Background: Artificial intelligence (AI) is changing continuing professional development (CPD) in health care and its interactions with the broader health care system. However, current scholarship lacks an integrated theoretical model that explains how AI impacts CPD as a complex sociotechnical system. Existing frameworks usually focus on isolated phenomena, such as ethics, literacy, or learning theory, leaving unaddressed the dynamics of how those phenomena interact in the complex sociotechnical AI-enhanced CPD system, as well as the new roles that AI-empowered patients and society play.
Objective: The objective of this study is to propose a comprehensive, theory-driven framework that provides insight into how AI transforms CPD systems. The goal was to integrate established AI constructs with Complexity Theory (CT) and Actor-Network Theory (ANT) to develop a model that guides practice, research, and policy.
Methods: We conducted a multimethod theory construction. The process started with identifying the AI-enhanced CPD as an established yet evolving phenomenon. Through a structured literature review, the main building blocks of AI-enhanced CPD were identified, as well as the ontological base (CT and ANT). The model was developed through iterative human-led and AI-assisted abductive analysis. The final model was abductively validated on a case study of a national organization pioneering AI use, demonstrating the theoretical model makes sense in practice. All conceptual decisions were reviewed collaboratively by the author group.
Results: The ALEERRT-CA framework is made of 6 pillars: AI literacy, explainability, ethics, readiness, reliability, and learning theories, and 2 theoretical lenses: CT and ANT. CT elucidates macro-level system behaviors in the AI-enhanced CPD system. Those behaviors include emergence, feedback loops, adaptation, and reality made of nested complex systems. ANT explains how localized interactions among human and nonhuman actors shape AI-enhanced CPD. Together, these lenses illustrate how AI redistributes agency, amplifies tensions, and generates emergent learning dynamics within CPD and the broader health care system.
Conclusions: This study presents a novel conceptual model of AI-enhanced CPD as a sociotechnical system. The integration of CT and ANT with AI constructs improves explanatory power of the ALEERRT-CA framework. Educators, program leaders, and policymakers can use the framework as a structured toolset to evaluate AI readiness, design responsible AI-enhanced CPD practices, and plan future empirical research. The framework provides a theoretical lens for observing the rapidly evolving field of AI-enhanced CPD and health care practice.
Background: Laparoscopic surgery has a flatter learning curve compared to traditional open surgery. Therefore, structured programs and realistic training models are imperative to ensure patients' safety. However, commercially available models are often too expensive or technically unrealistic for continuous surgical training.
Objective: The aim of this trial was to develop a cost-efficient and highly realistic uterus model to perform a total laparoscopic hysterectomy (TLH) and evaluate its applicability.
Methods: A training model (MaiSurge) for a TLH with salpingectomy or adenectomy was developed using a 3D printer and different cast materials. Polyvinyl alcohol was used to allow for the use of electrosurgery. To gather the first validity evidence, novice and expert gynecologists performed a TLH on the model. Operative time as well as surgical performance scores (Hysterectomy-Objective Structured Assessment of Technical Skills) were compared between both groups.
Results: A total of 12 participants in the novice group and 18 participants in the expert group completed the simulation. The experts obtained significantly better modified Hysterectomy-Objective Structured Assessment of Technical Skills scores (mean 74.0, SD 12.9 vs mean 60.3, SD 14.9; P=.049) and performed significantly faster (median 69.5, IQR 49.5-74.3 minutes vs median 37.5, IQR 30.5-38.8 minutes; P<.001). An excellent interrater reliability was observed (intraclass correlation coefficient=0.91). Approximately 92% (11/12) of novices felt that they had improved their surgical performance after training on the MaiSurge uterus model. Overall, all participants agreed that the new MaiSurge uterus model should be integrated into training curricula to improve the performance of residents on TLHs.
Conclusions: A new highly realistic and cost-effective training model (MaiSurge) to perform a TLH was developed. The model distinguishes between good and poor laparoscopic performances and, thus, can be used in training as well as assessment of surgical skills. The possibility of simulating even complex laparoscopic procedures in a realistic environment may be an opportunity to train a future generation of gynecologists without compromising patient safety or exhausting the limited availability of operating room time.
