JMIR Medical Education最新文献

Unlabelled: As artificial intelligence (AI) develops, the medical education community has begun defining the relevant forms of competency. Many experts emphasize the importance of optimizing AI tools' output or understanding the relevant technical and normative considerations around using AI tools. A recent publication in this journal showed that optimizing instructions for large language models may yield diminishing returns as such tools improve. This suggests the need for a new competency-one that focuses on choosing the appropriate AI tools. I briefly summarize the current competency domains and examples to contextualize the current state of AI competency development, highlighting the need for further synthesis. I then introduce a hierarchical framework of competencies that might assist with priority-setting around subsequent competency development work. It consists of cognitive, operational, and meta-AI domains, which respectively correspond with the knowledge around understanding, using, and choosing AI tools. The final section describes the potential challenges associated with the development of AI competency. These include traditional concerns around competency-based medical education: deciding whether and which competencies are meaningful for measuring the targets of interest; adjusting the relevant measurements to reflect the necessary temporal and institutional context; and setting up the relevant organizational support to encourage measurement of competency. This section also discusses the challenges of developing the relevant performance indicators for AI tools across different clinical contexts. Such indicators will be necessary for guiding the choice of AI tools for the clinical context, but medical educators may not have the skills to develop them. In addition to identifying potential sources for relevant indicators, the medical education community may shape physicians' norms of practice to drive the AI industry into producing the relevant indicators. The potential for physicians to incur higher medical liability from poor choice of AI may lead them to demand more nuanced performance indicators from AI suppliers. Physicians are also in a position to do so, since the competitive AI market may provide them more bargaining power.

Background: In the field of team-based care, pharmacists are vital for optimizing medication therapy. However, many medical professionals lack the opportunity to learn how to propose prescription changes with precision.

Objective: This study aimed to address this knowledge gap by developing and assessing a new educational program for pharmacy students focused on prescription support and interprofessional collaboration.

Methods: We recruited 191 fifth-year pharmaceutical students during the 2022-2024 academic years. The program featured a 7-day intensive curriculum that included learning how to assist with prescriptions, analyzing clinical data, and engaging in role-playing exercises. A web-based questionnaire and a paper test were used to evaluate students' awareness and knowledge both before and after the program. Statistical analyses were performed to verify the significance of changes; we utilized the Wilcoxon signed-rank test for the ordinal data derived from the specific behavioral objectives and 2-tailed paired t tests for the interval data from the knowledge tests. The magnitude of change was quantified using r for Wilcoxon tests and Cohen dz for 2-tailed t tests, with 95% CI calculated to ensure the stability and reliability of the observed results.

Results: Analysis of the primary outcome specific behavioral objectives revealed statistically significant effects across all items (Wilcoxon signed-rank test; P<.001). Effect sizes (r=0.505-0.835) ranged from moderate to large, with particularly large effects observed in identifying contents issue (r=0.835, 95% CI 0.126-0.330; P<.001). Knowledge test scores showed significant improvement in the following 3 subjects: pharmacology (r=-0.504, 95% CI -0.215 to 0.127; P<.001), organic chemistry (r=0.254, 95% CI -0.148 to -0.193; P=.004), and communication (r=0.221, 95% CI -0.151 to -0.190; P=.01). No significant changes were observed in pathology or pharmacokinetics.

Conclusions: This program provides strong evidence that practical, hands-on learning with hospital pharmacists helps improve pharmacy students' professional skills and optimize pharmaceutical therapies in interprofessional care. By teaching pharmacists to effectively propose prescription changes, the program equips them to become integral members of interprofessional care, ultimately leading to optimized pharmaceutical care for patients.

Unlabelled: Simulation has become an essential pedagogical tool in health professions education, traditionally valued for its ability to approximate clinical practice. Higher simulation fidelity is often assumed to directly enhance learner engagement and improve educational outcomes; however, this assumption oversimplifies a complex relationship. Fidelity is multidimensional, encompassing physical, emotional, and contextual dimensions, as well as qualitative and quantitative considerations, each influencing learners' perception of realism in distinct ways. Engagement is shaped not only by these dimensions of fidelity but also by intrinsic factors such as motivation, prior experience, stress, and emotional resilience, and by extrinsic factors including instructional design, facilitation, debriefing, and psychological safety. A central mediator in this process is the fiction contract, an implicit agreement that enables learners to suspend disbelief and engage authentically despite inherent limitations in realism. Technological sophistication alone does not necessarily translate into greater educational impact. Rather, fidelity should be intentionally aligned with learning objectives: advanced patient simulators may support procedural training, standardized patients may enhance communication skills, and task trainers may optimize focused psychomotor practice. This viewpoint advocates for a goal-oriented, multimodal approach that redefines high-fidelity simulation not as the pursuit of maximum realism, but as the deliberate alignment of fidelity with pedagogy to optimize learner engagement and educational effectiveness.

