Deep Learning Model for Automated Trainee Assessment During High-Fidelity Simulation.

Journal: Academic medicine : journal of the Association of American Medical Colleges
Published Date: Nov 1, 2023

Abstract

PROBLEM: Implementation of competency-based medical education has necessitated more frequent trainee assessments. Use of simulation as an assessment tool is limited by access to trained examiners, cost, and concerns with interrater reliability. Developing an automated tool for pass/fail assessment of trainees in simulation could improve accessibility and quality assurance of assessments. This study aimed to develop an automated assessment model using deep learning techniques to assess performance of anesthesiology trainees in a simulated critical event.

Authors

  • Asad Siddiqui
    A. Siddiqui is a pediatric anesthesiologist and assistant professor, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.
  • Zhoujie Zhao
    Z. Zhao was a graduate form, Department of Computer Sciences, University of Toronto, Toronto, Ontario, Canada, at the time of writing.
  • Chuer Pan
    C. Pan was an undergraduate student, Department of Engineering Sciences, University of Toronto, Toronto, Ontario, Canada, at the time of writing.
  • Frank Rudzicz
    University of Toronto, Toronto, Canada.
  • Tobias Everett
    T. Everett is a pediatric anesthesiologist and associate professor, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada.

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