@Article{info:doi/10.2196/52711, author="Hsieh, Hsing-yen and Lin, Chyi-her and Huang, Ruyi and Lin, Guan-chun and Lin, Jhen-Yu and Aldana, Clydie", title="Challenges for Medical Students in Applying Ethical Principles to Allocate Life-Saving Medical Devices During the COVID-19 Pandemic: Content Analysis", journal="JMIR Med Educ", year="2024", month="Jan", day="5", volume="10", pages="e52711", keywords="virtual patient", keywords="virtual patients", keywords="medical resources distribution", keywords="medical ethical education", keywords="COVID-19 pandemic", keywords="ethics", keywords="medical student", keywords="medical students", keywords="medical ethics", keywords="decision-making", keywords="ethical dilemna", keywords="simulation", keywords="reasoning", keywords="decision support", keywords="medical guideline", keywords="medical guidelines", keywords="medical devices", keywords="medical device", keywords="life-saving", keywords="thematic analysis", keywords="virtual platform", abstract="Background: The emergence of the COVID-19 pandemic has posed a significant ethical dilemma in the allocation of scarce, life-saving medical equipment to critically ill patients. It remains uncertain whether medical students are equipped to navigate this complex ethical process. Objective: This study aimed to assess the ability and confidence of medical students to apply principles of medical ethics in allocating critical medical devices through the scenario of virtual patients. Methods: The study recruited third- and fourth-year medical students during clinical rotation. We facilitated interactions between medical students and virtual patients experiencing respiratory failure due to COVID-19 infection. We assessed the students' ability to ethically allocate life-saving resources. Subsequently, we analyzed their written reports using thematic analysis to identify the ethical principles guiding their decision-making. Results: We enrolled a cohort of 67 out of 71 medical students with a mean age of 34 (SD 4.7) years, 60\% (n=40) of whom were female students. The principle of justice was cited by 73\% (n=49) of students while analyzing this scenario. A majority of them expressed hesitancy in determining which patient should receive life-saving resources, with 46\% (n=31) citing the principle of nonmaleficence, 31\% (n=21) advocating for a first-come-first-served approach, and 25\% (n=17) emphasizing respect for patient autonomy as key influencers in their decisions. Notably, medical students exhibited a lack of confidence in making ethical decisions concerning the distribution of medical resources. A minority, comprising 12\% (n=8), proposed the exploration of legal alternatives, while 4\% (n=3) suggested medical guidelines and collective decision-making as potential substitutes for individual ethical choices to alleviate the stress associated with personal decision-making. Conclusions: This study highlights the importance of improving ethical reasoning under time constraints using virtual platforms. More than 70\% of medical students identified justice as the predominant principle in allocating limited medical resources to critically ill patients. However, they exhibited a lack of confidence in making ethical determinations and leaned toward principles such as nonmaleficence, patient autonomy, adherence to legal and medical standards, and collective decision-making to mitigate the pressure associated with such decisions. ", doi="10.2196/52711", url="https://mededu.jmir.org/2024/1/e52711", url="http://www.ncbi.nlm.nih.gov/pubmed/38050366" } @Article{info:doi/10.2196/53961, author="Holderried, Friederike and Stegemann--Philipps, Christian and Herschbach, Lea and Moldt, Julia-Astrid and Nevins, Andrew and Griewatz, Jan and Holderried, Martin and Herrmann-Werner, Anne and Festl-Wietek, Teresa and Mahling, Moritz", title="A Generative Pretrained Transformer (GPT)--Powered Chatbot as a Simulated Patient to Practice History Taking: Prospective, Mixed Methods Study", journal="JMIR Med Educ", year="2024", month="Jan", day="16", volume="10", pages="e53961", keywords="simulated patient", keywords="GPT", keywords="generative pretrained transformer", keywords="ChatGPT", keywords="history taking", keywords="medical education", keywords="documentation", keywords="history", keywords="simulated", keywords="simulation", keywords="simulations", keywords="NLP", keywords="natural language processing", keywords="artificial intelligence", keywords="interactive", keywords="chatbot", keywords="chatbots", keywords="conversational agent", keywords="conversational agents", keywords="answer", keywords="answers", keywords="response", keywords="responses", keywords="human computer", keywords="human machine", keywords="usability", keywords="satisfaction", abstract="Background: Communication is a core competency of medical professionals and of utmost importance for patient safety. Although medical curricula emphasize communication training, traditional formats, such as real or simulated patient interactions, can present psychological stress and are limited in repetition. The recent emergence of large language models (LLMs), such as generative pretrained transformer (GPT), offers an opportunity to overcome these restrictions Objective: The aim of this study was to explore the feasibility of a GPT-driven chatbot to practice history taking, one of the core competencies of communication. Methods: We developed an interactive chatbot interface using GPT-3.5 and a specific prompt including a chatbot-optimized illness script and a behavioral component. Following a mixed methods approach, we invited medical students to voluntarily practice history taking. To determine whether GPT provides suitable answers as a simulated patient, the conversations were recorded and analyzed using quantitative and qualitative approaches. We analyzed the extent to which the questions and answers aligned with the provided script, as well as the medical plausibility of the answers. Finally, the students filled out the Chatbot Usability Questionnaire (CUQ). Results: A total of 28 students practiced with our chatbot (mean age 23.4, SD 2.9 years). We recorded a total of 826 question-answer pairs (QAPs), with a median of 27.5 QAPs per conversation and 94.7\% (n=782) pertaining to history taking. When questions were explicitly covered by the script (n=502, 60.3\%), the GPT-provided answers were mostly based on explicit script information (n=471, 94.4\%). For questions not covered by the script (n=195, 23.4\%), the GPT answers used 56.4\% (n=110) fictitious information. Regarding plausibility, 842 (97.9\%) of 860 QAPs were rated as plausible. Of the 14 (2.1\%) implausible answers, GPT provided answers rated as socially desirable, leaving role identity, ignoring script information, illogical reasoning, and calculation error. Despite these results, the CUQ revealed an overall positive user experience (77/100 points). Conclusions: Our data showed that LLMs, such as GPT, can provide a simulated patient experience and yield a good user experience and a majority of plausible answers. Our analysis revealed that GPT-provided answers use either explicit script information or are based on available information, which can be understood as abductive reasoning. Although rare, the GPT-based chatbot provides implausible information in some instances, with the major tendency being socially desirable instead of medically plausible information. ", doi="10.2196/53961", url="https://mededu.jmir.org/2024/1/e53961", url="http://www.ncbi.nlm.nih.gov/pubmed/38227363" } @Article{info:doi/10.2196/58753, author="Yamamoto, Akira and Koda, Masahide and Ogawa, Hiroko and Miyoshi, Tomoko and Maeda, Yoshinobu and Otsuka, Fumio and Ino, Hideo", title="Enhancing Medical Interview Skills Through AI-Simulated Patient Interactions: Nonrandomized Controlled Trial", journal="JMIR Med Educ", year="2024", month="Sep", day="23", volume="10", pages="e58753", keywords="medical interview", keywords="generative pretrained transformer", keywords="large language model", keywords="simulation-based learning", keywords="OSCE", keywords="artificial intelligence", keywords="medical education", keywords="simulated patients", keywords="nonrandomized controlled trial", abstract="Background: Medical interviewing is a critical skill in clinical practice, yet opportunities for practical training are limited in Japanese medical schools, necessitating urgent measures. Given advancements in artificial intelligence (AI) technology, its application in the medical field is expanding. However, reports on its application in medical interviews in medical education are scarce. Objective: This study aimed to investigate whether medical students' interview skills could be improved by engaging with AI-simulated patients using large language models, including the provision of feedback. Methods: This nonrandomized controlled trial was conducted with fourth-year medical students in Japan. A simulation program using large language models was provided to 35 students in the intervention group in 2023, while 110 students from 2022 who did not participate in the intervention were selected as the control group. The primary outcome was the score on the Pre-Clinical Clerkship Objective Structured Clinical Examination (pre-CC OSCE), a national standardized clinical skills examination, in medical interviewing. Secondary outcomes included surveys such as the Simulation-Based Training Quality Assurance Tool (SBT-QA10), administered at the start and end of the study. Results: The AI intervention group showed significantly higher scores on medical interviews than the control group (AI group vs control group: mean 28.1, SD 1.6 vs 27.1, SD 2.2; P=.01). There was a trend of inverse correlation between the SBT-QA10 and pre-CC OSCE scores (regression coefficient --2.0 to --2.1). No significant safety concerns were observed. Conclusions: Education through medical interviews using AI-simulated patients has demonstrated safety and a certain level of educational effectiveness. However, at present, the educational effects of this platform on nonverbal communication skills are limited, suggesting that it should be used as a supplementary tool to traditional simulation education. ", doi="10.2196/58753", url="https://mededu.jmir.org/2024/1/e58753", url="http://www.ncbi.nlm.nih.gov/pubmed/39312284" } @Article{info:doi/10.2196/54297, author="Zhou, You and Li, Si-Jia and Tang, Xing-Yi and He, Yi-Chen and Ma, Hao-Ming and Wang, Ao-Qi and Pei, Run-Yuan and Piao, Mei-Hua", title="Using ChatGPT in Nursing: Scoping Review of Current Opinions", journal="JMIR Med Educ", year="2024", month="Nov", day="19", volume="10", pages="e54297", keywords="ChatGPT", keywords="large language model", keywords="nursing", keywords="artificial intelligence", keywords="scoping review", keywords="generative AI", keywords="nursing education", abstract="Background: Since the release of ChatGPT in November 2022, this emerging technology has garnered a lot of attention in various fields, and nursing is no exception. However, to date, no study has comprehensively summarized the status and opinions of using ChatGPT across different nursing fields. Objective: We aim to synthesize the status and opinions of using ChatGPT according to different nursing fields, as well as assess ChatGPT's strengths, weaknesses, and the potential impacts it may cause. Methods: This scoping review was conducted following the framework of Arksey and O'Malley and guided by the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). A comprehensive literature research was conducted in 4 web-based databases (PubMed, Embase, Web of Science, and CINHAL) to identify studies reporting the opinions of using ChatGPT in nursing fields from 2022 to September 3, 2023. The references of the included studies were screened manually to further identify relevant studies. Two authors conducted studies screening, eligibility assessments, and data extraction independently. Results: A total of 30 studies were included. The United States (7 studies), Canada (5 studies), and China (4 studies) were countries with the most publications. In terms of fields of concern, studies mainly focused on ``ChatGPT and nursing education'' (20 studies), ``ChatGPT and nursing practice'' (10 studies), and ``ChatGPT and nursing research, writing, and examination'' (6 studies). Six studies addressed the use of ChatGPT in multiple nursing fields. Conclusions: As an emerging artificial intelligence technology, ChatGPT has great potential to revolutionize nursing education, nursing practice, and nursing research. However, researchers, institutions, and administrations still need to critically examine its accuracy, safety, and privacy, as well as academic misconduct and potential ethical issues that it may lead to before applying ChatGPT to practice. ", doi="10.2196/54297", url="https://mededu.jmir.org/2024/1/e54297" } @Article{info:doi/10.2196/52953, author="Shetty, Shishir and Bhat, Supriya and Al Bayatti, Saad and Al Kawas, Sausan and Talaat, Wael and El-Kishawi, Mohamed and Al Rawi, Natheer and Narasimhan, Sangeetha and Al-Daghestani, Hiba and Madi, Medhini and Shetty, Raghavendra", title="The Scope of Virtual Reality Simulators in Radiology Education: Systematic Literature Review", journal="JMIR Med Educ", year="2024", month="May", day="8", volume="10", pages="e52953", keywords="virtual reality", keywords="simulators", keywords="radiology education", keywords="medical imaging", keywords="radiology", keywords="education", keywords="systematic review", keywords="literature review", keywords="imaging", keywords="meta analysis", keywords="student", keywords="students", keywords="VR", keywords="PRISMA", keywords="Preferred Reporting Items for Systematic Reviews and Meta-Analyses", abstract="Background: In recent years, virtual reality (VR) has gained significant importance in medical education. Radiology education also has seen the induction of VR technology. However, there is no comprehensive review in this specific area. This review aims to fill this knowledge gap. Objective: This systematic literature review aims to explore the scope of VR use in radiology education. Methods: A literature search was carried out using PubMed, Scopus, ScienceDirect, and Google Scholar for articles relating to the use of VR in radiology education, published from database inception to September 1, 2023. The identified articles were then subjected to a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)--defined study selection process. Results: The database search identified 2503 nonduplicate articles. After PRISMA screening, 17 were included in the review for analysis, of which 3 (18\%) were randomized controlled trials, 7 (41\%) were randomized experimental trials, and 7 (41\%) were cross-sectional studies. Of the 10 randomized trials, 3 (30\%) had a low risk of bias, 5 (50\%) showed some concerns, and 2 (20\%) had a high risk of bias. Among the 7 cross-sectional studies, 2 (29\%) scored ``good'' in the overall quality and the remaining 5 (71\%) scored ``fair.'' VR was found to be significantly more effective than traditional methods of teaching in improving the radiographic and radiologic skills of students. The use of VR systems was found to improve the students' skills in overall proficiency, patient positioning, equipment knowledge, equipment handling, and radiographic techniques. Student feedback was also reported in the included studies. The students generally provided positive feedback about the utility, ease of use, and satisfaction of VR systems, as well as their perceived positive impact on skill and knowledge acquisition. Conclusions: The evidence from this review shows that the use of VR had significant benefit for students in various aspects of radiology education. However, the variable nature of the studies included in the review reduces the scope for a comprehensive recommendation of VR use in radiology education. ", doi="10.2196/52953", url="https://mededu.jmir.org/2024/1/e52953" } @Article{info:doi/10.2196/52224, author="He, Zonglin and Zhou, Botao and Feng, Haixiao and Bai, Jian and Wang, Yuechun", title="Inverted Classroom Teaching of Physiology in Basic Medical Education: Bibliometric Visual Analysis", journal="JMIR Med Educ", year="2024", month="Jun", day="25", volume="10", pages="e52224", keywords="flipped classroom", keywords="flipped classroom teaching", keywords="physiology", keywords="scientific knowledge map", keywords="hot topics", keywords="frontier progress", keywords="evolution trend", keywords="classroom-based", keywords="bibliometric visual analysis", keywords="bibliometric", keywords="visual analysis", keywords="medical education", keywords="teaching method", keywords="bibliometric analysis", keywords="visualization tool", keywords="academic", keywords="academic community", keywords="inverted classroom", abstract="Background: Over the last decade, there has been growing interest in inverted classroom teaching (ICT) and its various forms within the education sector. Physiology is a core course that bridges basic and clinical medicine, and ICT in physiology has been sporadically practiced to different extents globally. However, students' and teachers' responses and feedback to ICT in physiology are diverse, and the effectiveness of a modified ICT model integrated into regular teaching practice in physiology courses is difficult to assess objectively and quantitatively. Objective: This study aimed to explore the current status and development direction of ICT in physiology in basic medical education using bibliometric visual analysis of the related literature. Methods: A bibliometric analysis of the ICT-related literature in physiology published between 2000 and 2023 was performed using CiteSpace, a bibliometric visualization tool, based on the Web of Science database. Moreover, an in-depth review was performed to summarize the application of ICT in physiology courses worldwide, along with identification of research hot spots and development trends. Results: A total of 42 studies were included for this bibliometric analysis, with the year 2013 marking the commencement of the field. University staff and doctors working at affiliated hospitals represent the core authors of this field, with several research teams forming cooperative relationships and developing research networks. The development of ICT in physiology could be divided into several stages: the introduction stage (2013?2014), extensive practice stage (2015?2019), and modification and growth stage (2020?2023). Gopalan C is the author with the highest citation count of 5 cited publications and has published 14 relevant papers since 2016, with a significant surge from 2019 to 2022. Author collaboration is generally limited in this field, and most academic work has been conducted in independent teams, with minimal cross-team communication. Authors from the United States published the highest number of papers related to ICT in physiology (18 in total, accounting for over 43\% of the total papers), and their intermediary centrality was 0.24, indicating strong connections both within the country and internationally. Chinese authors ranked second, publishing 8 papers in the field, although their intermediary centrality was only 0.02, suggesting limited international influence and lower overall research quality. The topics of ICT in physiology research have been multifaceted, covering active learning, autonomous learning, student performance, teaching effect, blended teaching, and others. Conclusions: This bibliometric analysis and literature review provides a comprehensive overview of the history, development process, and future direction of the field of ICT in physiology. These findings can help to strengthen academic exchange and cooperation internationally, while promoting the diversification and effectiveness of ICT in physiology through building academic communities to jointly train emerging medical talents. ", doi="10.2196/52224", url="https://mededu.jmir.org/2024/1/e52224" } @Article{info:doi/10.2196/54987, author="Zhang, Fang and Liu, Xiaoliu and Wu, Wenyan and Zhu, Shiben", title="Evolution of Chatbots in Nursing Education: Narrative Review", journal="JMIR Med Educ", year="2024", month="Jun", day="13", volume="10", pages="e54987", keywords="nursing education", keywords="chatbots", keywords="artificial intelligence", keywords="narrative review", keywords="ChatGPT", abstract="Background: The integration of chatbots in nursing education is a rapidly evolving area with potential transformative impacts. This narrative review aims to synthesize and analyze the existing literature on chatbots in nursing education. Objective: This study aims to comprehensively examine the temporal trends, international distribution, study designs, and implications of chatbots in nursing education. Methods: A comprehensive search was conducted across 3 databases (PubMed, Web of Science, and Embase) following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram. Results: A total of 40 articles met the eligibility criteria, with a notable increase of publications in 2023 (n=28, 70\%). Temporal analysis revealed a notable surge in publications from 2021 to 2023, emphasizing the growing scholarly interest. Geographically, Taiwan province made substantial contributions (n=8, 20\%), followed by the United States (n=6, 15\%) and South Korea (n=4, 10\%). Study designs varied, with reviews (n=8, 20\%) and editorials (n=7, 18\%) being predominant, showcasing the richness of research in this domain. Conclusions: Integrating chatbots into nursing education presents a promising yet relatively unexplored avenue. This review highlights the urgent need for original research, emphasizing the importance of ethical considerations. ", doi="10.2196/54987", url="https://mededu.jmir.org/2024/1/e54987" } @Article{info:doi/10.2196/55737, author="Mainz, Anne and Nitsche, Julia and Weirauch, Vera and Meister, Sven", title="Measuring the Digital Competence of Health Professionals: Scoping Review", journal="JMIR Med Educ", year="2024", month="Mar", day="29", volume="10", pages="e55737", keywords="digital competence", keywords="digital literacy", keywords="digital health", keywords="health care", keywords="health care professional", keywords="health care professionals", keywords="scoping review", abstract="Background: Digital competence is listed as one of the key competences for lifelong learning and is increasing in importance not only in private life but also in professional life. There is consensus within the health care sector that digital competence (or digital literacy) is needed in various professional fields. However, it is still unclear what exactly the digital competence of health professionals should include and how it can be measured. Objective: This scoping review aims to provide an overview of the common definitions of digital literacy in scientific literature in the field of health care and the existing measurement instruments. Methods: Peer-reviewed scientific papers from the last 10 years (2013-2023) in English or German that deal with the digital competence of health care workers in both outpatient and inpatient care were included. The databases ScienceDirect, Scopus, PubMed, EBSCOhost, MEDLINE, OpenAIRE, ERIC, OAIster, Cochrane Library, CAMbase, APA PsycNet, and Psyndex were searched for literature. The review follows the JBI methodology for scoping reviews, and the description of the results is based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. Results: The initial search identified 1682 papers, of which 46 (2.73\%) were included in the synthesis. The review results show that there is a strong focus on technical skills and knowledge with regard to both the definitions of digital competence and the measurement tools. A wide range of competences were identified within the analyzed works and integrated into a validated competence model in the areas of technical, methodological, social, and personal competences. The measurement instruments mainly used self-assessment of skills and knowledge as an indicator of competence and differed greatly in their statistical quality. Conclusions: The identified multitude of subcompetences illustrates the complexity of digital competence in health care, and existing measuring instruments are not yet able to reflect this complexity. ", doi="10.2196/55737", url="https://mededu.jmir.org/2024/1/e55737", url="http://www.ncbi.nlm.nih.gov/pubmed/38551628" } @Article{info:doi/10.2196/56415, author="Mahsusi, Mahsusi and Hudaa, Syihaabul and Nuryani, Nuryani and Fahmi, Mustofa and Tsurayya, Ghina and Iqhrammullah, Muhammad", title="Global Rate of Willingness to Volunteer Among Medical and Health Students During Pandemic: Systemic Review and Meta-Analysis", journal="JMIR Med Educ", year="2024", month="Apr", day="15", volume="10", pages="e56415", keywords="COVID-19", keywords="education", keywords="health crisis", keywords="human resource management", keywords="volunteer", abstract="Background: During health crises such as the COVID-19 pandemic, shortages of health care workers often occur. Recruiting students as volunteers could be an option, but it is uncertain whether the idea is well-accepted. Objective: This study aims to estimate the global rate of willingness to volunteer among medical and health students in response to the COVID-19 pandemic. Methods: A systematic search was conducted on PubMed, Embase, Scopus, and Google Scholar for studies reporting the number of health students willing to volunteer during COVID-19 from 2019 to November 17, 2023. The meta-analysis was performed using a restricted maximum-likelihood model with logit transformation. Results: A total of 21 studies involving 26,056 health students were included in the meta-analysis. The pooled estimate of the willingness-to-volunteer rate among health students across multiple countries was 66.13\%, with an I2 of 98.99\% and P value of heterogeneity (P-Het)<.001. Removing a study with the highest influence led to the rate being 64.34\%. Our stratified analyses indicated that those with older age, being first-year students, and being female were more willing to volunteer (P<.001). From highest to lowest, the rates were 77.38\%, 77.03\%, 65.48\%, 64.11\%, 62.71\%, and 55.23\% in Africa, Western Europe, East and Southeast Asia, Middle East, and Eastern Europe, respectively. Because of the high heterogeneity, the evidence from this study has moderate strength. Conclusions: The majority of students are willing to volunteer during COVID-19, suggesting that volunteer recruitment is well-accepted. ", doi="10.2196/56415", url="https://mededu.jmir.org/2024/1/e56415", url="http://www.ncbi.nlm.nih.gov/pubmed/38621233" } @Article{info:doi/10.2196/54793, author="Tolentino, Raymond and Baradaran, Ashkan and Gore, Genevieve and Pluye, Pierre and Abbasgholizadeh-Rahimi, Samira", title="Curriculum Frameworks and Educational Programs in AI for Medical Students, Residents, and Practicing Physicians: Scoping Review", journal="JMIR Med Educ", year="2024", month="Jul", day="18", volume="10", pages="e54793", keywords="artificial intelligence", keywords="machine learning", keywords="curriculum", keywords="framework", keywords="medical education", keywords="review", abstract="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 ", doi="10.2196/54793", url="https://mededu.jmir.org/2024/1/e54793", url="http://www.ncbi.nlm.nih.gov/pubmed/39023999" } @Article{info:doi/10.2196/50667, author="Rohani, Narjes and Sowa, Stephen and Manataki, Areti", title="Identifying Learning Preferences and Strategies in Health Data Science Courses: Systematic Review", journal="JMIR Med Educ", year="2024", month="Aug", day="12", volume="10", pages="e50667", keywords="health data science", keywords="bioinformatics", keywords="learning approach", keywords="learning preference", keywords="learning tactic", keywords="learning strategy", keywords="interdisciplinary", keywords="systematic review", keywords="medical education", abstract="Background: Learning and teaching interdisciplinary health data science (HDS) is highly challenging, and despite the growing interest in HDS education, little is known about the learning experiences and preferences of HDS students. Objective: We conducted a systematic review to identify learning preferences and strategies in the HDS discipline. Methods: We searched 10 bibliographic databases (PubMed, ACM Digital Library, Web of Science, Cochrane Library, Wiley Online Library, ScienceDirect, SpringerLink, EBSCOhost, ERIC, and IEEE Xplore) from the date of inception until June 2023. We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and included primary studies written in English that investigated the learning preferences or strategies of students in HDS-related disciplines, such as bioinformatics, at any academic level. Risk of bias was independently assessed by 2 screeners using the Mixed Methods Appraisal Tool, and we used narrative data synthesis to present the study results. Results: After abstract screening and full-text reviewing of the 849 papers retrieved from the databases, 8 (0.9\%) studies, published between 2009 and 2021, were selected for narrative synthesis. The majority of these papers (7/8, 88\%) investigated learning preferences, while only 1 (12\%) paper studied learning strategies in HDS courses. The systematic review revealed that most HDS learners prefer visual presentations as their primary learning input. In terms of learning process and organization, they mostly tend to follow logical, linear, and sequential steps. Moreover, they focus more on abstract information, rather than detailed and concrete information. Regarding collaboration, HDS students sometimes prefer teamwork, and sometimes they prefer to work alone. Conclusions: The studies' quality, assessed using the Mixed Methods Appraisal Tool, ranged between 73\% and 100\%, indicating excellent quality overall. However, the number of studies in this area is small, and the results of all studies are based on self-reported data. Therefore, more research needs to be conducted to provide insight into HDS education. We provide some suggestions, such as using learning analytics and educational data mining methods, for conducting future research to address gaps in the literature. We also discuss implications for HDS educators, and we make recommendations for HDS course design; for example, we recommend including visual materials, such as diagrams and videos, and offering step-by-step instructions for students. ", doi="10.2196/50667", url="https://mededu.jmir.org/2024/1/e50667" } @Article{info:doi/10.2196/58165, author="Han, Qing", title="Topics and Trends of Health Informatics Education Research: Scientometric Analysis", journal="JMIR Med Educ", year="2024", month="Dec", day="11", volume="10", pages="e58165", keywords="health informatics education", keywords="scientometric analysis", keywords="structural topic model", keywords="health informatics", keywords="medical informatics", keywords="medical education", abstract="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. ", doi="10.2196/58165", url="https://mededu.jmir.org/2024/1/e58165" } @Article{info:doi/10.2196/53810, author="Timpka, Toomas", title="Time for Medicine and Public Health to Leave Platform X", journal="JMIR Med Educ", year="2024", month="May", day="24", volume="10", pages="e53810", keywords="internet", keywords="social media", keywords="medical informatics", keywords="knowledge translation", keywords="digital technology", keywords="clinical decision support", keywords="health services research", keywords="public health", keywords="digital health", keywords="perspective", keywords="medicine", doi="10.2196/53810", url="https://mededu.jmir.org/2024/1/e53810" } @Article{info:doi/10.2196/54507, author="Arango-Ibanez, Pablo Juan and Posso-Nu{\~n}ez, Alejandro Jose and D{\'i}az-Sol{\'o}rzano, Pablo Juan and Cruz-Su{\'a}rez, Gustavo", title="Evidence-Based Learning Strategies in Medicine Using AI", journal="JMIR Med Educ", year="2024", month="May", day="24", volume="10", pages="e54507", keywords="artificial intelligence", keywords="large language models", keywords="ChatGPT", keywords="active recall", keywords="memory cues", keywords="LLMs", keywords="evidence-based", keywords="learning strategy", keywords="medicine", keywords="AI", keywords="medical education", keywords="knowledge", keywords="relevance", doi="10.2196/54507", url="https://mededu.jmir.org/2024/1/e54507" } @Article{info:doi/10.2196/47438, author="Aqib, Ayma and Fareez, Faiha and Assadpour, Elnaz and Babar, Tubba and Kokavec, Andrew and Wang, Edward and Lo, Thomas and Lam, Jean-Paul and Smith, Christopher", title="Development of a Novel Web-Based Tool to Enhance Clinical Skills in Medical Education", journal="JMIR Med Educ", year="2024", month="Jun", day="20", volume="10", pages="e47438", keywords="medical education", keywords="objective structured clinical examination", keywords="OSCE", keywords="e-OSCE", keywords="Medical Council of Canada", keywords="MCC", keywords="virtual health", keywords="exam", keywords="examination", keywords="utility", keywords="usability", keywords="online learning", keywords="e-learning", keywords="medical student", keywords="medical students", keywords="clinical practice", keywords="clinical skills", keywords="clinical skill", keywords="OSCE tool", doi="10.2196/47438", url="https://mededu.jmir.org/2024/1/e47438" } @Article{info:doi/10.2196/53624, author="Jalali, Alireza and Nyman, Jacline and Loeffelholz, Ouida and Courtney, Chantelle", title="Data-Driven Fundraising: Strategic Plan for Medical Education", journal="JMIR Med Educ", year="2024", month="Jul", day="22", volume="10", pages="e53624", keywords="fundraising", keywords="philanthropy", keywords="crowdfunding", keywords="funding", keywords="charity", keywords="higher education", keywords="university", keywords="medical education", keywords="educators", keywords="advancement", keywords="data analytics", keywords="ethics", keywords="ethical", keywords="education", keywords="medical school", keywords="school", keywords="support", keywords="financial", keywords="community", doi="10.2196/53624", url="https://mededu.jmir.org/2024/1/e53624" } @Article{info:doi/10.2196/50111, author="Jafari, Mahtab", title="Can an Online Course, Life101: Mental and Physical Self-Care, Improve the Well-Being of College Students?", journal="JMIR Med Educ", year="2024", month="Jul", day="22", volume="10", pages="e50111", keywords="self-care course", keywords="stress management", keywords="student mental health", keywords="multimodal online course", keywords="mental health interventions", doi="10.2196/50111", url="https://mededu.jmir.org/2024/1/e50111" } @Article{info:doi/10.2196/48594, author="Tong, Wenting and Zhang, Xiaowen and Zeng, Haiping and Pan, Jianping and Gong, Chao and Zhang, Hui", title="Reforming China's Secondary Vocational Medical Education: Adapting to the Challenges and Opportunities of the AI Era", journal="JMIR Med Educ", year="2024", month="Aug", day="15", volume="10", pages="e48594", keywords="secondary vocational medical education", keywords="artificial intelligence", keywords="practical skills", keywords="critical thinking", keywords="AI", doi="10.2196/48594", url="https://mededu.jmir.org/2024/1/e48594" } @Article{info:doi/10.2196/54173, author="Lawrence, Katharine and Levine, L. Defne", title="The Digital Determinants of Health: A Guide for Competency Development in Digital Care Delivery for Health Professions Trainees", journal="JMIR Med Educ", year="2024", month="Aug", day="29", volume="10", pages="e54173", keywords="digital health", keywords="digital determinants of health", keywords="digital health competencies", keywords="medical education curriculum", keywords="competency development", keywords="digital health education", keywords="training competencies", keywords="digital health skills", keywords="digital care delivery", keywords="health professions training", doi="10.2196/54173", url="https://mededu.jmir.org/2024/1/e54173" } @Article{info:doi/10.2196/52346, author="Claman, Daniel and Sezgin, Emre", title="Artificial Intelligence in Dental Education: Opportunities and Challenges of Large Language Models and Multimodal Foundation Models", journal="JMIR Med Educ", year="2024", month="Sep", day="27", volume="10", pages="e52346", keywords="artificial intelligence", keywords="large language models", keywords="dental education", keywords="GPT", keywords="ChatGPT", keywords="periodontal health", keywords="AI", keywords="LLM", keywords="LLMs", keywords="chatbot", keywords="natural language", keywords="generative pretrained transformer", keywords="innovation", keywords="technology", keywords="large language model", doi="10.2196/52346", url="https://mededu.jmir.org/2024/1/e52346" } @Article{info:doi/10.2196/54112, author="Mun, Michelle and Chanchlani, Sonia and Lyons, Kayley and Gray, Kathleen", title="Transforming the Future of Digital Health Education: Redesign of a Graduate Program Using Competency Mapping", journal="JMIR Med Educ", year="2024", month="Oct", day="31", volume="10", pages="e54112", keywords="digital health", keywords="digital transformation", keywords="health care", keywords="clinical informatics", keywords="competencies", keywords="graduate education", doi="10.2196/54112", url="https://mededu.jmir.org/2024/1/e54112" } @Article{info:doi/10.2196/51446, author="Alli, Rabia Sauliha and Hossain, Qahh?r Soaad and Das, Sunit and Upshur, Ross", title="The Potential of Artificial Intelligence Tools for Reducing Uncertainty in Medicine and Directions for Medical Education", journal="JMIR Med Educ", year="2024", month="Nov", day="4", volume="10", pages="e51446", keywords="artificial intelligence", keywords="machine learning", keywords="uncertainty", keywords="clinical decision-making", keywords="medical education", keywords="generative AI", keywords="generative artificial intelligence", doi="10.2196/51446", url="https://mededu.jmir.org/2024/1/e51446" } @Article{info:doi/10.2196/55368, author="Weidener, Lukas and Fischer, Michael", title="Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education", journal="JMIR Med Educ", year="2024", month="Feb", day="9", volume="10", pages="e55368", keywords="artificial intelligence", keywords="AI", keywords="ethics", keywords="artificial intelligence ethics", keywords="AI ethics", keywords="medical education", keywords="medicine", keywords="medical artificial intelligence ethics", keywords="medical AI ethics", keywords="medical ethics", keywords="public health ethics", doi="10.2196/55368", url="https://mededu.jmir.org/2024/1/e55368", url="http://www.ncbi.nlm.nih.gov/pubmed/38285931" } @Article{info:doi/10.2196/52679, author="Thiesmeier, Robert and Orsini, Nicola", title="Rolling the DICE (Design, Interpret, Compute, Estimate): Interactive Learning of Biostatistics With Simulations", journal="JMIR Med Educ", year="2024", month="Apr", day="15", volume="10", pages="e52679", keywords="learning statistics", keywords="Monte Carlo simulation", keywords="simulation-based learning", keywords="survival analysis", keywords="Weibull", doi="10.2196/52679", url="https://mededu.jmir.org/2024/1/e52679", url="http://www.ncbi.nlm.nih.gov/pubmed/38619866" } @Article{info:doi/10.2196/48393, author="Grosjean, Julien and Benis, Arriel and Dufour, Jean-Charles and Lejeune, {\'E}meline and Disson, Flavien and Dahamna, Badisse and Cieslik, H{\'e}l{\`e}ne and L{\'e}guillon, Romain and Faure, Matthieu and Dufour, Frank and Staccini, Pascal and Darmoni, Jacques St{\'e}fan", title="Sharing Digital Health Educational Resources in a One-Stop Shop Portal: Tutorial on the Catalog and Index of Digital Health Teaching Resources (CIDHR) Semantic Search Engine", journal="JMIR Med Educ", year="2024", month="Mar", day="4", volume="10", pages="e48393", keywords="digital health", keywords="medical informatics", keywords="medical education", keywords="search engine", keywords="knowledge management", keywords="semantic web", keywords="language", keywords="teaching", keywords="vocabulary", keywords="controlled", keywords="students", keywords="educational personnel", keywords="French", keywords="curriculum", abstract="Background: Access to reliable and accurate digital health web-based resources is crucial. However, the lack of dedicated search engines for non-English languages, such as French, is a significant obstacle in this field. Thus, we developed and implemented a multilingual, multiterminology semantic search engine called Catalog and Index of Digital Health Teaching Resources (CIDHR). CIDHR is freely accessible to everyone, with a focus on French-speaking resources. CIDHR has been initiated to provide validated, high-quality content tailored to the specific needs of each user profile, be it students or professionals. Objective: This study's primary aim in developing and implementing the CIDHR is to improve knowledge sharing and spreading in digital health and health informatics and expand the health-related educational community, primarily French speaking but also in other languages. We intend to support the continuous development of initial (ie, bachelor level), advanced (ie, master and doctoral levels), and continuing training (ie, professionals and postgraduate levels) in digital health for health and social work fields. The main objective is to describe the development and implementation of CIDHR. The hypothesis guiding this research is that controlled vocabularies dedicated to medical informatics and digital health, such as the Medical Informatics Multilingual Ontology (MIMO) and the concepts structuring the French National Referential on Digital Health (FNRDH), to index digital health teaching and learning resources, are effectively increasing the availability and accessibility of these resources to medical students and other health care professionals. Methods: First, resource identification is processed by medical librarians from websites and scientific sources preselected and validated by domain experts and surveyed every week. Then, based on MIMO and FNRDH, the educational resources are indexed for each related knowledge domain. The same resources are also tagged with relevant academic and professional experience levels. Afterward, the indexed resources are shared with the digital health teaching and learning community. The last step consists of assessing CIDHR by obtaining informal feedback from users. Results: Resource identification and evaluation processes were executed by a dedicated team of medical librarians, aiming to collect and curate an extensive collection of digital health teaching and learning resources. The resources that successfully passed the evaluation process were promptly included in CIDHR. These resources were diligently indexed (with MIMO and FNRDH) and tagged for the study field and degree level. By October 2023, a total of 371 indexed resources were available on a dedicated portal. Conclusions: CIDHR is a multilingual digital health education semantic search engine and platform that aims to increase the accessibility of educational resources to the broader health care--related community. It focuses on making resources ``findable,'' ``accessible,'' ``interoperable,'' and ``reusable'' by using a one-stop shop portal approach. CIDHR has and will have an essential role in increasing digital health literacy. ", doi="10.2196/48393", url="https://mededu.jmir.org/2024/1/e48393", url="http://www.ncbi.nlm.nih.gov/pubmed/38437007" } @Article{info:doi/10.2196/51740, author="Srinivasa, Komal and Charlton, Amanda and Moir, Fiona and Goodyear-Smith, Felicity", title="How to Develop an Online Video for Teaching Health Procedural Skills: Tutorial for Health Educators New to Video Production", journal="JMIR Med Educ", year="2024", month="Aug", day="7", volume="10", pages="e51740", keywords="online video", keywords="developing video", keywords="procedural video", keywords="medical education", keywords="clinician educator", keywords="health education", abstract="Background: Clinician educators are experts in procedural skills that students need to learn. Some clinician educators are interested in creating their own procedural videos but are typically not experts in video production, and there is limited information on this topic in the clinical education literature. Therefore, we present a tutorial for clinician educators to develop a procedural video. Objective: We describe the steps needed to develop a medical procedural video from the perspective of a clinician educator new to creating videos, informed by best practices as evidenced by the literature. We also produce a checklist of elements that ensure a quality video. Finally, we identify the barriers and facilitators to making such a video. Methods: We used the example of processing a piece of skeletal muscle in a pathology laboratory to make a video. We developed the video by dividing it into 3 phases: preproduction, production, and postproduction. After writing the learning outcomes, we created a storyboard and script, which were validated by subject matter and audiovisual experts. Photos and videos were captured on a digital camera mounted on a monopod. Video editing software was used to sequence the video clips and photos, insert text and audio narration, and generate closed captions. The finished video was uploaded to YouTube (Google) and then inserted into open-source authoring software to enable an interactive quiz. Results: The final video was 4 minutes and 4 seconds long and took 70 hours to create. The final video included audio narration, closed captioning, bookmarks, and an interactive quiz. We identified that an effective video has six key factors: (1) clear learning outcomes, (2) being engaging, (3) being learner-centric, (4) incorporating principles of multimedia learning, (5) incorporating adult learning theories, and (6) being of high audiovisual quality. To ensure educational quality, we developed a checklist of elements that educators can use to develop a video. One of the barriers to creating procedural videos for a clinician educator who is new to making videos is the significant time commitment to build videography and editing skills. The facilitators for developing an online video include creating a community of practice and repeated skill-building rehearsals using simulations. Conclusions: We outlined the steps in procedural video production and developed a checklist of quality elements. These steps and the checklist can guide a clinician educator in creating a quality video while recognizing the time, technical, and cognitive requirements. ", doi="10.2196/51740", url="https://mededu.jmir.org/2024/1/e51740", url="http://www.ncbi.nlm.nih.gov/pubmed/39110488" } @Article{info:doi/10.2196/53151, author="Sahyouni, Amal and Zoukar, Imad and Dashash, Mayssoon", title="Evaluating the Effectiveness of an Online Course on Pediatric Malnutrition for Syrian Health Professionals: Qualitative Delphi Study", journal="JMIR Med Educ", year="2024", month="Oct", day="28", volume="10", pages="e53151", keywords="effectiveness", keywords="online course", keywords="pediatric", keywords="malnutrition", keywords="essential competencies", keywords="e-learning", keywords="health professional", keywords="Syria", keywords="pilot study", keywords="acquisition knowledge", abstract="Background: There is a shortage of competent health professionals in managing malnutrition. Online education may be a practical and flexible approach to address this gap. Objective: This study aimed to identify essential competencies and assess the effectiveness of an online course on pediatric malnutrition in improving the knowledge of pediatricians and health professionals. Methods: A focus group (n=5) and Delphi technique (n=21 health professionals) were used to identify 68 essential competencies. An online course consisting of 4 educational modules in Microsoft PowerPoint (Microsoft Corp) slide form with visual aids (photos and videos) was designed and published on the Syrian Virtual University platform website using an asynchronous e-learning system. The course covered