the first to third year (17/34, 50%), and the duration of the courses ranged from 1 hour to 3 academic years. Most of the studies (22/34, 65%) reported the use of blended education. A few of the studies (6/34, 18%) delivered courses entirely digitally by using online modules, offline learning, massive open online courses, and virtual patient simulations. The reported courses used various assessment approaches such as paper-based assessments, in-person observations, and online assessments. Most of the studies (30/34, 88%) evaluated courses mostly by using an uncontrolled before-and-after design and generally reported improvements in students’ learning outcomes. Conclusions: Digital health courses reported in literature are mostly elective, focus on a single area of digital health, and lack robust evaluation. They have diverse delivery, development, and assessment approaches. There is an urgent need for high-quality studies that evaluate digital health education. %M 34287206 %R 10.2196/28275 %U https://mededu.jmir.org/2021/3/e28275 %U https://doi.org/10.2196/28275 %U http://www.ncbi.nlm.nih.gov/pubmed/34287206 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 5 %P e26034 %T Objective Outcomes Evaluation of Innovative Digital Health Curricula. Comment on “Undergraduate Medical Competencies in Digital Health and Curricular Module Development: Mixed Methods Study” %A Grzeska,Alexander %A Ali,Shan %A Szmuda,Tomasz %A Słoniewski,Paweł %+ Medical University of Gdansk, ul M Skłodowskiej-Curie 3a, Gdańsk, 80-210, Poland, 48 572642516, alex.grzeska@gumed.edu.pl %K digital health %K eHealth %K mHealth %K digital health education %K elective module %K eHealth education %K curriculum %K medical school %K digital health mindset %K qualitative research %K interview %K survey %D 2021 %7 28.5.2021 %9 Letter to the Editor %J J Med Internet Res %G English %X %M 34047706 %R 10.2196/26034 %U https://www.jmir.org/2021/5/e26034 %U https://doi.org/10.2196/26034 %U http://www.ncbi.nlm.nih.gov/pubmed/34047706 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 7 %N 1 %P e25828 %T The Impact of Electronic Health Record–Based Simulation During Intern Boot Camp: Interventional Study %A Miller,Matthew E %A Scholl,Gretchen %A Corby,Sky %A Mohan,Vishnu %A Gold,Jeffrey A %+ Division of Pulmonary and Critical Care Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Rd, Mail code UHN67, Portland, OR, 97239, United States, 1 5034181496, goldje@ohsu.edu %K electronic health records %K medical education %K simulation %K usability %K training %D 2021 %7 9.3.2021 %9 Original Paper %J JMIR Med Educ %G English %X Background: Accurate data retrieval is an essential part of patient care in the intensive care unit (ICU). The electronic health record (EHR) is the primary method for data storage and data review. We previously reported that residents participating in EHR-based simulations have varied and nonstandard approaches to finding data in the ICU, with subsequent errors in recognizing patient safety issues. We hypothesized that a novel EHR simulation-based training exercise would decrease EHR use variability among intervention interns, irrespective of prior EHR experience. Objective: This study aims to understand the impact of a novel, short, high-fidelity, simulation-based EHR learning activity on the intern data gathering workflow and satisfaction. Methods: A total of 72 internal medicine interns across the 2018 and 2019 academic years underwent a dedicated EHR training session as part of a week-long boot camp early in their training. We collected data on previous EHR and ICU experience for all subjects. Training consisted of 1 hour of guided review of a high-fidelity, simulated ICU patient chart focusing on best navigation practices for data retrieval. Specifically, the activity focused on using high- and low-yield data visualization screens determined by expert consensus. The intervention group interns then had 20 minutes to review a new simulated patient chart before the group review. EHR screen navigation was captured using screen recording software and compared with data from existing ICU residents performing the same task on the same medical charts (N=62). Learners were surveyed immediately and 6 months after the activity to assess satisfaction and preferred EHR screen use. Results: Participants found the activity useful and enjoyable immediately and after 6 months. Intervention interns used more individual screens than reference residents (18 vs 20; P=.008), but the total number of screens used was the same (35 vs 38; P=.30). Significantly more intervention interns