Medical Education Online最新文献

Background: The practice of evidence-based medicine (EBM) has become pivotal in enhancing medical care and patient outcomes. With the diffusion of innovation in healthcare organizations, EBM can be expected to depend on medical professionals' competences with digital health (dHealth) and artificial intelligence (AI) technologies.

Objective: We aim to investigate the effect of dHealth competences and perceptions of AI on the adoption of EBM among prospective physicians. By focusing on dHealth and AI technologies, the study seeks to inform the redesign of medical curricula to better prepare students for the demands of evidence-based medical practice.

Methods: A cross-sectional survey was administered online to students at the University of Montreal's medical school, which has approximately 1,400 enrolled students. The survey included questions on students' dHealth competences, perceptions of AI, and their practice of EBM. Using structural equation modeling (SEM), we analyzed data from 177 respondents to test our research model.

Results: Our analysis indicates that medical students possess foundational knowledge competences of dHealth technologies and perceive AI to play an important role in the future of medicine. Yet, their experiential competences with dHealth technologies are limited. Our findings reveal that experiential dHealth competences are significantly related to the practice of EBM (β = 0.42, p < 0.001), as well as students' perceptions of the role of AI in the future of medicine (β = 0.39, p < 0.001), which, in turn, also affect EBM (β = 0.19, p < 0.05).

Conclusions: The study underscores the necessity of enhancing students' competences related to dHealth and considering their perceptions of the role of AI in the medical profession. In particular, the low levels of experiential dHealth competences highlight a promising starting point for training future physicians while simultaneously strengthening their practice of EBM. Accordingly, we suggest revising medical curricula to focus on providing students with practical experiences with dHealth and AI technologies.

Background: Medical Humanities (MH) curricula integrate humanities disciplines into medical education to nurture essential qualities in future physicians. However, the impact of MH on clinical competencies during formative training phases remains underexplored. This study aimed to determine the influence of MH curricula on internship performance.

Methods: The academic records of 1364 medical students across 8 years of admission cohorts were analyzed. Performance in basic sciences, clinical skills, MH, and internship rotations were investigated, including the subgroup analysis of MH curricula. Ten-fold cross-validation machine learning models (support vector machines, logistic regression, random forest) were performed to predict the internship grades. In addition, multiple variables regression was done to know the independent impact of MH on internship grades.

Results: MH showed the important roles in predicting internship performance in the machine learning model, with substantially reduced predictive accuracy after excluding MH variables (e.g. Area Under the Curve (AUC) declining from 0.781 to 0.742 in logistic regression). Multiple variables regression revealed that MH, after controlling for the scores of other subjects, has the highest odds ratio (OR: 1.29, p < 0.0001) on internship grades. MH explained 29.49% of the variance in internship grades as the primary variable in stepwise regression. In the subgroup analysis of MH curricula, Medical Sociology and Cultural Studies, as well as Communication Skills and Interpersonal Relationships, stood out with AUC values of 0.710 and 0.705, respectively, under logistic regression.

Conclusion: MH had the strongest predictive association with clinical competence during formative internship training, beyond basic medical sciences. Integrating humanities merits greater prioritization in medical curricula to nurture skilled, compassionate physicians. Further research should investigate the longitudinal impacts of humanities engagement.