definition, classification, epidemiology, anthropometrics, treatment, and consequences. Participants (n=10) completed a pretest of 40 multiple-choice questions, accessed the course, completed a posttest after a specified period, and filled out a questionnaire to measure their attitude and assess their satisfaction. Results: A total of 68 essential competencies were identified, categorized into 3 domains: knowledge (24 competencies), skills (29 competencies), and attitudes (15 competencies). These competencies were further classified based on their focus area: etiology (10 competencies), assessment and diagnosis (21 competencies), and management (37 competencies). Further, 10 volunteers, consisting of 5 pediatricians and 5 health professionals, participated in this study over a 2-week period. A statistically significant increase in knowledge was observed among participants following completion of the online course (pretest mean 24.2, SD 6.1, and posttest mean 35.2, SD 3.3; P<.001). Pediatricians demonstrated higher pre- and posttest scores compared to other health care professionals (all P values were <.05). Prior malnutrition training within the past year positively impacted pretest scores (P=.03). Participants highly rated the course (mean satisfaction score >3.0 on a 5-point Likert scale), with 60\% (6/10) favoring a blended learning approach. Conclusions: In total, 68 essential competencies are required for pediatricians to manage children who are malnourished. The online course effectively improved knowledge acquisition among health care professionals, with high participant satisfaction and approval of the e-learning environment. ", doi="10.2196/53151", url="https://mededu.jmir.org/2024/1/e53151" } @Article{info:doi/10.2196/49551, author="Ang, Darryl Wei How and Lim, Grace Zhi Qi and Lau, Tiang Siew and Dong, Jie and Lau, Ying", title="Unpacking the Experiences of Health Care Professionals About the Web-Based Building Resilience At Work Program During the COVID-19 Pandemic: Framework Analysis", journal="JMIR Med Educ", year="2024", month="Jan", day="31", volume="10", pages="e49551", keywords="resilience", keywords="intent to stay", keywords="employability", keywords="health care professionals", keywords="process evaluation", keywords="framework analysis", keywords="framework", keywords="stress", keywords="mental health disorder", keywords="prevention", keywords="training", keywords="qualitative study", keywords="web-based tool", keywords="tool", keywords="sustainability", abstract="Background: The COVID-19 pandemic has resulted in a greater workload in the health care system. Therefore, health care professionals (HCPs) continue to experience high levels of stress, resulting in mental health disorders. From a preventive perspective, building resilience has been associated with reduced stress and mental health disorders and promotes HCPs' intent to stay. Despite the benefits of resilience training, few studies provided an in-depth understanding of the contextual factors, implementation, and mechanisms of impact that influences the sustainability of resilience programs. Therefore, examining target users' experiences of the resilience program is important. This will provide meaningful information to refine and improve future resilience programs. Objective: This qualitative study aims to explore HCPs' experiences of participating in the web-based Building Resilience At Work (BRAW) program. In particular, this study aims to explore the contextual and implementational factors that would influence participants' interaction and outcome from the program. Methods: A descriptive qualitative approach using individual semistructured Zoom interviews was conducted with participants of the web-based resilience program. A framework analysis was conducted, and it is guided by the process evaluation framework. Results: A total of 33 HCPs participated in this qualitative study. Three themes depicting participants' experiences, interactions, and impacts from the BRAW program were elucidated from the framework analysis: learning from web-based tools, interacting with the BRAW program, and promoting participants' workforce readiness. Conclusions: Findings show that a web-based asynchronous and self-paced resilience program is an acceptable and feasible approach for HCPs. The program also led to encouraging findings on participants' resilience, intent to stay, and employability. However, continued refinements in the components of the web-based resilience program should be carried out to ensure the sustainability of this intervention. Trial Registration: ClinicalTrials.gov NCT05130879; https://clinicaltrials.gov/ct2/show/NCT05130879 ", doi="10.2196/49551", url="https://mededu.jmir.org/2024/1/e49551", url="http://www.ncbi.nlm.nih.gov/pubmed/38294866" } @Article{info:doi/10.2196/51173, author="Karabacak, Mert and Ozcan, Zeynep and Ozkara, Berksu Burak and Furkan, Sude Zeynep and Bisdas, Sotirios", title="A Pilot Project to Promote Research Competency in Medical Students Through Journal Clubs: Mixed Methods Study", journal="JMIR Med Educ", year="2024", month="Oct", day="31", volume="10", pages="e51173", keywords="medical student", keywords="research", keywords="peer education", keywords="student society", keywords="journal club", keywords="skills", keywords="scientific investigation", keywords="undergraduate", keywords="student-led", keywords="initiative", keywords="resources", keywords="research training", keywords="competency", keywords="continuing education", keywords="research improvement", keywords="motivation", keywords="mentor", keywords="mentorship", keywords="medical education", abstract="Background: Undergraduate medical students often lack hands-on research experience and fundamental scientific research skills, limiting their exposure to the practical aspects of scientific investigation. The Cerrahpasa Neuroscience Society introduced a program to address this deficiency and facilitate student-led research. Objective: The primary goal of this initiative was to enhance medical students' research output by enabling them to generate and publish peer-reviewed papers within the framework of this pilot project. The project aimed to provide an accessible, global model for research training through structured journal clubs, mentorship from experienced peers, and resource access. Methods: In January 2022, a total of 30 volunteer students from various Turkish medical schools participated in this course-based undergraduate research experience program. Students self-organized into 2 groups according to their preferred study type: original research or systematic review. Two final-year students with prior research experience led the project, developing training modules using selected materials. The project was implemented entirely online, with participants completing training modules before using their newly acquired theoretical knowledge to perform assigned tasks. Results: Based on student feedback, the project timeline was adjusted to allow for greater flexibility in meeting deadlines. Despite these adjustments, participants successfully completed their tasks, applying the theoretical knowledge they had gained to their respective assignments. As of April 2024, the initiative has culminated in 3 published papers and 3 more under peer review. The project has also seen an increase in student interest in further involvement and self-paced learning. Conclusions: This initiative leverages globally accessible resources for research training, effectively fostering research competency among participants. It has successfully demonstrated the potential for undergraduates to contribute to medical research output and paved the way for a self-sustaining, student-led research program. Despite some logistical challenges, the project provided valuable insights for future implementations, showcasing the potential for students to engage in meaningful, publishable research. ", doi="10.2196/51173", url="https://mededu.jmir.org/2024/1/e51173" } @Article{info:doi/10.2196/48507, author="Johnson, G. Susanne and Espehaug, Birgitte and Larun, Lillebeth and Ciliska, Donna and Olsen, Rydland Nina", title="Occupational Therapy Students' Evidence-Based Practice Skills as Reported in a Mobile App: Cross-Sectional Study", journal="JMIR Med Educ", year="2024", month="Feb", day="21", volume="10", pages="e48507", keywords="active learning strategies", keywords="application", keywords="cross-sectional study", keywords="development", keywords="education", keywords="higher education", keywords="interactive", keywords="mobile application", keywords="mobile app", keywords="occupational therapy students", keywords="occupational therapy", keywords="students", keywords="usability", keywords="use", abstract="Background: Evidence-based practice (EBP) is an important aspect of the health care education curriculum. EBP involves following the 5 EBP steps: ask, assess, appraise, apply, and audit. These 5 steps reflect the suggested core competencies covered in teaching and learning programs to support future health care professionals applying EBP. When implementing EBP teaching, assessing outcomes by documenting the student's performance and skills is relevant. This can be done using mobile devices. Objective: The aim of this study was to assess occupational therapy students' EBP skills as reported in a mobile app. Methods: We applied a cross-sectional design. Descriptive statistics were used to present frequencies, percentages, means, and ranges of data regarding EBP skills found in the EBPsteps app. Associations between students' ability to formulate the Population, Intervention, Comparison, and Outcome/Population, Interest, and Context (PICO/PICo) elements and identifying relevant research evidence were analyzed with the chi-square test. Results: Of 4 cohorts with 150 students, 119 (79.3\%) students used the app and produced 240 critically appraised topics (CATs) in the app. The EBP steps ``ask,'' ``assess,'' and ``appraise'' were often correctly performed. The clinical question was formulated correctly in 53.3\% (128/240) of the CATs, and students identified research evidence in 81.2\% (195/240) of the CATs. Critical appraisal checklists were used in 81.2\% (195/240) of the CATs, and most of these checklists were assessed as relevant for the type of research evidence identified (165/195, 84.6\%). The least frequently correctly reported steps were ``apply'' and ``audit.'' In 39.6\% (95/240) of the CATs, it was reported that research evidence was applied. Only 61\% (58/95) of these CATs described how the research was applied to clinical practice. Evaluation of practice changes was reported in 38.8\% (93/240) of the CATs. However, details about practice changes were lacking in all these CATs. A positive association was found between correctly reporting the ``population'' and ``interventions/interest'' elements of the PICO/PICo and identifying research evidence (P<.001). Conclusions: We assessed the students' EBP skills based on how they documented following the EBP steps in the EBPsteps app, and our results showed variations in how well the students mastered the steps. ``Apply'' and ``audit'' were the most difficult EBP steps for the students to perform, and this finding has implications and gives directions for further development of the app and educational instruction in EBP. The EBPsteps app is a new and relevant app for students to learn and practice EBP, and it can be used to assess students' EBP skills objectively. ", doi="10.2196/48507", url="https://mededu.jmir.org/2024/1/e48507", url="http://www.ncbi.nlm.nih.gov/pubmed/38381475" } @Article{info:doi/10.2196/51112, author="Rettinger, Lena and Putz, Peter and Aichinger, Lea and Javorszky, Maria Susanne and Widhalm, Klaus and Ertelt-Bach, Veronika and Huber, Andreas and Sargis, Sevan and Maul, Lukas and Radinger, Oliver and Werner, Franz and Kuhn, Sebastian", title="Telehealth Education in Allied Health Care and Nursing: Web-Based Cross-Sectional Survey of Students' Perceived Knowledge, Skills, Attitudes, and Experience", journal="JMIR Med Educ", year="2024", month="Mar", day="21", volume="10", pages="e51112", keywords="telehealth", keywords="health care education", keywords="student perspectives", keywords="curriculum", keywords="interdisciplinary education", abstract="Background: The COVID-19 pandemic has highlighted the growing relevance of telehealth in health care. Assessing health care and nursing students' telehealth competencies is crucial for its successful integration into education and practice. Objective: We aimed to assess students' perceived telehealth knowledge, skills, attitudes, and experiences. In addition, we aimed to examine students' preferences for telehealth content and teaching methods within their curricula. Methods: We conducted a cross-sectional web-based study in May 2022. A project-specific questionnaire, developed and refined through iterative feedback and face-validity testing, addressed topics such as demographics, personal perceptions, and professional experience with telehealth and solicited input on potential telehealth course content. Statistical analyses were conducted on surveys with at least a 50\% completion rate, including descriptive statistics of categorical variables, graphical representation of results, and Kruskal Wallis tests for central tendencies in subgroup analyses. Results: A total of 261 students from 7 bachelor's and 4 master's health care and nursing programs participated in the study. Most students expressed interest in telehealth (180/261, 69\% very or rather interested) and recognized its importance in their education (215/261, 82.4\% very or rather important). However, most participants reported limited knowledge of telehealth applications concerning their profession (only 7/261, 2.7\% stated profound knowledge) and limited active telehealth experience with various telehealth applications (between 18/261, 6.9\% and 63/261, 24.1\%). Statistically significant differences were found between study programs regarding telehealth interest (P=.005), knowledge (P<.001), perceived importance in education (P<.001), and perceived relevance after the pandemic (P=.004). Practical training with devices, software, and apps and telehealth case examples with various patient groups were perceived as most important for integration in future curricula. Most students preferred both interdisciplinary and program-specific courses. Conclusions: This study emphasizes the need to integrate telehealth into health care education curricula, as students state positive telehealth attitudes but seem to be not adequately prepared for its implementation. To optimally prepare future health professionals for the increasing role of telehealth in practice, the results of this study can be considered when designing telehealth curricula. ", doi="10.2196/51112", url="https://mededu.jmir.org/2024/1/e51112", url="http://www.ncbi.nlm.nih.gov/pubmed/38512310" } @Article{info:doi/10.2196/54427, author="Lee, Hwa Kye and Lee, Ho Jae and Lee, Yura and Lee, Hyunna and Lee, Sung Ji and Jang, Jeon Hye and Lee, Hee Kun and Han, Hyun Jeong and Jang, SuJung", title="Impact of Health Informatics Analyst Education on Job Role, Career Transition, and Skill Development: Survey Study", journal="JMIR Med Educ", year="2024", month="Sep", day="25", volume="10", pages="e54427", keywords="health informatics", keywords="health informatics training", keywords="informatics training", keywords="professional development", keywords="training program", keywords="digital health technology", keywords="informatics workforce", keywords="informatics competencies", keywords="competencies", keywords="job skills", keywords="continuing education", keywords="data science", abstract="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. ", doi="10.2196/54427", url="https://mededu.jmir.org/2024/1/e54427" } @Article{info:doi/10.2196/46740, author="Acharya, Amish and Black, Claire Ruth and Smithies, Alisdair and Darzi, Ara", title="Evaluating the Impact of the National Health Service Digital Academy on Participants' Perceptions of Their Identity as Leaders of Digital Health Change: Mixed Methods Study", journal="JMIR Med Educ", year="2024", month="Feb", day="21", volume="10", pages="e46740", keywords="digital leadership", keywords="professional identity", keywords="dissertation of practice", abstract="Background: The key to the digital leveling-up strategy of the National Health Service is the development of a digitally proficient leadership. The National Health Service Digital Academy (NHSDA) Digital Health Leadership program was designed to support emerging digital leaders to acquire the necessary skills to facilitate transformation. This study examined the influence of the program on professional identity formation as a means of creating a more proficient digital health leadership. Objective: This study aims to examine the impact of the NHSDA program on participants' perceptions of themselves as digital health leaders. Methods: We recruited 41 participants from 2 cohorts of the 2-year NHSDA program in this mixed methods study, all of whom had completed it >6 months before the study. The participants were initially invited to complete a web-based scoping questionnaire. This involved both quantitative and qualitative responses to prompts. Frequencies of responses were aggregated, while free-text comments from the questionnaire were analyzed inductively. The content of the 30 highest-scoring dissertations was also reviewed by 2 independent authors. A total of 14 semistructured interviews were then conducted with a subset of the cohort. These focused on individuals' perceptions of digital leadership and the influence of the course on the attainment of skills. In total, 3 in-depth focus groups were then conducted with participants to examine shared perceptions of professional identity as digital health leaders. The transcripts from the interviews and focus groups were aligned with a previously published examination of leadership as a framework. Results: Of the 41 participants, 42\% (17/41) were in clinical roles, 34\% (14/41) were in program delivery or management roles, 20\% (8/41) were in data science roles, and 5\% (2/41) were in ``other'' roles. Interviews and focus groups highlighted that the course influenced 8 domains of professional identity: commitment to the profession, critical thinking, goal orientation, mentoring, perception of the profession, socialization, reflection, and self-efficacy. The dissertation of the practice model, in which candidates undertake digital projects within their organizations supported by faculty, largely impacted metacognitive skill acquisition and goal orientation. However, the program also affected participants' values and direction within the wider digital health community. According to the questionnaire, after graduation, 59\% (24/41) of the participants changed roles in search of more prominence within digital leadership, with 46\% (11/24) reporting that the course was a strong determinant of this change. Conclusions: A digital leadership course aimed at providing attendees with the necessary attributes to guide transformation can have a significant impact on professional identity formation. This can create a sense of belonging to a wider health leadership structure and facilitate the attainment of organizational and national digital targets. This effect is diminished by a lack of locoregional support for professional development. ", doi="10.2196/46740", url="https://mededu.jmir.org/2024/1/e46740", url="http://www.ncbi.nlm.nih.gov/pubmed/38381477" } @Article{info:doi/10.2196/58126, author="R{\"o}ssler, Lena and Herrmann, Manfred and Wiegand, Annette and Kanzow, Philipp", title="Use of Multiple-Choice Items in Summative Examinations: Questionnaire Survey Among German Undergraduate Dental Training Programs", journal="JMIR Med Educ", year="2024", month="Jun", day="27", volume="10", pages="e58126", keywords="alternate-choice", keywords="assessment", keywords="best-answer", keywords="dental", keywords="dental schools", keywords="dental training", keywords="education", keywords="educational assessment", keywords="educational measurement", keywords="examination", keywords="German", keywords="Germany", keywords="k of n", keywords="Kprim", keywords="K'", keywords="medical education", keywords="medical student", keywords="MTF", keywords="Multiple-True-False", keywords="multiple choice", keywords="multiple-select", keywords="Pick-N", keywords="scoring", keywords="scoring system", keywords="single choice", keywords="single response", keywords="test", keywords="testing", keywords="true/false", keywords="true-false", keywords="Type A", keywords="Type K", keywords="Type K'", keywords="Type R", keywords="Type X", keywords="undergraduate", keywords="undergraduate curriculum", keywords="undergraduate education", abstract="Background: Multiple-choice examinations are frequently used in German dental schools. However, details regarding the used item types and applied scoring methods are lacking. Objective: This study aims to gain insight into the current use of multiple-choice items (ie, questions) in summative examinations in German undergraduate dental training programs. Methods: A paper-based 10-item questionnaire regarding the used assessment methods, multiple-choice item types, and applied scoring methods was designed. The pilot-tested questionnaire was mailed to the deans of studies and to the heads of the Department of Operative/Restorative Dentistry at all 30 dental schools in Germany in February 2023. Statistical analysis was performed using the Fisher exact test (P<.05). Results: The response rate amounted to 90\% (27/30 dental schools). All respondent dental schools used multiple-choice examinations for summative assessments. Examinations were delivered electronically by 70\% (19/27) of the dental schools. Almost all dental schools used single-choice Type A items (24/27, 89\%), which accounted for the largest number of items in approximately half of the dental schools (13/27, 48\%). Further item types (eg, conventional multiple-select items, Multiple-True-False, and Pick-N) were only used by fewer dental schools (?67\%, up to 18 out of 27 dental schools). For the multiple-select item types, the applied scoring methods varied considerably (ie, awarding [intermediate] partial credit and requirements for partial credit). Dental schools with the possibility of electronic examinations used multiple-select items slightly more often (14/19, 74\% vs 4/8, 50\%). However, this difference was statistically not significant (P=.38). Dental schools used items either individually or as key feature problems consisting of a clinical case scenario followed by a number of items focusing on critical treatment steps (15/27, 56\%). Not a single school used alternative testing methods (eg, answer-until-correct). A formal item review process was established at about half of the dental schools (15/27, 56\%). Conclusions: Summative assessment methods among German dental schools vary widely. Especially, a large variability regarding the use and scoring of multiple-select multiple-choice items was found. ", doi="10.2196/58126", url="https://mededu.jmir.org/2024/1/e58126" } @Article{info:doi/10.2196/52746, author="Wu, Zelin and Gan, Wenyi and Xue, Zhaowen and Ni, Zhengxin and Zheng, Xiaofei and Zhang, Yiyi", title="Performance of ChatGPT on Nursing Licensure Examinations in the United States and China: Cross-Sectional Study", journal="JMIR Med Educ", year="2024", month="Oct", day="3", volume="10", pages="e52746", keywords="artificial intelligence", keywords="ChatGPT", keywords="nursing licensure examination", keywords="nursing", keywords="LLMs", keywords="large language models", keywords="nursing education", keywords="AI", keywords="nursing student", keywords="large language model", keywords="licensing", keywords="observation", keywords="observational study", keywords="China", keywords="USA", keywords="United States of America", keywords="auxiliary tool", keywords="accuracy rate", keywords="theoretical", abstract="Background: The creation of large language models (LLMs) such as ChatGPT is an important step in the development of artificial intelligence, which shows great potential in medical education due to its powerful language understanding and generative capabilities. The purpose of this study was to quantitatively evaluate and comprehensively analyze ChatGPT's performance in handling questions for the National Nursing Licensure Examination (NNLE) in China and the United States, including the National Council Licensure Examination for Registered Nurses (NCLEX-RN) and the NNLE. Objective: This study aims to examine how well LLMs respond to the NCLEX-RN and the NNLE multiple-choice questions (MCQs) in various language inputs. To evaluate whether LLMs can be used as multilingual learning assistance for nursing, and to assess whether they possess a repository of professional knowledge applicable to clinical nursing practice. Methods: First, we compiled 150 NCLEX-RN Practical MCQs, 240 NNLE Theoretical MCQs, and 240 NNLE Practical MCQs. Then, the translation function of ChatGPT 3.5 was used to translate NCLEX-RN questions from English to Chinese and NNLE questions from Chinese to English. Finally, the original version and the translated version of the MCQs were inputted into ChatGPT 4.0, ChatGPT 3.5, and Google Bard. Different LLMs were compared according to the accuracy rate, and the differences between different language inputs were compared. Results: The accuracy rates of ChatGPT 4.0 for NCLEX-RN practical questions and Chinese-translated NCLEX-RN practical questions were 88.7\% (133/150) and 79.3\% (119/150), respectively. Despite the statistical significance of the difference (P=.03), the correct rate was generally satisfactory. Around 71.9\% (169/235) of NNLE Theoretical MCQs and 69.1\% (161/233) of NNLE Practical MCQs were correctly answered by ChatGPT 4.0. The accuracy of ChatGPT 4.0 in processing NNLE Theoretical MCQs and NNLE Practical MCQs translated into English was 71.5\% (168/235; P=.92) and 67.8\% (158/233; P=.77), respectively, and there was no statistically significant difference between the results of text input in different languages. ChatGPT 3.5 (NCLEX-RN P=.003, NNLE Theoretical P<.001, NNLE Practical P=.12) and Google Bard (NCLEX-RN P<.001, NNLE Theoretical P<.001, NNLE Practical P<.001) had lower accuracy rates for nursing-related MCQs than ChatGPT 4.0 in English input. English accuracy was higher when compared with ChatGPT 3.5's Chinese input, and the difference was statistically significant (NCLEX-RN P=.02, NNLE Practical P=.02). Whether submitted in Chinese or English, the MCQs from the NCLEX-RN and NNLE demonstrated that ChatGPT 4.0 had the highest number of unique correct responses and the lowest number of unique incorrect responses among the 3 LLMs. Conclusions: This study, focusing on 618 nursing MCQs including NCLEX-RN and NNLE exams, found that ChatGPT 4.0 outperformed ChatGPT 3.5 and Google Bard in accuracy. It excelled in processing English and Chinese inputs, underscoring its potential as a valuable tool in nursing education and clinical decision-making. ", doi="10.2196/52746", url="https://mededu.jmir.org/2024/1/e52746" } @Article{info:doi/10.2196/59902, author="Huang, Ting-Yun and Hsieh, Hsing Pei and Chang, Yung-Chun", title="Performance Comparison of Junior Residents and ChatGPT in the Objective Structured Clinical Examination (OSCE) for Medical History Taking and Documentation of Medical Records: Development and Usability Study", journal="JMIR Med Educ", year="2024", month="Nov", day="21", volume="10", pages="e59902", keywords="large language model", keywords="medical history taking", keywords="clinical documentation", keywords="simulation-based evaluation", keywords="OSCE standards", keywords="LLM", abstract="Background: This study explores the cutting-edge abilities of large language models (LLMs) such as ChatGPT in medical history taking and medical record documentation, with a focus on their practical effectiveness in clinical settings---an area vital for the progress of medical artificial intelligence. Objective: Our aim was to assess the capability of ChatGPT versions 3.5 and 4.0 in performing medical history taking and medical record documentation in simulated clinical environments. The study compared the performance of nonmedical individuals using ChatGPT with that of junior medical residents. Methods: A simulation involving standardized patients was designed to mimic authentic medical history--taking interactions. Five nonmedical participants used ChatGPT versions 3.5 and 4.0 to conduct medical histories and document medical records, mirroring the tasks performed by 5 junior residents in identical scenarios. A total of 10 diverse scenarios were examined. Results: Evaluation of the medical documentation created by laypersons with ChatGPT assistance and those created by junior residents was conducted by 2 senior emergency physicians using audio recordings and the final medical records. The assessment used the Objective Structured Clinical Examination benchmarks in Taiwan as a reference. ChatGPT-4.0 exhibited substantial enhancements over its predecessor and met or exceeded the performance of human counterparts in terms of both checklist and global assessment scores. Although the overall quality of human consultations remained higher, ChatGPT-4.0's proficiency in medical documentation was notably promising. Conclusions: The performance of ChatGPT 4.0 was on par with that of human participants in Objective Structured Clinical Examination evaluations, signifying its potential in medical history and medical record documentation. Despite this, the superiority of human consultations in terms of quality was evident. The study underscores both the promise and the current limitations of LLMs in the realm of clinical practice. ", doi="10.2196/59902", url="https://mededu.jmir.org/2024/1/e59902" } @Article{info:doi/10.2196/54401, author="Shikino, Kiyoshi and Nishizaki, Yuji and Fukui, Sho and Yokokawa, Daiki and Yamamoto, Yu and Kobayashi, Hiroyuki and Shimizu, Taro and Tokuda, Yasuharu", title="Development of a Clinical Simulation Video to Evaluate Multiple Domains of Clinical Competence: Cross-Sectional Study", journal="JMIR Med Educ", year="2024", month="Feb", day="29", volume="10", pages="e54401", keywords="discrimination index", keywords="General Medicine In-Training Examination", keywords="clinical simulation video", keywords="postgraduate medical education", keywords="video", keywords="videos", keywords="training", keywords="examination", keywords="examinations", keywords="medical education", keywords="resident", keywords="residents", keywords="postgraduate", keywords="postgraduates", keywords="simulation", keywords="simulations", keywords="diagnosis", keywords="diagnoses", keywords="diagnose", keywords="general medicine", keywords="general practice", keywords="general practitioner", keywords="skill", keywords="skills", abstract="Background: Medical students in Japan undergo a 2-year postgraduate residency program to acquire clinical knowledge and general medical skills. The General Medicine In-Training Examination (GM-ITE) assesses postgraduate residents' clinical knowledge. A clinical simulation video (CSV) may assess learners' interpersonal abilities. Objective: This study aimed to evaluate the relationship between GM-ITE scores and resident physicians' diagnostic skills by having them watch a CSV and to explore resident physicians' perceptions of the CSV's realism, educational value, and impact on their motivation to learn. Methods: The participants included 56 postgraduate medical residents who took the GM-ITE between January 21 and January 28, 2021; watched the CSV; and then provided a diagnosis. The CSV and GM-ITE scores were compared, and the validity of the simulations was examined using discrimination indices, wherein ?0.20 indicated high discriminatory power and >0.40 indicated a very good measure of the subject's qualifications. Additionally, we administered an anonymous questionnaire to ascertain participants' views on the realism and educational value of the CSV and its impact on their motivation to learn. Results: Of the 56 participants, 6 (11\%) provided the correct diagnosis, and all were from the second postgraduate year. All domains indicated high discriminatory power. The (anonymous) follow-up responses indicated that the CSV format was more suitable than the conventional GM-ITE for assessing clinical competence. The anonymous survey revealed that 12 (52\%) participants found the CSV format more suitable than the GM-ITE for assessing clinical competence, 18 (78\%) affirmed the realism of the video simulation, and 17 (74\%) indicated that the experience increased their motivation to learn. Conclusions: The findings indicated that CSV modules simulating real-world clinical examinations were successful in assessing examinees' clinical competence across multiple domains. The study demonstrated that the CSV not only augmented the assessment of diagnostic skills but also positively impacted learners' motivation, suggesting a multifaceted role for simulation in medical education. ", doi="10.2196/54401", url="https://mededu.jmir.org/2024/1/e54401", url="http://www.ncbi.nlm.nih.gov/pubmed/38421691" } @Article{info:doi/10.2196/56787, author="Mielitz, Annabelle and Kulau, Ulf and Bublitz, Lucas and Bittner, Anja and Friederichs, Hendrik and Albrecht, Urs-Vito", title="Teaching Digital Medicine to Undergraduate Medical Students With an Interprofessional and Interdisciplinary Approach: Development and Usability Study", journal="JMIR Med Educ", year="2024", month="Sep", day="30", volume="10", pages="e56787", keywords="medical education", keywords="digital medicine", keywords="digital health", abstract="Background: An integration of digital medicine into medical education can help future physicians shape the digital transformation of medicine. Objective: We aim to describe and evaluate a newly developed course for teaching digital medicine (the Bielefeld model) for the first time. Methods: The course was held with undergraduate medical students at Medical School Ostwestfalen-Lippe at Bielefeld University, Germany, in 2023 and evaluated via pretest-posttest surveys. The subjective and objective achievement of superordinate learning objectives and the objective achievement of subordinate learning objectives of the course, course design, and course importance were evaluated using 5-point Likert scales (1=strongly disagree; 5=strongly agree); reasons for absences were assessed using a multiple-choice format, and comments were collected. The superordinate objectives comprised (1) the understanding of factors driving the implementation of digital medical products and processes, (2) the application of this knowledge to a project, and (3) the empowerment to design such solutions in the future. The subordinate objectives comprised competencies related to the first superordinate objective. Results: In total, 10 undergraduate medical students (male: n=4, 40\%; female: n=6, 60\%; mean age 21.7, SD 2.1 years) evaluated the course. The superordinate objectives were achieved well to very well---the medians for the objective achievement were 4 (IQR 4-5), 4 (IQR 3-5), and 4 (IQR 4-4) scale units for the first, second, and third objectives, respectively, and the medians for the subjective achievement of the first, second, and third objectives were 4 (IQR 3-4), 4.5 (IQR 3-5), and 4 (IQR 3-5) scale units, respectively. Participants mastered the subordinate objectives, on average, better after the course than before (presurvey median 2.5, IQR 2-3 scale units; postsurvey median 4, IQR 3-4 scale units). The course concept was rated as highly suitable for achieving the superordinate objectives (median 5, IQR 4-5 scale units for the first, second, and third objectives). On average, the students strongly liked the course (median 5, IQR 4-5 scale units) and gained a benefit from it (median 4.5, IQR 4-5 scale units). All students fully agreed that the teaching staff was a strength of the course. The category positive feedback on the course or positive personal experience with the course received the most comments. Conclusions: The course framework shows promise in attaining learning objectives within the realm of digital medicine, notwithstanding the constraint of limited interpretability arising from a small sample size and further limitations. The course concept aligns with insights derived from teaching and learning research and the domain of digital medicine, albeit with identifiable areas for enhancement. A literature review indicates a dearth of publications pertaining to analogous courses in Germany. Future investigations should entail a more exhaustive evaluation of the course. In summary, this course constitutes a valuable contribution to incorporating digital medicine into medical education. ", doi="10.2196/56787", url="https://mededu.jmir.org/2024/1/e56787", url="http://www.ncbi.nlm.nih.gov/pubmed/39189929" } @Article{info:doi/10.2196/57077, author="Doueiri, Nadeem Zakaria and Bajra, Rika and Srinivasan, Malathi and Schillinger, Erika and Cuan, Nancy", title="Bridging the Telehealth Digital Divide With Collegiate Navigators: Mixed Methods Evaluation Study of a Service-Learning Health Disparities Course", journal="JMIR Med Educ", year="2024", month="Oct", day="1", volume="10", pages="e57077", keywords="service learning", keywords="medical education", keywords="access to care", keywords="telehealth", keywords="telemedicine", keywords="health disparities", keywords="social determinants of health", keywords="digital literacy", keywords="vulnerable populations", keywords="community engagement", keywords="value-added medical education", keywords="digital health", keywords="digital divide", keywords="health equity", keywords="collegiate navigator", keywords="experimental", keywords="education", keywords="student", keywords="qualitative analysis", keywords="technology", keywords="mobile phone", abstract="Background: Limited digital literacy is a barrier for vulnerable patients accessing health care. Objective: The Stanford Technology Access Resource Team (START), a service-learning course created to bridge the telehealth digital divide, trained undergraduate and graduate students to provide hands-on patient support to improve access to electronic medical records (EMRs) and video visits while learning about social determinants of health. Methods: START students reached out to 1185 patients (n=711, 60\% from primary care clinics of a large academic medical center and n=474, 40\% from a federally qualified health center). Registries consisted of patients without an EMR account (at primary care clinics) or patients with a scheduled telehealth visit (at a federally qualified health center). Patient outcomes were evaluated by successful EMR enrollments and video visit setups. Student outcomes were assessed by reflections coded for thematic content. Results: Over 6 academic quarters, 57 students reached out to 1185 registry patients. Of the 229 patients contacted, 141 desired technical support. START students successfully established EMR accounts and set up video visits for 78.7\% (111/141) of patients. After program completion, we reached out to 13.5\% (19/141) of patients to collect perspectives on program utility. The majority (18/19, 94.7\%) reported that START students were helpful, and 73.7\% (14/19) reported that they had successfully connected with their health care provider in a digital visit. Inability to establish access included a lack of Wi-Fi or device access, the absence of an interpreter, and a disability that precluded the use of video visits. Qualitative analysis of student reflections showed an impact on future career goals and improved awareness of health disparities of technology access. Conclusions: Of the patients who desired telehealth access, START improved access for 78.7\% (111/141) of patients. Students found that START broadened their understanding of health disparities and social determinants of health and influenced their future career goals. ", doi="10.2196/57077", url="https://mededu.jmir.org/2024/1/e57077" } @Article{info:doi/10.2196/57157, author="Miao, Jing and Thongprayoon, Charat and Garcia Valencia, Oscar and Craici, M. Iasmina and Cheungpasitporn, Wisit", title="Navigating Nephrology's Decline Through a GPT-4 Analysis of Internal Medicine Specialties in the United States: Qualitative Study", journal="JMIR Med Educ", year="2024", month="Oct", day="10", volume="10", pages="e57157", keywords="artificial intelligence", keywords="ChatGPT", keywords="nephrology fellowship training", keywords="fellowship matching", keywords="medical education", keywords="AI", keywords="nephrology", keywords="fellowship", keywords="United States", keywords="factor", keywords="chatbots", keywords="intellectual", keywords="complexity", keywords="work-life balance", keywords="procedural involvement", keywords="opportunity", keywords="career demand", keywords="financial compensation", abstract="Background: The 2024 Nephrology fellowship match data show the declining interest in nephrology in the United States, with an 11\% drop in candidates and a mere 66\% (321/488) of positions filled. Objective: The study aims to discern the factors influencing this trend using ChatGPT, a leading chatbot model, for insights into the comparative appeal of nephrology versus other internal medicine specialties. Methods: Using the GPT-4 model, the study compared nephrology with 13 other internal medicine specialties, evaluating each on 7 criteria including intellectual complexity, work-life balance, procedural involvement, research opportunities, patient relationships, career demand, and financial compensation. Each criterion was assigned scores from 1 to 10, with the cumulative score determining the ranking. The approach included counteracting potential bias by instructing GPT-4 to favor other specialties over nephrology in reverse scenarios. Results: GPT-4 ranked nephrology only above sleep medicine. While nephrology scored higher than hospice and palliative medicine, it fell short in key criteria such as work-life balance, patient relationships, and career demand. When examining the percentage of filled positions in the 2024 appointment year match, nephrology's filled rate was 66\%, only higher than the 45\% (155/348) filled rate of geriatric medicine. Nephrology's score decreased by 4\%?14\% in 5 criteria including intellectual challenge and complexity, procedural involvement, career opportunity and demand, research and academic opportunities, and financial compensation. Conclusions: ChatGPT does not favor nephrology over most internal medicine specialties, highlighting its diminishing appeal as a career choice. This trend raises significant concerns, especially considering the overall physician shortage, and prompts a reevaluation of factors affecting specialty choice among medical residents. ", doi="10.2196/57157", url="https://mededu.jmir.org/2024/1/e57157" } @Article{info:doi/10.2196/51915, author="Jones, Jennifer and Johnston, Sewan Jamie and Ndiaye, Yabsa Ngouille and Tokar, Anna and Singla, Saumya and Skinner, Ann Nadine and Strehlow, Matthew and Utunen, Heini", title="Health Care Workers' Motivations for Enrolling in Massive Open Online Courses During a Public Health Emergency: Descriptive Analysis", journal="JMIR Med Educ", year="2024", month="Jun", day="19", volume="10", pages="e51915", keywords="massive open online course", keywords="MOOC", keywords="online learning", keywords="online courses", keywords="online course", keywords="health care education", keywords="medical education", keywords="education", keywords="training", keywords="professional development", keywords="continuing education", keywords="COVID-19 training", keywords="infectious disease outbreak response", keywords="emergency", keywords="public health", keywords="crisis", keywords="crises", keywords="outbreak", keywords="pandemic", keywords="COVID-19", keywords="SARS-CoV-2", keywords="coronavirus", keywords="humanitarian emergency response", keywords="health care workers", keywords="nurse", keywords="nurses", keywords="practitioner", keywords="practitioners", keywords="clinician", keywords="clinicians", keywords="health care worker", keywords="medic", keywords="low-income", keywords="lower-middle income", keywords="LIC", keywords="LMIC", keywords="developing country", keywords="developing countries", keywords="developing nation", keywords="developing nations", keywords="case study", keywords="survey", keywords="surveys", keywords="descriptive analysis", keywords="descriptive analyses", keywords="motivation", keywords="motivations", keywords="lower-middle--income country", keywords="low-income country", abstract="Background: Massive open online courses (MOOCs) are increasingly used to educate health care workers during public health emergencies. In early 2020, the World Health Organization (WHO) developed a series of MOOCs for COVID-19, introducing the disease and strategies to control its outbreak, with 6 courses specifically targeting health care workers as learners. In 2020, Stanford University also launched a MOOC designed to deliver accurate and timely education on COVID-19, equipping health care workers across the globe to provide health care safely and effectively to patients with the novel infectious disease. Although the use of MOOCs for just-in-time training has expanded during the pandemic, evidence is limited regarding the factors motivating health care workers to enroll in and complete courses, particularly in low-income countries (LICs) and lower-middle--income countries (LMICs). Objective: This study seeks to gain insights on the characteristics and motivations of learners turning to MOOCs for just-in-time training, to provide evidence that can better inform MOOC design to meet the needs of health care workers. We examine data from learners in 1 Stanford University and 6 WHO COVID-19 courses to identify (1) the characteristics of health care workers completing the courses and (2) the factors motivating them to enroll. Methods: We analyze (1) course registration data of the 49,098 health care workers who completed the 7 focal courses and (2) survey responses from 6272 course completers. The survey asked respondents to rank their motivations for enrollment and share feedback about their learning experience. We use descriptive statistics to compare responses by health care profession and by World Bank country income classification. Results: Health care workers completed the focal courses from all regions of the world, with nearly one-third (14,159/49,098, 28.84\%) practicing in LICs and LMICs. Survey data revealed a diverse range of professional roles among the learners, including physicians (2171/6272, 34.61\%); nurses (1599/6272, 25.49\%); and other health care professionals such as allied health professionals, community health workers, paramedics, and pharmacists (2502/6272, 39.89\%). Across all health care professions, the primary motivation to enroll was for personal learning to improve clinical practice. Continuing education credit was also an important motivator, particularly for nonphysicians and learners in LICs and LMICs. Course cost (3423/6272, 54.58\%) and certification (4238/6272, 67.57\%) were also important to a majority of learners. Conclusions: Our results demonstrate that a diverse range of health care professionals accessed MOOCs for just-in-time training during a public health emergency. Although all health care workers were motivated to improve their clinical practice, different factors were influential across professions and locations. These factors should be considered in MOOC design to meet the needs of health care workers, particularly those in lower-resource settings where alternative avenues for training may be limited. ", doi="10.2196/51915", url="https://mededu.jmir.org/2024/1/e51915" } @Article{info:doi/10.2196/46507, author="Cardoso Pinto, M. Alexandra and Soussi, Daniella and Qasim, Subaan