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Educator's blueprint: Holistic applicant file review in undergraduate and postgraduate medical education.

Eric F ShappellKeme CarterYoon Soo ParkMicheal Gottlieb
Published in: AEM education and training (2023)
Medical schools and graduate medical education programs are tasked each year with selecting the next class of trainees, often from large applicant pools with enormous quantities of data to be processed. Review of applicant files must therefore be efficient, equitable, and effective in maximizing the likelihood of trainee success and alignment with institutional missions and values. In this article, we discuss 10 strategies to optimize the file review process for trainee selection. Using these strategies, educators can ensure rigorous and accountable file review processes for their training programs.
Keyphrases
  • medical education
  • public health
  • electronic health record
  • machine learning
  • deep learning
  • nursing students