Academic Medicine最新文献

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: 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: 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.

Personal statements for medical school and residency are often viewed as authentic accounts of an applicant's fit and interest within a specific medical context. Yet these narratives remain an inconsistent predictor of academic performance, and generative artificial intelligence (AI) may undermine their authenticity. Now is the time for admissions committees to convene stakeholders and reassess the value and purpose of personal statements for both admissions and applicants. While some may be tempted to do away with personal statements entirely, these statements can provide meaningful opportunities for reflection and growth. This Scholarly Perspective recommends that programs focus on the humanity of the writing process by: (1) viewing the personal statement as a learning opportunity for the applicant, (2) fostering community and collaboration in the writing process, (3) crafting more specific prompts, and (4) intentionally incorporating interview questions referencing the applicant's statement. By doing so, personal statements can humanize the admissions process, even in an era of AI.