Academic Medicine最新文献

{"title":"A \"Totally Different Beast\": The Effect of Time Variability on Clinical Competency Committee Decision-Making.","authors":"Benjamin Kinnear, Sally A Santen, Bailey DeCoursey, Sarah Ferris, Daniel J Schumacher","doi":"10.1097/ACM.0000000000005995","DOIUrl":"10.1097/ACM.0000000000005995","url":null,"abstract":"<p><strong>Purpose: </strong>Competency-based, time-variable training (CBTVT) uses educational outcomes rather than time to determine learner readiness to progress through training. Clinical competency committees (CCCs) are critical components of CBTVT because they ultimately render time-variable promotion decisions. However, the effect of time variability on CCC member decision-making remains unclear. In this study, the authors explored the effect of time variability on the decision-making of CCC members at an internal medicine residency program.</p><p><strong>Method: </strong>The authors used an evaluative case study approach to study the University of Cincinnati internal medicine residency program's CBTVT pilot, called Transitioning in Internal Medicine Education Leveraging Entrustment Scores Synthesis (TIMELESS), with 2 to 3 residents per postgraduate year participating longitudinally between October 2020 and March 2022. Taking a constructionist worldview, the authors used semistructured interviews with members of the TIMELESS CCC to explore how time variability affected their decision-making about resident promotion. Interviews and observations were completed between February 2021 and January 2022.</p><p><strong>Results: </strong>In total, the authors completed 16 interviews with 11 unique CCC members. Participants reported that time variability significantly affected their decision-making process. The authors developed 3 related themes. First, time variability created a sense of increased stakes of promotion decisions. Second, the increased perceived stakes of decisions led to more deliberate decision-making. Third, time in training and clinical experience still influenced time-variable promotion decisions.</p><p><strong>Conclusions: </strong>Time variability changed CCC member perception of the decision-making (more risk, higher stakes), with subsequent change in their behaviors (in-depth review, active decision-making). Although many time-based programs make entrustment determinations that are not linked to tangible consequences (i.e., Would I trust?), TIMELESS asked CCC members to make entrustment decisions that were consequential (i.e., Will I trust?).</p>","PeriodicalId":50929,"journal":{"name":"Academic Medicine","volume":" ","pages":"727-733"},"PeriodicalIF":5.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143442712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Let Me Hold Your Breath.","authors":"Anneliese Mair","doi":"10.1097/ACM.0000000000005996","DOIUrl":"10.1097/ACM.0000000000005996","url":null,"abstract":"","PeriodicalId":50929,"journal":{"name":"Academic Medicine","volume":" ","pages":"682"},"PeriodicalIF":5.3,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143558734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"It Takes More Than Enthusiasm: The Missing Infrastructure to Unlock AI's Potential in Medical Education.","authors":"Laurah Turner, Christine Zhou, Jesse Burk-Rafel","doi":"10.1097/ACM.0000000000006104","DOIUrl":"https://doi.org/10.1097/ACM.0000000000006104","url":null,"abstract":"<p><strong>Abstract: </strong>Generative artificial intelligence (AI), including large language models (LLMs), is rapidly transforming health care delivery, yet medical education remains unprepared to harness its potential or mitigate its risks. While AI holds immense potential to enhance medical education, unguided adoption of these tools without proper educational frameworks risks undermining learners' clinical reasoning development and professional growth, as was seen with the electronic health record. In this commentary, the authors argue that the primary barrier to effective AI integration in medical education is not technological sophistication, but rather 3 critical infrastructure deficiencies: institutional implementation structures, sustainable funding mechanisms, and rigorous research methodologies. The authors propose establishing dedicated educational informatics teams with executive authority, creating targeted funding streams modeled after clinical research investments, and developing rigorous assessment frameworks with clear benchmarks for educational outcomes. Without these foundational elements, AI integration risks exacerbating inequities between institutions, potentially compromising physician development, and ultimately failing to improve patient care. Recommendations developed at a Macy Foundation conference on AI and Medical Education provide a roadmap for addressing these challenges, but significant infrastructural support is required to realize their potential. The authors argue that failure to address these structural gaps would perpetuate a cycle of innovation without implementation, a challenge that has plagued medical education for decades. In an era when AI is reshaping clinical practice daily, trainees cannot afford another well-intentioned but under-resourced educational transformation. Transformative educational change demands more than enthusiasm-it requires institutional commitment, significant investment, and methodological rigor commensurate with the high stakes of physician preparation.