used the 10 most common screens (73% vs 45%; P=.001). Intervention interns used high-yield screens more often and low-yield screens less often than the reference residents, which are persistent on self-report 6 months later. Conclusions: A short, high-fidelity, simulation-based learning activity focused on provider-specific data gathering was found to be enjoyable and to modify navigation patterns persistently. This suggests that workflow-specific simulation-based EHR training throughout training is of educational benefit to residents. %M 33687339 %R 10.2196/25828 %U https://mededu.jmir.org/2021/1/e25828 %U https://doi.org/10.2196/25828 %U http://www.ncbi.nlm.nih.gov/pubmed/33687339 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 2 %P e24691 %T An Education Framework for Effective Implementation of a Health Information System: Scoping Review %A Jeyakumar,Tharshini %A McClure,Sarah %A Lowe,Mandy %A Hodges,Brian %A Fur,Katharine %A Javier-Brozo,Mariquita %A Tassone,Maria %A Anderson,Melanie %A Tripp,Tim %A Wiljer,David %+ University Health Network, 190 Elizabeth Street, R. Fraser Elliot Building RFE 3S-441, Toronto, ON, M5G 2C4, Canada, 1 416 340 4800 ext 6322, David.wiljer@uhn.ca %K health information system %K health care providers %K education %K learning %K patient care %D 2021 %7 24.2.2021 %9 Review %J J Med Internet Res %G English %X Background: To optimize their use of a new Health Information System (HIS), supporting health care providers require effective HIS education. Failure to provide this education can significantly hinder an organization’s HIS implementation and sustainability efforts. Objective: The aim of this review is to understand the most effective educational strategies and approaches to enable health care providers to optimally use an HIS. Methods: Ovid MEDLINE, Ovid Embase, EBSCO Cumulative Index to Nursing and Allied Health Literature, and EBSCO Education Resources Information Center were searched to identify relevant papers. Relevant studies were systematically reviewed and analyzed using a qualitative thematic analysis approach. Results: Of the 3539 studies screened, 17 were included for data extraction. The literature on the most effective approaches to enable health care providers to optimally use an HIS emphasized the importance of investing in engaging and understanding learners in the clinical context, maximizing the transfer of learning to care, and designing continuous and agile evaluation to meet the emerging demands of the clinical environment. Conclusions: This review supports the advancement of a new HIS learning framework that organizational leaders and educators can use to guide HIS education design and development. Future research should examine how this framework can be translated into practice. %M 33625370 %R 10.2196/24691 %U https://www.jmir.org/2021/2/e24691 %U https://doi.org/10.2196/24691 %U http://www.ncbi.nlm.nih.gov/pubmed/33625370 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 11 %P e22706 %T A Digitally Competent Health Workforce: Scoping Review of Educational Frameworks %A Nazeha,Nuraini %A Pavagadhi,Deepali %A Kyaw,Bhone Myint %A Car,Josip %A Jimenez,Geronimo %A Tudor Car,Lorainne %+ Centre for Population Health Sciences, Lee Kong Chian School of Medicine, Nanyang Technological University, 11 Mandalay Road, Singapore, Singapore, 65 69047005, josip.car@ntu.edu.sg %K digital health %K eHealth %K health professions education %K digital competency %K competency %K framework %K review %K medical education %D 2020 %7 5.11.2020 %9 Review %J J Med Internet Res %G English %X Background: Digital health technologies can be key to improving health outcomes, provided health care workers are adequately trained to use these technologies. There have been efforts to identify digital competencies for different health care worker groups; however, an overview of these efforts has yet to be consolidated and analyzed. Objective: The review aims to identify and study existing digital health competency frameworks for health care workers and provide recommendations for future digital health training initiatives and framework development. Methods: A literature search was performed to collate digital health competency frameworks published from 2000. A total of 6 databases including gray literature sources such as OpenGrey, ResearchGate, Google Scholar, Google, and websites of relevant associations were searched in November 2019. Screening and data extraction were performed in parallel by the reviewers. The included evidence is narratively described in terms of characteristics, evolution, and structural