and Dunin-Borkowska, Aleksandra and Rupasinghe, Thiara and Ubhi, Nicholas and Ranasinghe, Lasith", title="The Use of Animations Depicting Cardiac Electrical Activity to Improve Confidence in Understanding of Cardiac Pathology and Electrocardiography Traces Among Final-Year Medical Students: Nonrandomized Controlled Trial", journal="JMIR Med Educ", year="2024", month="Apr", day="23", volume="10", pages="e46507", keywords="medical education", keywords="cardiology", keywords="technology", keywords="clinical skills", keywords="cardiac", keywords="cardiac electrical activity", keywords="ECG", keywords="mixed methods study", keywords="students", keywords="education", keywords="medical professionals", keywords="development", keywords="web-based tutorial", keywords="teaching", keywords="cardiovascular", keywords="learning", keywords="electrocardiography", abstract="Background: Electrocardiography (ECG) interpretation is a fundamental skill for medical students and practicing medical professionals. Recognizing ECG pathologies promptly allows for quick intervention, especially in acute settings where urgent care is needed. However, many medical students find ECG interpretation and understanding of the underlying pathology challenging, with teaching methods varying greatly. Objective: This study involved the development of novel animations demonstrating the passage of electrical activity for well-described cardiac pathologies and showcased them alongside the corresponding live ECG traces during a web-based tutorial for final-year medical students. We aimed to assess whether the animations improved medical students' confidence in visualizing cardiac electrical activity and ECG interpretation, compared to standard ECG teaching methods. Methods: Final-year medical students at Imperial College London attended a web-based tutorial demonstrating the 7 animations depicting cardiac electrical activity and the corresponding ECG trace. Another tutorial without the animations was held to act as a control. Students completed a questionnaire assessing their confidence in interpreting ECGs and visualizing cardiovascular electrical transmission before and after the tutorial. Intervention-arm participants were also invited to a web-based focus group to explore their experiences of past ECG teaching and the tutorial, particularly on aspects they found helpful and what could be further improved in the tutorial and animations. Wilcoxon signed-rank tests and Mann-Whitney U tests were used to assess the statistical significance of any changes in confidence. Focus group transcripts were analyzed using inductive thematic analysis. Results: Overall, 19 students attended the intervention arm, with 15 (79\%) completing both the pre- and posttutorial questionnaires and 15 (79\%) participating in focus groups, whereas 14 students attended the control arm, with 13 (93\%) completing both questionnaires. Median confidence in interpreting ECGs in the intervention arm increased after the tutorial (2, IQR 1.5-3.0 vs 3, IQR 3-4.5; P<.001). Improvement was seen in both confidence in reviewing or diagnosing cardiac rhythms and the visualization of cardiac electrical activity. However, there was no significant difference between the intervention and control arms, for all pathologies (all P>.05). The main themes from the thematic analysis were that ECGs are a complex topic and past ECG teaching has focused on memorizing traces; the visualizations enabled deeper understanding of cardiac pathology; and ECG learning requires repetition, and clinical links remain essential. Conclusions: This study highlights the value of providing concise explanations of the meaning and pathophysiology behind ECG traces, both visually and verbally. ECG teaching that incorporates relevant pathophysiology, alongside vignettes with discussions regarding investigations and management options, is likely more helpful to students than practices based solely on pattern recognition. Although the animations supported student learning, the key element was the tutor's explanations. These animations may be more helpful as a supplement to teaching, for instance, as open-access videos. ", doi="10.2196/46507", url="https://mededu.jmir.org/2024/1/e46507" } @Article{info:doi/10.2196/50297, author="Dallora, Luiza Ana and Andersson, Kazimiera Ewa and Gregory Palm, Bruna and Bohman, Doris and Bj{\"o}rling, Gunilla and Marcinowicz, Ludmi?a and Stjernberg, Louise and Anderberg, Peter", title="Nursing Students' Attitudes Toward Technology: Multicenter Cross-Sectional Study", journal="JMIR Med Educ", year="2024", month="Apr", day="29", volume="10", pages="e50297", keywords="nursing education", keywords="technophilia", keywords="eHealth", keywords="technology anxiety", keywords="technology enthusiasm", keywords="mobile phone", abstract="Background: The growing presence of digital technologies in health care requires the health workforce to have proficiency in subjects such as informatics. This has implications in the education of nursing students, as their preparedness to use these technologies in clinical situations is something that course administrators need to consider. Thus, students' attitudes toward technology could be investigated to assess their needs regarding this proficiency. Objective: This study aims to investigate attitudes (enthusiasm and anxiety) toward technology among nursing students and to identify factors associated with those attitudes. Methods: Nursing students at 2 universities in Sweden and 1 university in Poland were invited to answer a questionnaire. Data about attitudes (anxiety and enthusiasm) toward technology, eHealth literacy, electronic device skills, and frequency of using electronic devices and sociodemographic data were collected. Descriptive statistics were used to characterize the data. The Spearman rank correlation coefficient and Mann-Whitney U test were used for statistical inferences. Results: In total, 646 students answered the questionnaire---342 (52.9\%) from the Swedish sites and 304 (47.1\%) from the Polish site. It was observed that the students' technology enthusiasm (techEnthusiasm) was on the higher end of the Technophilia instrument (score range 1-5): 3.83 (SD 0.90), 3.62 (SD 0.94), and 4.04 (SD 0.78) for the whole sample, Swedish students, and Polish students, respectively. Technology anxiety (techAnxiety) was on the midrange of the Technophilia instrument: 2.48 (SD 0.96), 2.37 (SD 1), and 2.60 (SD 0.89) for the whole sample, Swedish students, and Polish students, respectively. Regarding techEnthusiasm among the nursing students, a negative correlation with age was found for the Swedish sample (P<.001; $\rho$Swedish=?0.201) who were generally older than the Polish sample, and positive correlations with the eHealth Literacy Scale score (P<.001; $\rho$all=0.265; $\rho$Swedish=0.190; $\rho$Polish=0.352) and with the perceived skill in using computer devices (P<.001; $\rho$all=0.360; $\rho$Swedish=0.341; $\rho$Polish=0.309) were found for the Swedish, Polish, and total samples. Regarding techAnxiety among the nursing students, a positive correlation with age was found in the Swedish sample (P<.001; $\rho$Swedish=0.184), and negative correlations with eHealth Literacy Scale score (P<.001; $\rho$all=?0.196; $\rho$Swedish=?0.262; $\rho$Polish=?0.133) and with the perceived skill in using computer devices (P<.001; $\rho$all=?0.209; $\rho$Swedish=?0.347; $\rho$Polish=?0.134) were found for the Swedish, Polish, and total samples and with the semester only for the Swedish sample (P<.001; $\rho$Swedish=?0.124). Gender differences were found regarding techAnxiety in the Swedish sample, with women exhibiting a higher mean score than men (2.451, SD 1.014 and 1.987, SD 0.854, respectively). Conclusions: This study highlights nursing students' techEnthusiasm and techAnxiety, emphasizing correlations with various factors. With health care's increasing reliance on technology, integrating health technology--related topics into education is crucial for future professionals to address health care challenges effectively. International Registered Report Identifier (IRRID): RR2-10.2196/14643 ", doi="10.2196/50297", url="https://mededu.jmir.org/2024/1/e50297", url="http://www.ncbi.nlm.nih.gov/pubmed/38683660" } @Article{info:doi/10.2196/45468, author="Rahadiani, Pratiwi and Kekalih, Aria and Soemantri, Diantha and Krisnamurti, Budi Desak Gede", title="Exploring HTML5 Package Interactive Content in Supporting Learning Through Self-Paced Massive Open Online Courses on Healthy Aging: Mixed Methods Study", journal="JMIR Med Educ", year="2024", month="Aug", day="22", volume="10", pages="e45468", keywords="HTML5 package", keywords="H5P", keywords="students' perspectives", keywords="students' acceptance", keywords="massive open online courses", keywords="MOOCs", keywords="healthy aging", keywords="self-paced MOOC", keywords="student", keywords="perception", keywords="acceptance", keywords="opinion", keywords="attitude", keywords="MOOC", keywords="self-paced", keywords="self-guided", keywords="online course", keywords="online learning", keywords="geriatric", keywords="gerontology", keywords="gerontological", keywords="learning", abstract="Background: The rapidly aging population and the growth of geriatric medicine in the field of internal medicine are not supported by sufficient gerontological training in many health care disciplines. There is rising awareness about the education and training needed to adequately prepare health care professionals to address the needs of the older adult population. Massive open online courses (MOOCs) might be the best alternative method of learning delivery in this context. However, the diversity of MOOC participants poses a challenge for MOOC providers to innovate in developing learning content that suits the needs and characters of participants. Objective: The primary outcome of this study was to explore students' perceptions and acceptance of HTML5 package (H5P) interactive content in self-paced MOOCs and its association with students' characteristics and experience in using MOOCs. Methods: This study used a cross-sectional design, combining qualitative and quantitative approaches. Participants, predominantly general practitioners from various regions of Indonesia with diverse educational backgrounds and age groups, completed pretests, engaged with H5P interactive content, and participated in forum discussions and posttests. Data were retrieved from the online questionnaire attached to a selected MOOC course. Students' perceptions and acceptance of H5P interactive content were rated on a 6-point Likert scale from 1 (strongly disagree) to 6 (strongly agree). Data were analyzed using SPSS (IBM Corp) to examine demographics, computer literacy, acceptance, and perceptions of H5P interactive content. Quantitative analysis explored correlations, while qualitative analysis identified recurring themes from open-ended survey responses to determine students' perceptions. Results: In total, 184 MOOC participants agreed to participate in the study. Students demonstrated positive perceptions and a high level of acceptance of integrating H5P interactive content within the self-paced MOOC. Analysis of mean (SD) value across all responses consistently revealed favorable scores (greater than 5), ranging from 5.18 (SD 0.861) to 5.45 (SD 0.659) and 5.28 (SD 0.728) to 5.52 (SD 0.627), respectively. This finding underscores widespread satisfaction and robust acceptance of H5P interactive content. Students found the H5P interactive content more satisfying and fun, easier to understand, more effective, and more helpful in improving learning outcomes than material in the form of common documents and learning videos. There is a significant correlation between computer literacy, students' acceptance, and students' perceptions. Conclusions: Students from various backgrounds showed a high level of acceptance and positive perceptions of leveraging H5P interactive content in the self-paced MOOC. The findings suggest potential new uses of H5P interactive content in MOOCs, such as interactive videos with pop-up questions, to substitute for synchronous learning. The study underscores the significance of tailored educational strategies in supporting the professional development of health care professionals. ", doi="10.2196/45468", url="https://mededu.jmir.org/2024/1/e45468", url="http://www.ncbi.nlm.nih.gov/pubmed/39049507" } @Article{info:doi/10.2196/54067, author="Hudon, Alexandre and Kiepura, Barnab{\'e} and Pelletier, Myriam and Phan, V{\'e}ronique", title="Using ChatGPT in Psychiatry to Design Script Concordance Tests in Undergraduate Medical Education: Mixed Methods Study", journal="JMIR Med Educ", year="2024", month="Apr", day="4", volume="10", pages="e54067", keywords="psychiatry", keywords="artificial intelligence", keywords="medical education", keywords="concordance scripts", keywords="machine learning", keywords="ChatGPT", keywords="evaluation", keywords="education", keywords="medical learners", keywords="learning", keywords="teaching", keywords="design", keywords="support", keywords="tool", keywords="validation", keywords="educational", keywords="accuracy", keywords="clinical questions", keywords="educators", abstract="Background: Undergraduate medical studies represent a wide range of learning opportunities served in the form of various teaching-learning modalities for medical learners. A clinical scenario is frequently used as a modality, followed by multiple-choice and open-ended questions among other learning and teaching methods. As such, script concordance tests (SCTs) can be used to promote a higher level of clinical reasoning. Recent technological developments have made generative artificial intelligence (AI)--based systems such as ChatGPT (OpenAI) available to assist clinician-educators in creating instructional materials. Objective: The main objective of this project is to explore how SCTs generated by ChatGPT compared to SCTs produced by clinical experts on 3 major elements: the scenario (stem), clinical questions, and expert opinion. Methods: This mixed method study evaluated 3 ChatGPT-generated SCTs with 3 expert-created SCTs using a predefined framework. Clinician-educators as well as resident doctors in psychiatry involved in undergraduate medical education in Quebec, Canada, evaluated via a web-based survey the 6 SCTs on 3 criteria: the scenario, clinical questions, and expert opinion. They were also asked to describe the strengths and weaknesses of the SCTs. Results: A total of 102 respondents assessed the SCTs. There were no significant distinctions between the 2 types of SCTs concerning the scenario (P=.84), clinical questions (P=.99), and expert opinion (P=.07), as interpretated by the respondents. Indeed, respondents struggled to differentiate between ChatGPT- and expert-generated SCTs. ChatGPT showcased promise in expediting SCT design, aligning well with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria, albeit with a tendency toward caricatured scenarios and simplistic content. Conclusions: This study is the first to concentrate on the design of SCTs supported by AI in a period where medicine is changing swiftly and where technologies generated from AI are expanding much faster. This study suggests that ChatGPT can be a valuable tool in creating educational materials, and further validation is essential to ensure educational efficacy and accuracy. ", doi="10.2196/54067", url="https://mededu.jmir.org/2024/1/e54067" } @Article{info:doi/10.2196/55595, author="Wang, Shangqiguo and Mo, Changgeng and Chen, Yuan and Dai, Xiaolu and Wang, Huiyi and Shen, Xiaoli", title="Exploring the Performance of ChatGPT-4 in the Taiwan Audiologist Qualification Examination: Preliminary Observational Study Highlighting the Potential of AI Chatbots in Hearing Care", journal="JMIR Med Educ", year="2024", month="Apr", day="26", volume="10", pages="e55595", keywords="ChatGPT", keywords="medical education", keywords="artificial intelligence", keywords="AI", keywords="audiology", keywords="hearing care", keywords="natural language processing", keywords="large language model", keywords="Taiwan", keywords="hearing", keywords="hearing specialist", keywords="audiologist", keywords="examination", keywords="information accuracy", keywords="educational technology", keywords="healthcare services", keywords="chatbot", keywords="health care services", abstract="Background: Artificial intelligence (AI) chatbots, such as ChatGPT-4, have shown immense potential for application across various aspects of medicine, including medical education, clinical practice, and research. Objective: This study aimed to evaluate the performance of ChatGPT-4 in the 2023 Taiwan Audiologist Qualification Examination, thereby preliminarily exploring the potential utility of AI chatbots in the fields of audiology and hearing care services. Methods: ChatGPT-4 was tasked to provide answers and reasoning for the 2023 Taiwan Audiologist Qualification Examination. The examination encompassed six subjects: (1) basic auditory science, (2) behavioral audiology, (3) electrophysiological audiology, (4) principles and practice of hearing devices, (5) health and rehabilitation of the auditory and balance systems, and (6) auditory and speech communication disorders (including professional ethics). Each subject included 50 multiple-choice questions, with the exception of behavioral audiology, which had 49 questions, amounting to a total of 299 questions. Results: The correct answer rates across the 6 subjects were as follows: 88\% for basic auditory science, 63\% for behavioral audiology, 58\% for electrophysiological audiology, 72\% for principles and practice of hearing devices, 80\% for health and rehabilitation of the auditory and balance systems, and 86\% for auditory and speech communication disorders (including professional ethics). The overall accuracy rate for the 299 questions was 75\%, which surpasses the examination's passing criteria of an average 60\% accuracy rate across all subjects. A comprehensive review of ChatGPT-4's responses indicated that incorrect answers were predominantly due to information errors. Conclusions: ChatGPT-4 demonstrated a robust performance in the Taiwan Audiologist Qualification Examination, showcasing effective logical reasoning skills. Our results suggest that with enhanced information accuracy, ChatGPT-4's performance could be further improved. This study indicates significant potential for the application of AI chatbots in audiology and hearing care services. ", doi="10.2196/55595", url="https://mededu.jmir.org/2024/1/e55595" } @Article{info:doi/10.2196/54280, author="Enich, Michael and Morton, Cory and Jermyn, Richard", title="Naloxone Coprescribing and the Prevention of Opioid Overdoses: Quasi-Experimental Metacognitive Assessment of a Novel Education Initiative", journal="JMIR Med Educ", year="2024", month="Oct", day="28", volume="10", pages="e54280", keywords="naloxone", keywords="coprescribing", keywords="prescription", keywords="academic detailing", keywords="metacognition", keywords="metacognitive evaluation", keywords="pharmacotherapy", keywords="pharmaceutic", keywords="pharmaceutical", keywords="education", keywords="educational intervention", keywords="opioid", keywords="opioid overdose", keywords="harm reduction", abstract="Background: Critical evaluation of naloxone coprescription academic detailing programs has been positive, but little research has focused on how participant thinking changes during academic detailing. Objective: The dual purposes of this study were to (1) present a metacognitive evaluation of a naloxone coprescription academic detailing intervention and (2) describe the application of a metacognitive evaluation for future medical education interventions. Methods: Data were obtained from a pre-post knowledge assessment of a web-based, self-paced intervention designed to increase knowledge of clinical and organizational best practices for the coprescription of naloxone. To assess metacognition, items were designed with confidence-weighted true-false scoring. Multiple metacognitive scores were calculated: 3 content knowledge scores and 5 confidence-weighted true-false scores. Statistical analysis examined whether there were significant differences in scores before and after intervention. Analysis of overall content knowledge showed significant improvement at posttest. Results: There was a significant positive increase in absolute accuracy of participant confidence judgments, confidence in correct probability, and confidence in incorrect probability (all P values were <.05). Overall, results suggest an improvement in content knowledge scores after intervention and, metacognitively, suggest that individuals were more confident in their answer choices, regardless of correctness. Conclusions: Implications include the potential application of metacognitive evaluations to assess nuances in learner performance during academic detailing interventions and as a feedback mechanism to reinforce learning and guide curricular design. ", doi="10.2196/54280", url="https://mededu.jmir.org/2024/1/e54280" } @Article{info:doi/10.2196/60940, author="Hertel, Kay Amanda and Ajlan, S. Radwan", title="Impact of Ophthalmic Knowledge Assessment Program Scores and Surgical Volume on Subspecialty Fellowship Application in Ophthalmology Residency: Retrospective Cohort Study", journal="JMIR Med Educ", year="2024", month="Nov", day="13", volume="10", pages="e60940", keywords="residency", keywords="fellowship", keywords="ophthalmology", keywords="OKAP", keywords="surgical training", keywords="ophthalmology resident", keywords="ophthalmology residency program", keywords="examination", keywords="surgical volume exposure", keywords="fellowship training", keywords="surgical volume", keywords="exposure", keywords="Ophthalmic Knowledge Assessment Program", abstract="Background: Ophthalmology residents take the Ophthalmic Knowledge Assessment Program (OKAP) exam annually, which provides percentile rank for multiple categories and the total score. In addition, ophthalmology residency training programs have multiple subspecialty rotations with defined minimum procedure requirements. However, residents' surgical volumes vary, with some residents exceeding their peers in specific subspecialty rotations. Objective: This study aims to identify if there is a difference in OKAP examination scores and surgical volume exposure during ophthalmology residency training between nonfellowship and fellowship applicants and among various subspecialties. Methods: A retrospective review of OKAP scores and surgical procedure numbers of graduating residents in an accredited academic ophthalmology residency program in the Midwest United States was conducted. Data were collected from 2012 to 2022. Results: A total of 31 residents were identified. Most residents decided to pursue fellowship training upon graduation (20/31, 65\% residents), and the rest chose to practice comprehensive ophthalmology (11/31, 35\% residents). A total of 18/31 residents had OKAP score reports available. The fellowship group outperformed the nonfellowship group in multiple subsections and the total exam (P=.04). Those pursuing fellowship training in glaucoma performed higher on the Glaucoma section (P=.004) and the total exam (P=.005). Residents pursuing cornea performed higher on nearly all subsections, including External Disease and Cornea (P=.02) and the total exam (P=.007). The majority of the surgical volume exposure was identical between fellowship and nonfellowship groups. Those who pursued glaucoma fellowship performed more glaucoma filtering and shunting procedures (P=.03). Residents going into pediatrics fellowship were primary surgeons in more strabismus cases (P=.01), assisted in fewer strabismus cases (P<.001), and had no difference in the total number of strabismus surgeries. Conclusions: In our program, residents pursuing fellowship training had higher OKAP scores on multiple sections and the total exam. There was no significant difference in the overall surgical volume averages between fellowship and nonfellowship groups, but few differences existed in subspecialty procedures among fellowship applicants. Larger multicenter studies are needed to clarify the relationship between OKAP scores and ophthalmology fellowship decisions nationwide. ", doi="10.2196/60940", url="https://mededu.jmir.org/2024/1/e60940" } @Article{info:doi/10.2196/56132, author="Mehyar, Nimer and Awawdeh, Mohammed and Omair, Aamir and Aldawsari, Adi and Alshudukhi, Abdullah and Alzeer, Ahmed and Almutairi, Khaled and Alsultan, Sultan", title="Long-Term Knowledge Retention of Biochemistry Among Medical Students in Riyadh, Saudi Arabia: Cross-Sectional Survey", journal="JMIR Med Educ", year="2024", month="Dec", day="16", volume="10", pages="e56132", keywords="biochemistry", keywords="knowledge", keywords="retention", keywords="medical students", keywords="retention interval", keywords="Saudi Arabia", abstract="Background: Biochemistry is a cornerstone of medical education. Its knowledge is integral to the understanding of complex biological processes and how they are applied in several areas in health care. Also, its significance is reflected in the way it informs the practice of medicine, which can guide and help in both diagnosis and treatment. However, the retention of biochemistry knowledge over time remains a dilemma. Long-term retention of such crucial information is extremely important, as it forms the foundation upon which clinical skills are developed and refined. The effectiveness of biochemistry education, and consequently its long-term retention, is influenced by several factors. Educational methods play a critical role; interactional and integrative teaching approaches have been suggested to enhance retention compared with traditional didactic methods. The frequency and context in which biochemistry knowledge is applied in clinical settings can significantly impact its retention. Practical application reinforces theoretical understanding, making the knowledge more accessible in the long term. Prior knowledge (familiarity) of information suggests that it is stored in long-term memory, which makes its retention in the long term easier to recall. Objectives: This investigation was conducted at King Saud bin Abdulaziz University for Health Sciences in Riyadh, Saudi Arabia. The aim of the study is to understand the dynamics of long-term retention of biochemistry among medical students. Specifically, it looks for the association between students' familiarity with biochemistry content and actual knowledge retention levels. Methods: A cross-sectional correlational survey involving 240 students from King Saud bin Abdulaziz University for Health Sciences was conducted. Participants were recruited via nonprobability convenience sampling. A validated biochemistry assessment tool with 20 questions was used to gauge students' retention in biomolecules, catalysis, bioenergetics, and metabolism. To assess students' familiarity with the knowledge content of test questions, each question is accompanied by options that indicate students' prior knowledge of the content of the question. Statistical analyses tests such as Mann-Whitney U test, Kruskal-Wallis test, and chi-square tests were used. Results: Our findings revealed a significant correlation between students' familiarity of the content with their knowledge retention in the biomolecules (r=0.491; P<.001), catalysis (r=0.500; P<.001), bioenergetics (r=0.528; P<.001), and metabolism (r=0.564; P<.001) biochemistry knowledge domains. Conclusions: This study highlights the significance of familiarity (prior knowledge) in evaluating the retention of biochemistry knowledge. Although limited in terms of generalizability and inherent biases, the research highlights the crucial significance of student's familiarity in actual knowledge retention of several biochemistry domains. These results might be used by educators to customize instructional methods in order to improve students' long-term retention of biochemistry information and boost their clinical performance. ", doi="10.2196/56132", url="https://mededu.jmir.org/2024/1/e56132" } @Article{info:doi/10.2196/48135, author="Koester, MacKenzie and Motz, Rosemary and Porto, Ariel and Reyes Nieves, Nikita and Ashley, Karen", title="Using Project Extension for Community Healthcare Outcomes to Enhance Substance Use Disorder Care in Primary Care: Mixed Methods Study", journal="JMIR Med Educ", year="2024", month="Apr", day="1", volume="10", pages="e48135", keywords="continuing medical education", keywords="telementoring", keywords="substance use disorder treatment", keywords="substance use disorder", keywords="SUD", keywords="primary care", keywords="Extension for Community Healthcare Outcomes", keywords="Project ECHO", abstract="Background: Substance use and overdose deaths make up a substantial portion of injury-related deaths in the United States, with the state of Ohio leading the nation in rates of diagnosed substance use disorder (SUD). Ohio's growing epidemic has indicated a need to improve SUD care in a primary care setting through the engagement of multidisciplinary providers and the use of a comprehensive approach to care. Objective: The purpose of this study was to assess the ability of the Weitzman Extension for Community Healthcare Outcomes (ECHO): Comprehensive Substance Use Disorder Care program to both address and meet 7 series learning objectives and address substances by analyzing (1) the frequency of exposure to the learning objective topics and substance types during case discussions and (2) participants' change in knowledge, self-efficacy, attitudes, and skills related to the treatment of SUDs pre- to postseries. The 7 series learning objective themes included harm reduction, team-based care, behavioral techniques, medication-assisted treatment, trauma-informed care, co-occurring conditions, and social determinants of health. Methods: We used a mixed methods approach using a conceptual content analysis based on series learning objectives and substances and a 2-tailed paired-samples t test of participants' self-reported learner outcomes. The content analysis gauged the frequency and dose of learning objective themes and illicit and nonillicit substances mentioned in participant case presentations and discussions, and the paired-samples t test compared participants' knowledge, self-efficacy, attitudes, and skills associated with learning objectives and medication management of substances from pre- to postseries. Results: The results of the content analysis indicated that 3 learning objective themes---team-based care, harm reduction, and social determinants of health---resulted in the highest frequencies and dose, appearing in 100\% (n=22) of case presentations and discussions. Alcohol had the highest frequency and dose among the illicit and nonillicit substances, appearing in 81\% (n=18) of case presentations and discussions. The results of the paired-samples t test indicated statistically significant increases in knowledge domain statements related to polysubstance use (P=.02), understanding the approach other disciplines use in SUD care (P=.02), and medication management strategies for nicotine (P=.03) and opioid use disorder (P=.003). Statistically significant increases were observed for 2 self-efficacy domain statements regarding medication management for nicotine (P=.002) and alcohol use disorder (P=.02). Further, 1 statistically significant increase in the skill domain was observed regarding using the stages of change theory in interventions (P=.03). Conclusions: These findings indicate that the ECHO program's content aligned with its stated learning objectives; met its learning objectives for the 3 themes where significant improvements were measured; and met its intent to address multiple substances in case presentations and discussions. These results demonstrate that Project ECHO is a potential tool to educate multidisciplinary providers in a comprehensive approach to SUD care. ", doi="10.2196/48135", url="https://mededu.jmir.org/2024/1/e48135", url="http://www.ncbi.nlm.nih.gov/pubmed/38557477" } @Article{info:doi/10.2196/59009, author="Khamisy-Farah, Rola and Biras, Eden and Shehadeh, Rabie and Tuma, Ruba and Atwan, Hisham and Siri, Anna and Converti, Manlio and Chirico, Francesco and Szarpak, ?ukasz and Biz, Carlo and Farah, Raymond and Bragazzi, Nicola", title="Gender and Sexuality Awareness in Medical Education and Practice: Mixed Methods Study", journal="JMIR Med Educ", year="2024", month="Oct", day="8", volume="10", pages="e59009", keywords="gender medicine", keywords="medical education", keywords="clinical practice", keywords="gender-sensitive care", keywords="gender awareness", keywords="sexuality awareness", keywords="awareness", keywords="medical education and practice", keywords="healthcare", keywords="patient outcomes", keywords="patient", keywords="patients", keywords="medical professionals", keywords="training", keywords="educational interventions", keywords="status-based", keywords="survey", keywords="effectiveness", keywords="medical workforce", abstract="Background: The integration of gender and sexuality awareness in health care is increasingly recognized as vital for patient outcomes. Despite this, there is a notable lack of comprehensive data on the current state of physicians' training and perceptions in these areas, leading to a gap in targeted educational interventions and optimal health care delivery. Objective: The study's aim was to explore the experiences and perceptions of attending and resident physicians regarding the inclusion of gender and sexuality content in medical school curricula and professional practice in Israel. Methods: This cross-sectional survey targeted a diverse group of physicians across various specializations and experience levels. Distributed through Israeli Medical Associations and professional networks, it included sections on experiences with gender and sexuality content, perceptions of knowledge, the impact of medical school curricula on professional capabilities, and views on integrating gender medicine in medical education. Descriptive and correlational analyses, along with gender-based and medical status-based comparisons, were used, complemented, and enhanced by qualitative analysis of participants' replies. Results: The survey, encompassing 189 respondents, revealed low-to-moderate exposure to gender and sexuality content in medical school curricula, with a similar perception of preparedness. A need for more comprehensive training was widely recognized. The majority valued training in these areas for enhancing professional capabilities, identifying 10 essential gender-related knowledge areas. The preference for integrating gender medicine throughout medical education was significant. Gender-based analysis indicated variations in exposure and perceptions. Conclusions: The study highlights a crucial need for the inclusion of gender and sexuality awareness in medical education and practice. It suggests the necessity for curriculum development, targeted training programs, policy advocacy, mentorship initiatives, and research to evaluate the effectiveness of these interventions. The findings serve as a foundation for future directions in medical education, aiming for a more inclusive, aware, and prepared medical workforce. ", doi="10.2196/59009", url="https://mededu.jmir.org/2024/1/e59009", url="http://www.ncbi.nlm.nih.gov/pubmed/39152652" } @Article{info:doi/10.2196/57132, author="Tao, Wenjuan and Yang, Jinming and Qu, Xing", title="Utilization of, Perceptions on, and Intention to Use AI Chatbots Among Medical Students in China: National Cross-Sectional Study", journal="JMIR Med Educ", year="2024", month="Oct", day="28", volume="10", pages="e57132", keywords="medical education", keywords="artificial intelligence", keywords="UTAUT model", keywords="utilization", keywords="medical students", keywords="cross-sectional study", keywords="AI chatbots", keywords="China", keywords="acceptance", keywords="electronic survey", keywords="social media", keywords="medical information", keywords="risk", keywords="training", keywords="support", abstract="Background: Artificial intelligence (AI) chatbots are poised to have a profound impact on medical education. Medical students, as early adopters of technology and future health care providers, play a crucial role in shaping the future of health care. However, little is known about the utilization of, perceptions on, and intention to use AI chatbots among medical students in China. Objective: This study aims to explore the utilization of, perceptions on, and intention to use generative AI chatbots among medical students in China, using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. By conducting a national cross-sectional survey, we sought to identify the key determinants that influence medical students' acceptance of AI chatbots, thereby providing a basis for enhancing their integration into medical education. Understanding these factors is crucial for educators, policy makers, and technology developers to design and implement effective AI-driven educational tools that align with the needs and expectations of future health care professionals. Methods: A web-based electronic survey questionnaire was developed and distributed via social media to medical students across the country. The UTAUT was used as a theoretical framework to design the questionnaire and analyze the data. The relationship between behavioral intention to use AI chatbots and UTAUT predictors was examined using multivariable regression. Results: A total of 693 participants were from 57 universities covering 21 provinces or municipalities in China. Only a minority (199/693, 28.72\%) reported using AI chatbots for studying, with ChatGPT (129/693, 18.61\%) being the most commonly used. Most of the participants used AI chatbots for quickly obtaining medical information and knowledge (631/693, 91.05\%) and increasing learning efficiency (594/693, 85.71\%). Utilization behavior, social influence, facilitating conditions, perceived risk, and personal innovativeness showed significant positive associations with the behavioral intention to use AI chatbots (all P values were <.05). Conclusions: Chinese medical students hold positive perceptions toward and high intentions to use AI chatbots, but there are gaps between intention and actual adoption. This highlights the need for strategies to improve access, training, and support and provide peer usage examples to fully harness the potential benefits of chatbot technology. ", doi="10.2196/57132", url="https://mededu.jmir.org/2024/1/e57132" } @Article{info:doi/10.2196/53337, author="Bhavaraju, L. Vasudha and Panchanathan, Sarada and Willis, C. Brigham and Garcia-Filion, Pamela", title="Leveraging the Electronic Health Record to Measure Resident Clinical Experiences and Identify Training Gaps: Development and Usability Study", journal="JMIR Med Educ", year="2024", month="Nov", day="6", volume="10", pages="e53337", keywords="clinical informatics", keywords="electronic health record", keywords="pediatric resident", keywords="COVID-19", keywords="competence-based medical education", keywords="pediatric", keywords="children", keywords="SARS-CoV-2", keywords="clinic", keywords="urban", keywords="diagnosis", keywords="health informatics", keywords="EHR", keywords="individualized learning plan", abstract="Background: Competence-based medical education requires robust data to link competence with clinical experiences. The SARS-CoV-2 (COVID-19) pandemic abruptly altered the standard trajectory of clinical exposure in medical training programs. Residency program directors were tasked with identifying and addressing the resultant gaps in each trainee's experiences using existing tools. Objective: This study aims to demonstrate a feasible and efficient method to capture electronic health record (EHR) data that measure the volume and variety of pediatric resident clinical experiences from a continuity clinic; generate individual-, class-, and graduate-level benchmark data; and create a visualization for learners to quickly identify gaps in clinical experiences. Methods: This pilot was conducted in a large, urban pediatric residency program from 2016 to 2022. Through consensus, 5 pediatric faculty identified diagnostic groups that pediatric residents should see to be competent in outpatient pediatrics. Information technology consultants used International Classification of Diseases, Tenth Revision (ICD-10) codes corresponding with each diagnostic group to extract EHR patient encounter data as an indicator of exposure to the specific diagnosis. The frequency (volume) and diagnosis types (variety) seen by active residents (classes of 2020?2022) were compared with class and graduated resident (classes of 2016?2019) averages. These data were converted to percentages and translated to a radar chart visualization for residents to quickly compare their current clinical experiences with peers and graduates. Residents were surveyed on the use of these data and the visualization to identify training gaps. Results: Patient encounter data about clinical experiences for 102 residents (N=52 graduates) were extracted. Active residents (n=50) received data reports with radar graphs biannually: 3 for the classes of 2020 and 2021 and 2 for the class of 2022. Radar charts distinctly demonstrated gaps in diagnoses exposure compared with classmates and graduates. Residents found the visualization useful in setting clinical and learning goals. Conclusions: This pilot describes an innovative method of capturing and presenting data about resident clinical experiences, compared with peer and graduate benchmarks, to identify learning gaps that may result from disruptions or modifications in medical training. This methodology can be aggregated across specialties and institutions and potentially inform competence-based medical education. ", doi="10.2196/53337", url="https://mededu.jmir.org/2024/1/e53337" } @Article{info:doi/10.2196/52068, author="Yokokawa, Daiki and Shikino, Kiyoshi and Nishizaki, Yuji and Fukui, Sho and Tokuda, Yasuharu", title="Evaluation of a Computer-Based Morphological Analysis Method for Free-Text Responses in the General Medicine In-Training Examination: Algorithm Validation Study", journal="JMIR Med Educ", year="2024", month="Dec", day="5", volume="10", pages="e52068", keywords="General Medicine In-Training Examination", keywords="free-text response", keywords="morphological analysis", keywords="Situation, Background, Assessment, and Recommendation", keywords="video-based question", abstract="Background: The General Medicine In-Training Examination (GM-ITE) tests clinical knowledge in a 2-year postgraduate residency program in Japan. In the academic year 2021, as a domain of medical safety, the GM-ITE included questions regarding the diagnosis from medical history and physical findings through video viewing and the skills in presenting a case. Examinees watched a video or audio recording of a patient examination and provided free-text responses. However, the human cost of scoring free-text answers may limit the implementation of GM-ITE. A simple morphological analysis and word-matching model, thus, can be used to score free-text responses. Objective: This study aimed to compare human versus computer scoring of free-text responses and qualitatively evaluate the discrepancies between human- and machine-generated scores to assess the efficacy of machine scoring. Methods: After obtaining consent for participation in the study, the authors used text data from residents who voluntarily answered the GM-ITE patient reproduction video-based questions involving simulated patients. The GM-ITE used video-based questions to simulate a patient's consultation in the emergency room with a diagnosis of pulmonary embolism following a fracture. Residents provided statements for the case presentation. We obtained human-generated scores by collating the results of 2 independent scorers and machine-generated scores by converting the free-text responses into a word sequence through segmentation and morphological analysis and matching them with a prepared list of correct answers in 2022. Results: Of the 104 responses collected---63 for postgraduate year 1 and 41 for postgraduate year 2---39 cases remained for final analysis after excluding invalid responses. The authors found discrepancies between human and machine scoring in 14 questions (7.2\%); some were due to shortcomings in machine scoring that could be resolved by maintaining a list of correct words and dictionaries, whereas others were due to human error. Conclusions: Machine scoring is comparable to human scoring. It requires a simple program and calibration but can potentially reduce the cost of scoring free-text responses. ", doi="10.2196/52068", url="https://mededu.jmir.org/2024/1/e52068" } @Article{info:doi/10.2196/45413, author="Guinez-Molinos, Sergio and Espinoza, Sonia and Andrade, Jose and Medina, Alejandro", title="Design and Development of Learning Management System Huemul for Teaching Fast Healthcare Interoperability Resource: Algorithm Development and Validation Study", journal="JMIR Med Educ", year="2024", month="Jan", day="29", volume="10", pages="e45413", keywords="interoperability", keywords="health information system", keywords="Health Level Seven International", keywords="HL7", keywords="Fast Healthcare Interoperability Resource", keywords="FHIR", keywords="certification", keywords="training", keywords="interoperable", keywords="e-learning", keywords="application programming interface", keywords="API", abstract="Background: Interoperability between health information systems is a fundamental requirement to guarantee the continuity of health care for the population. The Fast Healthcare Interoperability Resource (FHIR) is the standard that enables the design and development of interoperable systems with broad adoption worldwide. However, FHIR training curriculums need an easily administered web-based self-learning platform with modules to create scenarios and questions that the learner answers. This paper proposes a system for teaching FHIR that automatically evaluates the answers, providing the learner with continuous feedback and progress. Objective: We are designing and developing a learning management system for creating, applying, deploying, and automatically assessing FHIR web-based courses. Methods: The system requirements for teaching FHIR were collected through interviews with experts involved in academic and professional FHIR activities (universities and health institutions). The interviews were semistructured, recording and documenting each meeting. In addition, we used an ad hoc instrument to register and analyze all the needs to elicit the requirements. Finally, the information obtained was triangulated with the available evidence. This analysis was carried out with Atlas-ti software. For design purposes, the requirements were divided into functional and nonfunctional. The functional requirements were (1) a test and question manager, (2) an application programming interface (API) to orchestrate components, (3) a test evaluator that automatically evaluates the responses, and (4) a client application for students. Security and usability are essential nonfunctional requirements to design functional and secure interfaces. The software development methodology was based on the traditional spiral model. The end users of the proposed system are (1) the system administrator for all technical aspects of the server, (2) the teacher designing the courses, and (3) the students interested in learning FHIR. Results: The main result described in this work is Huemul, a learning management system for training on FHIR, which includes the following components: (1) Huemul Admin: a web application to create users, tests, and questions and define scores; (2) Huemul API: module for communication between different software components (FHIR server, client, and