Background: Beyond its applications in other settings, virtual reality (VR) technology has gained attention in medical education, offering immersive learning experiences. Previous research has demonstrated its potential as an educational tool in medical settings, highlighting enhanced educational outcomes, skill acquisition and retention, standardized training experiences, and the promotion of active learning. However, there is still a dearth of research exploring various aspects of VR user experiences, with most studies focusing on its effect on skill acquisition. Limited qualitative research further hinders an in-depth understanding of user experiences, restricting a comprehensive overview of VR's potential in medical education.
Objective: This study explored subjective experiences with VR simulation training and its perceived benefits and challenges among medical students in the United Kingdom, using the 5 domains of the Immersive Technology Evaluation Measure (ITEM).
Methods: In July 2024, 15- to 20-minute in-person interviews were conducted with 11 medical students who had completed the immersive VR training consisting of the assessment and treatment of a virtual patient. Guided by the 5 domains of the ITEM as preconceived themes, a deductive thematic analysis was used to explore individual experiences with the training, embedded within narrative responses.
Results: Findings aligned with the 5 a priori ITEM domains of system usability, immersion, motivation, cognitive load, and debriefing. Within these predefined domains, new subthemes emerged that enhanced the understanding of user experience. Participants reported usability barriers involving accessibility, technical issues, and limited variability in scenarios. Immersion was generally strong due to realistic environments, although reduced interactivity constrained authenticity. Motivation was reflected in active engagement and a greater sense of preparedness for clinical practice. Cognitive load was associated with divided attention, physical effects, and a need for clearer guidance and familiarization. Ultimately, participants valued debriefing sessions as valuable opportunities for reflection and reinforcing knowledge.
Conclusions: VR training fosters immersion and motivation, but its effectiveness depends on balancing technical usability with cognitive demands. Future integration should prioritize design variability and structured debriefing to optimize learning outcomes. Refinement of immersive VR training in clinical education is also warranted, alongside further research in broader contexts and longitudinal use.
Unlabelled: Health care has widely adopted behavioral economics to influence clinical practice, with documented success using defaults and social comparison feedback in electronic health records. However, online medical education, now the dominant modality for continuing professional development, remains designed on assumptions of rational learning that behavioral science has disproven in clinical contexts. This viewpoint examines the paradox of applying sophisticated behavioral insights to clinical work while designing digital learning environments as if learners are immune to cognitive limitations. We propose digital choice architecture for medical education: intentional integration of behavioral design principles into learning management systems and online platforms. Drawing from clinical nudge units and implementation science, we demonstrate how defaults, social norms, and commitment devices can be systematically applied to digital continuing education. As medical education becomes increasingly technology-mediated, behavioral science provides the theoretical foundation and practical tools for designing online learning environments that align with how clinicians actually make decisions.
Background: Artificial intelligence (AI) shows promise in clinical diagnosis, treatment support, and health care efficiency. However, its adoption in real-world practice remains limited due to insufficient clinical validation and an unclear impact on practitioners' competence. Addressing these gaps is essential for effective, confident, and ethical integration of AI into modern health care settings.
Objective: This study aimed to evaluate the effectiveness of informed AI use, following a tailored AI training course, on the performance of general practitioners (GPs) and internists in test-based clinical competence assessments and their attitudes toward clinical AI applications.
Methods: A pre-post intervention study was conducted with 326 physicians from 39 countries. Participants completed a baseline test of clinical decision-making skills, covering diagnosis, treatment planning, and patient counseling; attended a 1.5-hour online training on effective AI use; and then took a similar postcourse test with AI assistance permitted (GPT-4.0). Test performance and time per question were compared before and after the training. Participants also rated AI accuracy, efficiency, perceived need for structured AI training, and their willingness to use AI in clinical practice before and after the course.
