Academic Medicine最新文献

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.

Purpose: This study aimed to assess the capacity of the Clinician Educator Milestones to distinguish between those who place less career emphasis on clinical education and those who have been recognized by their peers for their teaching abilities.

Method: The authors conducted a survey (January-April 2024) of clinician educators at Johns Hopkins University School of Medicine who were nominated by their peers for an institutional educator award between 2012 and 2023. Award nominees who responded were asked to identify 2 colleagues of similar academic rank who would not identify educator as their lead identity (control respondents). Respondents from both groups self-assessed from level 1 (novice) to 5 (expert) across the 20 subcompetencies of the Clinician Educator Milestones. The authors tested for differences between award nominees and the comparison group and for differences based on demographic characteristics. Statistical significance was set at P < .0025 to account for multiple comparisons.

Results: Of the 85 award nominees contacted, 71 (84%) completed the survey, compared to 91 (66%) of the 138 individuals in the control group. Award nominees rated themselves higher on 18 of the 20 subcompetencies compared to the control group. No statistically significant differences were found for commitment to professional responsibilities or leadership skills (P = .107 and .064, respectively). No subcompetencies reached the adjusted significance threshold when comparing across demographic groups.

Conclusions: The findings of this study suggest that the Clinician Educator Milestones can effectively differentiate between educators nominated by their peers for an institutional education award and other educators and showed few differences based on demographic characteristics. These findings support the milestones' potential utility for self-assessment and educator development. Further study of the milestones would add valuable information with which to compare these findings.

The accelerating integration of artificial intelligence (AI) into clinical practice presents a profound challenge to conventional medical education and assessment. Legacy assessment methods, focused on individual knowledge recall, fail to evaluate the competencies required for effective human-AI partnership. While numerous AI competency frameworks have emerged, they often overlook the synergistic wisdom needed for true collaboration. To address this gap, this article introduces and defines collaborative intelligence (CI), a dyad-centric construct focusing on the emergent capabilities of the human-AI team. CI represents the capability to critically appraise, ethically integrate, and judiciously act upon AI-derived insights. Its 5 core components are delineated: critical AI appraisal, information synthesis, adaptive judgment, ethical reasoning, and effective human-AI interaction. Subsequently, it proposes a novel assessment framework that discards rigid, time-based training stages in favor of a 4-stage competency spiral (novice to expert). This programmatic approach systematically maps assessment methods-including workplace-based assessments, high-fidelity simulations, and script concordance testing-to each CI component across the developmental spiral. Reengineering assessment to effectively measure CI is presented not merely as a technical adjustment but as a strategic imperative for cultivating physicians who possess the wisdom to harness algorithmic power while upholding the humanistic core of medicine, thereby ensuring safe and equitable care in the coming century.