engine); (3) Huemul Engine: component for answers evaluation to identify differences and validate the content; and (4) Huemul Client: the web application for users to show the test and questions. Huemul was successfully implemented with 416 students associated with the 10 active courses on the platform. In addition, the teachers have created 60 tests and 695 questions. Overall, the 416 students who completed their courses rated Huemul highly. Conclusions: Huemul is the first platform that allows the creation of courses, tests, and questions that enable the automatic evaluation and feedback of FHIR operations. Huemul has been implemented in multiple FHIR teaching scenarios for health care professionals. Professionals trained on FHIR with Huemul are leading successful national and international initiatives. ", doi="10.2196/45413", url="https://mededu.jmir.org/2024/1/e45413", url="http://www.ncbi.nlm.nih.gov/pubmed/38285492" } @Article{info:doi/10.2196/48566, author="Liu, Wa Justina Yat and Mak, Ying Pui and Chan, Kitty and Cheung, Ki Daphne Sze and Cheung, Kin and Fong, K. Kenneth N. and Kor, Kin Patrick Pui and Lai, Hung Timothy Kam and Maximo, Tulio", title="The Effects of Immersive Virtual Reality--Assisted Experiential Learning on Enhancing Empathy in Undergraduate Health Care Students Toward Older Adults With Cognitive Impairment: Multiple-Methods Study", journal="JMIR Med Educ", year="2024", month="Feb", day="15", volume="10", pages="e48566", keywords="immersive virtual reality", keywords="undergraduate health care education", keywords="empathy", keywords="cognitive impairment", abstract="Background: Immersive virtual reality (IVR)--assisted experiential learning has the potential to foster empathy among undergraduate health care students toward older adults with cognitive impairment by facilitating a sense of embodiment. However, the extent of its effectiveness, including enhancing students' learning experiences and achieving intended learning outcomes, remains underexplored. Objective: This study aims to evaluate the impacts of IVR-assisted experiential learning on the empathy of undergraduate health care students toward older people with cognitive impairment as the primary outcome (objective 1) and on their learning experience (objective 2) and their attainment of learning outcomes as the secondary outcomes (objective 3). Methods: A multiple-methods design was used, which included surveys, focus groups, and a review of the students' group assignments. Survey data were summarized using descriptive statistics, whereas paired 2-tailed t tests were used to evaluate differences in empathy scores before and after the 2-hour IVR tutorial (objective 1). Focus groups were conducted to evaluate the impacts of IVR-assisted experiential learning on the empathy of undergraduate health care students toward older people with cognitive impairment (objective 1). Descriptive statistics obtained from surveys and thematic analyses of focus groups were used to explore the students' learning experiences (objective 2). Thematic analysis of group assignments was conducted to identify learning outcomes (objective 3). Results: A total of 367 undergraduate nursing and occupational therapy students were recruited via convenience sampling. There was a significant increase in the students' empathy scores, measured using the Kiersma-Chen Empathy Scale, from 78.06 (SD 7.72) before to 81.17 (SD 8.93) after (P<.001). Students expressed high satisfaction with the IVR learning innovation, with a high satisfaction mean score of 20.68 (SD 2.55) and a high self-confidence mean score of 32.04 (SD 3.52) on the Student Satisfaction and Self-Confidence scale. Students exhibited a good sense of presence in the IVR learning environment, as reflected in the scores for adaptation (41.30, SD 6.03), interface quality (11.36, SD 3.70), involvement (62.00, SD 9.47), and sensory fidelity (31.47, SD 5.23) on the Presence Questionnaire version 2.0. In total, 3 major themes were identified from the focus groups, which involved 23 nursing students: enhanced sympathy toward older adults with cognitive impairment, improved engagement in IVR learning, and confidence in understanding the key concepts through the learning process. These themes supplement and align with the survey results. The analysis of the written assignments revealed that students attained the learning outcomes of understanding the challenges faced by older adults with cognitive impairment, the importance of providing person-centered care, and the need for an age-friendly society. Conclusions: IVR-assisted experiential learning enhances students' knowledge and empathy in caring for older adults with cognitive impairment. These findings suggest that IVR can be a valuable tool in professional health care education. ", doi="10.2196/48566", url="https://mededu.jmir.org/2024/1/e48566", url="http://www.ncbi.nlm.nih.gov/pubmed/38358800" } @Article{info:doi/10.2196/59213, author="Holderried, Friederike and Stegemann-Philipps, Christian and Herrmann-Werner, Anne and Festl-Wietek, Teresa and Holderried, Martin and Eickhoff, Carsten and Mahling, Moritz", title="A Language Model--Powered Simulated Patient With Automated Feedback for History Taking: Prospective Study", journal="JMIR Med Educ", year="2024", month="Aug", day="16", volume="10", pages="e59213", keywords="virtual patients communication", keywords="communication skills", keywords="technology enhanced education", keywords="TEL", keywords="medical education", keywords="ChatGPT", keywords="GPT: LLM", keywords="LLMs", keywords="NLP", keywords="natural language processing", keywords="machine learning", keywords="artificial intelligence", keywords="language model", keywords="language models", keywords="communication", keywords="relationship", keywords="relationships", keywords="chatbot", keywords="chatbots", keywords="conversational agent", keywords="conversational agents", keywords="history", keywords="histories", keywords="simulated", keywords="student", keywords="students", keywords="interaction", keywords="interactions", abstract="Background: Although history taking is fundamental for diagnosing medical conditions, teaching and providing feedback on the skill can be challenging due to resource constraints. Virtual simulated patients and web-based chatbots have thus emerged as educational tools, with recent advancements in artificial intelligence (AI) such as large language models (LLMs) enhancing their realism and potential to provide feedback. Objective: In our study, we aimed to evaluate the effectiveness of a Generative Pretrained Transformer (GPT) 4 model to provide structured feedback on medical students' performance in history taking with a simulated patient. Methods: We conducted a prospective study involving medical students performing history taking with a GPT-powered chatbot. To that end, we designed a chatbot to simulate patients' responses and provide immediate feedback on the comprehensiveness of the students' history taking. Students' interactions with the chatbot were analyzed, and feedback from the chatbot was compared with feedback from a human rater. We measured interrater reliability and performed a descriptive analysis to assess the quality of feedback. Results: Most of the study's participants were in their third year of medical school. A total of 1894 question-answer pairs from 106 conversations were included in our analysis. GPT-4's role-play and responses were medically plausible in more than 99\% of cases. Interrater reliability between GPT-4 and the human rater showed ``almost perfect'' agreement (Cohen $\kappa$=0.832). Less agreement ($\kappa$<0.6) detected for 8 out of 45 feedback categories highlighted topics about which the model's assessments were overly specific or diverged from human judgement. Conclusions: The GPT model was effective in providing structured feedback on history-taking dialogs provided by medical students. Although we unraveled some limitations regarding the specificity of feedback for certain feedback categories, the overall high agreement with human raters suggests that LLMs can be a valuable tool for medical education. Our findings, thus, advocate the careful integration of AI-driven feedback mechanisms in medical training and highlight important aspects when LLMs are used in that context. ", doi="10.2196/59213", url="https://mededu.jmir.org/2024/1/e59213" } @Article{info:doi/10.2196/57772, author="Ba, Hongjun and Zhang, Lili and He, Xiufang and Li, Shujuan", title="Knowledge Mapping and Global Trends in the Field of the Objective Structured Clinical Examination: Bibliometric and Visual Analysis (2004-2023)", journal="JMIR Med Educ", year="2024", month="Sep", day="30", volume="10", pages="e57772", keywords="Objective Structured Clinical Examination", keywords="OSCE", keywords="medical education assessment", keywords="bibliometric analysis", keywords="academic collaboration", keywords="health care professional training", keywords="medical education", keywords="medical knowledge", keywords="medical training", keywords="medical student", abstract="Background: The Objective Structured Clinical Examination (OSCE) is a pivotal tool for assessing health care professionals and plays an integral role in medical education. Objective: This study aims to map the bibliometric landscape of OSCE research, highlighting trends and key influencers. Methods: A comprehensive literature search was conducted for materials related to OSCE from January 2004 to December 2023, using the Web of Science Core Collection database. Bibliometric analysis and visualization were performed with VOSviewer and CiteSpace software tools. Results: Our analysis indicates a consistent increase in OSCE-related publications over the study period, with a notable surge after 2019, culminating in a peak of activity in 2021. The United States emerged as a significant contributor, responsible for 30.86\% (1626/5268) of total publications and amassing 44,051 citations. Coauthorship network analysis highlighted robust collaborations, particularly between the United States and the United Kingdom. Leading journals in this domain---BMC Medical Education, Medical Education, Academic Medicine, and Medical Teacher---featured the highest volume of papers, while The Lancet garnered substantial citations, reflecting its high impact factor (to be verified for accuracy). Prominent authors in the field include Sondra Zabar, Debra Pugh, Timothy J Wood, and Susan Humphrey-Murto, with Ronaldo M Harden, Brian D Hodges, and George E Miller being the most cited. The analysis of key research terms revealed a focus on ``education,'' ``performance,'' ``competence,'' and ``skills,'' indicating these are central themes in OSCE research. Conclusions: The study underscores a dynamic expansion in OSCE research and international collaboration, spotlighting influential countries, institutions, authors, and journals. These elements are instrumental in steering the evolution of medical education assessment practices and suggest a trajectory for future research endeavors. Future work should consider the implications of these findings for medical education and the potential areas for further investigation, particularly in underrepresented regions or emerging competencies in health care training. ", doi="10.2196/57772", url="https://mededu.jmir.org/2024/1/e57772" } @Article{info:doi/10.2196/54083, author="Hofstetter, Sebastian and Zilezinski, Max and Behr, Dominik and Kraft, Bernhard and Buhtz, Christian and Paulicke, Denny and Wolf, Anja and Klus, Christina and Stoevesandt, Dietrich and Schwarz, Karsten and Jahn, Patrick", title="Integrating Digital Assistive Technologies Into Care Processes: Mixed Methods Study", journal="JMIR Med Educ", year="2024", month="Oct", day="9", volume="10", pages="e54083", keywords="digital assistive technologies", keywords="education concept", keywords="intention to use", keywords="learning effects", keywords="digital transformation", abstract="Background: Current challenges in patient care have increased research on technology use in nursing and health care. Digital assistive technologies (DATs) are one option that can be incorporated into care processes. However, how the application of DATs should be introduced to nurses and care professionals must be clarified. No structured and effective education concepts for the patient-oriented integration of DATs in the nursing sector are currently available. Objective: This study aims to examine how a structured and guided integration and education concept, herein termed the sensitization, evaluative introduction, qualification, and implementation (SEQI) education concept, can support the integration of DATs into nursing practices. Methods: This study used an explanatory, sequential study design with a mixed methods approach. The SEQI intervention was run in 26 long-term care facilities oriented toward older adults in Germany after a 5-day training course in each. The participating care professionals were asked to test 1 of 6 DATs in real-world practice over 3 days. Surveys (n=112) were then administered that recorded the intention to use DATs at 3 measurement points, and guided qualitative interviews with care professionals (n=12) were conducted to evaluate the learning concepts and effects of the intervention. Results: As this was a pilot study, no sample size calculation was carried out, and P values were not reported. The participating care professionals were generally willing to integrate DATs---as an additional resource---into nursing processes even before the 4-stage SEQI intervention was presented. However, the intervention provided additional background knowledge and sensitized care professionals to the digital transformation, enabling them to evaluate how DATs fit in the health care sector, what qualifies these technologies for correct application, and what promotes their use. The care professionals expressed specific ideas and requirements for both technology-related education concepts and nursing DATs. Conclusions: Actively matching technical support, physical limitations, and patients' needs is crucial when selecting DATs and integrating them into nursing processes. To this end, using a structured process such as SEQI that strengthens care professionals' ability to integrate DATs can help improve the benefits of such technology in the health care setting. Practical, application-oriented learning can promote the long-term implementation of DATs. ", doi="10.2196/54083", url="https://mededu.jmir.org/2024/1/e54083" } @Article{info:doi/10.2196/57451, author="Jin, Kyung Hye and Kim, EunYoung", title="Performance of GPT-3.5 and GPT-4 on the Korean Pharmacist Licensing Examination: Comparison Study", journal="JMIR Med Educ", year="2024", month="Dec", day="4", volume="10", pages="e57451", keywords="GPT-3.5", keywords="GPT-4", keywords="Korean", keywords="Korean Pharmacist Licensing Examination", keywords="KPLE", abstract="Background: ChatGPT, a recently developed artificial intelligence chatbot and a notable large language model, has demonstrated improved performance on medical field examinations. However, there is currently little research on its efficacy in languages other than English or in pharmacy-related examinations. Objective: This study aimed to evaluate the performance of GPT models on the Korean Pharmacist Licensing Examination (KPLE). Methods: We evaluated the percentage of correct answers provided by 2 different versions of ChatGPT (GPT-3.5 and GPT-4) for all multiple-choice single-answer KPLE questions, excluding image-based questions. In total, 320, 317, and 323 questions from the 2021, 2022, and 2023 KPLEs, respectively, were included in the final analysis, which consisted of 4 units: Biopharmacy, Industrial Pharmacy, Clinical and Practical Pharmacy, and Medical Health Legislation. Results: The 3-year average percentage of correct answers was 86.5\% (830/960) for GPT-4 and 60.7\% (583/960) for GPT-3.5. GPT model accuracy was highest in Biopharmacy (GPT-3.5 77/96, 80.2\% in 2022; GPT-4 87/90, 96.7\% in 2021) and lowest in Medical Health Legislation (GPT-3.5 8/20, 40\% in 2022; GPT-4 12/20, 60\% in 2022). Additionally, when comparing the performance of artificial intelligence with that of human participants, pharmacy students outperformed GPT-3.5 but not GPT-4. Conclusions: In the last 3 years, GPT models have performed very close to or exceeded the passing threshold for the KPLE. This study demonstrates the potential of large language models in the pharmacy domain; however, extensive research is needed to evaluate their reliability and ensure their secure application in pharmacy contexts due to several inherent challenges. Addressing these limitations could make GPT models more effective auxiliary tools for pharmacy education. ", doi="10.2196/57451", url="https://mededu.jmir.org/2024/1/e57451" } @Article{info:doi/10.2196/56879, author="Gil-Hern{\'a}ndez, Eva and Carrillo, Irene and Guilabert, Mercedes and Bohomol, Elena and Serpa, C. Piedad and Ribeiro Neves, Vanessa and Maluenda Mart{\'i}nez, Maria and Martin-Delgado, Jimmy and P{\'e}rez-Esteve, Clara and Fern{\'a}ndez, C{\'e}sar and Mira, Joaqu{\'i}n Jos{\'e}", title="Development and Implementation of a Safety Incident Report System for Health Care Discipline Students During Clinical Internships: Observational Study", journal="JMIR Med Educ", year="2024", month="Jul", day="18", volume="10", pages="e56879", keywords="reporting systems", keywords="education", keywords="medical", keywords="nursing", keywords="undergraduate", keywords="patient safety", abstract="Background: Patient safety is a fundamental aspect of health care practice across global health systems. Safe practices, which include incident reporting systems, have proven valuable in preventing the recurrence of safety incidents. However, the accessibility of this tool for health care discipline students is not consistent, limiting their acquisition of competencies. In addition, there is no tools to familiarize students with analyzing safety incidents. Gamification has emerged as an effective strategy in health care education. Objective: This study aims to develop an incident reporting system tailored to the specific needs of health care discipline students, named Safety Incident Report System for Students. Secondary objectives included studying the performance of different groups of students in the use of the platform and training them on the correct procedures for reporting. Methods: This was an observational study carried out in 3 phases. Phase 1 consisted of the development of the web-based platform and the incident registration form. For this purpose, systems already developed and in use in Spain were taken as a basis. During phase 2, a total of 223 students in medicine and nursing with clinical internships from universities in Argentina, Brazil, Colombia, Ecuador, and Spain received an introductory seminar and were given access to the platform. Phase 3 ran in parallel and involved evaluation and feedback of the reports received as well as the opportunity to submit the students' opinion on the process. Descriptive statistics were obtained to gain information about the incidents, and mean comparisons by groups were performed to analyze the scores obtained. Results: The final form was divided into 9 sections and consisted of 48 questions that allowed for introducing data about the incident, its causes, and proposals for an improvement plan. The platform included a personal dashboard displaying submitted reports, average scores, progression, and score rankings. A total of 105 students participated, submitting 147 reports. Incidents were mainly reported in the hospital setting, with complications of care (87/346, 25.1\%) and effects of medication or medical products (82/346, 23.7\%) being predominant. The most repeated causes were related confusion, oversight, or distractions (49/147, 33.3\%) and absence of process verification (44/147, 29.9\%). Statistically significant differences were observed between the mean final scores received by country (P<.001) and sex (P=.006) but not by studies (P=.47). Overall, participants rated the experience of using the Safety Incident Report System for Students positively. Conclusions: This study presents an initial adaptation of reporting systems to suit the needs of students, introducing a guided and inspiring framework that has garnered positive acceptance among students. Through this endeavor, a pathway toward a safety culture within the faculty is established. A long-term follow-up would be desirable to check the real benefits of using the tool during education. Trial Registration: Trial Registration: ClinicalTrials.gov NCT05350345; https://clinicaltrials.gov/study/NCT05350345 ", doi="10.2196/56879", url="https://mededu.jmir.org/2024/1/e56879", url="http://www.ncbi.nlm.nih.gov/pubmed/39024005" } @Article{info:doi/10.2196/52461, author="He, Yuanhang and Xie, Zhihong and Li, Jiachen and Meng, Ziang and Xue, Dongbo and Hao, Chenjun", title="Global Trends in mHealth and Medical Education Research: Bibliometrics and Knowledge Graph Analysis", journal="JMIR Med Educ", year="2024", month="Jun", day="4", volume="10", pages="e52461", keywords="mHealth", keywords="mobile health", keywords="medical education", keywords="bibliometric", keywords="knowledge map", keywords="VOSviewer", abstract="Background: Mobile health (mHealth) is an emerging mobile communication and networking technology for health care systems. The integration of mHealth in medical education is growing extremely rapidly, bringing new changes to the field. However, no study has analyzed the publication and research trends occurring in both mHealth and medical education. Objective: The aim of this study was to summarize the current application and development trends of mHealth in medical education by searching and analyzing published articles related to both mHealth and medical education. Methods: The literature related to mHealth and medical education published from 2003 to 2023 was searched in the Web of Science core database, and 790 articles were screened according to the search strategy. The HistCite Pro 2.0 tool was used to analyze bibliometric indicators. VOSviewer, Pajek64, and SCImago Graphica software were used to visualize research trends and identify hot spots in the field. Results: In the past two decades, the number of published papers on mHealth in medical education has gradually increased, from only 3 papers in 2003 to 130 in 2022; this increase became particularly evident in 2007. The global citation score was determined to be 10,600, with an average of 13.42 citations per article. The local citation score was 96. The United States is the country with the most widespread application of mHealth in medical education, and most of the institutions conducting in-depth research in this field are also located in the United States, closely followed by China and the United Kingdom. Based on current trends, global coauthorship and research exchange will likely continue to expand. Among the research journals publishing in this joint field, journals published by JMIR Publications have an absolute advantage. A total of 105 keywords were identified, which were divided into five categories pointing to different research directions. Conclusions: Under the influence of COVID-19, along with the popularization of smartphones and modern communication technology, the field of combining mHealth and medical education has become a more popular research direction. The concept and application of digital health will be promoted in future developments of medical education. ", doi="10.2196/52461", url="https://mededu.jmir.org/2024/1/e52461" } @Article{info:doi/10.2196/50118, author="Laidsaar-Powell, Rebekah and Giunta, Sarah and Butow, Phyllis and Keast, Rachael and Koczwara, Bogda and Kay, Judy and Jefford, Michael and Turner, Sandra and Saunders, Christobel and Schofield, Penelope and Boyle, Frances and Yates, Patsy and White, Kate and Miller, Annie and Butt, Zoe and Bonnaudet, Melanie and Juraskova, Ilona", title="Development of Web-Based Education Modules to Improve Carer Engagement in Cancer Care: Design and User Experience Evaluation of the e-Triadic Oncology (eTRIO) Modules for Clinicians, Patients, and Carers", journal="JMIR Med Educ", year="2024", month="Apr", day="17", volume="10", pages="e50118", keywords="family carers", keywords="patient education", keywords="health professional education", keywords="web-based intervention", keywords="mobile phone", abstract="Background: Carers often assume key roles in cancer care. However, many carers report feeling disempowered and ill?equipped to support patients. Our group published evidence-based guidelines (the Triadic Oncology [TRIO] Guidelines) to improve oncology clinician engagement with carers and the management of challenging situations involving carers. Objective: To facilitate implementation of the TRIO Guidelines in clinical practice, we aimed to develop, iteratively refine, and conduct user testing of a suite of evidence-based and interactive web-based education modules for oncology clinicians (e-Triadic Oncology [eTRIO]), patients with cancer, and carers (eTRIO for Patients and Carers [eTRIO?pc]). These were designed to improve carer involvement, communication, and shared decision-making in the cancer management setting. Methods: The eTRIO education modules were based on extensive research, including systematic reviews, qualitative interviews, and consultation analyses. Guided by the person-based approach, module content and design were reviewed by an expert advisory group comprising academic and clinical experts (n=13) and consumers (n=5); content and design were continuously and iteratively refined. User experience testing (including ``think-aloud'' interviews and administration of the System Usability Scale [SUS]) of the modules was completed by additional clinicians (n=5), patients (n=3), and carers (n=3). Results: The final clinician module comprises 14 sections, requires approximately 1.5 to 2 hours to complete, and covers topics such as carer-inclusive communication and practices; supporting carer needs; and managing carer dominance, anger, and conflicting patient-carer wishes. The usability of the module was rated by 5 clinicians, with a mean SUS score of 75 (SD 5.3), which is interpreted as good. Clinicians often desired information in a concise format, divided into small ``snackable'' sections that could be easily recommenced if they were interrupted. The carer module features 11 sections; requires approximately 1.5 hours to complete; and includes topics such as the importance of carers, carer roles during consultations, and advocating for the patient. The patient module is an adaptation of the relevant carer module sections, comprising 7 sections and requiring 1 hour to complete. The average SUS score as rated by 6 patients and carers was 78 (SD 16.2), which is interpreted as good. Interactive activities, clinical vignette videos, and reflective learning exercises are incorporated into all modules. Patient and carer consumer advisers advocated for empathetic content and tone throughout their modules, with an easy-to-read and navigable module interface. Conclusions: The eTRIO suite of modules were rigorously developed using a person-based design methodology to meet the unique information needs and learning requirements of clinicians, patients, and carers, with the goal of improving effective and supportive carer involvement in cancer consultations and cancer care. ", doi="10.2196/50118", url="https://mededu.jmir.org/2024/1/e50118", url="http://www.ncbi.nlm.nih.gov/pubmed/38630531" } @Article{info:doi/10.2196/45291, author="Bhandoria, Geetu and Bilir, Esra and Uwins, Christina and Vidal-Alaball, Josep and Fuster-Casanovas, A{\"i}na and Ahmed, Wasim", title="Impact of a New Gynecologic Oncology Hashtag During Virtual-Only ASCO Annual Meetings: An X (Twitter) Social Network Analysis", journal="JMIR Med Educ", year="2024", month="Aug", day="14", volume="10", pages="e45291", keywords="social media", keywords="academic tweeting", keywords="hashtag", keywords="gynecologic oncology", keywords="Twitter", keywords="ASCO", keywords="gynecology", keywords="oncology", keywords="virtual", keywords="engagement", keywords="software application", keywords="users", keywords="cancer", keywords="social network", keywords="health promotion", abstract="Background: Official conference hashtags are commonly used to promote tweeting and social media engagement. The reach and impact of introducing a new hashtag during an oncology conference have yet to be studied. The American Society of Clinical Oncology (ASCO) conducts an annual global meeting, which was entirely virtual due to the COVID-19 pandemic in 2020 and 2021. Objective: This study aimed to assess the reach and impact (in the form of vertices and edges generated) and X (formerly Twitter) activity of the new hashtags \#goASCO20 and \#goASCO21 in the ASCO 2020 and 2021 virtual conferences. Methods: New hashtags (\#goASCO20 and \#goASCO21) were created for the ASCO virtual conferences in 2020 and 2021 to help focus gynecologic oncology discussion at the ASCO meetings. Data were retrieved using these hashtags (\#goASCO20 for 2020 and \#goASCO21 for 2021). A social network analysis was performed using the NodeXL software application. Results: The hashtags \#goASCO20 and \#goASCO21 had similar impacts on the social network. Analysis of the reach and impact of the individual hashtags found \#goASCO20 to have 150 vertices and 2519 total edges and \#goASCO20 to have 174 vertices and 2062 total edges. Mentions and tweets between 2020 and 2021 were also similar. The circles representing different users were spatially arranged in a more balanced way in 2021. Tweets using the \#goASCO21 hashtag received significantly more responses than tweets using \#goASCO20 (75 times in 2020 vs 360 times in 2021; z value=16.63 and P<.001). This indicates increased engagement in the subsequent year. Conclusions: Introducing a gynecologic oncology specialty--specific hashtag (\#goASCO20 and \#goASCO21) that is related but different from the official conference hashtag (\#ASCO20 and \#ASCO21) helped facilitate discussion on topics of interest to gynecologic oncologists during a virtual pan-oncology meeting. This impact was visible in the social network analysis. ", doi="10.2196/45291", url="https://mededu.jmir.org/2024/1/e45291" } @Article{info:doi/10.2196/55149, author="Clavier, Thomas and Chevalier, Emma and Demailly, Zo{\'e} and Veber, Benoit and Messaadi, Imad-Abdelkader and Popoff, Benjamin", title="Social Media Usage for Medical Education and Smartphone Addiction Among Medical Students: National Web-Based Survey", journal="JMIR Med Educ", year="2024", month="Oct", day="22", volume="10", pages="e55149", keywords="medical student", keywords="social network", keywords="social media", keywords="smartphone addiction", keywords="medical education", keywords="mobile addiction", keywords="social networks", abstract="Background: Social media (SoMe) have taken a major place in the medical field, and younger generations are increasingly using them as their primary source to find information. Objective: This study aimed to describe the use of SoMe for medical education among French medical students and assess the prevalence of smartphone addiction in this population. Methods: A cross-sectional web-based survey was conducted among French medical students (second to sixth year of study). The questionnaire collected information on SoMe use for medical education and professional behavior. Smartphone addiction was assessed using the Smartphone Addiction Scale Short-Version (SAS-SV) score. Results: A total of 762 medical students responded to the survey. Of these, 762 (100\%) were SoMe users, spending a median of 120 (IQR 60?150) minutes per day on SoMe; 656 (86.1\%) used SoMe for medical education, with YouTube, Instagram, and Facebook being the most popular platforms. The misuse of SoMe in a professional context was also identified; 27.2\% (207/762) of students posted hospital internship content, and 10.8\% (82/762) searched for a patient's name on SoMe. Smartphone addiction was prevalent among 29.1\% (222/762) of respondents, with a significant correlation between increased SoMe use and SAS-SV score (r=0.39, 95\% CI 0.33?0.45; P<.001). Smartphone-addicted students reported a higher impact on study time (211/222, 95\% vs 344/540, 63.6\%; P<.001) and a greater tendency to share hospital internship content on social networks (78/222, 35.1\% vs 129/540, 23.8\%; P=.002). Conclusions: Our findings reveal the extensive use of SoMe for medical education among French medical students, alongside a notable prevalence of smartphone addiction. These results highlight the need for medical schools and educators to address the responsible use of SoMe and develop strategies to mitigate the risks associated with excessive use and addiction. ", doi="10.2196/55149", url="https://mededu.jmir.org/2024/1/e55149" } @Article{info:doi/10.2196/52924, author="Elhariry, Maiar and Malhotra, Kashish and Goyal, Kashish and Bardus, Marco and Team, CoMICs SIMBA and and Kempegowda, Punith", title="A SIMBA CoMICs Initiative to Cocreating and Disseminating Evidence-Based, Peer-Reviewed Short Videos on Social Media: Mixed Methods Prospective Study", journal="JMIR Med Educ", year="2024", month="Oct", day="30", volume="10", pages="e52924", keywords="influencers", keywords="social media", keywords="public engagement", keywords="apps", keywords="healthcare", keywords="medical students", keywords="online medical information", keywords="simulation", keywords="peer-reviewed information", abstract="Background: Social media is a powerful platform for disseminating health information, yet it is often riddled with misinformation. Further, few guidelines exist for producing reliable, peer-reviewed content. This study describes a framework for creating and disseminating evidence-based videos on polycystic ovary syndrome (PCOS) and thyroid conditions to improve health literacy and tackle misinformation. Objective: The study aims to evaluate the creation, dissemination, and impact of evidence-based, peer-reviewed short videos on PCOS and thyroid disorders across social media. It also explores the experiences of content creators and assesses audience engagement. Methods: This mixed methods prospective study was conducted between December 2022 and May 2023 and comprised five phases: (1) script generation, (2) video creation, (3) cross-platform publication, (4) process evaluation, and (5) impact evaluation. The SIMBA-CoMICs (Simulation via Instant Messaging for Bedside Application--Combined Medical Information Cines) initiative provides a structured process where medical concepts are simplified and converted to visually engaging videos. The initiative recruited medical students interested in making visually appealing and scientifically accurate videos for social media. The students were then guided to create video scripts based on frequently searched PCOS- and thyroid-related topics. Once experts confirmed the accuracy of the scripts, the medical students produced the videos. The videos were checked by clinical experts and experts with lived experience to ensure clarity and engagement. The SIMBA-CoMICs team then guided the students in editing these videos to fit platform requirements before posting them on TikTok, Instagram, YouTube, and Twitter. Engagement metrics were tracked over 2 months. Content creators were interviewed, and thematic analysis was performed to explore their experiences. Results: The 20 videos received 718 likes, 120 shares, and 54,686 views across all platforms, with TikTok (19,458 views) and Twitter (19,678 views) being the most popular. Engagement increased significantly, with follower growth ranging from 5\% on Twitter to 89\% on TikTok. Thematic analysis of interviews with 8 out of 38 participants revealed 4 key themes: views on social media, advice for using social media, reasons for participating, and reflections on the project. Content creators highlighted the advantages of social media, such as large outreach (12 references), convenience (10 references), and accessibility to opportunities (7 references). Participants appreciated the nonrestrictive participation criteria, convenience (8 references), and the ability to record from home using prewritten scripts (6 references). Further recommendations to improve the content creation experience included awareness of audience demographics (9 references), sharing content on multiple platforms (5 references), and collaborating with organizations (3 references). Conclusions: This study demonstrates the effectiveness of the SIMBA CoMICs initiative in training medical students to create accurate medical information on PCOS and thyroid disorders for social media dissemination. The model offers a scalable solution to combat misinformation and improve health literacy. ", doi="10.2196/52924", url="https://mededu.jmir.org/2024/1/e52924" } @Article{info:doi/10.2196/59720, author="Nicolau, Abel and Jorge, In{\^e}s and Vieira-Marques, Pedro and Sa-Couto, Carla", title="Influence of Training With Corrective Feedback Devices on Cardiopulmonary Resuscitation Skills Acquisition and Retention: Systematic Review and Meta-Analysis", journal="JMIR Med Educ", year="2024", month="Dec", day="19", volume="10", pages="e59720", keywords="cardiopulmonary resuscitation", keywords="CPR quality", keywords="resuscitation training", keywords="corrective feedback devices", keywords="skills acquisition", keywords="skills retention", keywords="systematic review", keywords="evidence-based research", keywords="meta-analysis", keywords="feedback devices", keywords="PRISMA", abstract="Background: Several studies related to the use of corrective feedback devices in cardiopulmonary resuscitation training, with different populations, training methodologies, and equipment, present distinct results regarding the influence of this technology. Objective: This systematic review and meta-analysis aimed to examine the impact of corrective feedback devices in cardiopulmonary resuscitation skills acquisition and retention for laypeople and health care professionals. Training duration was also studied. Methods: The search was conducted in PubMed, Web of Science, and Scopus from January 2015 to December 2023. Eligible randomized controlled trials compared technology-based training incorporating corrective feedback with standard training. Outcomes of interest were the quality of chest compression--related components. The risk of bias was assessed using the Cochrane tool. A meta-analysis was used to explore the heterogeneity of the selected studies. Results: In total, 20 studies were included. Overall, it was reported that corrective feedback devices used during training had a positive impact on both skills acquisition and retention. Medium to high heterogeneity was observed. Conclusions: This systematic review and meta-analysis suggest that corrective feedback devices enhance skills acquisition and retention over time. Considering the medium to high heterogeneity observed, these findings should be interpreted with caution. More standardized, high-quality studies are needed. Trial Registration: PROSPERO CRD42021240953; https://www.crd.york.ac.uk/prospero/display\_record.php?RecordID=240953 ", doi="10.2196/59720", url="https://mededu.jmir.org/2024/1/e59720", url="http://www.ncbi.nlm.nih.gov/pubmed/39699935" } @Article{info:doi/10.2196/47127, author="Nguyen, Tuan Ba and Nguyen, Anh Van and Blizzard, Leigh Christopher and Palmer, Andrew and Nguyen, Tu Huu and Quyet, Cong Thang and Tran, Viet and Skinner, Marcus and Perndt, Haydn and Nelson, R. Mark", title="Using the Kirkpatrick Model to Evaluate the Effect of a Primary Trauma Care Course on Health Care Workers' Knowledge, Attitude, and Practice in Two Vietnamese Local Hospitals: Prospective Intervention Study", journal="JMIR Med Educ", year="2024", month="Jul", day="23", volume="10", pages="e47127", keywords="trauma care", keywords="emergency medicine", keywords="primary trauma care course", keywords="short course", keywords="medical education", keywords="trauma", keywords="emergency", keywords="urgent", keywords="professional development", keywords="workshop", keywords="injury", keywords="injured", keywords="injuries", keywords="primary care", abstract="Background: The Primary Trauma Care (PTC) course was originally developed to instruct health care workers in the management of patients with severe injuries in low- and middle-income countries (LMICs) with limited medical resources. PTC has now been taught for more than 25 years. Many studies have demonstrated that the 2-day PTC workshop is useful and informative to frontline health staff and has helped improve knowledge and confidence in trauma management; however, there is little evidence of the effect of the course on changes in clinical practice. The Kirkpatrick model (KM) and the knowledge, attitude, and practice (KAP) model are effective methods to evaluate this question. Objective: The aim of this study was to investigate how the 2-day PTC course impacts the satisfaction, knowledge, and skills of health care workers in 2 Vietnamese hospitals using a conceptual framework incorporating the KAP model and the 4-level KM as evaluation tools. Methods: The PTC course was delivered over 2 days in the emergency departments (EDs) of Thanh Hoa and Ninh Binh hospitals in February and March 2022, respectively. This study followed a prospective pre- and postintervention design. We used validated instruments to assess the participants' satisfaction, knowledge, and skills before, immediately after, and 6 months after course delivery. The Fisher exact test and the Wilcoxon matched-pairs signed rank test were used to compare the percentages and mean scores at the pretest, posttest, and 6-month postcourse follow-up time points among course participants. Results: A total of 80 health care staff members attended the 2-day PTC course and nearly 100\% of the participants were satisfied with the course. At level 2 of the KM (knowledge), the scores on multiple-choice questions and the confidence matrix improved significantly from 60\% to 77\% and from 59\% to 71\%, respectively (P<.001), and these improvements were seen in both subgroups (nurses and doctors). The focus of level 3 was on practice, demonstrating a significant incremental change, with scenarios checklist points increasing from a mean of 5.9 (SD 1.9) to 9.0 (SD 0.9) and bedside clinical checklist points increasing from a mean of 5 (SD 1.5) to 8.3 (SD 0.8) (both P<.001). At the 6-month follow-up, the scores for multiple-choice questions, the confidence matrix, and scenarios checklist all remained unchanged, except for the multiple-choice question score in the nurse subgroup (P=.005). Conclusions: The PTC course undertaken in 2 local hospitals in Vietnam was successful in demonstrating improvements at 3 levels of the KM for ED health care staff. The improvements in the confidence matrix and scenarios checklist were maintained for at least 6 months after the course. PTC courses should be effective in providing and sustaining improvement in knowledge and trauma care practice in other LMICs such as Vietnam. Trial Registration: Australian New Zealand Clinical Trial Registry (ANZCTR) ACTRN 12621000371897; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=380970 ", doi="10.2196/47127", url="https://mededu.jmir.org/2024/1/e47127" } @Article{info:doi/10.2196/60767, author="Wang, Y. Ellen and Qian, Daniel and Zhang, Lijin and Li, S-K Brian and Ko, Brian and Khoury, Michael and Renavikar, Meghana and Ganesan, Avani and Caruso, J. Thomas", title="Acceptance of Virtual Reality in Trainees Using a Technology Acceptance Model: Survey Study", journal="JMIR Med Educ", year="2024", month="Dec", day="23", volume="10", pages="e60767", keywords="virtual reality", keywords="technology assessment", keywords="graduate medical education trainees", keywords="medical education", keywords="technology adoption", keywords="Technology Acceptance Model", keywords="factor analysis", keywords="VR", keywords="TAM", keywords="United Theory of Acceptance and Use of Technology", keywords="UTAUT", abstract="Background: Virtual reality (VR) technologies have demonstrated therapeutic usefulness across a variety of health care settings. However, graduate medical education (GME) trainee perspectives on VR acceptability and usability are limited. The behavioral intentions of GME trainees with regard to VR as an anxiolytic tool have not been characterized through a theoretical framework of technology adoption. Objective: The primary aim of this study was to apply a hybrid Technology Acceptance Model (TAM) and a United Theory of Acceptance and Use of Technology (UTAUT) model to evaluate factors that predict the behavioral intentions of GME trainees to use VR for patient anxiolysis. The secondary aim was to assess the reliability of the TAM-UTAUT. Methods: Participants were surveyed in June 2023. GME trainees participated in a VR experience used to reduce perioperative anxiety. Participants then completed a survey evaluating demographics, perceptions, attitudes, environmental factors, and behavioral intentions that influence the adoption of new technologies. Results: In total, 202 of 1540 GME trainees participated. Only 198 participants were included in the final analysis (12.9\% participation rate). Perceptions of usefulness, ease of use, and enjoyment; social influence; and facilitating conditions predicted intention to use VR. Age, past use, price willing to pay, and curiosity were less strong predictors of intention to use. All confirmatory factor analysis models demonstrated a good fit. All domain measurements demonstrated acceptable reliability. Conclusions: This TAM-UTAUT demonstrated validity and reliability for predicting the behavioral intentions of GME trainees to use VR as a therapeutic anxiolytic in clinical practice. Social influence and facilitating conditions are modifiable factors that present opportunities to advance VR adoption, such as fostering exposure to new technologies and offering relevant training and social encouragement. Future investigations should study the model's reliability within specialties in different geographic locations. ", doi="10.2196/60767", url="https://mededu.jmir.org/2024/1/e60767" } @Article{info:doi/10.2196/46220, author="Lonati, Caterina and Wellhausen, Marie and Pennig, Stefan and R{\"o}hr{\ss}en, Thomas and Kircelli, Fatih and Arendt, Svenja and Tschulena, Ulrich", title="The Use of a Novel Virtual Reality Training Tool for Peritoneal Dialysis: Qualitative Assessment Among Health Care Professionals", journal="JMIR Med Educ", year="2024", month="Aug", day="6", volume="10", pages="e46220", keywords="peritoneal dialysis", keywords="virtual reality", keywords="patient education", keywords="patient training", keywords="chronic kidney disease", keywords="nursing", keywords="qualitative assessment", abstract="Background: Effective peritoneal dialysis (PD) training is essential for performing dialysis at home and reducing the risk of peritonitis and other PD-related infections. Virtual reality (VR) is an innovative learning tool that is able to combine theoretical information, interactivity, and behavioral instructions while offering a playful learning environment. To improve patient training for PD, Fresenius Medical Care launched the stay{\textbullet}safe MyTraining VR, a novel educational program based on the use of a VR headset and a handheld controller. Objective: This qualitative assessment aims to investigate opinions toward the new tool among the health care professionals (HCPs) who were responsible for implementing the VR application. Methods: We recruited nursing staff and nephrologists who have gained practical experience with the stay{\textbullet}safe MyTraining VR within pilot dialysis centers. Predetermined open-ended questions were administered during individual and group video interviews. Results: We interviewed 7 HCPs who have 2 to 20 years of experience in