composition of frameworks. A thematic analysis was also performed to identify common themes across the included frameworks. Results: In total, 30 frameworks were included in this review, a majority of which aimed at nurses, originated from high-income countries, were published since 2016, and were developed via literature reviews, followed by expert consultations. The thematic analysis uncovered 28 digital health competency domains across the included frameworks. The most prevalent domains pertained to basic information technology literacy, health information management, digital communication, ethical, legal, or regulatory requirements, and data privacy and security. The Health Information Technology Competencies framework was found to be the most comprehensive framework, as it presented 21 out of the 28 identified domains, had the highest number of competencies, and targeted a wide variety of health care workers. Conclusions: Digital health training initiatives should focus on competencies relevant to a particular health care worker group, role, level of seniority, and setting. The findings from this review can inform and guide digital health training initiatives. The most prevalent competency domains identified represent essential interprofessional competencies to be incorporated into health care workers’ training. Digital health frameworks should be regularly updated with novel digital health technologies, be applicable to low- and middle-income countries, and include overlooked health care worker groups such as allied health professionals. %M 33151152 %R 10.2196/22706 %U https://www.jmir.org/2020/11/e22706 %U https://doi.org/10.2196/22706 %U http://www.ncbi.nlm.nih.gov/pubmed/33151152 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 22 %N 10 %P e22161 %T Undergraduate Medical Competencies in Digital Health and Curricular Module Development: Mixed Methods Study %A Poncette,Akira-Sebastian %A Glauert,Daniel Leon %A Mosch,Lina %A Braune,Katarina %A Balzer,Felix %A Back,David Alexander %+ Dieter Scheffner Center for Medical Education and Educational Research, Charité – Universitätsmedizin Berlin, Corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, Berlin, 10117, Germany, 49 30 2841 1240, david.back@charite.de %K digital health %K eHealth %K mHealth %K digital health education %K elective module %K eHealth education %K curriculum %K medical school %K digital health mindset %K qualitative research %K interview %K survey %D 2020 %7 29.10.2020 %9 Original Paper %J J Med Internet Res %G English %X Background: Owing to an increase in digital technologies in health care, recently leveraged by the COVID-19 pandemic, physicians are required to use these technologies appropriately and to be familiar with their implications on patient care, the health system, and society. Therefore, medical students should be confronted with digital health during their medical education. However, corresponding teaching formats and concepts are still largely lacking in the medical curricula. Objective: This study aims to introduce digital health as a curricular module at a German medical school and to identify undergraduate medical competencies in digital health and their suitable teaching methods. Methods: We developed a 3-week curricular module on digital health for third-year medical students at a large German medical school, taking place for the first time in January 2020. Semistructured interviews with 5 digital health experts were recorded, transcribed, and analyzed using an abductive approach. We obtained feedback from the participating students and lecturers of the module through a 17-item survey questionnaire. Results: The module received overall positive feedback from both students and lecturers who expressed the need for further digital health education and stated that the field is very important for clinical care and is underrepresented in the current medical curriculum. We extracted a detailed overview of digital health competencies, skills, and knowledge to teach the students from the expert interviews. They also contained suggestions for teaching methods and statements supporting the urgency of the implementation of digital health education in the mandatory curriculum. Conclusions: An elective class seems to be a suitable format for the timely introduction of digital health education. However, a longitudinal implementation in the mandatory curriculum should be the goal. Beyond training future physicians in digital skills and teaching them digital health’s ethical, legal, and social implications, the experience-based development of a critical