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A Systematic Review of Metrics Utilized in the Selection and Prediction of Future Performance of Residents in the United States.

Jeremy M LipmanColleen Y ColbertRendell W AshtonJudith C FrenchChristine WarrenMonica Yepes-RiosRachel S KingS Beth BiererTheresa KlineJames K Stoller
Published in: Journal of graduate medical education (2023)
Background Aligning resident and training program attributes is critical. Many programs screen and select residents using assessment tools not grounded in available evidence. This can introduce bias and inappropriate trainee recruitment. Prior reviews of this literature did not include the important lens of diversity, equity, and inclusion (DEI). Objective This study's objective is to summarize the evidence linking elements in the Electronic Residency Application Service (ERAS) application with selection and training outcomes, including DEI factors. Methods A systematic review was conducted on March 30, 2022, concordant with PRISMA guidelines, to identify the data supporting the use of elements contained in ERAS and interviews for residency training programs in the United States. Studies were coded into the topics of research, awards, United States Medical Licensing Examination (USMLE) scores, personal statement, letters of recommendation, medical school transcripts, work and volunteer experiences, medical school demographics, DEI, and presence of additional degrees, as well as the interview. Results The 2599 identified unique studies were reviewed by 2 authors with conflicts adjudicated by a third. Ultimately, 231 meeting inclusion criteria were included (kappa=0.53). Conclusions Based on the studies reviewed, low-quality research supports use of the interview, Medical Student Performance Evaluation, personal statement, research productivity, prior experience, and letters of recommendation in resident selection, while USMLE scores, grades, national ranking, attainment of additional degrees, and receipt of awards should have a limited role in this process.
Keyphrases
  • quality improvement
  • case control
  • mental health
  • healthcare
  • patient safety
  • public health
  • systematic review
  • high throughput
  • virtual reality
  • type diabetes
  • current status
  • medical students
  • glycemic control