</p>","PeriodicalId":50929,"journal":{"name":"Academic Medicine","volume":" ","pages":""},"PeriodicalIF":5.3,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Data on Electronic Residency Application Service Experiences Illuminate the \"Shadow Economy of Effort\".","authors":"Jennifer X Hong, Ish Sethi, C Jessica Dine","doi":"10.1097/ACM.0000000000006111","DOIUrl":"https://doi.org/10.1097/ACM.0000000000006111","url":null,"abstract":"","PeriodicalId":50929,"journal":{"name":"Academic Medicine","volume":" ","pages":""},"PeriodicalIF":5.3,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Enhanced Rural Medical Education: Bridging Gaps and Building Trust.","authors":"Natasha N Bray, Denna Wheeler, Justin Zumwalt","doi":"10.1097/ACM.0000000000006105","DOIUrl":"https://doi.org/10.1097/ACM.0000000000006105","url":null,"abstract":"<p><strong>Abstract: </strong>Persistent geographic and specialty-based disparities in health care workforce distribution have created critical gaps in rural health care access, resulting in disproportionate morbidity and mortality in underserved communities. This commentary examines how strategically implemented artificial intelligence (AI)-based technologies can facilitate the development of competent, community-oriented physicians capable of delivering high-quality care in rural settings.Key challenges in rural medical education are identified, including resource limitations, infrastructure deficiencies, and difficulty attracting qualified educators, while acknowledging rural training's unique benefits, including hands-on procedural experience, broader clinical scope, and meaningful mentoring relationships. AI-powered tools can transform rural medical education through virtual patient simulations, personalized learning platforms, clinical decision support systems, and automated performance evaluation-technologies that democratize access to educational resources, standardize learning experiences across geographic locations, and provide personalized learning pathways.AI can foster the development of rural community-focused and trained physicians by analyzing community health patterns, enhancing cultural sensitivity training, and simulating real-world rural health care barriers. However, this technology is rapidly evolving, and implementation must address ethical considerations including data privacy, algorithmic bias, transparency, and equitable access. By integrating AI responsibly, medical educators can prepare physicians to thrive in rural practice-potentially increasing patient-physician trust and reducing health care disparities.</p>","PeriodicalId":50929,"journal":{"name":"Academic Medicine","volume":" ","pages":""},"PeriodicalIF":5.3,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210153","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From the World Wide Web to AI: Why We Must Learn From Our Past to Transform the Future of Medical Education.","authors":"Lisa D Howley, Alison J Whelan","doi":"10.1097/ACM.0000000000006103","DOIUrl":"https://doi.org/10.1097/ACM.0000000000006103","url":null,"abstract":"<p><strong>Abstract: </strong>In 1989, the World Wide Web was proposed as a practical solution to connect scientists globally. That innovation later transformed how knowledge was accessed and shared across all sectors, including health care and education. While initially met with skepticism, the Web ultimately revolutionized broad sectors including health care and education. The Web reimagined medical education and enabled digital learning platforms, open-access resources, and new forms of collaboration. A new paradigm, artificial intelligence (AI)-particularly generative AI, is now poised to redefine how knowledge is created, synthesized, and applied in medical education and health care.This commentary draws parallels between medical educators' initial resistance to the Web and current hesitations with AI. Current concerns are echoing our past, such as misinformation and lack of credibility; technology replacing traditional teaching; inequity in access to the new technologies; and doubts about learning efficacy. We argue that these lessons learned from the Web era offer important insights into the ways AI in and for medical education will benefit