The use of narrative comments in medical education poses a unique challenge: comments are intended to provide formative feedback to learners while also being used for summative grades. Given student and internal medicine (IM) grading committee concerns about narrative comment quality, we offered an interactive IM Grand Rounds (GR) session aimed at improving comment quality. We undertook this study to determine the quality of comments submitted by faculty and post-graduate trainees on students' IM Clerkship clinical assessments, and to explore the potential impact of our IM-GR. Archived comments from clerkship cohorts prior to and immediately following IM-GR were reviewed. Clinical clerkship assessment comments include three sections: Medical Student Performance Assessment (MSPE), Areas of Strength, and Areas for Improvement. We adapted a previously published comment assessment tool and identified the performance domain(s) discussed, inclusion of specific examples of student performance, evidence that the comment was based on direct observations, and, when applicable, the inclusion of actionable recommendations. Scoring was based on the number of domains represented and whether an example within that domain was provided (maximum score = 10). Analysis included descriptive statistics, t-test, and Pearson correlation coefficients. We scored 697 comments. Overall, section ratings were MSPE 2.51 (SD 1.52, range 0-9), Areas of Strength 1.53 (SD 1.09, range 0-6), and Areas for Improvement 1.27 (SD 1.06, range 0-8). Significant differences were noted after Grand Rounds only in the MSPE mean scores. Within domains, trends toward increased use of specific examples in the post-GR narratives were noted. Assessment of both the breadth and depth of the included comments revealed low-quality narratives offered by our faculty and resident instructors. A focused session on best practices in writing narratives offered minimal change in the overall narrative quality, although we did notice a trend toward the inclusion of explanative examples.

Introduction: While children of medical professionals are globally overrepresented in medical schools, evidence from China remains limited. This study examines parental medical background prevalence among Chinese medical undergraduates, its association with admission outcomes, and disparities in pre-admission preparedness within China's meritocratic National College Entrance Examination (NCEE) system - a critical context given its role as the primary gateway to higher education.

Methods: Using data from the 2021 China Medical Student Survey (CMSS), a nationally representative sample of 19,299 clinical medical students was analyzed. Linear and logistic regression models were employed to assess the relationship between parental medical background and admission outcomes/pre-admission preparedness, controlling for socio-demographic covariates (e.g. gender, urban/rural residency, family income) and institutional/provincial fixed effects.

Results: Children of medical professionals were significantly overrepresented (11.60% vs. 0.34% national physician-population ratio). Parental medical background did not predict advantages in NCEE scores or admission to long-term programs. However, paternal medical background was associated with higher pre-admission preparedness in clinical practice (β = 0.199, p < 0.05), health and society (β = 0.205, p < 0.01), professionalism (β = 0.130, p < 0.05), and a greater likelihood of understanding the major (OR = 0.724, p < 0.01), while maternal background only correlated with understanding of the major (OR = 0.623, p < 0.01).

Conclusions: In the context of China's NCEE-based student selection system, parental medical background has no direct influence on admission results, yet intergenerational disparities in preparedness persist. To foster substantive equity, China's meritocratic system could integrate targeted interventions (e.g. pre-med mentorship for disadvantaged students). These findings underscore the global imperative to balance meritocracy with policies addressing structural inequities in medical student selection.

Large language models (LLMs) have shown promising capabilities across medical disciplines, yet their performance in basic medical sciences remains incompletely characterized. Medical histology, requiring factual knowledge and interpretative skills, provides a unique domain for evaluating AI capabilities in medical education. To evaluate and compare the performance of five current LLMs: GPT-4.1, Claude 3.7 Sonnet, Gemini 2.0 Flash, Copilot, and DeepSeek R1 on correctly answering medical histology multiple choice questions (MCQs). This cross-sectional comparative study used 200 USMLE-style histology MCQs across 20 topics. Each LLM completed all the questions in three separate attempts. Performance metrics included accuracy rates, test-retest reliability (ICC), and topic-specific analysis. Statistical analysis employed ANOVA with post-hoc Tukey's tests and two-way mixed ANOVA for system-topic interactions. All LLMs achieved exceptionally high accuracy (Mean 91.1%, SD 7.2). Gemini performed best (92.0%), followed by Claude (91.5%), Copilot (91.0%), GPT-4 (90.8%), and DeepSeek (90.3%), with no significant differences between systems (p > 0.05). Claude showed the highest reliability (ICC = 0.931), followed by GPT-4 (ICC = 0.882). Complete accuracy and reproducibility (100%) were detected in Histological Methods, Blood and Hemopoiesis, and Circulatory System, while Muscle tissue (76.0%) and Lymphoid System (84.7%) presented the greatest challenges. LLMs demonstrate exceptional accuracy and reliability in answering histological MCQs, significantly outperforming other medical disciplines. Minimal inter-system variability suggests technological maturity, though topic-specific challenges and reliability concerns indicate the continued need for human expertise. These findings reflect rapid AI advancement and identify histology as particularly suitable for AI-assisted medical education.Clinical trial number: The clinical trial number is not pertinent to this study as it does not involve medicinal products or therapeutic interventions.