Background: Radial artery puncture is a common clinical procedure essential for assessing gas exchange but is frequently perceived as stressful by inexperienced operators, who fear causing pain to their patients. Despite its practical relevance, formal training in this procedure is inconsistently integrated into medical curricula. This study evaluated whether a structured training program-combining theoretical instruction, simulation-based practice, and debriefing-could influence students' procedural confidence and decision-making and patient experience during their first clinical arterial puncture.

Objective: This study aimed to determine whether structured simulation-based training influences medical students' anxiety, confidence, and technical performance and patient experience during their first arterial puncture.

Methods: Third-year medical students who had never performed an arterial puncture were assigned to 1 of 2 groups: a structured training group (group 1) or a control group receiving informal or no specific training (group 2). After performing their first arterial puncture under supervision, students completed a questionnaire assessing apprehension, satisfaction, and confidence. The decision to use local anesthesia, puncture success, and patient-rated pain and apprehension were also recorded. A total of 67 students participated (group 1: n=24, 35.8%; group 2: n=43, 64.2%), with 61 patients included. Statistical comparisons were performed using the Fisher exact and nonparametric Mann-Whitney U tests (α=.05).

Results: Self-reported apprehension and confidence were similar between groups. However, group 1 students were significantly less likely to use local anesthesia compared to group 2 students (7/20, 35% vs 28/36, 77.8%, respectively; P=.003), suggesting greater procedural confidence. First-attempt success rates were comparable (group 1: 3/13, 23.1%; group 2: 14/29, 48.3%; P=.18). Median patient-reported pain scores were numerically but not statistically significantly lower when anesthesia was used (2.1, IQR 1.2-4.0 vs 4.8, IQR 2.1-6.4; P=.08).

Conclusions: Structured training influenced students' behavior during their first arterial puncture, reducing reliance on anesthesia despite similar levels of self-reported apprehension. Although confidence ratings did not differ, behavioral indicators suggested improved self-efficacy and readiness for clinical performance. These findings support the behavioral impact of structured procedural education and call for future research using validated assessment tools and long-term follow-up.

Unlabelled: Artificial intelligence (AI) is increasingly influencing medical education by enabling adaptive learning, AI-assisted assessment, and scalable instructional tools. Natural language processing, machine learning, and generative large language models offer innovative ways to support teaching and learning, yet their integration raises ethical, pedagogical, and infrastructural challenges. This viewpoint article aims to examine the current applications, benefits, and challenges of AI in medical education and propose strategies for responsible and effective integration. AI tools such as chatbots, virtual patients, and intelligent tutoring systems enhance personalized and immersive learning. Automated grading and predictive analytics support efficient evaluations, while AI-assisted writing tools streamline content creation. Despite these advances, concerns persist around data privacy, algorithmic bias, unequal access, and diminished critical thinking. Key solutions include AI literacy training, data oversight, equitable infrastructure, and curriculum reform. The FACETS framework offers 6 dimensions (ie, form, application, context, instructional mode, technology, and the SAMR [substitution, augmentation, modification, redefinition model]) to evaluate AI integration effectively. AI offers substantial opportunities to transform medical education, but its adoption must be ethical, equitable, and pedagogically grounded. Strategic frameworks such as FACETS, combined with institutional governance and cross-sector collaboration, are essential to guide implementation so that AI enhances learning outcomes while preserving the humanistic foundations of medical practice.

Unlabelled: Trust is increasingly recognized as a cornerstone for the successful implementation of digital public health initiatives, from mobile apps to the use of artificial intelligence in medicine, yet it remains underrepresented in educational curricula. In the course of our research and teaching activities in the field of trust in digital public health and medicine, we identified a gap in existing educational resources that aimed at supporting students in conducting structured trust analyses. Digitalization introduces new complexities into trust relationships, as interactions become increasingly mediated by digital tools. Preparing future professionals, therefore, demands fostering a critical understanding of how trust operates within digital systems, especially in the health sector. To address this gap, we developed and tested the first Trust Analysis Canvas for Teaching (TACT), a tool designed to guide students in conducting trust analyses of case studies in digital public health and medicine. Grounded in conceptual research on trust in health systems and health data sharing, we (1) developed the canvas content and reviewed it with two trust researchers; (2) tested and iteratively refined the tool with 23 students (3 BSc, 14 MSc, and 6 PhD) from diverse disciplines and academic levels through in-person and online focus groups at the universities of Zurich and Bern; (3) collaborated with a graphic designer to optimize its visual layout; and (4) translated the final canvas into French, Italian, German, and Spanish to ensure accessibility across disciplines, academic levels, and languages while maintaining a clear and engaging visual design. This paper introduces TACT, a canvas comprising 16 guiding questions organized around 6 core dimensions, designed to enable students from diverse disciplinary backgrounds and academic levels to engage with the complex concept of trust in a structured and guided manner, thereby addressing the identified gap in the current curricula. We outline the development process and provide a practical, step-by-step tutorial demonstrating its application through a written trust analysis of a digital health case study, supported by references to relevant literature.