PD training. The number of patients trained with the stay{\textbullet}safe MyTraining VR ranged from 2 to 5 for each professional. The stay{\textbullet}safe MyTraining VR was well accepted and perceived as a valuable supplementary tool for PD training. From the respondents' perspective, the technology improved patients' learning experience by facilitating the internalization of both medical information and procedural skills. HCPs highlighted that the opportunity offered by VR to reiterate training activities in a positive and safe learning environment, according to each patient's needs, can facilitate error correction and implement a standardized training curriculum. However, VR had limited use in the final phase of the patient PD training program, where learners need to get familiar with the handling of the materials. Moreover, the traditional PD training was still considered essential to manage the emotional and motivational aspects and address any patient-specific application-oriented questions. In addition to its use within PD training, VR was perceived as a useful tool to support the decision-making process of patients and train other HCPs. Moreover, VR introduction was associated with increased efficiency and productivity of HCPs because it enabled them to perform other activities while the patient was practicing with the device. As for patients' acceptance of the new tool, interviewees reported positive feedback, including that of older adults. Limited use with patients experiencing dementia or severe visual impairment or lacking sensomotoric competence was mentioned. Conclusions: The stay{\textbullet}safe MyTraining VR is suggested to improve training efficiency and efficacy and thus could have a positive impact in the PD training scenario. Our study offers a process proposal that can serve as a guide to the implementation of a VR-based PD training program within other dialysis centers. Dedicated research is needed to assess the operational benefits and the consequences on patient management. ", doi="10.2196/46220", url="https://mededu.jmir.org/2024/1/e46220", url="http://www.ncbi.nlm.nih.gov/pubmed/39106093" } @Article{info:doi/10.2196/56844, author="M{\o}rk, Gry and Bonsaksen, Tore and Larsen, S{\o}nnik Ole and Kunnikoff, Martin Hans and Lie, Stangeland Silje", title="Virtual Reality Simulation in Undergraduate Health Care Education Programs: Usability Study", journal="JMIR Med Educ", year="2024", month="Nov", day="19", volume="10", pages="e56844", keywords="360{\textdegree} videos", keywords="health professions education", keywords="virtual reality", keywords="usability study", keywords="undergraduates", keywords="university", keywords="students", keywords="simulation", abstract="Background: Virtual reality (VR) is increasingly being used in higher education for clinical skills training and role-playing among health care students. Using 360{\textdegree} videos in VR headsets, followed by peer debrief and group discussions, may strengthen students' social and emotional learning. Objective: This study aimed to explore student-perceived usability of VR simulation in three health care education programs in Norway. Methods: Students from one university participated in a VR simulation program. Of these, students in social education (n=74), nursing (n=45), and occupational therapy (n=27) completed a questionnaire asking about their perceptions of the usability of the VR simulation and the related learning activities. Differences between groups of students were examined with Pearson chi-square tests and with 1-way ANOVA. Qualitative content analysis was used to analyze data from open-ended questions. Results: The nursing students were most satisfied with the usability of the VR simulation, while the occupational therapy students were least satisfied. The nursing students had more often prior experience from using VR technology (60\%), while occupational therapy students less often had prior experience (37\%). Nevertheless, high mean scores indicated that the students experienced the VR simulation and the related learning activities as very useful. The results also showed that by using realistic scenarios in VR simulation, health care students can be prepared for complex clinical situations in a safe environment. Also, group debriefing sessions are a vital part of the learning process that enhance active involvement with peers. Conclusions: VR simulation has promise and potential as a pedagogical tool in health care education, especially for training soft skills relevant for clinical practice, such as communication, decision-making, time management, and critical thinking. ", doi="10.2196/56844", url="https://mededu.jmir.org/2024/1/e56844" } @Article{info:doi/10.2196/52631, author="Halim, Freda and Widysanto, Allen and Wahjoepramono, Perdana Petra Octavian and Candrawinata, Siulinda Valeska and Budihardja, Setiawan Andi and Irawan, Andry and Sudirman, Taufik and Christina, Natalia and Koerniawan, Sutanto Heru and Tobing, Lumban Jephtah Furano and Sungono, Veli and Marlina, Mona and Wahjoepramono, Julianta Eka", title="Objective Comparison of the First-Person--View Live Streaming Method Versus Face-to-Face Teaching Method in Improving Wound Suturing Skills for Skin Closure in Surgical Clerkship Students: Randomized Controlled Trial", journal="JMIR Med Educ", year="2024", month="Aug", day="30", volume="10", pages="e52631", keywords="teaching method", keywords="live streaming", keywords="first-person view", keywords="face-to-face", keywords="simple wound suturing", abstract="Background: The use of digital online teaching media in improving the surgical skills of medical students is indispensable, yet it is still not widely explored objectively. The first-person--view online teaching method may be more effective as it provides more realism to surgical clerkship students in achieving basic surgical skills. Objective: This study aims to objectively assess the effectiveness of the first-person--view live streaming (LS) method using a GoPro camera compared to the standard face-to-face (FTF) teaching method in improving simple wound suturing skills in surgical clerkship students. Methods: A prospective, parallel, nonblinded, single-center, randomized controlled trial was performed. Between January and April 2023, clerkship students of the Department of Surgery, Pelita Harapan University, were randomly selected and recruited into either the LS or FTF teaching method for simple interrupted suturing skills. All the participants were assessed objectively before and 1 week after training, using the direct observational procedural skills (DOPS) method. DOPS results and poststudy questionnaires were analyzed. Results: A total of 74 students were included in this study, with 37 (50\%) participants in each group. Paired analysis of each participant's pre-experiment and postexperiment DOPS scores revealed that the LS method's outcome is comparable to the FTF method's outcome (LS: mean 27.5, SD 20.6 vs FTF: mean 24.4, SD 16.7; P=.48) in improving the students' surgical skills. Conclusions: First-person--view LS training sessions could enhance students' ability to master simple procedural skills such as simple wound suturing and has comparable results to the current FTF teaching method. Teaching a practical skill using the LS method also gives more confidence for the participants to perform the procedure independently. Other advantages of the LS method, such as the ability to study from outside the sterile environment, are also promising. We recommend improvements in the audiovisual quality of the camera and a stable internet connection before performing the LS teaching method. Trial Registration: ClinicalTrials.gov NCT06221917; https://clinicaltrials.gov/study/NCT06221917 ", doi="10.2196/52631", url="https://mededu.jmir.org/2024/1/e52631" } @Article{info:doi/10.2196/52230, author="Chien, Cheng-Yu and Tsai, Shang-Li and Huang, Chien-Hsiung and Wang, Ming-Fang and Lin, Chi-Chun and Chen, Chen-Bin and Tsai, Li-Heng and Tseng, Hsiao-Jung and Huang, Yan-Bo and Ng, Chip-Jin", title="Effectiveness of Blended Versus Traditional Refresher Training for Cardiopulmonary Resuscitation: Prospective Observational Study", journal="JMIR Med Educ", year="2024", month="Apr", day="29", volume="10", pages="e52230", keywords="cardiopulmonary resuscitation", keywords="blended method", keywords="blended", keywords="hybrid", keywords="refresher", keywords="refreshers", keywords="teaching", keywords="instruction", keywords="observational", keywords="training", keywords="professional development", keywords="continuing education", keywords="retraining", keywords="traditional method", keywords="self-directed learning", keywords="resuscitation", keywords="CPR", keywords="emergency", keywords="rescue", keywords="life support", keywords="cardiac", keywords="cardiopulmonary", abstract="Background: Generally, cardiopulmonary resuscitation (CPR) skills decline substantially over time. By combining web-based self-regulated learning with hands-on practice, blended training can be a time- and resource-efficient approach enabling individuals to acquire or refresh CPR skills at their convenience. However, few studies have evaluated the effectiveness of blended CPR refresher training compared with that of the traditional method. Objective: This study investigated and compared the effectiveness of traditional and blended CPR training through 6-month and 12-month refresher sessions with CPR ability indicators. Methods: This study recruited participants aged ?18 years from the Automated External Defibrillator Donation Project. The participants were divided into 4 groups based on the format of the CPR training and refresher training received: (1) initial traditional training (a 30-minute instructor-led, hands-on session) and 6-month traditional refresher training (Traditional6 group), (2) initial traditional training and 6-month blended refresher training (an 18-minute e-learning module; Mixed6 group), (3) initial traditional training and 12-month blended refresher training (Mixed12 group), and (4) initial blended training and 6-month blended refresher training (Blended6 group). CPR knowledge and performance were evaluated immediately after initial training. For each group, following initial training but before refresher training, a learning effectiveness assessment was conducted at 12 and 24 months. CPR knowledge was assessed using a written test with 15 multiple-choice questions, and CPR performance was assessed through an examiner-rated skill test and objectively through manikin feedback. A generalized estimating equation model was used to analyze changes in CPR ability indicators. Results: This study recruited 1163 participants (mean age 41.82, SD 11.6 years; n=725, 62.3\% female), with 332 (28.5\%), 270 (23.2\%), 258 (22.2\%), and 303 (26.1\%) participants in the Mixed6, Traditional6, Mixed12, and Blended6 groups, respectively. No significant between-group difference was observed in knowledge acquisition after initial training (P=.23). All groups met the criteria for high-quality CPR skills (ie, average compression depth: 5-6 cm; average compression rate: 100-120 beats/min; chest recoil rate: >80\%); however, a higher proportion (98/303, 32.3\%) of participants receiving blended training initially demonstrated high-quality CPR skills. At 12 and 24 months, CPR skills had declined in all the groups, but the decline was significantly higher in the Mixed12 group, whereas the differences were not significant between the other groups. This finding indicates that frequent retraining can maintain high-quality CPR skills and that blended refresher training is as effective as traditional refresher training. Conclusions: Our findings indicate that 6-month refresher training sessions for CPR are more effective for maintaining high-quality CPR skills, and that as refreshers, self-learning e-modules are as effective as instructor-led sessions. Although the blended learning approach is cost and resource effective, factors such as participant demographics, training environment, and level of engagement must be considered to maximize the potential of this approach. Trial Registration: IGOGO NCT05659108; https://www.cgmh-igogo.tw ", doi="10.2196/52230", url="https://mededu.jmir.org/2024/1/e52230", url="http://www.ncbi.nlm.nih.gov/pubmed/38683663" } @Article{info:doi/10.2196/57696, author="Dsouza, Maria Jeanne", title="A Student's Viewpoint on ChatGPT Use and Automation Bias in Medical Education", journal="JMIR Med Educ", year="2024", month="Apr", day="15", volume="10", pages="e57696", keywords="AI", keywords="artificial intelligence", keywords="ChatGPT", keywords="medical education", doi="10.2196/57696", url="https://mededu.jmir.org/2024/1/e57696" } @Article{info:doi/10.2196/58743, author="De Martinis, Massimo and Ginaldi, Lia", title="Digital Skills to Improve Levels of Care and Renew Health Care Professions", journal="JMIR Med Educ", year="2024", month="May", day="1", volume="10", pages="e58743", keywords="digital competence", keywords="telehealth", keywords="nursing", keywords="health care workforce", keywords="health care professionals", keywords="informatics", keywords="education", keywords="curriculum", keywords="interdisciplinary education", keywords="health care education", doi="10.2196/58743", url="https://mededu.jmir.org/2024/1/e58743" } @Article{info:doi/10.2196/58370, author="Pendergrast, Tricia and Chalmers, Zachary", title="Authors' Reply: A Use Case for Generative AI in Medical Education", journal="JMIR Med Educ", year="2024", month="Jun", day="7", volume="10", pages="e58370", keywords="ChatGPT", keywords="undergraduate medical education", keywords="large language models", doi="10.2196/58370", url="https://mededu.jmir.org/2024/1/e58370" } @Article{info:doi/10.2196/56117, author="Sekhar, C. Tejas and Nayak, R. Yash and Abdoler, A. Emily", title="A Use Case for Generative AI in Medical Education", journal="JMIR Med Educ", year="2024", month="Jun", day="7", volume="10", pages="e56117", keywords="medical education", keywords="med ed", keywords="generative artificial intelligence", keywords="artificial intelligence", keywords="GAI", keywords="AI", keywords="Anki", keywords="flashcard", keywords="undergraduate medical education", keywords="UME", doi="10.2196/56117", url="https://mededu.jmir.org/2024/1/e56117" } @Article{info:doi/10.2196/50156, author="Mareli{\'c}, Marko and Klasni{\'c}, Ksenija and Vuku{\vs}i{\'c} Rukavina, Tea", title="Measuring e-Professional Behavior of Doctors of Medicine and Dental Medicine on Social Networking Sites: Indexes Construction With Formative Indicators", journal="JMIR Med Educ", year="2024", month="Feb", day="27", volume="10", pages="e50156", keywords="e-professionalism", keywords="social media", keywords="formative index", keywords="social networking", keywords="doctors", keywords="medical", keywords="dental medicine", abstract="Background: Previous studies have predominantly measured e-professionalism through perceptions or attitudes, yet there exists no validated measure specifically targeting the actual behaviors of health care professionals (HCPs) in this realm. This study addresses this gap by constructing a normative framework, drawing from 3 primary sources to define e-professional behavior across 6 domains. Four domains pertain to the dangers of social networking sites (SNSs), encompassing confidentiality, privacy, patient interaction, and equitable resource allocation. Meanwhile, 2 domains focus on the opportunities of SNSs, namely, the proactive dissemination of public health information and maintaining scientific integrity. Objective: This study aims to develop and validate 2 new measures assessing the e-professional behavior of doctors of medicine (MDs) and doctors of dental medicine (DMDs), focusing on both the dangers and opportunities associated with SNSs. Methods: The study used a purposive sample of MDs and DMDs in Croatia who were users of at least one SNS. Data collection took place in 2021 through an online survey. Validation of both indexes used a formative approach, which involved a 5-step methodology: content specification, indicators definition with instructions for item coding and index construction, indicators collinearity check using the variance inflation factor (VIF), external validity test using multiple indicators multiple causes (MIMIC) model, and external validity test by checking the relationships of the indexes with the scale of attitude toward SNSs using Pearson correlation coefficients. Results: A total of 753 responses were included in the analysis. The first e-professionalism index, assessing the dangers associated with SNSs, comprises 14 items. During the indicators collinearity check, all indicators displayed acceptable VIF values below 2.5. The MIMIC model showed good fit ($\chi$213=9.4, P=.742; $\chi$2/df=0.723; root-mean-square error of approximation<.001; goodness-of-fit index=0.998; comparative fit index=1.000). The external validity of the index is supported by a statistically significant negative correlation with the scale measuring attitudes toward SNSs (r=--0.225, P<.001). Following the removal of 1 item, the second e-professionalism index, focusing on the opportunities associated with SNSs, comprises 5 items. During the indicators collinearity check, all indicators exhibited acceptable VIF values below 2.5. Additionally, the MIMIC model demonstrated a good fit ($\chi$24=2.5, P=.718; $\chi$2/df=0.637; root-mean-square error of approximation<0.001; goodness-of-fit index=0.999; comparative fit index=1.000). The external validity of the index is supported by a statistically significant positive correlation with the scale of attitude toward SNSs (r=0.338; P<.001). Conclusions: Following the validation process, the instrument designed for gauging the e-professional behavior of MDs and DMDs consists of 19 items, which contribute to the formation of 2 distinct indexes: the e-professionalism index, focusing on the dangers associated with SNSs, comprising 14 items, and the e-professionalism index, highlighting the opportunities offered by SNSs, consisting of 5 items. These indexes serve as valid measures of the e-professional behavior of MDs and DMDs, with the potential for further refinement to encompass emerging forms of unprofessional behavior that may arise over time. ", doi="10.2196/50156", url="https://mededu.jmir.org/2024/1/e50156", url="http://www.ncbi.nlm.nih.gov/pubmed/38412021" } @Article{info:doi/10.2196/54071, author="Landis-Lewis, Zach and Andrews, A. Chris and Gross, A. Colin and Friedman, P. Charles and Shah, J. Nirav", title="Exploring Anesthesia Provider Preferences for Precision Feedback: Preference Elicitation Study", journal="JMIR Med Educ", year="2024", month="Jun", day="11", volume="10", pages="e54071", keywords="audit and feedback", keywords="dashboard", keywords="motivation", keywords="visualization", keywords="anesthesia care", keywords="anesthesia", keywords="feedback", keywords="engagement", keywords="effectiveness", keywords="precision feedback", keywords="experimental design", keywords="design", keywords="clinical practice", keywords="motivational", keywords="performance", keywords="performance data", abstract="Background: Health care professionals must learn continuously as a core part of their work. As the rate of knowledge production in biomedicine increases, better support for health care professionals' continuous learning is needed. In health systems, feedback is pervasive and is widely considered to be essential for learning that drives improvement. Clinical quality dashboards are one widely deployed approach to delivering feedback, but engagement with these systems is commonly low, reflecting a limited understanding of how to improve the effectiveness of feedback about health care. When coaches and facilitators deliver feedback for improving performance, they aim to be responsive to the recipient's motivations, information needs, and preferences. However, such functionality is largely missing from dashboards and feedback reports. Precision feedback is the delivery of high-value, motivating performance information that is prioritized based on its motivational potential for a specific recipient, including their needs and preferences. Anesthesia care offers a clinical domain with high-quality performance data and an abundance of evidence-based quality metrics. Objective: The objective of this study is to explore anesthesia provider preferences for precision feedback. Methods: We developed a test set of precision feedback messages with balanced characteristics across 4 performance scenarios. We created an experimental design to expose participants to contrasting message versions. We recruited anesthesia providers and elicited their preferences through analysis of the content of preferred messages. Participants additionally rated their perceived benefit of preferred messages to clinical practice on a 5-point Likert scale. Results: We elicited preferences and feedback message benefit ratings from 35 participants. Preferences were diverse across participants but largely consistent within participants. Participants' preferences were consistent for message temporality ($\alpha$=.85) and display format ($\alpha$=.80). Ratings of participants' perceived benefit to clinical practice of preferred messages were high (mean rating 4.27, SD 0.77). Conclusions: Health care professionals exhibited diverse yet internally consistent preferences for precision feedback across a set of performance scenarios, while also giving messages high ratings of perceived benefit. A ``one-size-fits-most approach'' to performance feedback delivery would not appear to satisfy these preferences. Precision feedback systems may hold potential to improve support for health care professionals' continuous learning by accommodating feedback preferences. ", doi="10.2196/54071", url="https://mededu.jmir.org/2024/1/e54071" } @Article{info:doi/10.2196/59454, author="Sahan, Fatma and Guthardt, Lisa and Panitz, Karin and Siegel-Kianer, Anna and Eichhof, Isabel and Schmitt, D. Bj{\"o}rn and Apolinario-Hagen, Jennifer", title="Enhancing Digital Health Awareness and mHealth Competencies in Medical Education: Proof-of-Concept Study and Summative Process Evaluation of a Quality Improvement Project", journal="JMIR Med Educ", year="2024", month="Sep", day="20", volume="10", pages="e59454", keywords="medical students", keywords="digital health", keywords="design thinking", keywords="digital health literacy", keywords="medical education", keywords="digital health competencies", keywords="mobile phone", abstract="Background: Currently, there is a need to optimize knowledge on digital transformation in mental health care, including digital therapeutics (eg, prescription apps), in medical education. However, in Germany, digital health has not yet been systematically integrated into medical curricula and is taught in a relatively small number of electives. Challenges for lecturers include the dynamic field as well as lacking guidance on how to efficiently apply innovative teaching formats for these new digital competencies. Quality improvement projects provide options to pilot-test novel educational offerings, as little is known about the acceptability of participatory approaches in conventional medical education. Objective: This quality improvement project addressed the gap in medical school electives on digital health literacy by introducing and evaluating an elective scoping study on the systematic development of different health app concepts designed by students to cultivate essential skills for future health care professionals (ie, mobile health [mHealth] competencies). Methods: This proof-of-concept study describes the development, optimization, implementation, and evaluation of a web-based elective on digital (mental) health competencies in medical education. Implemented as part of a quality improvement project, the elective aimed to guide medical students in developing app concepts applying a design thinking approach at a German medical school from January 2021 to January 2024. Topics included defining digital (mental) health, quality criteria for health apps, user perspective, persuasive design, and critical reflection on digitization in medical practice. The elective was offered 6 times within 36 months, with continuous evaluation and iterative optimization using both process and outcome measures, such as web-based questionnaires. We present examples of app concepts designed by students and summarize the quantitative and qualitative evaluation results. Results: In total, 60 students completed the elective and developed 25 health app concepts, most commonly targeting stress management and depression. In addition, disease management and prevention apps were designed for various somatic conditions such as diabetes and chronic pain. The results indicated high overall satisfaction across the 6 courses according to the evaluation questionnaire, with lower scores indicating higher satisfaction on a scale ranging from 1 to 6 (mean 1.70, SD 0.68). Students particularly valued the content, flexibility, support, and structure. While improvements in group work, submissions, and information transfer were suggested, the results underscore the usefulness of the web-based elective. Conclusions: This quality improvement project provides insights into relevant features for the successful user-centered and creative integration of mHealth competencies into medical education. Key factors for the satisfaction of students involved the participatory mindset, focus on competencies, discussions with app providers, and flexibility. Future efforts should define important learning objectives for digital health literacy and provide recommendations for integration rather than debating the need for digital health integration. ", doi="10.2196/59454", url="https://mededu.jmir.org/2024/1/e59454" } @Article{info:doi/10.2196/60031, author="Wosny, Marie and Strasser, Maria Livia and Kraehenmann, Simone and Hastings, Janna", title="Practical Recommendations for Navigating Digital Tools in Hospitals: Qualitative Interview Study", journal="JMIR Med Educ", year="2024", month="Nov", day="27", volume="10", pages="e60031", keywords="health care", keywords="hospital", keywords="information system", keywords="information technology", keywords="technology implementation", keywords="training", keywords="medical education", keywords="digital literacy", keywords="curriculum development", keywords="health care workforce development", keywords="mobile phone", abstract="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. ", doi="10.2196/60031", url="https://mededu.jmir.org/2024/1/e60031" } @Article{info:doi/10.2196/55048, author="Rojas, Marcos and Rojas, Marcelo and Burgess, Valentina and Toro-P{\'e}rez, Javier and Salehi, Shima", title="Exploring the Performance of ChatGPT Versions 3.5, 4, and 4 With Vision in the Chilean Medical Licensing Examination: Observational Study", journal="JMIR Med Educ", year="2024", month="Apr", day="29", volume="10", pages="e55048", keywords="artificial intelligence", keywords="AI", keywords="generative artificial intelligence", keywords="medical education", keywords="ChatGPT", keywords="EUNACOM", keywords="medical licensure", keywords="medical license", keywords="medical licensing exam", abstract="Background: The deployment of OpenAI's ChatGPT-3.5 and its subsequent versions, ChatGPT-4 and ChatGPT-4 With Vision (4V; also known as ``GPT-4 Turbo With Vision''), has notably influenced the medical field. Having demonstrated remarkable performance in medical examinations globally, these models show potential for educational applications. However, their effectiveness in non-English contexts, particularly in Chile's medical licensing examinations---a critical step for medical practitioners in Chile---is less explored. This gap highlights the need to evaluate ChatGPT's adaptability to diverse linguistic and cultural contexts. Objective: This study aims to evaluate the performance of ChatGPT versions 3.5, 4, and 4V in the EUNACOM (Examen {\'U}nico Nacional de Conocimientos de Medicina), a major medical examination in Chile. Methods: Three official practice drills (540 questions) from the University of Chile, mirroring the EUNACOM's structure and difficulty, were used to test ChatGPT versions 3.5, 4, and 4V. The 3 ChatGPT versions were provided 3 attempts for each drill. Responses to questions during each attempt were systematically categorized and analyzed to assess their accuracy rate. Results: All versions of ChatGPT passed the EUNACOM drills. Specifically, versions 4 and 4V outperformed version 3.5, achieving average accuracy rates of 79.32\% and 78.83\%, respectively, compared to 57.53\% for version 3.5 (P<.001). Version 4V, however, did not outperform version 4 (P=.73), despite the additional visual capabilities. We also evaluated ChatGPT's performance in different medical areas of the EUNACOM and found that versions 4 and 4V consistently outperformed version 3.5. Across the different medical areas, version 3.5 displayed the highest accuracy in psychiatry (69.84\%), while versions 4 and 4V achieved the highest accuracy in surgery (90.00\% and 86.11\%, respectively). Versions 3.5 and 4 had the lowest performance in internal medicine (52.74\% and 75.62\%, respectively), while version 4V had the lowest performance in public health (74.07\%). Conclusions: This study reveals ChatGPT's ability to pass the EUNACOM, with distinct proficiencies across versions 3.5, 4, and 4V. Notably, advancements in artificial intelligence (AI) have not significantly led to enhancements in performance on image-based questions. The variations in proficiency across medical fields suggest the need for more nuanced AI training. Additionally, the study underscores the importance of exploring innovative approaches to using AI to augment human cognition and enhance the learning process. Such advancements have the potential to significantly influence medical education, fostering not only knowledge acquisition but also the development of critical thinking and problem-solving skills among health care professionals. ", doi="10.2196/55048", url="https://mededu.jmir.org/2024/1/e55048" } @Article{info:doi/10.2196/50545, author="Thomae, V. Anita and Witt, M. Claudia and Barth, J{\"u}rgen", title="Integration of ChatGPT Into a Course for Medical Students: Explorative Study on Teaching Scenarios, Students' Perception, and Applications", journal="JMIR Med Educ", year="2024", month="Aug", day="22", volume="10", pages="e50545", keywords="medical education", keywords="ChatGPT", keywords="artificial intelligence", keywords="information for patients", keywords="critical appraisal", keywords="evaluation", keywords="blended learning", keywords="AI", keywords="digital skills", keywords="teaching", abstract="Background: Text-generating artificial intelligence (AI) such as ChatGPT offers many opportunities and challenges in medical education. Acquiring practical skills necessary for using AI in a clinical context is crucial, especially for medical education. Objective: This explorative study aimed to investigate the feasibility of integrating ChatGPT into teaching units and to evaluate the course and the importance of AI-related competencies for medical students. Since a possible application of ChatGPT in the medical field could be the generation of information for patients, we further investigated how such information is perceived by students in terms of persuasiveness and quality. Methods: ChatGPT was integrated into 3 different teaching units of a blended learning course for medical students. Using a mixed methods approach, quantitative and qualitative data were collected. As baseline data, we assessed students' characteristics, including their openness to digital innovation. The students evaluated the integration of ChatGPT into the course and shared their thoughts regarding the future of text-generating AI in medical education. The course was evaluated based on the Kirkpatrick Model, with satisfaction, learning progress, and applicable knowledge considered as key assessment levels. In ChatGPT-integrating teaching units, students evaluated videos featuring information for patients regarding their persuasiveness on treatment expectations in a self-experience experiment and critically reviewed information for patients written using ChatGPT 3.5 based on different prompts. Results: A total of 52 medical students participated in the study. The comprehensive evaluation of the course revealed elevated levels of satisfaction, learning progress, and applicability specifically in relation to the ChatGPT-integrating teaching units. Furthermore, all evaluation levels demonstrated an association with each other. Higher openness to digital innovation was associated with higher satisfaction and, to a lesser extent, with higher applicability. AI-related competencies in other courses of the medical curriculum were perceived as highly important by medical students. Qualitative analysis highlighted potential use cases of ChatGPT in teaching and learning. In ChatGPT-integrating teaching units, students rated information for patients generated using a basic ChatGPT prompt as ``moderate'' in terms of comprehensibility, patient safety, and the correct application of communication rules taught during the course. The students' ratings were considerably improved using an extended prompt. The same text, however, showed the smallest increase in treatment expectations when compared with information provided by humans (patient, clinician, and expert) via videos. Conclusions: This study offers valuable insights into integrating the development of AI competencies into a blended learning course. Integration of ChatGPT enhanced learning experiences for medical students. ", doi="10.2196/50545", url="https://mededu.jmir.org/2024/1/e50545" } @Article{info:doi/10.2196/50869, author="Erren, C. Thomas", title="Patients, Doctors, and Chatbots", journal="JMIR Med Educ", year="2024", month="Jan", day="4", volume="10", pages="e50869", keywords="chatbot", keywords="ChatGPT", keywords="medical advice", keywords="ethics", keywords="patients", keywords="doctors", doi="10.2196/50869", url="https://mededu.jmir.org/2024/1/e50869", url="http://www.ncbi.nlm.nih.gov/pubmed/38175695" } @Article{info:doi/10.2196/51183, author="Blease, Charlotte and Torous, John and McMillan, Brian and H{\"a}gglund, Maria and Mandl, D. Kenneth", title="Generative Language Models and Open Notes: Exploring the Promise and Limitations", journal="JMIR Med Educ", year="2024", month="Jan", day="4", volume="10", pages="e51183", keywords="ChatGPT", keywords="generative language models", keywords="large language models", keywords="medical education", keywords="Open Notes", keywords="online record access", keywords="patient-centered care", keywords="empathy", keywords="language model", keywords="documentation", keywords="communication tool", keywords="clinical documentation", doi="10.2196/51183", url="https://mededu.jmir.org/2024/1/e51183", url="http://www.ncbi.nlm.nih.gov/pubmed/38175688" } @Article{info:doi/10.2196/51148, author="Knoedler, Leonard and Alfertshofer, Michael and Knoedler, Samuel and Hoch, C. Cosima and Funk, F. Paul and Cotofana, Sebastian and Maheta, Bhagvat and Frank, Konstantin and Br{\'e}bant, Vanessa and Prantl, Lukas and Lamby, Philipp", title="Pure Wisdom or Potemkin Villages? A Comparison of ChatGPT 3.5 and ChatGPT 4 on USMLE Step 3 Style Questions: Quantitative Analysis", journal="JMIR Med Educ", year="2024", month="Jan", day="5", volume="10", pages="e51148", keywords="ChatGPT", keywords="United States Medical Licensing Examination", keywords="artificial intelligence", keywords="USMLE", keywords="USMLE Step 1", keywords="OpenAI", keywords="medical education", keywords="clinical decision-making", abstract="Background: The United States Medical Licensing Examination (USMLE) has been critical in medical education since 1992, testing various aspects of a medical student's knowledge and skills through different steps, based on their training level. Artificial intelligence (AI) tools, including chatbots like ChatGPT, are emerging technologies with potential applications in medicine. However, comprehensive studies analyzing ChatGPT's performance on USMLE Step 3 in large-scale scenarios and comparing different versions of ChatGPT are limited. Objective: This paper aimed to analyze ChatGPT's performance on USMLE Step 3 practice test questions to better elucidate the strengths and weaknesses of AI use in medical education and deduce evidence-based strategies to counteract AI cheating. Methods: A total of 2069 USMLE Step 3 practice questions were extracted from the AMBOSS study platform. After including 229 image-based questions, a total of 1840 text-based questions were further categorized and entered into ChatGPT 3.5, while a subset of 229 questions were entered into ChatGPT 4. Responses were recorded, and the accuracy of ChatGPT answers as well as its performance in different test question categories and for different difficulty levels were compared between both versions. Results: Overall, ChatGPT 4 demonstrated a statistically significant superior performance compared to ChatGPT 3.5, achieving an accuracy of 84.7\% (194/229) and 56.9\% (1047/1840), respectively. A noteworthy correlation was observed between the length of test questions and the performance of ChatGPT 3.5 ($\rho$=--0.069; P=.003), which was absent in ChatGPT 4 (P=.87). Additionally, the difficulty of test questions, as categorized by AMBOSS hammer ratings, showed a statistically significant correlation with performance for both ChatGPT versions, with $\rho$=--0.289 for ChatGPT 3.5 and $\rho$=--0.344 for ChatGPT 4. ChatGPT 4 surpassed ChatGPT 3.5 in all levels of test question difficulty, except for the 2 highest difficulty tiers (4 and 5 hammers), where statistical significance was not reached. Conclusions: In this study, ChatGPT 4 demonstrated remarkable proficiency in taking the USMLE Step 3, with an accuracy rate of 84.7\% (194/229), outshining ChatGPT 3.5 with an accuracy rate of 56.9\% (1047/1840). Although ChatGPT 4 performed exceptionally, it encountered difficulties in questions requiring the application of theoretical concepts, particularly in cardiology and neurology. These insights are pivotal for the development of examination strategies that are resilient to AI and underline the promising role of AI in the realm of medical education and diagnostics. ", doi="10.2196/51148", url="https://mededu.jmir.org/2024/1/e51148", url="http://www.ncbi.nlm.nih.gov/pubmed/38180782" } @Article{info:doi/10.2196/51247, author="Weidener, Lukas and Fischer, Michael", title="Artificial Intelligence in Medicine: Cross-Sectional Study Among Medical Students on Application, Education, and Ethical Aspects", journal="JMIR Med Educ", year="2024", month="Jan", day="5", volume="10", pages="e51247", keywords="artificial intelligence", keywords="AI technology", keywords="medicine", keywords="medical education", keywords="medical curriculum", keywords="medical school", keywords="AI ethics", keywords="ethics", abstract="Background: The use of artificial intelligence (AI) in medicine not only directly impacts the medical profession but is also increasingly associated with various potential ethical aspects. In addition, the expanding use of AI and AI-based applications such as ChatGPT demands a corresponding shift in medical education to adequately prepare future practitioners for the effective use of these tools and address the associated ethical challenges they present. Objective: This study aims to explore how medical students from Germany, Austria, and Switzerland perceive the use of AI in medicine and the teaching of AI and AI ethics in medical education in accordance with their use of AI-based chat applications, such as ChatGPT. Methods: This cross-sectional study, conducted from June 15 to July 15, 2023, surveyed medical students across Germany, Austria, and Switzerland using a web-based survey. This study aimed to assess students' perceptions of AI in medicine and the integration of AI and AI ethics into medical education. The survey, which included 53 items across 6 sections, was developed and pretested. Data analysis used descriptive statistics (median, mode, IQR, total number, and percentages) and either the chi-square or Mann-Whitney U tests, as appropriate. Results: Surveying 487 medical students across Germany, Austria, and Switzerland revealed limited formal education on AI or AI ethics within medical curricula, although 38.8\% (189/487) had prior experience with AI-based chat applications, such as ChatGPT. Despite varied prior exposures, 71.7\% (349/487) anticipated a positive impact of AI on medicine. There was widespread consensus (385/487, 74.9\%) on the need for AI and AI ethics instruction in medical education, although the current offerings were deemed inadequate. Regarding the AI ethics education content, all proposed topics were rated as highly relevant. Conclusions: This study revealed a pronounced discrepancy between the use of AI-based (chat) applications, such as ChatGPT, among medical students in Germany, Austria, and Switzerland and the teaching of AI in medical education. To adequately prepare future medical professionals, there is an urgent need to integrate the teaching of AI and AI ethics into the medical curricula. ", doi="10.2196/51247", url="https://mededu.jmir.org/2024/1/e51247", url="http://www.ncbi.nlm.nih.gov/pubmed/38180787" } @Article{info:doi/10.2196/51308, author="Zaleski, L. Amanda and Berkowsky, Rachel and Craig, Thomas Kelly Jean and Pescatello, S. Linda", title="Comprehensiveness, Accuracy, and Readability of Exercise Recommendations Provided by an AI-Based Chatbot: Mixed Methods Study", journal="JMIR Med Educ", year="2024", month="Jan", day="11", volume="10", pages="e51308", keywords="exercise prescription", keywords="health literacy", keywords="large language model", keywords="patient education", keywords="artificial intelligence", keywords="AI", keywords="chatbot", abstract="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. ", doi="10.2196/51308", url="https://mededu.jmir.org/2024/1/e51308", url="http://www.ncbi.nlm.nih.gov/pubmed/38206661" } @Article{info:doi/10.2196/47339, author="Al-Worafi, Mohammed Yaser and Goh, Wen Khang and Hermansyah, Andi and Tan, Siang Ching and Ming, Chiau Long", title="The Use of ChatGPT for Education Modules on Integrated Pharmacotherapy of Infectious Disease: Educators' Perspectives", journal="JMIR Med Educ", year="2024", month="Jan", day="12", volume="10", pages="e47339", keywords="innovation and technology", keywords="quality education", keywords="sustainable communities", keywords="innovation and infrastructure", keywords="partnerships for the goals", keywords="sustainable education", keywords="social justice", keywords="ChatGPT", keywords="artificial intelligence", keywords="feasibility", abstract="Background: Artificial Intelligence (AI) plays an important role in many fields, including medical education, practice, and research. Many medical educators started using ChatGPT at the end of 2022 for many purposes. Objective: The aim of this study was to explore the potential uses, benefits, and risks of using ChatGPT in education modules on integrated pharmacotherapy of infectious disease. Methods: A content analysis was conducted to investigate the applications of ChatGPT in education modules on integrated pharmacotherapy of infectious disease. Questions pertaining to curriculum development, syllabus design, lecture note preparation, and examination construction were posed during data collection. Three experienced professors rated the appropriateness and precision of the answers provided by ChatGPT. The consensus rating was considered. The professors also discussed the prospective applications, benefits, and risks of ChatGPT in this educational setting. Results: ChatGPT demonstrated the ability to contribute to various aspects of curriculum design, with ratings ranging from 50\% to 92\% for appropriateness and accuracy. However, there were limitations and risks associated with its use, including incomplete syllabi, the absence of essential learning objectives, and the inability to design valid questionnaires and qualitative studies. It was suggested that educators use ChatGPT as a resource rather than relying primarily on its output. There are recommendations for effectively incorporating ChatGPT into the curriculum of the education modules on integrated pharmacotherapy of infectious disease. Conclusions: Medical and health sciences educators can use ChatGPT as a guide in many aspects related to the development of the curriculum of the education modules on integrated pharmacotherapy of infectious disease, syllabus design, lecture notes preparation, and examination preparation with caution. ", doi="10.2196/47339", url="https://mededu.jmir.org/2024/1/e47339", url="http://www.ncbi.nlm.nih.gov/pubmed/38214967" } @Article{info:doi/10.2196/49970, author="Long, Cai and Lowe, Kayle and Zhang, Jessica and Santos, dos Andr{\'e} and Alanazi, Alaa and O'Brien, Daniel and Wright, D. Erin and Cote, David", title="A Novel Evaluation Model for Assessing ChatGPT on Otolaryngology--Head and Neck Surgery Certification Examinations: Performance Study", journal="JMIR Med Educ", year="2024", month="Jan", day="16", volume="10", pages="e49970", keywords="medical licensing", keywords="otolaryngology", keywords="otology", keywords="laryngology", keywords="ear", keywords="nose", keywords="throat", keywords="ENT", keywords="surgery", keywords="surgical", keywords="exam", keywords="exams", keywords="response", keywords="responses", keywords="answer", keywords="answers", keywords="chatbot", keywords="chatbots", keywords="examination", keywords="examinations", keywords="medical education", keywords="otolaryngology/head and neck surgery", keywords="OHNS", keywords="artificial intelligence", keywords="AI", keywords="ChatGPT", keywords="medical examination", keywords="large language models", keywords="language model", keywords="LLM", keywords="LLMs", keywords="wide range information", keywords="patient safety", keywords="clinical implementation", keywords="safety", keywords="machine learning", keywords="NLP", keywords="natural language processing", abstract="Background: ChatGPT is among the most popular large language models (LLMs), exhibiting proficiency in various standardized tests, including multiple-choice medical board examinations. However, its performance on otolaryngology--head and neck surgery (OHNS) certification examinations and open-ended medical board certification examinations has not been reported. Objective: We aimed to evaluate the performance of ChatGPT on OHNS board examinations and propose a novel method to assess an AI model's performance on open-ended medical board examination questions. Methods: Twenty-one open-ended questions were adopted from the Royal College of Physicians and Surgeons of Canada's sample examination to query ChatGPT on April 11, 2023, with and without prompts. A new model, named Concordance, Validity, Safety, Competency (CVSC), was developed to evaluate its performance. Results: In an open-ended question assessment, ChatGPT achieved a passing mark (an average of 75\% across 3 trials) in the attempts and demonstrated higher accuracy with prompts. The model demonstrated high concordance (92.06\%) and satisfactory validity. While demonstrating considerable consistency in regenerating answers, it often provided only partially correct responses. Notably, concerning features such as hallucinations and self-conflicting answers were observed. Conclusions: ChatGPT achieved a passing score in the sample examination and demonstrated the potential to pass the OHNS certification examination of the Royal College of Physicians and Surgeons of Canada. Some concerns remain due to its hallucinations, which could pose risks to patient safety. Further adjustments are necessary to yield safer and more accurate answers for clinical