Background: Project ECHO has emerged as a leading telementoring modality for continuing medical education, particularly for providers practicing in rural and underserved areas with limited access to specialty care. The efficacy and utility of the ECHO model in healthcare training is well documented, though there is less literature focused on the determinants of ECHO implementation.

Objective: This study aims to assess facilitators and barriers to implementing the ECHO model.

Methods: We conducted virtual focus groups with eight Project ECHO implementation teams (n = 29 individuals) across the United States. Guided by the Consolidated Framework for Implementation Research (CFIR), focus groups explored experiences implementing the ECHO model and assessed facilitators and barriers to program uptake, delivery, and sustainability.

Results: Qualitative analysis revealed implementation determinants across CFIR levels. Participants recognized the advantage of ECHO's virtual, learner-centric, case-based learning approach compared to other continuing medical education modalities. Participants recommended recruiting subject matter expert presenters with skills as educators and understanding of the ECHO model. Because of Project ECHO's emphasis on case-based learning, participants highlighted the importance of balancing didactics with case presentations and discussion. Scheduling and finding time to participate was reported as a challenge for provider engagement, though most participants suggested that the length, frequency of sessions, and number of participants can be tailored for each program to accommodate needs. Providing CME credit and setting expectations for attendance and case presentation were said to improve provider engagement. Support and mentorship from the ECHO Institute was described as a facilitator in planning for ECHO implementation and delivery. Funding was reported as a barrier to sustainability.

Conclusion: By addressing barriers prior to implementing the ECHO model, future ECHOs can be tailored to leverage program resources, maximize attendance, expand reach, and ultimately improve outcomes.

Background: Studies of evidence-based medicine (EBM) curricula in graduate medical education are common, but little consensus exists on the best methods to teach EBM.

Objective: The purpose of the current study was to evaluate EBM teaching approaches for graduate medical trainees and to update a 2014 systematic review.

Methods: We conducted a systematic literature search of major health and education databases for articles published from January 2014 through October 2022. Articles were independently screened to ensure they described an experimental or quasi-experimental evaluation of EBM teaching for graduate medical trainees. Quality of included studies was appraised using the Medical Education Research Study Quality Instrument. Data were extracted and synthesized using Coomarasamy and Khan's hierarchy of EBM teaching and learning.

Results: Over 1400 articles were screened; 35 met eligibility criteria and were included in our review. Interactive, classroom-based teaching approaches were most common (23/35, 66%). Only 2 (6%) studies used a clinically integrated teaching approach. Most studies reported positive short-term outcomes in EBM knowledge, skills, attitudes, or learner satisfaction. Few studies evaluated EBM behaviors, and none measured long-term application of EBM principles.

Conclusions: Reviewed studies had low to moderate study quality, often limited by small sample size and lack of validated measures. Although commonly encouraged as a teaching approach, few studies used clinically integrated EBM teaching. Instead of reporting individual, site-specific efforts, future studies should examine the broader culture of EBM in graduate medical education and prioritize sustained application of EBM into practice as a key outcome.

