Academic Medicine最新文献

Purpose: The volume of residency applications and data per applicant are increasing with emphasis on holistic review and application inflation. Studies have shown artificial intelligence (AI) could augment human review in resident selection and reveal successful candidates who may otherwise be overlooked. This study determines whether AI successfully predicts match outcomes for ophthalmology residents and could be a valid means of improving the objectivity and efficiency of the residency match process.

Method: This was a prospective study of 642 applicants in the 2023-2024 San Francisco Match cycle. A total of 129 US doctors of ophthalmology and foreign medical graduates were excluded from analysis. The application data of the 513 US medical doctor graduates was studied to predict their match outcomes. Data were received from applicants on September 1, 2023, and were prospectively analyzed by both AI and faculty until the match results were released on February 6, 2024. Faculty utilized a standardized rubric to generate a rank list. In late September 2024, GPT 3.5 Turbo (Azure OpenAI) was given 5 main criteria and no prior examples to prevent bias. Both faculty and AI had access to the entire San Francisco Match application. Main outcome was predictiveness of rank list on match outcome of each applicant.

Results: Both the AI rank list and faculty rank list were predictive of matching to an ophthalmology residency spot (P values < .001). Each 10-percentile increase of the AI ranking had a 23% increase in the odds of a match (odds ratio = 1.23; 95% CI, 1.15-1.32), and each 10-percentile increase of the faculty rank list had a 41% increase in the odds of a match (odds ratio = 1.41; 95% CI, 1.29-1.53).

Conclusions: AI accurately predicts match outcomes and can be used as an adjunct aide to faculty review of applications to reduce the immense administrative workload and human bias.Teaser TextThe residency application process is growing increasingly complex as applicant volume and emphasis on holistic review continue to rise, placing significant strain on faculty reviewers. Artificial intelligence (AI) has the potential to augment human judgment by improving efficiency and objectivity in resident selection. In this prospective study of over 500 U.S. allopathic applicants in the 2023-2024 ophthalmology SF Match cycle, we evaluated whether an AI model could predict match outcomes using complete application data. AI-generated rankings were prospectively compared with faculty rankings created using a standardized rubric. Both approaches were significantly predictive of match success. These findings demonstrate that AI can meaningfully support residency application review, offering a scalable adjunct to faculty assessment that may reduce administrative burden and help mitigate human bias while preserving faculty oversight.

Purpose: Although theoretical scholarship has examined professional identity formation (PIF) as an objective and outcome of medical student education, few empirical studies exist to support these models. Medical students' families have been identified as an important source of identity; however, PIF frameworks provide limited elaboration on the roles of family in identity formation. This study examined how medical students perceive the influence of their families on their PIF.

Method: In this retrospective, qualitative study, data were drawn from recorded transcripts of semistructured interviews performed in spring 2019 with 53 graduating students from 4 US medical schools regarding factors influencing their PIF. After students identified family as a contributor to PIF in early transcript reviews, secondary analysis identified interview excerpts relevant to families of origin, families of procreation, and extended families. Open coding was used to categorize the impact of family members and family relationships on medical students' PIF. After initial group coding training, pairs of investigators coded transcripts independently, compared codes, and resolved differences through discussion with the entire team.

Results: Of the 53 students, 52 (98.1%) referred to their family during interviews. Family of origin was referenced in 207 of 289 excerpts (71.6%), and family of procreation was referenced in 96 of 289 (33.2%). Families affected medical students' PIF by providing support; influencing students' motivations, skills, and attitudes affecting their socialization in medicine; and providing reference points from which students marked their PIF progress. Families could also set expectations that contributed to tension as students' PIF evolved.

Conclusions: This study found that medical students' family relationships have a substantial influence on their PI development. This enhanced understanding of influences can enable students and educators to facilitate the positive impact families can have and explore ways to mitigate the challenges students experience as their PI develops throughout medical school.

Student satisfaction surveys have become a cornerstone of medical education, influencing critical decisions such as faculty promotion and institutional accreditation. However, the systemic overreliance on satisfaction surveys, especially student evaluations of teaching, is concerning. These self-report surveys, initially designed for formative feedback, are now used to make high-stakes decisions despite their well-documented biases and weak correlations with actual learning outcomes. This overreliance not only diminishes their usefulness but also risks fostering a culture where feedback is devalued, data are underused, and educational quality is potentially compromised. Moreover, the inherent biases in student evaluations of teaching-driven by irrelevant factors such as teacher demographics and other contextual factors-undermine their validity, disproportionately affecting marginalized groups and perpetuating systemic inequities. The overreliance on student satisfaction data in faculty promotion and medical school accreditation further exacerbates these issues, encouraging superficial strategies to boost satisfaction scores rather than substantive improvements in teaching, learning, and assessment. In this Scholarly Perspective, the authors argue for a critical rethinking of the role of student satisfaction surveys in medical education. They encourage the medical education community to reduce reliance on these instruments in favor of more holistic evaluation processes that better reflect educational quality. Although the authors recognize the value of student feedback, they argue it should be integrated within a broader, more evidence-informed evaluation system to provide more meaningful insights. Given the high stakes, the authors advocate for timely, meaningful reform in how the medical education community evaluates educational quality and accredits MD programs in the United States.