implementation. ", doi="10.2196/49970", url="https://mededu.jmir.org/2024/1/e49970", url="http://www.ncbi.nlm.nih.gov/pubmed/38227351" } @Article{info:doi/10.2196/51388, author="Kuo, I-Hsien Nicholas and Perez-Concha, Oscar and Hanly, Mark and Mnatzaganian, Emmanuel and Hao, Brandon and Di Sipio, Marcus and Yu, Guolin and Vanjara, Jash and Valerie, Cerelia Ivy and de Oliveira Costa, Juliana and Churches, Timothy and Lujic, Sanja and Hegarty, Jo and Jorm, Louisa and Barbieri, Sebastiano", title="Enriching Data Science and Health Care Education: Application and Impact of Synthetic Data Sets Through the Health Gym Project", journal="JMIR Med Educ", year="2024", month="Jan", day="16", volume="10", pages="e51388", keywords="medical education", keywords="generative model", keywords="generative adversarial networks", keywords="privacy", keywords="antiretroviral therapy (ART)", keywords="human immunodeficiency virus (HIV)", keywords="data science", keywords="educational purposes", keywords="accessibility", keywords="data privacy", keywords="data sets", keywords="sepsis", keywords="hypotension", keywords="HIV", keywords="science education", keywords="health care AI", doi="10.2196/51388", url="https://mededu.jmir.org/2024/1/e51388", url="http://www.ncbi.nlm.nih.gov/pubmed/38227356" } @Article{info:doi/10.2196/50842, author="Haddad, Firas and Saade, S. Joanna", title="Performance of ChatGPT on Ophthalmology-Related Questions Across Various Examination Levels: Observational Study", journal="JMIR Med Educ", year="2024", month="Jan", day="18", volume="10", pages="e50842", keywords="ChatGPT", keywords="artificial intelligence", keywords="AI", keywords="board examinations", keywords="ophthalmology", keywords="testing", abstract="Background: ChatGPT and language learning models have gained attention recently for their ability to answer questions on various examinations across various disciplines. The question of whether ChatGPT could be used to aid in medical education is yet to be answered, particularly in the field of ophthalmology. Objective: The aim of this study is to assess the ability of ChatGPT-3.5 (GPT-3.5) and ChatGPT-4.0 (GPT-4.0) to answer ophthalmology-related questions across different levels of ophthalmology training. Methods: Questions from the United States Medical Licensing Examination (USMLE) steps 1 (n=44), 2 (n=60), and 3 (n=28) were extracted from AMBOSS, and 248 questions (64 easy, 122 medium, and 62 difficult questions) were extracted from the book, Ophthalmology Board Review Q\&A, for the Ophthalmic Knowledge Assessment Program and the Board of Ophthalmology (OB) Written Qualifying Examination (WQE). Questions were prompted identically and inputted to GPT-3.5 and GPT-4.0. Results: GPT-3.5 achieved a total of 55\% (n=210) of correct answers, while GPT-4.0 achieved a total of 70\% (n=270) of correct answers. GPT-3.5 answered 75\% (n=33) of questions correctly in USMLE step 1, 73.33\% (n=44) in USMLE step 2, 60.71\% (n=17) in USMLE step 3, and 46.77\% (n=116) in the OB-WQE. GPT-4.0 answered 70.45\% (n=31) of questions correctly in USMLE step 1, 90.32\% (n=56) in USMLE step 2, 96.43\% (n=27) in USMLE step 3, and 62.90\% (n=156) in the OB-WQE. GPT-3.5 performed poorer as examination levels advanced (P<.001), while GPT-4.0 performed better on USMLE steps 2 and 3 and worse on USMLE step 1 and the OB-WQE (P<.001). The coefficient of correlation (r) between ChatGPT answering correctly and human users answering correctly was 0.21 (P=.01) for GPT-3.5 as compared to --0.31 (P<.001) for GPT-4.0. GPT-3.5 performed similarly across difficulty levels, while GPT-4.0 performed more poorly with an increase in the difficulty level. Both GPT models performed significantly better on certain topics than on others. Conclusions: ChatGPT is far from being considered a part of mainstream medical education. Future models with higher accuracy are needed for the platform to be effective in medical education. ", doi="10.2196/50842", url="https://mededu.jmir.org/2024/1/e50842", url="http://www.ncbi.nlm.nih.gov/pubmed/38236632" } @Article{info:doi/10.2196/51344, author="Kavadella, Argyro and Dias da Silva, Antonio Marco and Kaklamanos, G. Eleftherios and Stamatopoulos, Vasileios and Giannakopoulos, Kostis", title="Evaluation of ChatGPT's Real-Life Implementation in Undergraduate Dental Education: Mixed Methods Study", journal="JMIR Med Educ", year="2024", month="Jan", day="31", volume="10", pages="e51344", keywords="ChatGPT", keywords="large language models", keywords="LLM", keywords="natural language processing", keywords="artificial Intelligence", keywords="dental education", keywords="higher education", keywords="learning assignments", keywords="dental students", keywords="AI pedagogy", keywords="dentistry", keywords="university", abstract="Background: The recent artificial intelligence tool ChatGPT seems to offer a range of benefits in academic education while also raising concerns. Relevant literature encompasses issues of plagiarism and academic dishonesty, as well as pedagogy and educational affordances; yet, no real-life implementation of ChatGPT in the educational process has been reported to our knowledge so far. Objective: This mixed methods study aimed to evaluate the implementation of ChatGPT in the educational process, both quantitatively and qualitatively. Methods: In March 2023, a total of 77 second-year dental students of the European University Cyprus were divided into 2 groups and asked to compose a learning assignment on ``Radiation Biology and Radiation Protection in the Dental Office,'' working collaboratively in small subgroups, as part of the educational semester program of the Dentomaxillofacial Radiology module. Careful planning ensured a seamless integration of ChatGPT, addressing potential challenges. One group searched the internet for scientific resources to perform the task and the other group used ChatGPT for this purpose. Both groups developed a PowerPoint (Microsoft Corp) presentation based on their research and presented it in class. The ChatGPT group students additionally registered all interactions with the language model during the prompting process and evaluated the final outcome; they also answered an open-ended evaluation questionnaire, including questions on their learning experience. Finally, all students undertook a knowledge examination on the topic, and the grades between the 2 groups were compared statistically, whereas the free-text comments of the questionnaires were thematically analyzed. Results: Out of the 77 students, 39 were assigned to the ChatGPT group and 38 to the literature research group. Seventy students undertook the multiple choice question knowledge examination, and examination grades ranged from 5 to 10 on the 0-10 grading scale. The Mann-Whitney U test showed that students of the ChatGPT group performed significantly better (P=.045) than students of the literature research group. The evaluation questionnaires revealed the benefits (human-like interface, immediate response, and wide knowledge base), the limitations (need for rephrasing the prompts to get a relevant answer, general content, false citations, and incapability to provide images or videos), and the prospects (in education, clinical practice, continuing education, and research) of ChatGPT. Conclusions: Students using ChatGPT for their learning assignments performed significantly better in the knowledge examination than their fellow students who used the literature research methodology. Students adapted quickly to the technological environment of the language model, recognized its opportunities and limitations, and used it creatively and efficiently. Implications for practice: the study underscores the adaptability of students to technological innovations including ChatGPT and its potential to enhance educational outcomes. Educators should consider integrating ChatGPT into curriculum design; awareness programs are warranted to educate both students and educators about the limitations of ChatGPT, encouraging critical engagement and responsible use. ", doi="10.2196/51344", url="https://mededu.jmir.org/2024/1/e51344", url="http://www.ncbi.nlm.nih.gov/pubmed/38111256" } @Article{info:doi/10.2196/50705, author="Gray, Megan and Baird, Austin and Sawyer, Taylor and James, Jasmine and DeBroux, Thea and Bartlett, Michelle and Krick, Jeanne and Umoren, Rachel", title="Increasing Realism and Variety of Virtual Patient Dialogues for Prenatal Counseling Education Through a Novel Application of ChatGPT: Exploratory Observational Study", journal="JMIR Med Educ", year="2024", month="Feb", day="1", volume="10", pages="e50705", keywords="prenatal counseling", keywords="virtual health", keywords="virtual patient", keywords="simulation", keywords="neonatology", keywords="ChatGPT", keywords="AI", keywords="artificial intelligence", abstract="Background: Using virtual patients, facilitated by natural language processing, provides a valuable educational experience for learners. Generating a large, varied sample of realistic and appropriate responses for virtual patients is challenging. Artificial intelligence (AI) programs can be a viable source for these responses, but their utility for this purpose has not been explored. Objective: In this study, we explored the effectiveness of generative AI (ChatGPT) in developing realistic virtual standardized patient dialogues to teach prenatal counseling skills. Methods: ChatGPT was prompted to generate a list of common areas of concern and questions that families expecting preterm delivery at 24 weeks gestation might ask during prenatal counseling. ChatGPT was then prompted to generate 2 role-plays with dialogues between a parent expecting a potential preterm delivery at 24 weeks and their counseling physician using each of the example questions. The prompt was repeated for 2 unique role-plays: one parent was characterized as anxious and the other as having low trust in the medical system. Role-play scripts were exported verbatim and independently reviewed by 2 neonatologists with experience in prenatal counseling, using a scale of 1-5 on realism, appropriateness, and utility for virtual standardized patient responses. Results: ChatGPT generated 7 areas of concern, with 35 example questions used to generate role-plays. The 35 role-play transcripts generated 176 unique parent responses (median 5, IQR 4-6, per role-play) with 268 unique sentences. Expert review identified 117 (65\%) of the 176 responses as indicating an emotion, either directly or indirectly. Approximately half (98/176, 56\%) of the responses had 2 or more sentences, and half (88/176, 50\%) included at least 1 question. More than half (104/176, 58\%) of the responses from role-played parent characters described a feeling, such as being scared, worried, or concerned. The role-plays of parents with low trust in the medical system generated many unique sentences (n=50). Most of the sentences in the responses were found to be reasonably realistic (214/268, 80\%), appropriate for variable prenatal counseling conversation paths (233/268, 87\%), and usable without more than a minimal modification in a virtual patient program (169/268, 63\%). Conclusions: Generative AI programs, such as ChatGPT, may provide a viable source of training materials to expand virtual patient programs, with careful attention to the concerns and questions of patients and families. Given the potential for unrealistic or inappropriate statements and questions, an expert should review AI chat outputs before deploying them in an educational program. ", doi="10.2196/50705", url="https://mededu.jmir.org/2024/1/e50705", url="http://www.ncbi.nlm.nih.gov/pubmed/38300696" } @Article{info:doi/10.2196/50965, author="Meyer, Annika and Riese, Janik and Streichert, Thomas", title="Comparison of the Performance of GPT-3.5 and GPT-4 With That of Medical Students on the Written German Medical Licensing Examination: Observational Study", journal="JMIR Med Educ", year="2024", month="Feb", day="8", volume="10", pages="e50965", keywords="ChatGPT", keywords="artificial intelligence", keywords="large language model", keywords="medical exams", keywords="medical examinations", keywords="medical education", keywords="LLM", keywords="public trust", keywords="trust", keywords="medical accuracy", keywords="licensing exam", keywords="licensing examination", keywords="improvement", keywords="patient care", keywords="general population", keywords="licensure examination", abstract="Background: The potential of artificial intelligence (AI)--based large language models, such as ChatGPT, has gained significant attention in the medical field. This enthusiasm is driven not only by recent breakthroughs and improved accessibility, but also by the prospect of democratizing medical knowledge and promoting equitable health care. However, the performance of ChatGPT is substantially influenced by the input language, and given the growing public trust in this AI tool compared to that in traditional sources of information, investigating its medical accuracy across different languages is of particular importance. Objective: This study aimed to compare the performance of GPT-3.5 and GPT-4 with that of medical students on the written German medical licensing examination. Methods: To assess GPT-3.5's and GPT-4's medical proficiency, we used 937 original multiple-choice questions from 3 written German medical licensing examinations in October 2021, April 2022, and October 2022. Results: GPT-4 achieved an average score of 85\% and ranked in the 92.8th, 99.5th, and 92.6th percentiles among medical students who took the same examinations in October 2021, April 2022, and October 2022, respectively. This represents a substantial improvement of 27\% compared to GPT-3.5, which only passed 1 out of the 3 examinations. While GPT-3.5 performed well in psychiatry questions, GPT-4 exhibited strengths in internal medicine and surgery but showed weakness in academic research. Conclusions: The study results highlight ChatGPT's remarkable improvement from moderate (GPT-3.5) to high competency (GPT-4) in answering medical licensing examination questions in German. While GPT-4's predecessor (GPT-3.5) was imprecise and inconsistent, it demonstrates considerable potential to improve medical education and patient care, provided that medically trained users critically evaluate its results. As the replacement of search engines by AI tools seems possible in the future, further studies with nonprofessional questions are needed to assess the safety and accuracy of ChatGPT for the general population. ", doi="10.2196/50965", url="https://mededu.jmir.org/2024/1/e50965", url="http://www.ncbi.nlm.nih.gov/pubmed/38329802" } @Article{info:doi/10.2196/48514, author="Yu, Peng and Fang, Changchang and Liu, Xiaolin and Fu, Wanying and Ling, Jitao and Yan, Zhiwei and Jiang, Yuan and Cao, Zhengyu and Wu, Maoxiong and Chen, Zhiteng and Zhu, Wengen and Zhang, Yuling and Abudukeremu, Ayiguli and Wang, Yue and Liu, Xiao and Wang, Jingfeng", title="Performance of ChatGPT on the Chinese Postgraduate Examination for Clinical Medicine: Survey Study", journal="JMIR Med Educ", year="2024", month="Feb", day="9", volume="10", pages="e48514", keywords="ChatGPT", keywords="Chinese Postgraduate Examination for Clinical Medicine", keywords="medical student", keywords="performance", keywords="artificial intelligence", keywords="medical care", keywords="qualitative feedback", keywords="medical education", keywords="clinical decision-making", abstract="Background: ChatGPT, an artificial intelligence (AI) based on large-scale language models, has sparked interest in the field of health care. Nonetheless, the capabilities of AI in text comprehension and generation are constrained by the quality and volume of available training data for a specific language, and the performance of AI across different languages requires further investigation. While AI harbors substantial potential in medicine, it is imperative to tackle challenges such as the formulation of clinical care standards; facilitating cultural transitions in medical education and practice; and managing ethical issues including data privacy, consent, and bias. Objective: The study aimed to evaluate ChatGPT's performance in processing Chinese Postgraduate Examination for Clinical Medicine questions, assess its clinical reasoning ability, investigate potential limitations with the Chinese language, and explore its potential as a valuable tool for medical professionals in the Chinese context. Methods: A data set of Chinese Postgraduate Examination for Clinical Medicine questions was used to assess the effectiveness of ChatGPT's (version 3.5) medical knowledge in the Chinese language, which has a data set of 165 medical questions that were divided into three categories: (1) common questions (n=90) assessing basic medical knowledge, (2) case analysis questions (n=45) focusing on clinical decision-making through patient case evaluations, and (3) multichoice questions (n=30) requiring the selection of multiple correct answers. First of all, we assessed whether ChatGPT could meet the stringent cutoff score defined by the government agency, which requires a performance within the top 20\% of candidates. Additionally, in our evaluation of ChatGPT's performance on both original and encoded medical questions, 3 primary indicators were used: accuracy, concordance (which validates the answer), and the frequency of insights. Results: Our evaluation revealed that ChatGPT scored 153.5 out of 300 for original questions in Chinese, which signifies the minimum score set to ensure that at least 20\% more candidates pass than the enrollment quota. However, ChatGPT had low accuracy in answering open-ended medical questions, with only 31.5\% total accuracy. The accuracy for common questions, multichoice questions, and case analysis questions was 42\%, 37\%, and 17\%, respectively. ChatGPT achieved a 90\% concordance across all questions. Among correct responses, the concordance was 100\%, significantly exceeding that of incorrect responses (n=57, 50\%; P<.001). ChatGPT provided innovative insights for 80\% (n=132) of all questions, with an average of 2.95 insights per accurate response. Conclusions: Although ChatGPT surpassed the passing threshold for the Chinese Postgraduate Examination for Clinical Medicine, its performance in answering open-ended medical questions was suboptimal. Nonetheless, ChatGPT exhibited high internal concordance and the ability to generate multiple insights in the Chinese language. Future research should investigate the language-based discrepancies in ChatGPT's performance within the health care context. ", doi="10.2196/48514", url="https://mededu.jmir.org/2024/1/e48514", url="http://www.ncbi.nlm.nih.gov/pubmed/38335017" } @Article{info:doi/10.2196/48949, author="Giunti, Guido and Doherty, P. Colin", title="Cocreating an Automated mHealth Apps Systematic Review Process With Generative AI: Design Science Research Approach", journal="JMIR Med Educ", year="2024", month="Feb", day="12", volume="10", pages="e48949", keywords="generative artificial intelligence", keywords="mHealth", keywords="ChatGPT", keywords="evidence-base", keywords="apps", keywords="qualitative study", keywords="design science research", keywords="eHealth", keywords="mobile device", keywords="AI", keywords="language model", keywords="mHealth intervention", keywords="generative AI", keywords="AI tool", keywords="software code", keywords="systematic review", abstract="Background: The use of mobile devices for delivering health-related services (mobile health [mHealth]) has rapidly increased, leading to a demand for summarizing the state of the art and practice through systematic reviews. However, the systematic review process is a resource-intensive and time-consuming process. Generative artificial intelligence (AI) has emerged as a potential solution to automate tedious tasks. Objective: This study aimed to explore the feasibility of using generative AI tools to automate time-consuming and resource-intensive tasks in a systematic review process and assess the scope and limitations of using such tools. Methods: We used the design science research methodology. The solution proposed is to use cocreation with a generative AI, such as ChatGPT, to produce software code that automates the process of conducting systematic reviews. Results: A triggering prompt was generated, and assistance from the generative AI was used to guide the steps toward developing, executing, and debugging a Python script. Errors in code were solved through conversational exchange with ChatGPT, and a tentative script was created. The code pulled the mHealth solutions from the Google Play Store and searched their descriptions for keywords that hinted toward evidence base. The results were exported to a CSV file, which was compared to the initial outputs of other similar systematic review processes. Conclusions: This study demonstrates the potential of using generative AI to automate the time-consuming process of conducting systematic reviews of mHealth apps. This approach could be particularly useful for researchers with limited coding skills. However, the study has limitations related to the design science research methodology, subjectivity bias, and the quality of the search results used to train the language model. ", doi="10.2196/48949", url="https://mededu.jmir.org/2024/1/e48949", url="http://www.ncbi.nlm.nih.gov/pubmed/38345839" } @Article{info:doi/10.2196/51391, author="Abdullahi, Tassallah and Singh, Ritambhara and Eickhoff, Carsten", title="Learning to Make Rare and Complex Diagnoses With Generative AI Assistance: Qualitative Study of Popular Large Language Models", journal="JMIR Med Educ", year="2024", month="Feb", day="13", volume="10", pages="e51391", keywords="clinical decision support", keywords="rare diseases", keywords="complex diseases", keywords="prompt engineering", keywords="reliability", keywords="consistency", keywords="natural language processing", keywords="language model", keywords="Bard", keywords="ChatGPT 3.5", keywords="GPT-4", keywords="MedAlpaca", keywords="medical education", keywords="complex diagnosis", keywords="artificial intelligence", keywords="AI assistance", keywords="medical training", keywords="prediction model", abstract="Background: Patients with rare and complex diseases often experience delayed diagnoses and misdiagnoses because comprehensive knowledge about these diseases is limited to only a few medical experts. In this context, large language models (LLMs) have emerged as powerful knowledge aggregation tools with applications in clinical decision support and education domains. Objective: This study aims to explore the potential of 3 popular LLMs, namely Bard (Google LLC), ChatGPT-3.5 (OpenAI), and GPT-4 (OpenAI), in medical education to enhance the diagnosis of rare and complex diseases while investigating the impact of prompt engineering on their performance. Methods: We conducted experiments on publicly available complex and rare cases to achieve these objectives. We implemented various prompt strategies to evaluate the performance of these models using both open-ended and multiple-choice prompts. In addition, we used a majority voting strategy to leverage diverse reasoning paths within language models, aiming to enhance their reliability. Furthermore, we compared their performance with the performance of human respondents and MedAlpaca, a generative LLM specifically designed for medical tasks. Results: Notably, all LLMs outperformed the average human consensus and MedAlpaca, with a minimum margin of 5\% and 13\%, respectively, across all 30 cases from the diagnostic case challenge collection. On the frequently misdiagnosed cases category, Bard tied with MedAlpaca but surpassed the human average consensus by 14\%, whereas GPT-4 and ChatGPT-3.5 outperformed MedAlpaca and the human respondents on the moderately often misdiagnosed cases category with minimum accuracy scores of 28\% and 11\%, respectively. The majority voting strategy, particularly with GPT-4, demonstrated the highest overall score across all cases from the diagnostic complex case collection, surpassing that of other LLMs. On the Medical Information Mart for Intensive Care-III data sets, Bard and GPT-4 achieved the highest diagnostic accuracy scores, with multiple-choice prompts scoring 93\%, whereas ChatGPT-3.5 and MedAlpaca scored 73\% and 47\%, respectively. Furthermore, our results demonstrate that there is no one-size-fits-all prompting approach for improving the performance of LLMs and that a single strategy does not universally apply to all LLMs. Conclusions: Our findings shed light on the diagnostic capabilities of LLMs and the challenges associated with identifying an optimal prompting strategy that aligns with each language model's characteristics and specific task requirements. The significance of prompt engineering is highlighted, providing valuable insights for researchers and practitioners who use these language models for medical training. Furthermore, this study represents a crucial step toward understanding how LLMs can enhance diagnostic reasoning in rare and complex medical cases, paving the way for developing effective educational tools and accurate diagnostic aids to improve patient care and outcomes. ", doi="10.2196/51391", url="https://mededu.jmir.org/2024/1/e51391", url="http://www.ncbi.nlm.nih.gov/pubmed/38349725" } @Article{info:doi/10.2196/48989, author="Chen, Chih-Wei and Walter, Paul and Wei, Cheng-Chung James", title="Using ChatGPT-Like Solutions to Bridge the Communication Gap Between Patients With Rheumatoid Arthritis and Health Care Professionals", journal="JMIR Med Educ", year="2024", month="Feb", day="27", volume="10", pages="e48989", keywords="rheumatoid arthritis", keywords="ChatGPT", keywords="artificial intelligence", keywords="communication gap", keywords="privacy", keywords="data management", doi="10.2196/48989", url="https://mededu.jmir.org/2024/1/e48989", url="http://www.ncbi.nlm.nih.gov/pubmed/38412022" } @Article{info:doi/10.2196/51426, author="Willms, Amanda and Liu, Sam", title="Exploring the Feasibility of Using ChatGPT to Create Just-in-Time Adaptive Physical Activity mHealth Intervention Content: Case Study", journal="JMIR Med Educ", year="2024", month="Feb", day="29", volume="10", pages="e51426", keywords="ChatGPT", keywords="digital health", keywords="mobile health", keywords="mHealth", keywords="physical activity", keywords="application", keywords="mobile app", keywords="mobile apps", keywords="content creation", keywords="behavior change", keywords="app design", abstract="Background: Achieving physical activity (PA) guidelines' recommendation of 150 minutes of moderate-to-vigorous PA per week has been shown to reduce the risk of many chronic conditions. Despite the overwhelming evidence in this field, PA levels remain low globally. By creating engaging mobile health (mHealth) interventions through strategies such as just-in-time adaptive interventions (JITAIs) that are tailored to an individual's dynamic state, there is potential to increase PA levels. However, generating personalized content can take a long time due to various versions of content required for the personalization algorithms. ChatGPT presents an incredible opportunity to rapidly produce tailored content; however, there is a lack of studies exploring its feasibility. Objective: This study aimed to (1) explore the feasibility of using ChatGPT to create content for a PA JITAI mobile app and (2) describe lessons learned and future recommendations for using ChatGPT in the development of mHealth JITAI content. Methods: During phase 1, we used Pathverse, a no-code app builder, and ChatGPT to develop a JITAI app to help parents support their child's PA levels. The intervention was developed based on the Multi-Process Action Control (M-PAC) framework, and the necessary behavior change techniques targeting the M-PAC constructs were implemented in the app design to help parents support their child's PA. The acceptability of using ChatGPT for this purpose was discussed to determine its feasibility. In phase 2, we summarized the lessons we learned during the JITAI content development process using ChatGPT and generated recommendations to inform future similar use cases. Results: In phase 1, by using specific prompts, we efficiently generated content for 13 lessons relating to increasing parental support for their child's PA following the M-PAC framework. It was determined that using ChatGPT for this case study to develop PA content for a JITAI was acceptable. In phase 2, we summarized our recommendations into the following six steps when using ChatGPT to create content for mHealth behavior interventions: (1) determine target behavior, (2) ground the intervention in behavior change theory, (3) design the intervention structure, (4) input intervention structure and behavior change constructs into ChatGPT, (5) revise the ChatGPT response, and (6) customize the response to be used in the intervention. Conclusions: ChatGPT offers a remarkable opportunity for rapid content creation in the context of an mHealth JITAI. Although our case study demonstrated that ChatGPT was acceptable, it is essential to approach its use, along with other language models, with caution. Before delivering content to population groups, expert review is crucial to ensure accuracy and relevancy. Future research and application of these guidelines are imperative as we deepen our understanding of ChatGPT and its interactions with human input. ", doi="10.2196/51426", url="https://mededu.jmir.org/2024/1/e51426", url="http://www.ncbi.nlm.nih.gov/pubmed/38421689" } @Article{info:doi/10.2196/51151, author="Magalh{\~a}es Araujo, Sabrina and Cruz-Correia, Ricardo", title="Incorporating ChatGPT in Medical Informatics Education: Mixed Methods Study on Student Perceptions and Experiential Integration Proposals", journal="JMIR Med Educ", year="2024", month="Mar", day="20", volume="10", pages="e51151", keywords="education", keywords="medical informatics", keywords="artificial intelligence", keywords="AI", keywords="generative language model", keywords="ChatGPT", abstract="Background: The integration of artificial intelligence (AI) technologies, such as ChatGPT, in the educational landscape has the potential to enhance the learning experience of medical informatics students and prepare them for using AI in professional settings. The incorporation of AI in classes aims to develop critical thinking by encouraging students to interact with ChatGPT and critically analyze the responses generated by the chatbot. This approach also helps students develop important skills in the field of biomedical and health informatics to enhance their interaction with AI tools. Objective: The aim of the study is to explore the perceptions of students regarding the use of ChatGPT as a learning tool in their educational context and provide professors with examples of prompts for incorporating ChatGPT into their teaching and learning activities, thereby enhancing the educational experience for students in medical informatics courses. Methods: This study used a mixed methods approach to gain insights from students regarding the use of ChatGPT in education. To accomplish this, a structured questionnaire was applied to evaluate students' familiarity with ChatGPT, gauge their perceptions of its use, and understand their attitudes toward its use in academic and learning tasks. Learning outcomes of 2 courses were analyzed to propose ChatGPT's incorporation in master's programs in medicine and medical informatics. Results: The majority of students expressed satisfaction with the use of ChatGPT in education, finding it beneficial for various purposes, including generating academic content, brainstorming ideas, and rewriting text. While some participants raised concerns about potential biases and the need for informed use, the overall perception was positive. Additionally, the study proposed integrating ChatGPT into 2 specific courses in the master's programs in medicine and medical informatics. The incorporation of ChatGPT was envisioned to enhance student learning experiences and assist in project planning, programming code generation, examination preparation, workflow exploration, and technical interview preparation, thus advancing medical informatics education. In medical teaching, it will be used as an assistant for simplifying the explanation of concepts and solving complex problems, as well as for generating clinical narratives and patient simulators. Conclusions: The study's valuable insights into medical faculty students' perspectives and integration proposals for ChatGPT serve as an informative guide for professors aiming to enhance medical informatics education. The research delves into the potential of ChatGPT, emphasizes the necessity of collaboration in academic environments, identifies subject areas with discernible benefits, and underscores its transformative role in fostering innovative and engaging learning experiences. The envisaged proposals hold promise in empowering future health care professionals to work in the rapidly evolving era of digital health care. ", doi="10.2196/51151", url="https://mededu.jmir.org/2024/1/e51151", url="http://www.ncbi.nlm.nih.gov/pubmed/38506920" } @Article{info:doi/10.2196/56128, author="Goodings, James Anthony and Kajitani, Sten and Chhor, Allison and Albakri, Ahmad and Pastrak, Mila and Kodancha, Megha and Ives, Rowan and Lee, Bin Yoo and Kajitani, Kari", title="Assessment of ChatGPT-4 in Family Medicine Board Examinations Using Advanced AI Learning and Analytical Methods: Observational Study", journal="JMIR Med Educ", year="2024", month="Oct", day="8", volume="10", pages="e56128", keywords="ChatGPT-4", keywords="Family Medicine Board Examination", keywords="artificial intelligence in medical education", keywords="AI performance assessment", keywords="prompt engineering", keywords="ChatGPT", keywords="artificial intelligence", keywords="AI", keywords="medical education", keywords="assessment", keywords="observational", keywords="analytical method", keywords="data analysis", keywords="examination", abstract="Background: This research explores the capabilities of ChatGPT-4 in passing the American Board of Family Medicine (ABFM) Certification Examination. Addressing a gap in existing literature, where earlier artificial intelligence (AI) models showed limitations in medical board examinations, this study evaluates the enhanced features and potential of ChatGPT-4, especially in document analysis and information synthesis. Objective: The primary goal is to assess whether ChatGPT-4, when provided with extensive preparation resources and when using sophisticated data analysis, can achieve a score equal to or above the passing threshold for the Family Medicine Board Examinations. Methods: In this study, ChatGPT-4 was embedded in a specialized subenvironment, ``AI Family Medicine Board Exam Taker,'' designed to closely mimic the conditions of the ABFM Certification Examination. This subenvironment enabled the AI to access and analyze a range of relevant study materials, including a primary medical textbook and supplementary web-based resources. The AI was presented with a series of ABFM-type examination questions, reflecting the breadth and complexity typical of the examination. Emphasis was placed on assessing the AI's ability to interpret and respond to these questions accurately, leveraging its advanced data processing and analysis capabilities within this controlled subenvironment. Results: In our study, ChatGPT-4's performance was quantitatively assessed on 300 practice ABFM examination questions. The AI achieved a correct response rate of 88.67\% (95\% CI 85.08\%-92.25\%) for the Custom Robot version and 87.33\% (95\% CI 83.57\%-91.10\%) for the Regular version. Statistical analysis, including the McNemar test (P=.45), indicated no significant difference in accuracy between the 2 versions. In addition, the chi-square test for error-type distribution (P=.32) revealed no significant variation in the pattern of errors across versions. These results highlight ChatGPT-4's capacity for high-level performance and consistency in responding to complex medical examination questions under controlled conditions. Conclusions: The study demonstrates that ChatGPT-4, particularly when equipped with specialized preparation and when operating in a tailored subenvironment, shows promising potential in handling the intricacies of medical board examinations. While its performance is comparable with the expected standards for passing the ABFM Certification Examination, further enhancements in AI technology and tailored training methods could push these capabilities to new heights. This exploration opens avenues for integrating AI tools such as ChatGPT-4 in medical education and assessment, emphasizing the importance of continuous advancement and specialized training in medical applications of AI. ", doi="10.2196/56128", url="https://mededu.jmir.org/2024/1/e56128" } @Article{info:doi/10.2196/63430, author="Bicknell, T. Brenton and Butler, Danner and Whalen, Sydney and Ricks, James and Dixon, J. Cory and Clark, B. Abigail and Spaedy, Olivia and Skelton, Adam and Edupuganti, Neel and Dzubinski, Lance and Tate, Hudson and Dyess, Garrett and Lindeman, Brenessa and Lehmann, Soleymani Lisa", title="ChatGPT-4 Omni Performance in USMLE Disciplines and Clinical Skills: Comparative Analysis", journal="JMIR Med Educ", year="2024", month="Nov", day="6", volume="10", pages="e63430", keywords="large language model", keywords="ChatGPT", keywords="medical education", keywords="USMLE", keywords="AI in medical education", keywords="medical student resources", keywords="educational technology", keywords="artificial intelligence in medicine", keywords="clinical skills", keywords="LLM", keywords="medical licensing examination", keywords="medical students", keywords="United States Medical Licensing Examination", keywords="ChatGPT 4 Omni", keywords="ChatGPT 4", keywords="ChatGPT 3.5", abstract="Background: Recent studies, including those by the National Board of Medical Examiners, have highlighted the remarkable capabilities of recent large language models (LLMs) such as ChatGPT in passing the United States Medical Licensing Examination (USMLE). However, there is a gap in detailed analysis of LLM performance in specific medical content areas, thus limiting an assessment of their potential utility in medical education. Objective: This study aimed to assess and compare the accuracy of successive ChatGPT versions (GPT-3.5, GPT-4, and GPT-4 Omni) in USMLE disciplines, clinical clerkships, and the clinical skills of diagnostics and management. Methods: This study used 750 clinical vignette-based multiple-choice questions to characterize the performance of successive ChatGPT versions (ChatGPT 3.5 [GPT-3.5], ChatGPT 4 [GPT-4], and ChatGPT 4 Omni [GPT-4o]) across USMLE disciplines, clinical clerkships, and in clinical skills (diagnostics and management). Accuracy was assessed using a standardized protocol, with statistical analyses conducted to compare the models' performances. Results: GPT-4o achieved the highest accuracy across 750 multiple-choice questions at 90.4\%, outperforming GPT-4 and GPT-3.5, which scored 81.1\% and 60.0\%, respectively. GPT-4o's highest performances were in social sciences (95.5\%), behavioral and neuroscience (94.2\%), and pharmacology (93.2\%). In clinical skills, GPT-4o's diagnostic accuracy was 92.7\% and management accuracy was 88.8\%, significantly higher than its predecessors. Notably, both GPT-4o and GPT-4 significantly outperformed the medical student average accuracy of 59.3\% (95\% CI 58.3?60.3). Conclusions: GPT-4o's performance in USMLE disciplines, clinical clerkships, and clinical skills indicates substantial improvements over its predecessors, suggesting significant potential for the use of this technology as an educational aid for medical students. These findings underscore the need for careful consideration when integrating LLMs into medical education, emphasizing the importance of structured curricula to guide their appropriate use and the need for ongoing critical analyses to ensure their reliability and effectiveness. ", doi="10.2196/63430", url="https://mededu.jmir.org/2024/1/e63430" } @Article{info:doi/10.2196/56762, author="Ros-Arlanz{\'o}n, Pablo and Perez-Sempere, Angel", title="Evaluating AI Competence in Specialized Medicine: Comparative Analysis of ChatGPT and Neurologists in a Neurology Specialist Examination in Spain", journal="JMIR Med Educ", year="2024", month="Nov", day="14", volume="10", pages="e56762", keywords="artificial intelligence", keywords="ChatGPT", keywords="clinical decision-making", keywords="medical education", keywords="medical knowledge assessment", keywords="OpenAI", abstract="Background: With the rapid advancement of artificial intelligence (AI) in various fields, evaluating its application in specialized medical contexts becomes crucial. ChatGPT, a large language model developed by OpenAI, has shown potential in diverse applications, including medicine. Objective: This study aims to compare the performance of ChatGPT with that of attending neurologists in a real neurology specialist examination conducted in the Valencian Community, Spain, assessing the AI's capabilities and limitations in medical knowledge. Methods: We conducted a comparative analysis using the 2022 neurology specialist examination results from 120 neurologists and responses generated by ChatGPT versions 3.5 and 4. The examination consisted of 80 multiple-choice questions, with a focus on clinical neurology and health legislation. Questions were classified according to Bloom's Taxonomy. Statistical analysis of performance, including the $\kappa$ coefficient for response consistency, was performed. Results: Human participants exhibited a median score of 5.91 (IQR: 4.93-6.76), with 32 neurologists failing to pass. ChatGPT-3.5 ranked 116th out of 122, answering 54.5\% of questions correctly (score 3.94). ChatGPT-4 showed marked improvement, ranking 17th with 81.8\% of correct answers (score 7.57), surpassing several human specialists. No significant variations were observed in the performance on lower-order questions versus higher-order questions. Additionally, ChatGPT-4 demonstrated increased interrater reliability, as reflected by a higher $\kappa$ coefficient of 0.73, compared to ChatGPT-3.5's coefficient of 0.69. Conclusions: This study underscores the evolving capabilities of AI in medical knowledge assessment, particularly in specialized fields. ChatGPT-4's performance, outperforming the median score of human participants in a rigorous neurology examination, represents a significant milestone in AI development, suggesting its potential as an effective tool in specialized medical education and assessment. ", doi="10.2196/56762", url="https://mededu.jmir.org/2024/1/e56762" } @Article{info:doi/10.2196/51433, author="Ehrett, Carl and Hegde, Sudeep and Andre, Kwame and Liu, Dixizi and Wilson, Timothy", title="Leveraging Open-Source Large Language Models for Data Augmentation in Hospital Staff Surveys: Mixed Methods Study", journal="JMIR Med Educ", year="2024", month="Nov", day="19", volume="10", pages="e51433", keywords="data augmentation", keywords="large language models", keywords="medical education", keywords="natural language processing", keywords="data security", keywords="ethics", keywords="AI", keywords="artificial intelligence", keywords="data privacy", keywords="medical staff", abstract="Background: Generative large language models (LLMs) have the potential to revolutionize medical education by generating tailored learning materials, enhancing teaching efficiency, and improving learner engagement. However, the application of LLMs in health care settings, particularly for augmenting small datasets in text classification tasks, remains underexplored, particularly for cost- and privacy-conscious applications that do not permit the use of third-party services such as OpenAI's ChatGPT. Objective: This study aims to explore the use of open-source LLMs, such as Large Language Model Meta AI (LLaMA) and Alpaca models, for data augmentation in a specific text classification task related to hospital staff surveys. Methods: The surveys were designed to elicit narratives of everyday adaptation by frontline radiology staff during the initial phase of the COVID-19 pandemic. A 2-step process of data augmentation and text classification was conducted. The study generated synthetic data similar to the survey reports using 4 generative LLMs for data augmentation. A different set of 3 classifier LLMs was then used to classify the augmented text for thematic categories. The study evaluated performance on the classification task. Results: The overall best-performing combination of LLMs, temperature, classifier, and number of synthetic data cases is via augmentation with LLaMA 7B at temperature 0.7 with 100 augments, using Robustly Optimized BERT Pretraining Approach (RoBERTa) for the classification task, achieving an average area under the receiver operating characteristic (AUC) curve of 0.87 (SD 0.02; ie, 1 SD). The results demonstrate that open-source LLMs can enhance text classifiers' performance for small datasets in health care contexts, providing promising pathways for improving medical education processes and patient care practices. Conclusions: The study demonstrates the value of data augmentation with open-source LLMs, highlights the importance of privacy and ethical considerations when using LLMs, and suggests future directions for research in this field. ", doi="10.2196/51433", url="https://mededu.jmir.org/2024/1/e51433" } @Article{info:doi/10.2196/52784, author="Ming, Shuai and Guo, Qingge and Cheng, Wenjun and Lei, Bo", title="Influence of Model Evolution and System Roles on ChatGPT's Performance in Chinese Medical Licensing Exams: Comparative Study", journal="JMIR Med Educ", year="2024", month="Aug", day="13", volume="10", pages="e52784", keywords="ChatGPT", keywords="Chinese National Medical Licensing Examination", keywords="large language models", keywords="medical education", keywords="system role", keywords="LLM", keywords="LLMs", keywords="language model", keywords="language models", keywords="artificial intelligence", keywords="chatbot", keywords="chatbots", keywords="conversational agent", keywords="conversational agents", keywords="exam", keywords="exams", keywords="examination", keywords="examinations", keywords="OpenAI", keywords="answer", keywords="answers", keywords="response", keywords="responses", keywords="accuracy", keywords="performance", keywords="China", keywords="Chinese", abstract="Background: With the increasing application of large language models like ChatGPT in various industries, its potential in the medical domain, especially in standardized examinations, has become a focal point of research. Objective: The aim of this study is to assess the clinical performance of ChatGPT, focusing on its accuracy and reliability in the Chinese National Medical Licensing Examination (CNMLE). Methods: The CNMLE 2022 question set, consisting of 500 single-answer multiple choices questions, were reclassified into 15 medical subspecialties. Each question was tested 8 to 12 times in Chinese on the OpenAI platform from April 24 to May 15, 2023. Three key factors were considered: the version of GPT-3.5 and 4.0, the prompt's designation of system roles tailored to medical subspecialties, and repetition for coherence. A passing accuracy threshold was established as 60\%. The $\chi$2 tests and $\kappa$ values were employed to evaluate the model's accuracy and consistency. Results: GPT-4.0 achieved a passing accuracy of 72.7\%, which was significantly higher than that of GPT-3.5 (54\%; P<.001). The variability rate of repeated responses from GPT-4.0 was lower than that of GPT-3.5 (9\% vs 19.5\%; P<.001). However, both models showed relatively good response coherence, with $\kappa$ values of 0.778 and 0.610, respectively. System roles numerically increased accuracy for both GPT-4.0 (0.3\%?3.7\%) and