digital health mindset with openness to innovation and the ability to assess ever-changing health technologies through a broad transdisciplinary approach to translate research into clinical routine seem more important. Therefore, the teaching of digital health should be as practice-based as possible and involve the educational cooperation of different institutions and academic disciplines. %M 33118935 %R 10.2196/22161 %U http://www.jmir.org/2020/10/e22161/ %U https://doi.org/10.2196/22161 %U http://www.ncbi.nlm.nih.gov/pubmed/33118935 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 6 %N 2 %P e17030 %T Understanding Medical Students’ Attitudes Toward Learning eHealth: Questionnaire Study %A Vossen,Kjeld %A Rethans,Jan-Joost %A van Kuijk,Sander M J %A van der Vleuten,Cees P %A Kubben,Pieter L %+ Maastricht University Medical Center, P. Debyelaan 25, Maastricht, Netherlands, 31 628091727, kjeldvossen@hotmail.com %K eHealth %K student opinion %K mHealth %K medical education %K students %K medicine %K curriculum %K digital skills %D 2020 %7 1.10.2020 %9 Original Paper %J JMIR Med Educ %G English %X Background: Several publications on research into eHealth demonstrate promising results. Prior researchers indicated that the current generation of doctors is not trained to take advantage of eHealth in clinical practice. Therefore, training and education for everyone using eHealth are key factors to its successful implementation. We set out to review whether medical students feel prepared to take advantage of eHealth innovations in medicine. Objective: Our objective was to evaluate whether medical students desire a dedicated eHealth curriculum during their medical studies. Methods: A questionnaire assessing current education, the need for education about eHealth topics, and the didactical forms for teaching these topics was developed. Questionnaire items were scored on a scale from 1 (fully disagree with a topic) to 10 (fully agree with a topic). This questionnaire was distributed among 1468 medical students of Maastricht University in the Netherlands. R version 3.5.0 (The R Foundation) was used for all statistical procedures. Results: A total of 303 students out of 1468, representing a response rate of 20.64%, replied to our questionnaire. The aggregate statement “I feel prepared to take advantage of the technological developments within the medical field” was scored at a mean value of 4.8 out of 10. Mean scores regarding the need for education about eHealth topics ranged from 6.4 to 7.3. Medical students did not favor creating their own health apps or mobile apps; the mean score was 4.9 for this topic. The most popular didactical option, with a mean score 7.2, was to remotely follow a real-life patient under the supervision of a doctor. Conclusions: To the best of our knowledge, this is the largest evaluation of students’ opinions on eHealth training in a medical undergraduate curriculum. We found that medical students have positives attitudes toward incorporating eHealth into the medical curriculum. %M 33001034 %R 10.2196/17030 %U http://mededu.jmir.org/2020/2/e17030/ %U https://doi.org/10.2196/17030 %U http://www.ncbi.nlm.nih.gov/pubmed/33001034 %0 Journal Article %@ 2369-3762 %I JMIR Publications %V 6 %N 2 %P e20027 %T Medical Student Training in eHealth: Scoping Review %A Echelard,Jean-François %A Méthot,François %A Nguyen,Hue-Anh %A Pomey,Marie-Pascale %+ Faculty of Medicine, Université de Montréal, 2900 Edouard Montpetit Blvd, Montréal, QC, H3T 1J4, Canada, 1 514 343 6111, jfechelard@hotmail.com %K medical education %K eHealth %K digital health %K mHealth %K health apps %K telehealth %K artificial intelligence %K electronic health records %K programming %K internet of things %D 2020 %7 11.9.2020 %9 Original Paper %J JMIR Med Educ %G English %X Background: eHealth is the use of information and communication technologies to enable and improve health and health care services. It is crucial that medical students receive adequate training in eHealth as they will work in clinical environments that are increasingly being enabled by technology. This trend is especially accelerated by the COVID-19 pandemic as it complicates traditional face-to-face medical consultations and highlights the need for innovative approaches in health care. Objective: This review aims to evaluate the extent and nature of the existing literature on medical student training in eHealth. In detail, it aims to examine what this education consists of, the barriers, enhancing factors, and propositions for improving the medical curriculum. This review focuses primarily on