Agitation in healthcare, particularly in psychiatric settings, is a prevalent and escalating global concern. Despite its significance, healthcare students often feel underprepared to manage agitation, citing fear, stigma, and limited clinical exposure. Traditional teaching methods, such as lectures or simulations, are resource-intensive and offer limited opportunities for repeated practice in low-risk environments. Virtual reality (VR) offers a promising alternative, providing immersive, standardised, and repeatable training for high-stress clinical scenarios. In response, the education team at [redacted for peer review], developed the Managing AGgression using Immersive Content (MAGIC) programme. This three-hour blended learning workshop, a mandatory component of the psychiatry curriculum for medical and nursing students, integrates didactic teaching, role-play, and the Virtual Reality in Agitation Management (VRAM) activity. Through experiential learning, MAGIC aims to enhance healthcare students' confidence, empathy, mental health literacy, and competence in managing agitation in psychiatric healthcare settings. Using a pre- and post-test quasi-experimental design, we evaluated the programme's effectiveness among 152 medical and nursing students. Results demonstrated significant improvements in mental health literacy, self-perceived proficiency, and confidence in managing agitated patients; there was also a marked reduction in stigma towards individuals with mental health conditions. In addition, participants responded positively to all aspects of the VRAM software, underscoring its usability and educational value. These findings highlight the potential of integrating immersive VR technology with traditional pedagogical methods to transform healthcare education by fostering deeper engagement, enhancing clinical competence, and ultimately improving patient outcomes.

Interprofessional teaching rounds are a practical application of interprofessional education in bedside teaching, yet there is a lack of research on how interprofessional teaching rounds should be implemented into medical education. This study aimed to describe our experience in developing and implementing interprofessional teaching rounds during a clerkship rotation for medical students, and compares its strengths and weaknesses relative to traditional teaching rounds. Medical students were assigned to either the interprofessional teaching round group (n = 24) or the traditional teaching round group (n = 25), and each group participated in their assigned type of teaching round. A quiz including medical knowledge of gynecological and obstetric diseases was used to assess the students' diagnostic and treatment abilities after teaching rounds. Additionally, a survey was conducted among students to evaluate whether the interprofessional teaching rounds were helpful. The results showed that when using interprofessional teaching rounds, the test score for medical knowledge related to the diagnosis and treatment of gynecological and obstetric diseases was significantly higher than the traditional teaching round group (85.5 ± 11.2 vs 78.3 ± 12.5, p = 0.038). Additionally, the interprofessional teaching rounds significantly enhanced understanding of clinical application, identification, and appropriate problem-solving in cases, as well as examination of different disciplinary aspects of a case, and improvement of interdisciplinary collaboration skills compared to traditional teaching rounds. Our study demonstrates that interprofessional teaching rounds can serve as an effective teaching method for enhancing medical students' ability to collaborate interprofessionally and to solve clinical problems comprehensively.

Purpose: Recommendations to ensure diverse, equitable, and inclusive content in Continuing Medical Education (CME) have been developed, however, learners' perception of these efforts are unknown. Learner recognition of biased or non-inclusive content and satisfaction with activity diversity provides insight into the success of bias mitigation efforts during CME planning and delivery. This study's objective was to evaluate the types of bias identified by learners, and to evaluate learners' perception of inclusivity and satisfaction with the diversity of CME activities.

Study design: This study was a retrospective mixed methods analysis of post-activity evaluation comments from 210 CME activities and 5,284 evaluations at a large Accreditation Council for Continuing Medical Education (ACCME)-accredited academic healthcare system from September 1, 2022 to December 31, 2023.

Results: Learners were satisfied with speaker and content diversity in 98.9% of activities. The qualitative analysis included 967 comments and demonstrated four main categories of perceived bias or lack of diversity identified by the CME activity learners: 1) Bias related to social identity factors, of which racial, ethnic, and gender bias were the most common forms identified by learners; 2) Lack of diversity in speakers, content and delivery; 3) Resistance to bias and inclusion evaluation questions; and 4) Commercial/industry bias. Further, some learners noted the instructional design of certain activities was not inclusive of all learners.

Conclusion: These findings suggest that some CME activity learners perceive various forms of bias and lack of inclusivity and diversity despite efforts to review and mitigate bias in the planning and delivery of CME. While most CME activity learners were satisfied with speaker and content diversity, the data can inform more targeted efforts during the CME planning phase that focus on speaker and content diversity and screening for bias that goes beyond traditional industry/commercial bias.