GPT-3.5 (1.3\%?4.5\%), and reduced variability by 1.7\% and 1.8\%, respectively (P>.05). In subgroup analysis, ChatGPT achieved comparable accuracy among different question types (P>.05). GPT-4.0 surpassed the accuracy threshold in 14 of 15 subspecialties, while GPT-3.5 did so in 7 of 15 on the first response. Conclusions: GPT-4.0 passed the CNMLE and outperformed GPT-3.5 in key areas such as accuracy, consistency, and medical subspecialty expertise. Adding a system role insignificantly enhanced the model's reliability and answer coherence. GPT-4.0 showed promising potential in medical education and clinical practice, meriting further study. ", doi="10.2196/52784", url="https://mededu.jmir.org/2024/1/e52784" } @Article{info:doi/10.2196/56342, author="Burke, B. Harry and Hoang, Albert and Lopreiato, O. Joseph and King, Heidi and Hemmer, Paul and Montgomery, Michael and Gagarin, Viktoria", title="Assessing the Ability of a Large Language Model to Score Free-Text Medical Student Clinical Notes: Quantitative Study", journal="JMIR Med Educ", year="2024", month="Jul", day="25", volume="10", pages="e56342", keywords="medical education", keywords="generative artificial intelligence", keywords="natural language processing", keywords="ChatGPT", keywords="generative pretrained transformer", keywords="standardized patients", keywords="clinical notes", keywords="free-text notes", keywords="history and physical examination", keywords="large language model", keywords="LLM", keywords="medical student", keywords="medical students", keywords="clinical information", keywords="artificial intelligence", keywords="AI", keywords="patients", keywords="patient", keywords="medicine", abstract="Background: Teaching medical students the skills required to acquire, interpret, apply, and communicate clinical information is an integral part of medical education. A crucial aspect of this process involves providing students with feedback regarding the quality of their free-text clinical notes. Objective: The goal of this study was to assess the ability of ChatGPT 3.5, a large language model, to score medical students' free-text history and physical notes. Methods: This is a single-institution, retrospective study. Standardized patients learned a prespecified clinical case and, acting as the patient, interacted with medical students. Each student wrote a free-text history and physical note of their interaction. The students' notes were scored independently by the standardized patients and ChatGPT using a prespecified scoring rubric that consisted of 85 case elements. The measure of accuracy was percent correct. Results: The study population consisted of 168 first-year medical students. There was a total of 14,280 scores. The ChatGPT incorrect scoring rate was 1.0\%, and the standardized patient incorrect scoring rate was 7.2\%. The ChatGPT error rate was 86\%, lower than the standardized patient error rate. The ChatGPT mean incorrect scoring rate of 12 (SD 11) was significantly lower than the standardized patient mean incorrect scoring rate of 85 (SD 74; P=.002). Conclusions: ChatGPT demonstrated a significantly lower error rate compared to standardized patients. This is the first study to assess the ability of a generative pretrained transformer (GPT) program to score medical students' standardized patient-based free-text clinical notes. It is expected that, in the near future, large language models will provide real-time feedback to practicing physicians regarding their free-text notes. GPT artificial intelligence programs represent an important advance in medical education and medical practice. ", doi="10.2196/56342", url="https://mededu.jmir.org/2024/1/e56342" } @Article{info:doi/10.2196/58355, author="Moldt, Julia-Astrid and Festl-Wietek, Teresa and Fuhl, Wolfgang and Zabel, Susanne and Claassen, Manfred and Wagner, Samuel and Nieselt, Kay and Herrmann-Werner, Anne", title="Assessing AI Awareness and Identifying Essential Competencies: Insights From Key Stakeholders in Integrating AI Into Medical Education", journal="JMIR Med Educ", year="2024", month="Jun", day="12", volume="10", pages="e58355", keywords="AI in medicine", keywords="artificial intelligence", keywords="medical education", keywords="medical students", keywords="qualitative approach", keywords="qualitative analysis", keywords="needs assessment", abstract="Background: The increasing importance of artificial intelligence (AI) in health care has generated a growing need for health care professionals to possess a comprehensive understanding of AI technologies, requiring an adaptation in medical education. Objective: This paper explores stakeholder perceptions and expectations regarding AI in medicine and examines their potential impact on the medical curriculum. This study project aims to assess the AI experiences and awareness of different stakeholders and identify essential AI-related topics in medical education to define necessary competencies for students. Methods: The empirical data were collected as part of the T{\"u}KITZMed project between August 2022 and March 2023, using a semistructured qualitative interview. These interviews were administered to a diverse group of stakeholders to explore their experiences and perspectives of AI in medicine. A qualitative content analysis of the collected data was conducted using MAXQDA software. Results: Semistructured interviews were conducted with 38 participants (6 lecturers, 9 clinicians, 10 students, 6 AI experts, and 7 institutional stakeholders). The qualitative content analysis revealed 6 primary categories with a total of 24 subcategories to answer the research questions. The evaluation of the stakeholders' statements revealed several commonalities and differences regarding their understanding of AI. Crucial identified AI themes based on the main categories were as follows: possible curriculum contents, skills, and competencies; programming skills; curriculum scope; and curriculum structure. Conclusions: The analysis emphasizes integrating AI into medical curricula to ensure students' proficiency in clinical applications. Standardized AI comprehension is crucial for defining and teaching relevant content. Considering diverse perspectives in implementation is essential to comprehensively define AI in the medical context, addressing gaps and facilitating effective solutions for future AI use in medical studies. The results provide insights into potential curriculum content and structure, including aspects of AI in medicine. ", doi="10.2196/58355", url="https://mededu.jmir.org/2024/1/e58355" } @Article{info:doi/10.2196/51282, author="Jo, Eunbeen and Song, Sanghoun and Kim, Jong-Ho and Lim, Subin and Kim, Hyeon Ju and Cha, Jung-Joon and Kim, Young-Min and Joo, Joon Hyung", title="Assessing GPT-4's Performance in Delivering Medical Advice: Comparative Analysis With Human Experts", journal="JMIR Med Educ", year="2024", month="Jul", day="8", volume="10", pages="e51282", keywords="GPT-4", keywords="medical advice", keywords="ChatGPT", keywords="cardiology", keywords="cardiologist", keywords="heart", keywords="advice", keywords="recommendation", keywords="recommendations", keywords="linguistic", keywords="linguistics", keywords="artificial intelligence", keywords="NLP", keywords="natural language processing", keywords="chatbot", keywords="chatbots", keywords="conversational agent", keywords="conversational agents", keywords="response", keywords="responses", abstract="Background: Accurate medical advice is paramount in ensuring optimal patient care, and misinformation can lead to misguided decisions with potentially detrimental health outcomes. The emergence of large language models (LLMs) such as OpenAI's GPT-4 has spurred interest in their potential health care applications, particularly in automated medical consultation. Yet, rigorous investigations comparing their performance to human experts remain sparse. Objective: This study aims to compare the medical accuracy of GPT-4 with human experts in providing medical advice using real-world user-generated queries, with a specific focus on cardiology. It also sought to analyze the performance of GPT-4 and human experts in specific question categories, including drug or medication information and preliminary diagnoses. Methods: We collected 251 pairs of cardiology-specific questions from general users and answers from human experts via an internet portal. GPT-4 was tasked with generating responses to the same questions. Three independent cardiologists (SL, JHK, and JJC) evaluated the answers provided by both human experts and GPT-4. Using a computer interface, each evaluator compared the pairs and determined which answer was superior, and they quantitatively measured the clarity and complexity of the questions as well as the accuracy and appropriateness of the responses, applying a 3-tiered grading scale (low, medium, and high). Furthermore, a linguistic analysis was conducted to compare the length and vocabulary diversity of the responses using word count and type-token ratio. Results: GPT-4 and human experts displayed comparable efficacy in medical accuracy (``GPT-4 is better'' at 132/251, 52.6\% vs ``Human expert is better'' at 119/251, 47.4\%). In accuracy level categorization, humans had more high-accuracy responses than GPT-4 (50/237, 21.1\% vs 30/238, 12.6\%) but also a greater proportion of low-accuracy responses (11/237, 4.6\% vs 1/238, 0.4\%; P=.001). GPT-4 responses were generally longer and used a less diverse vocabulary than those of human experts, potentially enhancing their comprehensibility for general users (sentence count: mean 10.9, SD 4.2 vs mean 5.9, SD 3.7; P<.001; type-token ratio: mean 0.69, SD 0.07 vs mean 0.79, SD 0.09; P<.001). Nevertheless, human experts outperformed GPT-4 in specific question categories, notably those related to drug or medication information and preliminary diagnoses. These findings highlight the limitations of GPT-4 in providing advice based on clinical experience. Conclusions: GPT-4 has shown promising potential in automated medical consultation, with comparable medical accuracy to human experts. However, challenges remain particularly in the realm of nuanced clinical judgment. Future improvements in LLMs may require the integration of specific clinical reasoning pathways and regulatory oversight for safe use. Further research is needed to understand the full potential of LLMs across various medical specialties and conditions. ", doi="10.2196/51282", url="https://mededu.jmir.org/2024/1/e51282" } @Article{info:doi/10.2196/53308, author="Hassanipour, Soheil and Nayak, Sandeep and Bozorgi, Ali and Keivanlou, Mohammad-Hossein and Dave, Tirth and Alotaibi, Abdulhadi and Joukar, Farahnaz and Mellatdoust, Parinaz and Bakhshi, Arash and Kuriyakose, Dona and Polisetty, D. Lakshmi and Chimpiri, Mallika and Amini-Salehi, Ehsan", title="The Ability of ChatGPT in Paraphrasing Texts and Reducing Plagiarism: A Descriptive Analysis", journal="JMIR Med Educ", year="2024", month="Jul", day="8", volume="10", pages="e53308", keywords="ChatGPT", keywords="paraphrasing", keywords="text generation", keywords="prompts", keywords="academic journals", keywords="plagiarize", keywords="plagiarism", keywords="paraphrase", keywords="wording", keywords="LLM", keywords="LLMs", keywords="language model", keywords="language models", keywords="prompt", keywords="generative", keywords="artificial intelligence", keywords="NLP", keywords="natural language processing", keywords="rephrase", keywords="plagiarizing", keywords="honesty", keywords="integrity", keywords="texts", keywords="text", keywords="textual", keywords="generation", keywords="large language model", keywords="large language models", abstract="Background: The introduction of ChatGPT by OpenAI has garnered significant attention. Among its capabilities, paraphrasing stands out. Objective: This study aims to investigate the satisfactory levels of plagiarism in the paraphrased text produced by this chatbot. Methods: Three texts of varying lengths were presented to ChatGPT. ChatGPT was then instructed to paraphrase the provided texts using five different prompts. In the subsequent stage of the study, the texts were divided into separate paragraphs, and ChatGPT was requested to paraphrase each paragraph individually. Lastly, in the third stage, ChatGPT was asked to paraphrase the texts it had previously generated. Results: The average plagiarism rate in the texts generated by ChatGPT was 45\% (SD 10\%). ChatGPT exhibited a substantial reduction in plagiarism for the provided texts (mean difference ?0.51, 95\% CI ?0.54 to ?0.48; P<.001). Furthermore, when comparing the second attempt with the initial attempt, a significant decrease in the plagiarism rate was observed (mean difference ?0.06, 95\% CI ?0.08 to ?0.03; P<.001). The number of paragraphs in the texts demonstrated a noteworthy association with the percentage of plagiarism, with texts consisting of a single paragraph exhibiting the lowest plagiarism rate (P<.001). Conclusion: Although ChatGPT demonstrates a notable reduction of plagiarism within texts, the existing levels of plagiarism remain relatively high. This underscores a crucial caution for researchers when incorporating this chatbot into their work. ", doi="10.2196/53308", url="https://mededu.jmir.org/2024/1/e53308" } @Article{info:doi/10.2196/58758, author="Shikino, Kiyoshi and Shimizu, Taro and Otsuka, Yuki and Tago, Masaki and Takahashi, Hiromizu and Watari, Takashi and Sasaki, Yosuke and Iizuka, Gemmei and Tamura, Hiroki and Nakashima, Koichi and Kunitomo, Kotaro and Suzuki, Morika and Aoyama, Sayaka and Kosaka, Shintaro and Kawahigashi, Teiko and Matsumoto, Tomohiro and Orihara, Fumina and Morikawa, Toru and Nishizawa, Toshinori and Hoshina, Yoji and Yamamoto, Yu and Matsuo, Yuichiro and Unoki, Yuto and Kimura, Hirofumi and Tokushima, Midori and Watanuki, Satoshi and Saito, Takuma and Otsuka, Fumio and Tokuda, Yasuharu", title="Evaluation of ChatGPT-Generated Differential Diagnosis for Common Diseases With Atypical Presentation: Descriptive Research", journal="JMIR Med Educ", year="2024", month="Jun", day="21", volume="10", pages="e58758", keywords="atypical presentation", keywords="ChatGPT", keywords="common disease", keywords="diagnostic accuracy", keywords="diagnosis", keywords="patient safety", abstract="Background: The persistence of diagnostic errors, despite advances in medical knowledge and diagnostics, highlights the importance of understanding atypical disease presentations and their contribution to mortality and morbidity. Artificial intelligence (AI), particularly generative pre-trained transformers like GPT-4, holds promise for improving diagnostic accuracy, but requires further exploration in handling atypical presentations. Objective: This study aimed to assess the diagnostic accuracy of ChatGPT in generating differential diagnoses for atypical presentations of common diseases, with a focus on the model's reliance on patient history during the diagnostic process. Methods: We used 25 clinical vignettes from the Journal of Generalist Medicine characterizing atypical manifestations of common diseases. Two general medicine physicians categorized the cases based on atypicality. ChatGPT was then used to generate differential diagnoses based on the clinical information provided. The concordance between AI-generated and final diagnoses was measured, with a focus on the top-ranked disease (top 1) and the top 5 differential diagnoses (top 5). Results: ChatGPT's diagnostic accuracy decreased with an increase in atypical presentation. For category 1 (C1) cases, the concordance rates were 17\% (n=1) for the top 1 and 67\% (n=4) for the top 5. Categories 3 (C3) and 4 (C4) showed a 0\% concordance for top 1 and markedly lower rates for the top 5, indicating difficulties in handling highly atypical cases. The $\chi$2 test revealed no significant difference in the top 1 differential diagnosis accuracy between less atypical (C1+C2) and more atypical (C3+C4) groups ($\chi${\texttwosuperior}1=2.07; n=25; P=.13). However, a significant difference was found in the top 5 analyses, with less atypical cases showing higher accuracy ($\chi${\texttwosuperior}1=4.01; n=25; P=.048). Conclusions: ChatGPT-4 demonstrates potential as an auxiliary tool for diagnosing typical and mildly atypical presentations of common diseases. However, its performance declines with greater atypicality. The study findings underscore the need for AI systems to encompass a broader range of linguistic capabilities, cultural understanding, and diverse clinical scenarios to improve diagnostic utility in real-world settings. ", doi="10.2196/58758", url="https://mededu.jmir.org/2024/1/e58758" } @Article{info:doi/10.2196/52674, author="Fukuzawa, Fumitoshi and Yanagita, Yasutaka and Yokokawa, Daiki and Uchida, Shun and Yamashita, Shiho and Li, Yu and Shikino, Kiyoshi and Tsukamoto, Tomoko and Noda, Kazutaka and Uehara, Takanori and Ikusaka, Masatomi", title="Importance of Patient History in Artificial Intelligence--Assisted Medical Diagnosis: Comparison Study", journal="JMIR Med Educ", year="2024", month="Apr", day="8", volume="10", pages="e52674", keywords="medical diagnosis", keywords="ChatGPT", keywords="AI in medicine", keywords="diagnostic accuracy", keywords="patient history", keywords="medical history", keywords="artificial intelligence", keywords="AI", keywords="physical examination", keywords="physical examinations", keywords="laboratory investigation", keywords="laboratory investigations", keywords="mHealth", keywords="accuracy", keywords="public health", keywords="United States", keywords="AI diagnosis", keywords="treatment", keywords="male", keywords="female", keywords="child", keywords="children", keywords="youth", keywords="adolescent", keywords="adolescents", keywords="teen", keywords="teens", keywords="teenager", keywords="teenagers", keywords="older adult", keywords="older adults", keywords="elder", keywords="elderly", keywords="older person", keywords="older people", keywords="investigative", keywords="mobile health", keywords="digital health", abstract="Background: Medical history contributes approximately 80\% to a diagnosis, although physical examinations and laboratory investigations increase a physician's confidence in the medical diagnosis. The concept of artificial intelligence (AI) was first proposed more than 70 years ago. Recently, its role in various fields of medicine has grown remarkably. However, no studies have evaluated the importance of patient history in AI-assisted medical diagnosis. Objective: This study explored the contribution of patient history to AI-assisted medical diagnoses and assessed the accuracy of ChatGPT in reaching a clinical diagnosis based on the medical history provided. Methods: Using clinical vignettes of 30 cases identified in The BMJ, we evaluated the accuracy of diagnoses generated by ChatGPT. We compared the diagnoses made by ChatGPT based solely on medical history with the correct diagnoses. We also compared the diagnoses made by ChatGPT after incorporating additional physical examination findings and laboratory data alongside history with the correct diagnoses. Results: ChatGPT accurately diagnosed 76.6\% (23/30) of the cases with only the medical history, consistent with previous research targeting physicians. We also found that this rate was 93.3\% (28/30) when additional information was included. Conclusions: Although adding additional information improves diagnostic accuracy, patient history remains a significant factor in AI-assisted medical diagnosis. Thus, when using AI in medical diagnosis, it is crucial to include pertinent and correct patient histories for an accurate diagnosis. Our findings emphasize the continued significance of patient history in clinical diagnoses in this age and highlight the need for its integration into AI-assisted medical diagnosis systems. ", doi="10.2196/52674", url="https://mededu.jmir.org/2024/1/e52674" } @Article{info:doi/10.2196/46500, author="Abid, Areeba and Murugan, Avinash and Banerjee, Imon and Purkayastha, Saptarshi and Trivedi, Hari and Gichoya, Judy", title="AI Education for Fourth-Year Medical Students: Two-Year Experience of a Web-Based, Self-Guided Curriculum and Mixed Methods Study", journal="JMIR Med Educ", year="2024", month="Feb", day="20", volume="10", pages="e46500", keywords="medical education", keywords="machine learning", keywords="artificial intelligence", keywords="elective curriculum", keywords="medical student", keywords="student", keywords="students", keywords="elective", keywords="electives", keywords="curricula", keywords="curriculum", keywords="lesson plan", keywords="lesson plans", keywords="educators", keywords="educator", keywords="teacher", keywords="teachers", keywords="teaching", keywords="computer programming", keywords="programming", keywords="coding", keywords="programmer", keywords="programmers", keywords="self guided", keywords="self directed", abstract="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. ", doi="10.2196/46500", url="https://mededu.jmir.org/2024/1/e46500", url="http://www.ncbi.nlm.nih.gov/pubmed/38376896" } @Article{info:doi/10.2196/51523, author="Farhat, Faiza and Chaudhry, Moalla Beenish and Nadeem, Mohammad and Sohail, Saquib Shahab and Madsen, {\O}ivind Dag", title="Evaluating Large Language Models for the National Premedical Exam in India: Comparative Analysis of GPT-3.5, GPT-4, and Bard", journal="JMIR Med Educ", year="2024", month="Feb", day="21", volume="10", pages="e51523", keywords="accuracy", keywords="AI model", keywords="artificial intelligence", keywords="Bard", keywords="ChatGPT", keywords="educational task", keywords="GPT-4", keywords="Generative Pre-trained Transformers", keywords="large language models", keywords="medical education, medical exam", keywords="natural language processing", keywords="performance", keywords="premedical exams", keywords="suitability", abstract="Background: Large language models (LLMs) have revolutionized natural language processing with their ability to generate human-like text through extensive training on large data sets. These models, including Generative Pre-trained Transformers (GPT)-3.5 (OpenAI), GPT-4 (OpenAI), and Bard (Google LLC), find applications beyond natural language processing, attracting interest from academia and industry. Students are actively leveraging LLMs to enhance learning experiences and prepare for high-stakes exams, such as the National Eligibility cum Entrance Test (NEET) in India. Objective: This comparative analysis aims to evaluate the performance of GPT-3.5, GPT-4, and Bard in answering NEET-2023 questions. Methods: In this paper, we evaluated the performance of the 3 mainstream LLMs, namely GPT-3.5, GPT-4, and Google Bard, in answering questions related to the NEET-2023 exam. The questions of the NEET were provided to these artificial intelligence models, and the responses were recorded and compared against the correct answers from the official answer key. Consensus was used to evaluate the performance of all 3 models. Results: It was evident that GPT-4 passed the entrance test with flying colors (300/700, 42.9\%), showcasing exceptional performance. On the other hand, GPT-3.5 managed to meet the qualifying criteria, but with a substantially lower score (145/700, 20.7\%). However, Bard (115/700, 16.4\%) failed to meet the qualifying criteria and did not pass the test. GPT-4 demonstrated consistent superiority over Bard and GPT-3.5 in all 3 subjects. Specifically, GPT-4 achieved accuracy rates of 73\% (29/40) in physics, 44\% (16/36) in chemistry, and 51\% (50/99) in biology. Conversely, GPT-3.5 attained an accuracy rate of 45\% (18/40) in physics, 33\% (13/26) in chemistry, and 34\% (34/99) in biology. The accuracy consensus metric showed that the matching responses between GPT-4 and Bard, as well as GPT-4 and GPT-3.5, had higher incidences of being correct, at 0.56 and 0.57, respectively, compared to the matching responses between Bard and GPT-3.5, which stood at 0.42. When all 3 models were considered together, their matching responses reached the highest accuracy consensus of 0.59. Conclusions: The study's findings provide valuable insights into the performance of GPT-3.5, GPT-4, and Bard in answering NEET-2023 questions. GPT-4 emerged as the most accurate model, highlighting its potential for educational applications. Cross-checking responses across models may result in confusion as the compared models (as duos or a trio) tend to agree on only a little over half of the correct responses. Using GPT-4 as one of the compared models will result in higher accuracy consensus. The results underscore the suitability of LLMs for high-stakes exams and their positive impact on education. Additionally, the study establishes a benchmark for evaluating and enhancing LLMs' performance in educational tasks, promoting responsible and informed use of these models in diverse learning environments. ", doi="10.2196/51523", url="https://mededu.jmir.org/2024/1/e51523", url="http://www.ncbi.nlm.nih.gov/pubmed/38381486" } @Article{info:doi/10.2196/54393, author="Nakao, Takahiro and Miki, Soichiro and Nakamura, Yuta and Kikuchi, Tomohiro and Nomura, Yukihiro and Hanaoka, Shouhei and Yoshikawa, Takeharu and Abe, Osamu", title="Capability of GPT-4V(ision) in the Japanese National Medical Licensing Examination: Evaluation Study", journal="JMIR Med Educ", year="2024", month="Mar", day="12", volume="10", pages="e54393", keywords="AI", keywords="artificial intelligence", keywords="LLM", keywords="large language model", keywords="language model", keywords="language models", keywords="ChatGPT", keywords="GPT-4", keywords="GPT-4V", keywords="generative pretrained transformer", keywords="image", keywords="images", keywords="imaging", keywords="response", keywords="responses", keywords="exam", keywords="examination", keywords="exams", keywords="examinations", keywords="answer", keywords="answers", keywords="NLP", keywords="natural language processing", keywords="chatbot", keywords="chatbots", keywords="conversational agent", keywords="conversational agents", keywords="medical education", abstract="Background: Previous research applying large language models (LLMs) to medicine was focused on text-based information. Recently, multimodal variants of LLMs acquired the capability of recognizing images. Objective: We aim to evaluate the image recognition capability of generative pretrained transformer (GPT)-4V, a recent multimodal LLM developed by OpenAI, in the medical field by testing how visual information affects its performance to answer questions in the 117th Japanese National Medical Licensing Examination. Methods: We focused on 108 questions that had 1 or more images as part of a question and presented GPT-4V with the same questions under two conditions: (1) with both the question text and associated images and (2) with the question text only. We then compared the difference in accuracy between the 2 conditions using the exact McNemar test. Results: Among the 108 questions with images, GPT-4V's accuracy was 68\% (73/108) when presented with images and 72\% (78/108) when presented without images (P=.36). For the 2 question categories, clinical and general, the accuracies with and those without images were 71\% (70/98) versus 78\% (76/98; P=.21) and 30\% (3/10) versus 20\% (2/10; P?.99), respectively. Conclusions: The additional information from the images did not significantly improve the performance of GPT-4V in the Japanese National Medical Licensing Examination. ", doi="10.2196/54393", url="https://mededu.jmir.org/2024/1/e54393", url="http://www.ncbi.nlm.nih.gov/pubmed/38470459" } @Article{info:doi/10.2196/57054, author="Noda, Masao and Ueno, Takayoshi and Koshu, Ryota and Takaso, Yuji and Shimada, Dias Mari and Saito, Chizu and Sugimoto, Hisashi and Fushiki, Hiroaki and Ito, Makoto and Nomura, Akihiro and Yoshizaki, Tomokazu", title="Performance of GPT-4V in Answering the Japanese Otolaryngology Board Certification Examination Questions: Evaluation Study", journal="JMIR Med Educ", year="2024", month="Mar", day="28", volume="10", pages="e57054", keywords="artificial intelligence", keywords="GPT-4v", keywords="large language model", keywords="otolaryngology", keywords="GPT", keywords="ChatGPT", keywords="LLM", keywords="LLMs", keywords="language model", keywords="language models", keywords="head", keywords="respiratory", keywords="ENT: ear", keywords="nose", keywords="throat", keywords="neck", keywords="NLP", keywords="natural language processing", keywords="image", keywords="images", keywords="exam", keywords="exams", keywords="examination", keywords="examinations", keywords="answer", keywords="answers", keywords="answering", keywords="response", keywords="responses", abstract="Background: Artificial intelligence models can learn from medical literature and clinical cases and generate answers that rival human experts. However, challenges remain in the analysis of complex data containing images and diagrams. Objective: This study aims to assess the answering capabilities and accuracy of ChatGPT-4 Vision (GPT-4V) for a set of 100 questions, including image-based questions, from the 2023 otolaryngology board certification examination. Methods: Answers to 100 questions from the 2023 otolaryngology board certification examination, including image-based questions, were generated using GPT-4V. The accuracy rate was evaluated using different prompts, and the presence of images, clinical area of the questions, and variations in the answer content were examined. Results: The accuracy rate for text-only input was, on average, 24.7\% but improved to 47.3\% with the addition of English translation and prompts (P<.001). The average nonresponse rate for text-only input was 46.3\%; this decreased to 2.7\% with the addition of English translation and prompts (P<.001). The accuracy rate was lower for image-based questions than for text-only questions across all types of input, with a relatively high nonresponse rate. General questions and questions from the fields of head and neck allergies and nasal allergies had relatively high accuracy rates, which increased with the addition of translation and prompts. In terms of content, questions related to anatomy had the highest accuracy rate. For all content types, the addition of translation and prompts increased the accuracy rate. As for the performance based on image-based questions, the average of correct answer rate with text-only input was 30.4\%, and that with text-plus-image input was 41.3\% (P=.02). Conclusions: Examination of artificial intelligence's answering capabilities for the otolaryngology board certification examination improves our understanding of its potential and limitations in this field. Although the improvement was noted with the addition of translation and prompts, the accuracy rate for image-based questions was lower than that for text-based questions, suggesting room for improvement in GPT-4V at this stage. Furthermore, text-plus-image input answers a higher rate in image-based questions. Our findings imply the usefulness and potential of GPT-4V in medicine; however, future consideration of safe use methods is needed. ", doi="10.2196/57054", url="https://mededu.jmir.org/2024/1/e57054", url="http://www.ncbi.nlm.nih.gov/pubmed/38546736" } @Article{info:doi/10.2196/52483, author="Wu, Yijun and Zheng, Yue and Feng, Baijie and Yang, Yuqi and Kang, Kai and Zhao, Ailin", title="Embracing ChatGPT for Medical Education: Exploring Its Impact on Doctors and Medical Students", journal="JMIR Med Educ", year="2024", month="Apr", day="10", volume="10", pages="e52483", keywords="artificial intelligence", keywords="AI", keywords="ChatGPT", keywords="medical education", keywords="doctors", keywords="medical students", doi="10.2196/52483", url="https://mededu.jmir.org/2024/1/e52483", url="http://www.ncbi.nlm.nih.gov/pubmed/38598263" } @Article{info:doi/10.2196/52818, author="Cherif, Hela and Moussa, Chirine and Missaoui, Mouhaymen Abdel and Salouage, Issam and Mokaddem, Salma and Dhahri, Besma", title="Appraisal of ChatGPT's Aptitude for Medical Education: Comparative Analysis With Third-Year Medical Students in a Pulmonology Examination", journal="JMIR Med Educ", year="2024", month="Jul", day="23", volume="10", pages="e52818", keywords="medical education", keywords="ChatGPT", keywords="GPT", keywords="artificial intelligence", keywords="natural language processing", keywords="NLP", keywords="pulmonary medicine", keywords="pulmonary", keywords="lung", keywords="lungs", keywords="respiratory", keywords="respiration", keywords="pneumology", keywords="comparative analysis", keywords="large language models", keywords="LLMs", keywords="LLM", keywords="language model", keywords="generative AI", keywords="generative artificial intelligence", keywords="generative", keywords="exams", keywords="exam", keywords="examinations", keywords="examination", abstract="Background: The rapid evolution of ChatGPT has generated substantial interest and led to extensive discussions in both public and academic domains, particularly in the context of medical education. Objective: This study aimed to evaluate ChatGPT's performance in a pulmonology examination through a comparative analysis with that of third-year medical students. Methods: In this cross-sectional study, we conducted a comparative analysis with 2 distinct groups. The first group comprised 244 third-year medical students who had previously taken our institution's 2020 pulmonology examination, which was conducted in French. The second group involved ChatGPT-3.5 in 2 separate sets of conversations: without contextualization (V1) and with contextualization (V2). In both V1 and V2, ChatGPT received the same set of questions administered to the students. Results: V1 demonstrated exceptional proficiency in radiology, microbiology, and thoracic surgery, surpassing the majority of medical students in these domains. However, it faced challenges in pathology, pharmacology, and clinical pneumology. In contrast, V2 consistently delivered more accurate responses across various question categories, regardless of the specialization. ChatGPT exhibited suboptimal performance in multiple choice questions compared to medical students. V2 excelled in responding to structured open-ended questions. Both ChatGPT conversations, particularly V2, outperformed students in addressing questions of low and intermediate difficulty. Interestingly, students showcased enhanced proficiency when confronted with highly challenging questions. V1 fell short of passing the examination. Conversely, V2 successfully achieved examination success, outperforming 139 (62.1\%) medical students. Conclusions: While ChatGPT has access to a comprehensive web-based data set, its performance closely mirrors that of an average medical student. Outcomes are influenced by question format, item complexity, and contextual nuances. The model faces challenges in medical contexts requiring information synthesis, advanced analytical aptitude, and clinical judgment, as well as in non-English language assessments and when confronted with data outside mainstream internet sources. ", doi="10.2196/52818", url="https://mededu.jmir.org/2024/1/e52818" } @Article{info:doi/10.2196/51157, author="McBee, C. Joseph and Han, Y. Daniel and Liu, Li and Ma, Leah and Adjeroh, A. Donald and Xu, Dong and Hu, Gangqing", title="Assessing ChatGPT's Competency in Addressing Interdisciplinary Inquiries on Chatbot Uses in Sports Rehabilitation: Simulation Study", journal="JMIR Med Educ", year="2024", month="Aug", day="7", volume="10", pages="e51157", keywords="ChatGPT", keywords="chatbots", keywords="multirole-playing", keywords="interdisciplinary inquiry", keywords="medical education", keywords="sports medicine", abstract="Background: ChatGPT showcases exceptional conversational capabilities and extensive cross-disciplinary knowledge. In addition, it can perform multiple roles in a single chat session. This unique multirole-playing feature positions ChatGPT as a promising tool for exploring interdisciplinary subjects. Objective: The aim of this study was to evaluate ChatGPT's competency in addressing interdisciplinary inquiries based on a case study exploring the opportunities and challenges of chatbot uses in sports rehabilitation. Methods: We developed a model termed PanelGPT to assess ChatGPT's competency in addressing interdisciplinary topics through simulated panel discussions. Taking chatbot uses in sports rehabilitation as an example of an interdisciplinary topic, we prompted ChatGPT through PanelGPT to role-play a physiotherapist, psychologist, nutritionist, artificial intelligence expert, and athlete in a simulated panel discussion. During the simulation, we posed questions to the panel while ChatGPT acted as both the panelists for responses and the moderator for steering the discussion. We performed the simulation using ChatGPT-4 and evaluated the responses by referring to the literature and our human expertise. Results: By tackling questions related to chatbot uses in sports rehabilitation with respect to patient education, physiotherapy, physiology, nutrition, and ethical considerations, responses from the ChatGPT-simulated panel discussion reasonably pointed to various benefits such as 24/7 support, personalized advice, automated tracking, and reminders. ChatGPT also correctly emphasized the importance of patient education, and identified challenges such as limited interaction modes, inaccuracies in emotion-related advice, assurance of data privacy and security, transparency in data handling, and fairness in model training. It also stressed that chatbots are to assist as a copilot, not to replace human health care professionals in the rehabilitation process. Conclusions: ChatGPT exhibits strong competency in addressing interdisciplinary inquiry by simulating multiple experts from complementary backgrounds, with significant implications in assisting medical education. ", doi="10.2196/51157", url="https://mededu.jmir.org/2024/1/e51157", url="http://www.ncbi.nlm.nih.gov/pubmed/39042885" } @Article{info:doi/10.2196/59133, author="Takahashi, Hiromizu and Shikino, Kiyoshi and Kondo, Takeshi and Komori, Akira and Yamada, Yuji and Saita, Mizue and Naito, Toshio", title="Educational Utility of Clinical Vignettes Generated in Japanese by ChatGPT-4: Mixed Methods Study", journal="JMIR Med Educ", year="2024", month="Aug", day="13", volume="10", pages="e59133", keywords="generative AI", keywords="ChatGPT-4", keywords="medical case generation", keywords="medical education", keywords="clinical vignettes", keywords="AI", keywords="artificial intelligence", keywords="Japanese", keywords="Japan", abstract="Background: Evaluating the accuracy and educational utility of artificial intelligence--generated medical cases, especially those produced by large language models such as ChatGPT-4 (developed by OpenAI), is crucial yet underexplored. Objective: This study aimed to assess the educational utility of ChatGPT-4--generated clinical vignettes and their applicability in educational settings. Methods: Using a convergent mixed methods design, a web-based survey was conducted from January 8 to 28, 2024, to evaluate 18 medical cases generated by ChatGPT-4 in Japanese. In the survey, 6 main question items were used to evaluate the quality of the generated clinical vignettes and their educational utility, which are information quality, information accuracy, educational usefulness, clinical match, terminology accuracy (TA), and diagnosis difficulty. Feedback was solicited from physicians specializing in general internal medicine or general medicine and experienced in medical education. Chi-square and Mann-Whitney U tests were performed to identify differences among cases, and linear regression was used to examine trends associated with physicians' experience. Thematic analysis of qualitative feedback was performed to identify areas for improvement and confirm the educational utility of the cases. Results: Of the 73 invited participants, 71 (97\%) responded. The respondents, primarily male (64/71, 90\%), spanned a broad range of practice years (from 1976 to 2017) and represented diverse hospital sizes throughout Japan. The majority deemed the information quality (mean 0.77, 95\% CI 0.75-0.79) and information accuracy (mean 0.68, 95\% CI 0.65-0.71) to be satisfactory, with these responses being based on binary data. The average scores assigned were 3.55 (95\% CI 3.49-3.60) for educational usefulness, 3.70 (95\% CI 3.65-3.75) for clinical match, 3.49 (95\% CI 3.44-3.55) for TA, and 2.34 (95\% CI 2.28-2.40) for diagnosis difficulty, based on a 5-point Likert scale. Statistical analysis showed significant variability in content quality and relevance across the cases (P<.001 after Bonferroni correction). Participants suggested improvements in generating physical findings, using natural language, and enhancing medical TA. The thematic analysis highlighted the need for clearer documentation, clinical information consistency, content relevance, and patient-centered case presentations. Conclusions: ChatGPT-4--generated medical cases written in Japanese possess considerable potential as resources in medical education, with recognized adequacy in quality and accuracy. Nevertheless, there is a notable need for enhancements in the precision and realism of case details. This study emphasizes ChatGPT-4's value as an adjunctive educational tool in the medical field, requiring expert oversight for optimal application. ", doi="10.2196/59133", url="https://mededu.jmir.org/2024/1/e59133", url="http://www.ncbi.nlm.nih.gov/pubmed/39137031" } @Article{info:doi/10.2196/51757, author="Cherrez-Ojeda, Ivan and Gallardo-Bastidas, C. Juan and Robles-Velasco, Karla and Osorio, F. Mar{\'i}a and Velez Leon, Maria Eleonor and Leon Velastegui, Manuel and Pauletto, Patr{\'i}cia and Aguilar-D{\'i}az, C. F. and Squassi, Aldo and Gonz{\'a}lez Eras, Patricia Susana and Cordero Carrasco, Erita and Chavez Gonzalez, Leonor Karol and Calderon, C. Juan and Bousquet, Jean and Bedbrook, Anna and Faytong-Haro, Marco", title="Understanding Health Care Students' Perceptions, Beliefs, and Attitudes Toward AI-Powered Language Models: Cross-Sectional Study", journal="JMIR Med Educ", year="2024", month="Aug", day="13", volume="10", pages="e51757", keywords="artificial intelligence", keywords="ChatGPT", keywords="education", keywords="health care", keywords="students", abstract="Background: ChatGPT was not intended for use in health care, but it has potential benefits that depend on end-user understanding and acceptability, which is where health care students become crucial. There is still a limited amount of research in this area. Objective: The primary aim of our study was to assess the frequency of ChatGPT use, the perceived level of knowledge, the perceived risks associated with its use, and the ethical issues, as well as attitudes toward the use of ChatGPT in the context of education in the field of health. In addition, we aimed to examine whether there were differences across groups based on demographic variables. The second part of the study aimed to assess the association between the frequency of use, the level of perceived knowledge, the level of risk perception, and the level of perception of ethics as predictive factors for participants' attitudes toward the use of ChatGPT. Methods: A cross-sectional survey was conducted from May to June 2023 encompassing students of medicine, nursing, dentistry, nutrition, and laboratory science across the Americas. The study used descriptive analysis, chi-square tests, and ANOVA to assess statistical significance across different categories. The study used several ordinal logistic regression models to analyze the impact of predictive factors (frequency of use, perception of knowledge, perception of risk, and ethics perception scores) on attitude as the dependent variable. The models were adjusted for gender, institution type, major, and country. Stata was used to conduct all the analyses. Results: Of 2661 health care students, 42.99\% (n=1144) were unaware of ChatGPT. The median score of knowledge was ``minimal'' (median 2.00, IQR 1.00-3.00). Most respondents (median 2.61, IQR 2.11-3.11) regarded ChatGPT as neither ethical nor unethical. Most participants (median 3.89, IQR 3.44-4.34) ``somewhat agreed'' that ChatGPT (1) benefits health care settings, (2) provides trustworthy data, (3) is a helpful tool for clinical and educational medical information access, and (4) makes the work easier. In total, 70\% (7/10) of people used it for homework. As the perceived knowledge of ChatGPT increased, there was a stronger tendency with regard to having a favorable attitude toward ChatGPT. Higher ethical consideration perception ratings increased the likelihood of considering ChatGPT as a source of trustworthy health care information (odds ratio [OR] 1.620, 95\% CI 1.498-1.752), beneficial in medical issues (OR 1.495, 95\% CI 1.452-1.539), and useful for medical literature (OR 1.494, 95\% CI 1.426-1.564; P<.001 for all results). Conclusions: Over 40\% of American health care students (1144/2661, 42.99\%) were unaware of ChatGPT despite its extensive use in the health field. Our data revealed the positive attitudes toward ChatGPT and the desire to learn more about it. Medical educators must explore how chatbots may be included in undergraduate health care education programs. ", doi="10.2196/51757", url="https://mededu.jmir.org/2024/1/e51757", url="http://www.ncbi.nlm.nih.gov/pubmed/39137029" } @Article{info:doi/10.2196/56859, author="Yoon, Soo-Hyuk and Oh, Kyeong Seok and Lim, Gun Byung and Lee, Ho-Jin", title="Performance of ChatGPT in the In-Training Examination for Anesthesiology and Pain Medicine Residents in South Korea: Observational Study", journal="JMIR Med Educ", year="2024", month="Sep", day="16", volume="10", pages="e56859", keywords="AI tools", keywords="problem solving", keywords="anesthesiology", keywords="artificial intelligence", keywords="pain medicine", keywords="ChatGPT", keywords="health care", keywords="medical education", keywords="South Korea", abstract="Background: ChatGPT has been tested in health care, including the US Medical Licensing Examination and specialty exams, showing near-passing results. Its performance in the field of anesthesiology has been assessed using English board examination questions; however, its effectiveness in Korea remains unexplored. Objective: This study investigated the problem-solving performance of ChatGPT in the fields of anesthesiology and pain medicine in the Korean language context, highlighted advancements in artificial intelligence (AI), and explored its potential applications in medical education. Methods: We investigated the performance (number of correct answers/number of questions) of GPT-4, GPT-3.5, and CLOVA X in the fields of anesthesiology and pain medicine, using in-training examinations that have been administered to Korean anesthesiology residents over the past 5 years, with an annual composition of 100 questions. Questions containing images, diagrams, or photographs were excluded from the analysis. Furthermore, to assess the performance differences of the GPT across different languages, we conducted a comparative analysis of the GPT-4's problem-solving proficiency using both the original Korean texts and their English translations. Results: A total of 398 questions were analyzed. GPT-4 (67.8\%) demonstrated a significantly better overall performance than GPT-3.5 (37.2\%) and CLOVA-X (36.7\%). However, GPT-3.5 and CLOVA X did not show significant differences in their overall performance. Additionally, the GPT-4 showed superior performance on questions translated into English, indicating a language processing discrepancy (English: 75.4\% vs Korean: 67.8\%; difference 7.5\%; 95\% CI 3.1\%-11.9\%; P=.001). Conclusions: This study underscores the potential of AI tools, such as ChatGPT, in medical education and practice but emphasizes the need for cautious application and further refinement, especially in non-English medical contexts. The findings suggest that