Results: The average test scores improved from 56.9% (SD 15.7%) to 77.6% (SD 12.7%; P<.001), and the pass rate increased from 6.4% (21/326) to 58.6% (191/326), with larger gains observed among GPs and younger physicians. All skill domains (diagnosis, treatment planning, and patient counseling) improved significantly (all P<.001), while time taken to complete the test increased slightly from before to after the course (mean 40.25, SD 16.14 min vs 42.29, SD 14.02 min; P=.03). By the end of the intervention, physicians viewed AI more favorably, reporting increased confidence in its accuracy and time efficiency, greater appreciation for the need for structured AI training, and increased confidence and willingness to integrate AI into patient care.
Conclusions: Informed use of AI, based on tailored training, was associated with higher performance in test-based clinical decision-making assessments and greater confidence in using AI among GPs and internists. Building on previous research that often lacked structured training, focused primarily on model performance, or was limited in clinical scope, this study provides empirical evidence of both competence and perceptual improvement following informed AI use in a large, multinational cohort, enhancing the generalizability. These findings support the integration of structured AI training into medical education and continuing professional development to improve clinical performance and promote competent use of AI in clinical practice.
Background: Physician empathy is important not only for improving patient satisfaction and health outcomes but also for increasing physician job satisfaction and protecting against burnout. However, amid concerns over declining empathy levels in medical education, there is a need for innovative teaching approaches that address the empathy gap, a critical element in patient-centered care.
Objective: This study aimed to use a mixed-methods analysis to explore the effectiveness of a virtual reality (VR) intervention versus traditional lecture methods in enhancing empathy among medical students.
Methods: Overall, 50 first- and second-year medical students were randomized to either a VR intervention, which simulated patient experiences, or a control group receiving traditional empathy lectures. Both groups watch 2 videos with reflections gathered after each video to capture students' experiential learning. Empathy was measured using the Jefferson Scale of Empathy-Student Version before and after the intervention.
Results: Quantitative analysis revealed significant increases in empathy scores post intervention for both groups (lecture group: mean increase 4.71, SD 11.01; VR group: mean increase 5.6, SD 10.02; P<.001), indicating that both interventions enhanced empathy. The VR group exhibited a significant difference in qualitative empathy coding after the second video (U=165.5; P<.001) compared to the lecture group. Qualitative feedback from the VR group emphasized a more profound emotional and cognitive engagement with the patient perspective than the lecture group.
Conclusions: This study supports the integration of VR into medical education as a complementary approach to traditional teaching methods for empathy training. VR immersion provides a valuable platform for students to develop a deeper, more nuanced understanding of empathy. These findings advocate for further exploration into VR's long-term impact on empathy in clinical practice.
Background: The medical education of French family medicine residents involves active, socioconstructivist-inspired small-group courses useful for skill acquisition. This is challenged by the increasing gap between the growing number of residents and the limited number of teachers. Blended courses have the potential to address this issue by reducing the duration of face-to-face sessions while preserving small-group courses.
Objective: This study aimed to compare the effects of blended vs traditional, face-to-face, active, socioconstructivist learning on the acquisition of knowledge and skills by family medicine residents.
Methods: We conducted a randomized controlled trial to compare a blended course and a traditional course. The blended course involved 2.5 hours of asynchronous e-learning and a 3-hour face-to-face session. The traditional course involved 5.5 hours of face-to-face teaching. Both courses were grounded in socioconstructivist principles and actively engaged residents. The primary outcome was residents' self-assessment of knowledge and skills. Secondary outcomes included satisfaction with knowledge- or skill-related learning objectives and academic achievement at 6 months.
Results: We included 155 family medicine residents (n=78, 50.3% in the blended course and n=77, 49.7% in the traditional course). There was no significant difference between groups regarding the primary outcome (mean difference 0.40 [maximum mean difference 20] points, 95% CI -0.21 to 1.02; P=.19; Cohen d=0.21). No significant differences were observed for the secondary outcomes except for knowledge self-assessment, which was higher in the blended course but not educationally meaningful (mean difference 0.40 [maximum possible 10] points, 95% CI 0.07-0.71; P=.02; Cohen d=0.39).
Conclusions: Blended courses can help sustain socioconstructivist small-group teaching methods while accommodating a growing family medicine resident population, with no deleterious impact on knowledge and skill self-assessments.
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：