Purpose: Teaser: As the largest MD granting institution in the US, Indiana University School of Medicine has 8 regional medical campuses (RMC) and a main medical campus (MMC) in Indianapolis. RMC students comprise more than half of each class and can choose to spend 2 years or all 4 years at their RMC. While RMC students gain unique benefits compared to MMC students, concerns exist about inequities in residency match outcomes. The authors analyzed 8 years of residency placement data (2,653 students) focusing on highly competitive specialties (HCS), primary care (PC), and unmatched status. Students from RMCs and MMCs had no difference in unmatched and PC match rates. However, students who spent all 4 years at an RMC were less likely to match into HCS compared to those who spent their clinical years at the MMC. The authors discuss possible explanations for their findings and offer recommendations for schools with RMC systems.Although regional medical campus (RMC) students gain unique benefits compared with students at main medical campuses (MMCs), concerns exist about possible inequities in match outcomes. This study compared the residency match outcomes of MMC versus RMC students, focusing on unmatched status, primary care, and competitive specialties.

Method: This study used cumulative match data from 8 Indiana University School of Medicine graduating classes (2016-2023). Graduates were categorized based on campus attended (4 years at the MMC, 4 years at the RMCs, phase 1 [preclinical education] at the RMCs, and phases 2 and 3 [clinical education] at the MMC) and match type (primary care, non-primary care, postgraduate year 1 only, or unmatched). Non-primary care was divided into highly competitive specialties and other non-primary care specialties.

Results: Of the 2,478 students (93%) who matched, 937 (38%) matched into primary care, 1,470 (59%) into non-primary care, and 71 (3%) into postgraduate year 1-only positions, with 295 students (12%) entering highly competitive specialties. Highly competitive specialties constituted only 38 students (8%) who attended an RMC all 4 years versus 104 (12%) who attended an RMC for preclinical years and the MMC for clinical years (??  =  0.06, P = .02) and 151 (15%) who attended the MMC all 4 years (??  =  0.11, P < .001). There was no difference for matching into a highly competitive specialty between students who spent preclinical years at an RMC and clinical years at the MMC versus those who spent all 4 years at the MMC.

Conclusions: Students from RMCs and the MMC had comparable rates in overall match success and entering primary care residencies. However, students who spent their clinical years at the MMC were more likely to enter highly competitive specialties versus those who spent all 4 years at an RMC.

Purpose: Competency committees serve as a holistic mechanism for determining whether learners have reached thresholds for advancement. In the United States, standards for competency committee operations are mandated throughout graduate medical education (GME) programs. Although similar committees have become increasingly prevalent in undergraduate medical education (UME), there is no external standard for their operations, and research is limited. This study aimed to address gaps in understanding the purpose, structure, and function of UME competency committees in US medical schools.

Method: This study was part of a larger focused ethnography conducted across 7 US MD-granting programs. Data were collected between April 2023 and November 2024 from site-specific documents (n = 21) and semistructured interviews with chairs (n = 7), an administrator (n = 1), and founders (n = 7) of the respective competency committees. Interviews were recorded and transcribed verbatim. The authors incorporated an interpretative approach to analyze data: generating and refining codes, developing institutional case summaries, and identifying emergent analytic ideas.

Results: Participants developed competency committees to serve as a critical component toward the aim of realizing a true competency-based education model. Despite the similar purpose, outcomes were variable, ranging from feedback and coaching to advancement and remediation. Competency was defined and operationalized locally; some identified numerical thresholds, whereas others favored individual judgment. Workload was commonly distributed between subgroups or subcommittees to navigate the large learner volume. Other critical considerations included the relationship between the competency committee and advancement committees, roles and responsibilities of members, and mitigation of conflicts.

Conclusions: Although significant variability existed, participants encountered similar decision points, which illustrate key considerations for competency committee implementation in UME. This work provides a detailed description of competency committees in US medical schools, with important contrasts to GME, laying the groundwork for future research and best practices.