although AI advancements are promising, they require careful evaluation and development to ensure acceptable performance across diverse linguistic and professional settings. ", doi="10.2196/56859", url="https://mededu.jmir.org/2024/1/e56859" } @Article{info:doi/10.2196/68503, author="Carrillo, Irene and Skoumalov{\'a}, Ivana and Bruus, Ireen and Klemm, Victoria and Guerra-Paiva, Sofia and Kne?evi{\'c}, Bojana and Jankauskiene, Augustina and Jocic, Dragana and Tella, Susanna and Buttigieg, C. Sandra and Srulovici, Einav and Madarasov{\'a} Geckov{\'a}, Andrea and P{\~o}lluste, Kaja and Strametz, Reinhard and Sousa, Paulo and Odalovic, Marina and Mira, Joaqu{\'i}n Jos{\'e}", title="Correction: Psychological Safety Competency Training During the Clinical Internship From the Perspective of Health Care Trainee Mentors in 11 Pan-European Countries: Mixed Methods Observational Study", journal="JMIR Med Educ", year="2024", month="Nov", day="15", volume="10", pages="e68503", doi="10.2196/68503", url="https://mededu.jmir.org/2024/1/e68503" } @Article{info:doi/10.2196/57594, author="Gilson, Aidan and Safranek, W. Conrad and Huang, Thomas and Socrates, Vimig and Chi, Ling and Taylor, Andrew Richard and Chartash, David", title="Correction: How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment", journal="JMIR Med Educ", year="2024", month="Feb", day="27", volume="10", pages="e57594", doi="10.2196/57594", url="https://mededu.jmir.org/2024/1/e57594", url="http://www.ncbi.nlm.nih.gov/pubmed/38412478" } @Article{info:doi/10.2196/59919, author="Rettinger, Lena and Putz, Peter and Aichinger, Lea and Javorszky, Maria Susanne and Widhalm, Klaus and Ertelt-Bach, Veronika and Huber, Andreas and Sargis, Sevan and Maul, Lukas and Radinger, Oliver and Werner, Franz and Kuhn, Sebastian", title="Correction: Telehealth Education in Allied Health Care and Nursing: Web-Based Cross-Sectional Survey of Students' Perceived Knowledge, Skills, Attitudes, and Experience", journal="JMIR Med Educ", year="2024", month="Apr", day="26", volume="10", pages="e59919", doi="10.2196/59919", url="https://mededu.jmir.org/2024/1/e59919", url="http://www.ncbi.nlm.nih.gov/pubmed/38669670" } @Article{info:doi/10.2196/50174, author="Nguyen, Tina", title="ChatGPT in Medical Education: A Precursor for Automation Bias?", journal="JMIR Med Educ", year="2024", month="Jan", day="17", volume="10", pages="e50174", keywords="ChatGPT", keywords="artificial intelligence", keywords="AI", keywords="medical students", keywords="residents", keywords="medical school curriculum", keywords="medical education", keywords="automation bias", keywords="large language models", keywords="LLMs", keywords="bias", doi="10.2196/50174", url="https://mededu.jmir.org/2024/1/e50174", url="http://www.ncbi.nlm.nih.gov/pubmed/38231545" } @Article{info:doi/10.2196/48518, author="Devlin, M. Paulina and Akingbola, Oluwabukola and Stonehocker, Jody and Fitzgerald, T. James and Winkel, Ford Abigail and Hammoud, M. Maya and Morgan, K. Helen", title="Opportunities to Improve Communication With Residency Applicants: Cross-Sectional Study of Obstetrics and Gynecology Residency Program Websites", journal="JMIR Med Educ", year="2024", month="Oct", day="21", volume="10", pages="e48518", keywords="obstetrics and gynecology", keywords="residency program", keywords="residency application", keywords="website", keywords="program signals", keywords="communication best practices", abstract="Background: As part of the residency application process in the United States, many medical specialties now offer applicants the opportunity to send program signals that indicate high interest to a limited number of residency programs. To determine which residency programs to apply to, and which programs to send signals to, applicants need accurate information to determine which programs align with their future training goals. Most applicants use a program's website to review program characteristics and criteria, so describing the current state of residency program websites can inform programs of best practices. Objective: This study aims to characterize information available on obstetrics and gynecology residency program websites and to determine whether there are differences in information available between different types of residency programs. Methods: This was a cross-sectional observational study of all US obstetrics and gynecology residency program website content. The authorship group identified factors that would be useful for residency applicants around program demographics and learner trajectories; application criteria including standardized testing metrics, residency statistics, and benefits; and diversity, equity, and inclusion mission statements and values. Two authors examined all available websites from November 2011 through March 2022. Data analysis consisted of descriptive statistics and one-way ANOVA, with P<.05 considered significant. Results: Among 290 programs, 283 (97.6\%) had websites; 238 (82.1\%) listed medical schools of current residents; 158 (54.5\%) described residency alumni trajectories; 107 (36.9\%) included guidance related to the preferred United States Medical Licensing Examination Step 1 scores; 53 (18.3\%) included guidance related to the Comprehensive Osteopathic Medical Licensing Examination Level 1 scores; 185 (63.8\%) included international applicant guidance; 132 (45.5\%) included a program-specific mission statement; 84 (29\%) included a diversity, equity, and inclusion statement; and 167 (57.6\%) included program-specific media or links to program social media on their websites. University-based programs were more likely to include a variety of information compared to community-based university-affiliated and community-based programs, including medical schools of current residents (113/123, 91.9\%, university-based; 85/111, 76.6\%, community-based university-affiliated; 40/56, 71.4\%, community-based; P<.001); alumni trajectories (90/123, 73.2\%, university-based; 51/111, 45.9\%, community-based university-affiliated; 17/56, 30.4\%, community-based; P<.001); the United States Medical Licensing Examination Step 1 score guidance (58/123, 47.2\%, university-based; 36/111, 32.4\%, community-based university-affiliated; 13/56, 23.2\%, community-based; P=.004); and diversity, equity, and inclusion statements (57/123, 46.3\%, university-based; 19/111, 17.1\%, community-based university-affiliated; 8/56, 14.3\%, community-based; P<.001). Conclusions: There are opportunities to improve the quantity and quality of data on residency websites. From this work, we propose best practices for what information should be included on residency websites that will enable applicants to make informed decisions. ", doi="10.2196/48518", url="https://mededu.jmir.org/2024/1/e48518" } @Article{info:doi/10.2196/43705, author="Monahan, Ken and Gould, Edward and Rice, Todd and Wright, Patty and Vasilevskis, Eduard and Harrell, Frank and Drago, Monique and Mitchell, Sarah", title="Impact of the COVID-19 Pandemic on Medical Grand Rounds Attendance: Comparison of In-Person and Remote Conferences", journal="JMIR Med Educ", year="2024", month="Jan", day="3", volume="10", pages="e43705", keywords="continuing medical education", keywords="COVID-19", keywords="distance education", keywords="professional development", keywords="virtual learning", abstract="Background: Many academic medical centers transitioned from in-person to remote conferences due to the COVID-19 pandemic, but the impact on faculty attendance is unknown. Objective: This study aims to evaluate changes in attendance at medical grand rounds (MGR) following the transition from an in-person to remote format and as a function of the COVID-19 census at Vanderbilt Medical Center. Methods: We obtained the faculty attendee characteristics from Department of Medicine records. Attendance was recorded using a SMS text message--based system. The daily COVID-19 census was recorded independently by hospital administration. The main attendance metric was the proportion of eligible faculty that attended each MGR. Comparisons were made for the entire cohort and for individual faculty. Results: The observation period was from March 2019 to June 2021 and included 101 MGR conferences with more than 600 eligible faculty. Overall attendance was unchanged during the in-person and remote formats (12,536/25,808, 48.6\% vs 16,727/32,680, 51.2\%; P=.44) and did not change significantly during a surge in the COVID-19 census. Individual faculty members attendance rates varied widely. Absolute differences between formats were less than --20\% or greater than 20\% for one-third (160/476, 33.6\%) of faculty. Pulmonary or critical care faculty attendance increased during the remote format compared to in person (1450/2616, 55.4\% vs 1004/2045, 49.1\%; P<.001). A cloud-based digital archive of MGR lectures was accessed by <1\% of faculty per conference. Conclusions: Overall faculty attendance at MGR did not change following the transition to a remote format, regardless of the COVID-19 census, but individual attendance habits fluctuated in a bidirectional manner. Incentivizing the use of a digital archive may represent an opportunity to increase faculty consumption of MGR. ", doi="10.2196/43705", url="https://mededu.jmir.org/2024/1/e43705", url="http://www.ncbi.nlm.nih.gov/pubmed/38029287" } @Article{info:doi/10.2196/56568, author="dos Santos, Albano Luiz Ricardo and de Oliveira, Maicon Alan and dos Santos, Costa Luana Michelly Aparecida and Aguilar, Jos{\'e} Guilherme and Costa, Lemos Wilbert Dener and Donato, Barros Dantony de Castro and Bollela, Roberto Valdes", title="Collaborative Development of an Electronic Portfolio to Support the Assessment and Development of Medical Undergraduates", journal="JMIR Med Educ", year="2024", month="Apr", day="4", volume="10", pages="e56568", keywords="e-portfolio", keywords="education", keywords="health education", keywords="learning", keywords="medical students", keywords="medical school curriculum", keywords="medical education", keywords="student support", keywords="software", doi="10.2196/56568", url="https://mededu.jmir.org/2024/1/e56568" } @Article{info:doi/10.2196/54283, author="Takagi, Soshi and Koda, Masahide and Watari, Takashi", title="The Performance of ChatGPT-4V in Interpreting Images and Tables in the Japanese Medical Licensing Exam", journal="JMIR Med Educ", year="2024", month="May", day="23", volume="10", pages="e54283", keywords="ChatGPT", keywords="medical licensing examination", keywords="generative artificial intelligence", keywords="medical education", keywords="large language model", keywords="images", keywords="tables", keywords="artificial intelligence", keywords="AI", keywords="Japanese", keywords="reliability", keywords="medical application", keywords="medical applications", keywords="diagnostic", keywords="diagnostics", keywords="online data", keywords="web-based data", doi="10.2196/54283", url="https://mededu.jmir.org/2024/1/e54283" } @Article{info:doi/10.2196/53193, author="Fukui, Sho and Shimizu, Taro and Nishizaki, Yuji and Shikino, Kiyoshi and Yamamoto, Yu and Kobayashi, Hiroyuki and Tokuda, Yasuharu", title="The Utility of Wearable Cameras in Developing Examination Questions and Answers on Physical Examinations: Preliminary Study", journal="JMIR Med Educ", year="2024", month="Jul", day="19", volume="10", pages="e53193", keywords="medical education", keywords="medical technology", keywords="wearable device", keywords="wearable camera", keywords="medical examination", keywords="exam", keywords="examination", keywords="exams", keywords="examinations", keywords="physical", keywords="resident physicians", keywords="wearable", keywords="wearables", keywords="camera", keywords="cameras", keywords="video", keywords="videos", keywords="innovation", keywords="innovations", keywords="innovative", keywords="recording", keywords="recordings", keywords="survey", keywords="surveys", doi="10.2196/53193", url="https://mededu.jmir.org/2024/1/e53193" } @Article{info:doi/10.2196/51435, author="Dzuali, Fiatsogbe and Seiger, Kira and Novoa, Roberto and Aleshin, Maria and Teng, Joyce and Lester, Jenna and Daneshjou, Roxana", title="ChatGPT May Improve Access to Language-Concordant Care for Patients With Non--English Language Preferences", journal="JMIR Med Educ", year="2024", month="Dec", day="10", volume="10", pages="e51435", keywords="ChatGPT", keywords="artificial intelligence", keywords="language", keywords="translation", keywords="health care disparity", keywords="natural language model", keywords="survey", keywords="patient education", keywords="preference", keywords="human language", keywords="language-concordant care", doi="10.2196/51435", url="https://mededu.jmir.org/2024/1/e51435" } @Article{info:doi/10.2196/63129, author="Miyazaki, Yuki and Hata, Masahiro and Omori, Hisaki and Hirashima, Atsuya and Nakagawa, Yuta and Eto, Mitsuhiro and Takahashi, Shun and Ikeda, Manabu", title="Performance of ChatGPT-4o on the Japanese Medical Licensing Examination: Evalution of Accuracy in Text-Only and Image-Based Questions", journal="JMIR Med Educ", year="2024", month="Dec", day="24", volume="10", pages="e63129", keywords="medical education", keywords="artificial intelligence", keywords="clinical decision-making", keywords="GPT-4o", keywords="medical licensing examination", keywords="Japan", keywords="images", keywords="accuracy", keywords="AI technology", keywords="application", keywords="decision-making", keywords="image-based", keywords="reliability", keywords="ChatGPT", doi="10.2196/63129", url="https://mededu.jmir.org/2024/1/e63129" } @Article{info:doi/10.2196/52155, author="Kumar, Ajay and Burr, Pierce and Young, Michael Tim", title="Using AI Text-to-Image Generation to Create Novel Illustrations for Medical Education: Current Limitations as Illustrated by Hypothyroidism and Horner Syndrome", journal="JMIR Med Educ", year="2024", month="Feb", day="22", volume="10", pages="e52155", keywords="artificial intelligence", keywords="AI", keywords="medical illustration", keywords="medical images", keywords="medical education", keywords="image", keywords="images", keywords="illustration", keywords="illustrations", keywords="photo", keywords="photos", keywords="photographs", keywords="face", keywords="facial", keywords="paralysis", keywords="photograph", keywords="photography", keywords="Horner's syndrome", keywords="Horner syndrome", keywords="Bernard syndrome", keywords="Bernard's syndrome", keywords="miosis", keywords="oculosympathetic", keywords="ptosis", keywords="ophthalmoplegia", keywords="nervous system", keywords="autonomic", keywords="eye", keywords="eyes", keywords="pupil", keywords="pupils", keywords="neurologic", keywords="neurological", doi="10.2196/52155", url="https://mededu.jmir.org/2024/1/e52155", url="http://www.ncbi.nlm.nih.gov/pubmed/38386400" } @Article{info:doi/10.2196/51411, author="Wang, Shuang and Yang, Liuying and Li, Min and Zhang, Xinghe and Tai, Xiantao", title="Medical Education and Artificial Intelligence: Web of Science--Based Bibliometric Analysis (2013-2022)", journal="JMIR Med Educ", year="2024", month="Oct", day="10", volume="10", pages="e51411", keywords="artificial intelligence", keywords="medical education", keywords="bibliometric analysis", keywords="CiteSpace", keywords="VOSviewer", abstract="Background: Incremental advancements in artificial intelligence (AI) technology have facilitated its integration into various disciplines. In particular, the infusion of AI into medical education has emerged as a significant trend, with noteworthy research findings. Consequently, a comprehensive review and analysis of the current research landscape of AI in medical education is warranted. Objective: This study aims to conduct a bibliometric analysis of pertinent papers, spanning the years 2013?2022, using CiteSpace and VOSviewer. The study visually represents the existing research status and trends of AI in medical education. Methods: Articles related to AI and medical education, published between 2013 and 2022, were systematically searched in the Web of Science core database. Two reviewers scrutinized the initially retrieved papers, based on their titles and abstracts, to eliminate papers unrelated to the topic. The selected papers were then analyzed and visualized for country, institution, author, reference, and keywords using CiteSpace and VOSviewer. Results: A total of 195 papers pertaining to AI in medical education were identified from 2013 to 2022. The annual publications demonstrated an increasing trend over time. The United States emerged as the most active country in this research arena, and Harvard Medical School and the University of Toronto were the most active institutions. Prolific authors in this field included Vincent Bissonnette, Charlotte Blacketer, Rolando F Del Maestro, Nicole Ledows, Nykan Mirchi, Alexander Winkler-Schwartz, and Recai Yilamaz. The paper with the highest citation was ``Medical Students' Attitude Towards Artificial Intelligence: A Multicentre Survey.'' Keyword analysis revealed that ``radiology,'' ``medical physics,'' ``ehealth,'' ``surgery,'' and ``specialty'' were the primary focus, whereas ``big data'' and ``management'' emerged as research frontiers. Conclusions: The study underscores the promising potential of AI in medical education research. Current research directions encompass radiology, medical information management, and other aspects. Technological progress is expected to broaden these directions further. There is an urgent need to bolster interregional collaboration and enhance research quality. These findings offer valuable insights for researchers to identify perspectives and guide future research directions. ", doi="10.2196/51411", url="https://mededu.jmir.org/2024/1/e51411" } @Article{info:doi/10.2196/54105, author="Livesay, Karen and Walter, Ruby and Petersen, Sacha and Abdolkhani, Robab and Zhao, Lin and Butler-Henderson, Kerryn", title="Challenges and Needs in Digital Health Practice and Nursing Education Curricula: Gap Analysis Study", journal="JMIR Med Educ", year="2024", month="Sep", day="13", volume="10", pages="e54105", keywords="nursing", keywords="digital health", keywords="capability", keywords="workforce", keywords="framework", keywords="nursing education", keywords="education", keywords="digital health practice", keywords="clinicians", keywords="nurse", keywords="nurse graduates", keywords="clinical nurses", keywords="nurses", keywords="nurse educators", keywords="teach", keywords="teaching", keywords="learning", keywords="nursing students", keywords="student", keywords="students", abstract="Background: Australian nursing programs aim to introduce students to digital health requirements for practice. However, innovation in digital health is more dynamic than education providers' ability to respond. It is uncertain whether what is taught and demonstrated in nursing programs meets the needs and expectations of clinicians with regard to the capability of the nurse graduates. Objective: This study aims to identify gaps in the National Nursing and Midwifery Digital Health Capability Framework , based on the perspectives of clinical nurses, and in nurse educators' confidence and knowledge to teach. The findings will direct a future co-design process. Methods: This study triangulated the findings from 2 studies of the Digital Awareness in Simulated Health project and the National Nursing and Midwifery Digital Capability Framework. The first was a qualitative study that considered the experiences of nurses with digital health technologies during the COVID-19 pandemic, and the second was a survey of nurse educators who identified their confidence and knowledge to teach and demonstrate digital health concepts. Results: The results were categorized by and presented from the perspectives of nurse clinicians, nurse graduates, and nurse educators. Findings were listed against each of the framework capabilities, and omissions from the framework were identified. A series of statements and questions were formulated from the gap analysis to direct a future co-design process with nursing stakeholders to develop a digital health capability curriculum for nurse educators. Conclusions: Further work to evaluate nursing digital health opportunities for nurse educators is indicated by the gaps identified in this study. ", doi="10.2196/54105", url="https://mededu.jmir.org/2024/1/e54105" } @Article{info:doi/10.2196/52906, author="Curran, Vernon and Glynn, Robert and Whitton, Cindy and Hollett, Ann", title="An Approach to the Design and Development of an Accredited Continuing Professional Development e-Learning Module on Virtual Care", journal="JMIR Med Educ", year="2024", month="Aug", day="8", volume="10", pages="e52906", keywords="virtual care", keywords="continuing professional development", keywords="needs assessment", keywords="remote care", keywords="medical education", keywords="continuing medical education", keywords="CME", keywords="CPD", keywords="PD", keywords="professional development", keywords="integration", keywords="implementation", keywords="training", keywords="eHealth", keywords="e-health", keywords="telehealth", keywords="telemedicine", keywords="ICT", keywords="information and communication technology", keywords="provider", keywords="providers", keywords="healthcare professional", keywords="healthcare professionals", keywords="accreditation", keywords="instructional", keywords="teaching", keywords="module", keywords="modules", keywords="e-learning", keywords="eLearning", keywords="online learning", keywords="distance learning", doi="10.2196/52906", url="https://mededu.jmir.org/2024/1/e52906" } @Article{info:doi/10.2196/54137, author="Butler-Henderson, Kerryn and Gray, Kathleen and Arabi, Salma", title="Roles and Responsibilities of the Global Specialist Digital Health Workforce: Analysis of Global Census Data", journal="JMIR Med Educ", year="2024", month="Jul", day="25", volume="10", pages="e54137", keywords="workforce", keywords="functions", keywords="digital health", keywords="census", keywords="census data", keywords="workforce survey", keywords="survey", keywords="support", keywords="development", keywords="use", keywords="management", keywords="health data", keywords="health information", keywords="health knowledge", keywords="health technology", keywords="Australia", keywords="New Zealand", keywords="online content", keywords="digital data", abstract="Background: The Global Specialist Digital Health Workforce Census is the largest workforce survey of the specialist roles that support the development, use, management, and governance of health data, health information, health knowledge, and health technology. Objective: This paper aims to present an analysis of the roles and functions reported by respondents in the 2023 census. Methods: The 2023 census was deployed using Qualtrics and was open from July 1 to August 13, 2023. A broad definition was provided to guide respondents about who is in the specialist digital health workforce. Anyone who self-identifies as being part of this workforce could undertake the survey. The data was analyzed using descriptive statistical analysis and thematic analysis of the functions respondents reported in their roles. Results: A total of 1103 respondents completed the census, with data reported about their demographic information and their roles. The majority of respondents lived in Australia (n=870, 78.9\%) or New Zealand (n=130, 11.8\%), with most (n=620, 56.3\%) aged 35?54 years and identifying as female (n=720, 65.3\%). The top four occupational specialties were health informatics (n=179, 20.2\%), health information management (n=175, 19.8\%), health information technology (n=128, 14.4\%), and health librarianship (n=104, 11.7\%). Nearly all (n=797, 90\%) participants identified as a manager or professional. Less than half (430/1019, 42.2\%) had a formal qualification in a specialist digital health area, and only one-quarter (244/938, 26\%) held a credential in a digital health area. While two-thirds (502/763, 65.7\%) reported undertaking professional development in the last year, most were self-directed activities, such as seeking information or consuming online content. Work undertaken by specialist digital health workers could be classified as either leadership, functional, occupational, or technological. Conclusions: Future specialist digital health workforce capability frameworks should include the aspects of leadership, function, occupation, and technology. This largely unqualified workforce is undertaking little formal professional development to upskill them to continue to support the safe delivery and management of health and care through the use of digital data and technology. ", doi="10.2196/54137", url="https://mededu.jmir.org/2024/1/e54137" } @Article{info:doi/10.2196/51389, author="Kr{\"o}plin, Juliane and Maier, Leonie and Lenz, Jan-Hendrik and Romeike, Bernd", title="Knowledge Transfer and Networking Upon Implementation of a Transdisciplinary Digital Health Curriculum in a Unique Digital Health Training Culture: Prospective Analysis", journal="JMIR Med Educ", year="2024", month="Apr", day="15", volume="10", pages="e51389", keywords="big data", keywords="digital didactics", keywords="digital health applications", keywords="digital leadership", keywords="digital literacy", keywords="generative artificial intelligence", keywords="mobile working", keywords="robotics", keywords="telemedicine", keywords="wearables", abstract="Background: Digital health has been taught at medical faculties for a few years. However, in general, the teaching of digital competencies in medical education and training is still underrepresented. Objective: This study aims to analyze the objective acquisition of digital competencies through the implementation of a transdisciplinary digital health curriculum as a compulsory elective subject at a German university. The main subject areas of digital leadership and management, digital learning and didactics, digital communication, robotics, and generative artificial intelligence were developed and taught in a transdisciplinary manner over a period of 1 semester. Methods: The participants evaluated the relevant content of the curriculum regarding the competencies already taught in advance during the study, using a Likert scale. The participants' increase in digital competencies were examined with a pre-post test consisting of 12 questions. Statistical analysis was performed using an unpaired 2-tailed Student t test. A P value of <.05 was considered statistically significant. Furthermore, an analysis of the acceptance of the transdisciplinary approach as well as the application of an alternative examination method (term paper instead of a test with closed and open questions) was carried out. Results: In the first year after the introduction of the compulsory elective subject, students of human medicine (n=15), dentistry (n=3), and medical biotechnology (n=2) participated in the curriculum. In total, 13 participants were women (7 men), and 61.1\% (n=11) of the participants in human medicine and dentistry were in the preclinical study stage (clinical: n=7, 38.9\%). All the aforementioned learning objectives were largely absent in all study sections (preclinical: mean 4.2; clinical: mean 4.4; P=.02). The pre-post test comparison revealed a significant increase of 106\% in knowledge (P<.001) among the participants. Conclusions: The transdisciplinary teaching of a digital health curriculum, including digital teaching methods, considers perspectives and skills from different disciplines. Our new curriculum facilitates an objective increase in knowledge regarding the complex challenges of the digital transformation of our health care system. Of the 16 student term papers arising from the course, robotics and artificial intelligence attracted the most interest, accounting for 9 of the submissions. ", doi="10.2196/51389", url="https://mededu.jmir.org/2024/1/e51389" } @Article{info:doi/10.2196/52207, author="Kataoka, Koshi and Nishizaki, Yuji and Shimizu, Taro and Yamamoto, Yu and Shikino, Kiyoshi and Nojima, Masanori and Nagasaki, Kazuya and Fukui, Sho and Nishiguchi, Sho and Katayama, Kohta and Kurihara, Masaru and Ueda, Rieko and Kobayashi, Hiroyuki and Tokuda, Yasuharu", title="Hospital Use of a Web-Based Clinical Knowledge Support System and In-Training Examination Performance Among Postgraduate Resident Physicians in Japan: Nationwide Observational Study", journal="JMIR Med Educ", year="2024", month="May", day="30", volume="10", pages="e52207", keywords="clinical knowledge support system", keywords="GM-ITE", keywords="postgraduate clinical resident", keywords="in-training examination performance", keywords="exam", keywords="exams", keywords="examination", keywords="examinations", keywords="resident", keywords="residents", keywords="cross-sectional", keywords="national", keywords="nationwide", keywords="postgraduate", keywords="decision support", keywords="point-of-care", keywords="UpToDate", keywords="DynaMed", keywords="knowledge support", keywords="medical education", keywords="performance", keywords="information behavior", keywords="information behaviour", keywords="information seeking", keywords="teaching", keywords="pedagogy", keywords="pedagogical", keywords="log", keywords="logs", keywords="usage", keywords="evidence-based medicine", keywords="EBM", keywords="educational", keywords="decision support system", keywords="clinical decision support", keywords="Japan", keywords="General Medicine In-Training Examination", abstract="Background: The relationship between educational outcomes and the use of web-based clinical knowledge support systems in teaching hospitals remains unknown in Japan. A previous study on this topic could have been affected by recall bias because of the use of a self-reported questionnaire. Objective: We aimed to explore the relationship between the use of the Wolters Kluwer UpToDate clinical knowledge support system in teaching hospitals and residents' General Medicine In-Training Examination (GM-ITE) scores. In this study, we objectively evaluated the relationship between the total number of UpToDate hospital use logs and the GM-ITE scores. Methods: This nationwide cross-sectional study included postgraduate year--1 and --2 residents who had taken the examination in the 2020 academic year. Hospital-level information was obtained from published web pages, and UpToDate hospital use logs were provided by Wolters Kluwer. We evaluated the relationship between the total number of UpToDate hospital use logs and residents' GM-ITE scores. We analyzed 215 teaching hospitals with at least 5 GM-ITE examinees and hospital use logs from 2017 to 2019. Results: The study population consisted of 3013 residents from 215 teaching hospitals with at least 5 GM-ITE examinees and web-based resource use log data from 2017 to 2019. High-use hospital residents had significantly higher GM-ITE scores than low-use hospital residents (mean 26.9, SD 2.0 vs mean 26.2, SD 2.3; P=.009; Cohen d=0.35, 95\% CI 0.08-0.62). The GM-ITE scores were significantly correlated with the total number of hospital use logs (Pearson r=0.28; P<.001). The multilevel analysis revealed a positive association between the total number of logs divided by the number of hospital physicians and the GM-ITE scores (estimated coefficient=0.36, 95\% CI 0.14-0.59; P=.001). Conclusions: The findings suggest that the development of residents' clinical reasoning abilities through UpToDate is associated with high GM-ITE scores. Thus, higher use of UpToDate may lead physicians and residents in high-use hospitals to increase the implementation of evidence-based medicine, leading to high educational outcomes. ", doi="10.2196/52207", url="https://mededu.jmir.org/2024/1/e52207" } @Article{info:doi/10.2196/52290, author="Doll, Joy and Anzalone, Jerrod A. and Clarke, Martina and Cooper, Kathryn and Polich, Ann and Siedlik, Jacob", title="A Call for a Health Data--Informed Workforce Among Clinicians", journal="JMIR Med Educ", year="2024", month="Jun", day="17", volume="10", pages="e52290", keywords="health data--informed workforce", keywords="health data", keywords="health informaticist", keywords="data literacy", keywords="workforce development", doi="10.2196/52290", url="https://mededu.jmir.org/2024/1/e52290" } @Article{info:doi/10.2196/52993, author="DeLaRosby, Anna and Mulcahy, Julie and Norwood, Todd", title="A Proposed Decision-Making Framework for the Translation of In-Person Clinical Care to Digital Care: Tutorial", journal="JMIR Med Educ", year="2024", month="Jun", day="26", volume="10", pages="e52993", keywords="clinical decision-making", keywords="digital health", keywords="telehealth", keywords="telerehab", keywords="framework", keywords="digital medicine", keywords="cognitive process", keywords="telemedicine", keywords="clinical training", doi="10.2196/52993", url="https://mededu.jmir.org/2024/1/e52993" } @Article{info:doi/10.2196/53997, author="Grosjean, Julien and Dufour, Frank and Benis, Arriel and Januel, Jean-Marie and Staccini, Pascal and Darmoni, Jacques St{\'e}fan", title="Digital Health Education for the Future: The SaNuRN (Sant{\'e} Num{\'e}rique Rouen-Nice) Consortium's Journey", journal="JMIR Med Educ", year="2024", month="Apr", day="30", volume="10", pages="e53997", keywords="digital health", keywords="medical informatics", keywords="education", keywords="health education", keywords="curriculum", keywords="students", keywords="teaching materials", keywords="hybrid learning", keywords="program development", keywords="capacity building", keywords="access to information", keywords="e-learning", keywords="open access", keywords="open data", keywords="skills framework", keywords="competency-based learning", keywords="telemedicine training", keywords="medical simulation", keywords="objective structured clinical examination", keywords="OSCE", keywords="script concordance test", keywords="SCT", keywords="virtual patient", doi="10.2196/53997", url="https://mededu.jmir.org/2024/1/e53997" } @Article{info:doi/10.2196/53106, author="Yeo, Ji-Young and Nam, Hyeongil and Park, Jong-Il and Han, Soo-Yeon", title="Multidisciplinary Design--Based Multimodal Virtual Reality Simulation in Nursing Education: Mixed Methods Study", journal="JMIR Med Educ", year="2024", month="Jul", day="26", volume="10", pages="e53106", keywords="multidisciplinary", keywords="multimodal", keywords="nursing", keywords="simulation", keywords="virtual reality", keywords="VR", keywords="education", keywords="allied health", keywords="educational", keywords="simulations", keywords="pediatric", keywords="pediatrics", keywords="paediatric", keywords="paediatrics", keywords="feasibility", keywords="nurse", keywords="nurses", keywords="qualitative", keywords="interview", keywords="interviews", keywords="development", keywords="develop", keywords="teaching", keywords="educator", keywords="educators", keywords="user test", keywords="user testing", keywords="module", keywords="modules", keywords="usability", keywords="satisfaction", abstract="Background: The COVID-19 pandemic underscored the necessity for innovative educational methods in nursing. Our study takes a unique approach using a multidisciplinary simulation design, which offers a systematic and comprehensive strategy for developing virtual reality (VR) simulations in nursing education. Objective: The aim of this study is to develop VR simulation content for a pediatric nursing module based on a multidisciplinary simulation design and to evaluate its feasibility for nursing education. Methods: This study used a 1-group, posttest-only design. VR content for pediatric nursing practice was developed by integrating the technological characteristics of a multimodal VR system with the learning elements of traditional nursing simulation, combining various disciplines, including education, engineering, and nursing. A user test was conducted with 12 nursing graduates (preservice nurses) followed by post hoc surveys (assessing presence, VR systems, VR sickness, and simulation satisfaction) and in-depth, one-on-one interviews. Results: User tests showed mean scores of 4.01 (SD 1.43) for presence, 4.91 (SD 0.81) for the VR system, 0.64 (SD 0.35) for VR sickness, and 5.00 (SD 1.00) for simulation satisfaction. In-depth interviews revealed that the main strengths of the immersive VR simulation for pediatric pneumonia nursing were effective visualization and direct experience through hands-on manipulation; the drawback was keyword-based voice interaction. To improve VR simulation quality, participants suggested increasing the number of nursing techniques and refining them in more detail. Conclusions: This VR simulation content for a pediatric nursing practice using a multidisciplinary educational design model was confirmed to have positive educational potential. Further research is needed to confirm the specific learning effects of immersive nursing content based on multidisciplinary design models. ", doi="10.2196/53106", url="https://mededu.jmir.org/2024/1/e53106" } @Article{info:doi/10.2196/53254, author="Kleib, Manal and Arnaert, Antonia and Nagle, M. Lynn and Darko, Mirekuwaa Elizabeth and Idrees, Sobia and da Costa, Daniel and Ali, Shamsa", title="Resources to Support Canadian Nurses to Deliver Virtual Care: Environmental Scan", journal="JMIR Med Educ", year="2024", month="Aug", day="13", volume="10", pages="e53254", keywords="virtual care", keywords="digital health", keywords="nursing practice", keywords="environmental scan", keywords="telehealth", keywords="nurses", keywords="Canada", keywords="health care", abstract="Background: Regulatory and professional nursing associations have an important role in ensuring that nurses provide safe, competent, and ethical care and are capable of adapting to emerging phenomena that influence society and population health needs. Telehealth and more recently virtual care are 2 digital health modalities that have gained momentum during the COVID-19 pandemic. Telehealth refers to telecommunications and digital communication technologies used to deliver health care, support health care provider and patient education, and facilitate self-care. Virtual care facilitates the delivery of health care services via any remote communication between patients and health care providers and among health care providers, either synchronously or asynchronously, through information and communication technologies. Despite nurses' adaptability to delivering virtual care, many have also reported challenges. Objective: This study aims to describe resources about virtual care, digital health, and nursing informatics (ie, practice guidelines and fact sheets) available to Canadian nurses through their regulatory and professional associations. Methods: An environmental scan was conducted between March and July 2023. The websites of nursing regulatory bodies across 13 Canadian provinces and territories and relevant nursing and a few nonnursing professional associations were searched. Data were extracted from the websites of these organizations to map out educational materials, training opportunities, and guidelines made available for nurses to learn and adapt to the ongoing digitalization of the health care system. Information from each source was summarized and analyzed using an inductive content analysis approach to identify categories and themes. The Virtual Health Competency Framework was applied to support the analysis process. Results: Seven themes were identified: (1) types of resources available about virtual care, (2) terminologies used in virtual care resources, (3) currency of virtual care resources identified, (4) requirements for providing virtual care between provinces, (5) resources through professional nursing associations and other relevant organizations, (6) regulatory guidance versus competency in virtual care, and (7) resources about digital health and nursing informatics. Results also revealed that practice guidance for delivering telehealth existed before the COVID-19 pandemic, but it was further expanded during the pandemic. Differences were noted across available resources with respect to terms used (eg, telenursing, telehealth, or virtual care), types of documents (eg, guideline vs fact sheet), and the depth of information shared. Only 2 associations provided comprehensive telenursing practice guidelines. Resources relative to digital health and nursing informatics exist, but variations between provinces were also noted. Conclusions: The use of telehealth and virtual care services is becoming mainstream in Canadian health care. Despite variations across jurisdictions, the existing nursing practice guidance resources for delivering telehealth and virtual care are substantial and can serve as a beginning step for developing a standardized set of practice requirements or competencies to inform nursing practice and the education of future nurses. ", doi="10.2196/53254", url="https://mededu.jmir.org/2024/1/e53254", url="http://www.ncbi.nlm.nih.gov/pubmed/39137026" } @Article{info:doi/10.2196/53258, author="Kleib, Manal and Arnaert, Antonia and Nagle, M. Lynn and Sugars, Rebecca and da Costa, Daniel", title="Newly Qualified Canadian Nurses' Experiences With Digital Health in the Workplace: Comparative Qualitative Analysis", journal="JMIR Med Educ", year="2024", month="Aug", day="19", volume="10", pages="e53258", keywords="digital health", keywords="new graduate nurses", keywords="nursing practice", keywords="workplace", keywords="informatics", abstract="Background: Clinical practice settings have increasingly become dependent on the use of digital or eHealth technologies such as electronic health records. It is vitally important to support nurses in adapting to digitalized health care systems; however, little is known about nursing graduates' experiences as they transition to the workplace. Objective: This study aims to (1) describe newly qualified nurses' experiences with digital health in the workplace, and (2) identify strategies that could help support new graduates' transition and practice with digital health. Methods: An exploratory descriptive qualitative design was used. A total of 14 nurses from Eastern and Western Canada participated in semistructured interviews and data were analyzed using inductive content analysis. Results: Three themes were identified: (1) experiences before becoming a registered nurse, (2) experiences upon joining the workplace, and (3) suggestions for bridging the gap in transition to digital health practice. Findings revealed more similarities than differences between participants with respect to gaps in digital health education, technology-related challenges, and their influence on nursing practice. Conclusions: Digital health is the foundation of contemporary health care; therefore, comprehensive education during nursing school and throughout professional nursing practice, as well as organizational support and policy, are critical pillars. Health systems investing in digital health technologies must create supportive work environments for nurses to thrive in technologically rich environments and increase their capacity to deliver the digital health future. ", doi="10.2196/53258", url="https://mededu.jmir.org/2024/1/e53258" } @Article{info:doi/10.2196/53462, author="Saig{\'i}-Rubi{\'o}, Francesc and Romeu, Teresa and Hern{\'a}ndez Encuentra, Eul{\`a}lia and Guitert, Montse and Andr{\'e}s, Erik and Reixach, Elisenda", title="Design, Implementation, and Analysis of an Assessment and Accreditation Model to Evaluate a Digital Competence Framework for Health Professionals: Mixed Methods Study", journal="JMIR Med Educ", year="2024", month="Oct", day="17", volume="10", pages="e53462", keywords="eHealth literacy", keywords="eHealth competencies", keywords="digital health", keywords="competencies", keywords="eHealth", keywords="health literacy", keywords="digital technology", keywords="health care professionals", keywords="health care workers", abstract="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. ", doi="10.2196/53462", url="https://mededu.jmir.org/2024/1/e53462", url="http://www.ncbi.nlm.nih.gov/pubmed/39418092" } @Article{info:doi/10.2196/64125, author="Carrillo, Irene and Skoumalov{\'a}, Ivana and Bruus, Ireen and Klemm, Victoria and Guerra-Paiva, Sofia and Kne?evi{\'c}, Bojana and Jankauskiene, Augustina and Jocic, Dragana and Tella, Susanna and Buttigieg, C. Sandra and Srulovici, Einav and Madarasov{\'a} Geckov{\'a}, Andrea and P{\~o}lluste, Kaja and Strametz, Reinhard and Sousa, Paulo and Odalovic, Marina and Mira, Joaqu{\'i}n Jos{\'e}", title="Psychological Safety Competency Training During the Clinical Internship From the Perspective of Health Care Trainee Mentors in 11 Pan-European Countries: Mixed Methods Observational Study", journal="JMIR Med Educ", year="2024", month="Oct", day="7", volume="10", pages="e64125", keywords="psychological safety", keywords="speaking up", keywords="professional competence", keywords="patient safety", keywords="education", keywords="adverse event", abstract="Background: In the field of research, psychological safety has been widely recognized as a contributing factor to improving the quality of care and patient safety. However, its consideration in the curricula and traineeship pathways of residents and health care students is scarce. Objective: This study aims to determine the extent to which health care trainees acquire psychological safety competencies during their internships in clinical settings and identify what measures can be taken to promote their learning. Methods: A mixed methods observational study based on a consensus conference and an open-ended survey among a sample of health care trainee mentors from health care institutions in a pan-European context was conducted. First, we administered an ad hoc questionnaire to assess the perceived degree of acquisition or implementation and significance of competencies (knowledge, attitudes, and skills) and institutional interventions in psychological safety. Second, we asked mentors to propose measures to foster among trainees those competencies that, in the first phase of the study, obtained an average acquisition score of <3.4 (scale of 1-5). A content analysis of the information collected was carried out, and the spontaneity of each category and theme was determined. Results: In total, 173 mentors from 11 pan-European countries completed the first questionnaire (response rate: 173/256, 67.6\%), of which 63 (36.4\%) participated in the second consultation. The competencies with the lowest acquisition level were related to warning a professional that their behavior posed a risk to the patient, managing their possible bad reaction, and offering support to a colleague who becomes a second victim. The mentors' proposals for improvement of this competency gap referred to training in communication skills and patient safety, safety culture, work climate, individual attitudes, a reference person for trainees, formal incorporation into the curricula of health care degrees and specialization pathways, specific systems and mechanisms to give trainees a voice, institutional risk management, regulations, guidelines and standards, supervision, and resources to support trainees. In terms of teaching methodology, the mentors recommended innovative strategies, many of them based on technological tools or solutions, including videos, seminars, lectures, workshops, simulation learning or role-playing with or without professional actors, case studies, videos with practical demonstrations or model situations, panel discussions, clinical sessions for joint analysis of patient safety incidents, and debriefings to set and discuss lessons learned. Conclusions: This study sought to promote psychological safety competencies as a formal part of the training of future health care professionals, facilitating the translation of international guidelines into practice and clinical settings in the pan-European context. ", doi="10.2196/64125", url="https://mededu.jmir.org/2024/1/e64125", url="http://www.ncbi.nlm.nih.gov/pubmed/39374073" }