our community.The authors build on insights and recommendations from the November 2024 Josiah Macy Jr. Foundation conference on AI in medical education, emphasizing the urgent need for a national set of AI competencies for and in medical education. These competencies should include knowledge of AI tools, their evaluation and ethical use, and their integration into clinical and educational processes.Ultimately, an AI-drive future is not predetermined-it is ours to shape. By learning from the past, medical educators can more effectively lead the ethical and equitable adoption of AI, ensuring it strengthens CBME and enhances the preparation of future physicians. This work represents both a challenge and an exciting opportunity to define the next chapter in the evolution of medical education.</p>","PeriodicalId":50929,"journal":{"name":"Academic Medicine","volume":" ","pages":""},"PeriodicalIF":5.3,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effective Engagement With AI Is the Only Path Forward for Clinician-Educators.","authors":"Adam Rodman, Cornelius A James","doi":"10.1097/ACM.0000000000006102","DOIUrl":"https://doi.org/10.1097/ACM.0000000000006102","url":null,"abstract":"<p><strong>Abstract: </strong>There is broad agreement that the cognitive abilities of generative artificial intelligence (GAI) technologies are existential opportunities for medical education, threatening to reshape both the clinical practice environment and the nature of learning. Recent improvements in the abilities of these models-most notably so-called reasoning models, which can perform many cognitive tasks with equal or better performance compared to humans-outpaces the abilities of both medical schools, professional societies, and consensus organizations to adapt. In this commentary, the authors propose that this fact should compel individual clinician-educators to become early adopters and actively engage with GAI technologies, with their learners, patients, and educational communities, and provide practical strategies for engagement.</p>","PeriodicalId":50929,"journal":{"name":"Academic Medicine","volume":" ","pages":""},"PeriodicalIF":5.3,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Marathon Without a Finish Line: A Learner's Perspective on AI in Medical Education.","authors":"Joy Jiang","doi":"10.1097/ACM.0000000000006100","DOIUrl":"https://doi.org/10.1097/ACM.0000000000006100","url":null,"abstract":"<p><strong>Abstract: </strong>Artificial intelligence (AI) is rapidly evolving and increasingly being integrated into health care. Medical students and residents must develop a foundational understanding of AI's applications and implications for their education and training, professional practice, and the health of their patients. In this commentary, the author, a current MD/PhD student, calls for the inclusion of AI in educational programming and suggests approaches to 5 inherent tensions in efforts to develop AI curricula. Curricula should not fixate on any one tool or technology; however, a core AI curriculum should provide all students and residents with the needed AI literacy and skills, enabling graduates to engage with, question, and responsibly use AI tools in their clinical practice. Learners also should be offered opportunities that prepare them for a variety of roles ranging from users to developers of AI models. Institutional differences in AI resources and expertise pose a challenge, underscoring the importance of unifying national modules and co-production of curriculum with interested learners who have AI experience. Finally, all AI curricula must promote among developers and users an awareness of ethical principles when contextualizing and evaluating AI models.</p>","PeriodicalId":50929,"journal":{"name":"Academic Medicine","volume":" ","pages":""},"PeriodicalIF":5.3,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Helping Medical Education Faculty Navigate a Post-Academic Landing Pad.","authors":"Kimberly Skarupski, Janet Bickel","doi":"10.1097/ACM.0000000000006108","DOIUrl":"https://doi.org/10.1097/ACM.0000000000006108","url":null,"abstract":"","PeriodicalId":50929,"journal":{"name":"Academic Medicine","volume":" ","pages":""},"PeriodicalIF":5.3,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144163595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Are Generative Artificial Intelligence-Aided Letters of Recommendation in the Recommender's Voice?","authors":"Shigeki Matsubara","doi":"10.1097/ACM.0000000000006106","DOIUrl":"https://doi.org/10.1097/ACM.0000000000006106","url":null,"abstract":"","PeriodicalId":50929,"journal":{"name":"Academic Medicine","volume":" ","pages":""},"PeriodicalIF":5.3,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144163585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"教育学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}