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Association of Interview and Holistic Review Metrics With Resident Performance-Related Difficulties in an Internal Medicine Residency Program.

Reena HemrajaniTheresa VetteseKaren LawSara D Turbow
Published in: Journal of graduate medical education (2023)
Background The utility of traditional academic factors to predict residency candidates' performance is unclear. Many programs utilize holistic review processes assessing applicants on an expanded range of application and interview characteristics. Determining which characteristics might predict performance-related difficulty in residency is needed. Objective We aim to elucidate factors associated with residency performance-related difficulty in a large academic internal medicine residency program. Methods In 2022, we conducted a retrospective cohort study of Electronic Residency Application Service and interview data for residents matriculating between 2018 and 2020. The primary outcome was a composite of performance-related difficulty during residency (referral to the Clinical Competency Committee; any rotation evaluation score of 2 out of 5 or lower; and/or a confidential "comment of concern" to the program director). Logistic regression models were fit to assess associations between resident characteristics and the composite outcome. Results Thirty-eight of 117 residents met the composite outcome. Gold Humanism Honor Society (odds ratio [OR] 0.24, 95% confidence interval [CI] 0.16-0.87) or Alpha Omega Alpha (OR 0.36, 95% CI 0.14-0.99) members were less likely to have performance-related difficulty, as were residents with higher United States Medical Licensing Examination Step 2 Clinical Knowledge scores (OR 0.97, 95% CI 0.47-1.00). One-point increases in general faculty overall interview score, leadership competency score, and leadership overall score were associated with 41% to 63% lower odds of meeting the composite outcome. Interview or file review "flags" had an OR of 2.82 (95% CI 1.37-5.80) for the composite outcome. Conclusions Seven metrics were associated with the composite outcome of resident performance-related difficulty.
Keyphrases
  • quality improvement
  • medical students
  • healthcare
  • patient safety
  • public health
  • machine learning
  • deep learning
  • tyrosine kinase
  • artificial intelligence