some key technologies such as mobile health (mHealth), the internet of things (IoT), telehealth, and artificial intelligence (AI). Methods: Searches were performed on 4 databases, and articles were selected based on the eligibility criteria. Studies had to be related to the training of medical students in eHealth. The eligibility criteria were studies published since 2014, from a peer-reviewed journal, and written in either English or French. A grid was used to extract and chart data. Results: The search resulted in 25 articles. The most studied aspect was mHealth. eHealth as a broad concept, the IoT, AI, and programming were least covered. A total of 52% (13/25) of all studies contained an intervention, mostly regarding mHealth, electronic health records, web-based medical resources, and programming. The findings included various barriers, enhancing factors, and propositions for improving the medical curriculum. Conclusions: Trends have emerged regarding the suboptimal present state of eHealth training and barriers, enhancing factors, and propositions for optimal training. We recommend that additional studies be conducted on the following themes: barriers, enhancing factors, propositions for optimal training, competencies that medical students should acquire, learning outcomes from eHealth training, and patient care outcomes from this training. Additional studies should be conducted on eHealth and each of its aspects, especially on the IoT, AI, programming, and eHealth as a broad concept. Training in eHealth is critical to medical practice in clinical environments that are increasingly being enabled by technology. The need for innovative approaches in health care during the COVID-19 pandemic further highlights the relevance of this training. %M 32915154 %R 10.2196/20027 %U https://mededu.jmir.org/2020/2/e20027 %U https://doi.org/10.2196/20027 %U http://www.ncbi.nlm.nih.gov/pubmed/32915154 %0 Journal Article %@ 2291-9694 %I JMIR Publications %V 8 %N 1 %P e15748 %T Teaching Hands-On Informatics Skills to Future Health Informaticians: A Competency Framework Proposal and Analysis of Health Care Informatics Curricula %A Sapci,A Hasan %A Sapci,H Aylin %+ Adelphi University, 1 South Avenue, Garden City, NY, 11530, United States, 1 5168338156, sapci@adelphi.edu %K health informatics curriculum %K skill-based training %K hands-on health informatics training %D 2020 %7 21.1.2020 %9 Original Paper %J JMIR Med Inform %G English %X Background: Existing health informatics curriculum requirements mostly use a competency-based approach rather than a skill-based one. Objective: The main objective of this study was to assess the current skills training requirements in graduate health informatics curricula to evaluate graduate students’ confidence in specific health informatics skills. Methods: A quantitative cross-sectional observational study was developed to evaluate published health informatics curriculum requirements and to determine the comprehensive health informatics skill sets required in a research university in New York, United States. In addition, a questionnaire to assess students’ confidence about specific health informatics skills was developed and sent to all enrolled and graduated Master of Science students in a health informatics program. Results: The evaluation was performed in a graduate health informatics program, and analysis of the students’ self-assessments questionnaire showed that 79.4% (81/102) of participants were not confident (not at all confident or slightly confident) about developing an artificial intelligence app, 58.8% (60/102) were not confident about designing and developing databases, and 54.9% (56/102) were not confident about evaluating privacy and security infrastructure. Less than one-third of students (24/105, 23.5%) were confident (extremely confident and very confident) that they could evaluate the use of data capture technologies and develop mobile health informatics apps (10/102, 9.8%). Conclusions: Health informatics programs should consider specialized tracks that include specific skills to meet the complex health care delivery and market demand, and specific training components should be defined for different specialties. There is a need to determine new competencies and skill sets that promote inductive and deductive reasoning from diverse and various data platforms and to develop a comprehensive curriculum framework for health informatics skills training. %M 31961328 %R 10.2196/15748 %U http://medinform.jmir.org/2020/1/e15748/ %U https://doi.org/10.2196/15748 %U http://www.ncbi.nlm.nih.gov/pubmed/31961328