Using Machine Learning to Evaluate Attending Feedback on Resident Performance.

Journal: Anesthesia and analgesia
Published Date: Feb 1, 2021

Abstract

BACKGROUND: High-quality and high-utility feedback allows for the development of improvement plans for trainees. The current manual assessment of the quality of this feedback is time consuming and subjective. We propose the use of machine learning to rapidly distinguish the quality of attending feedback on resident performance.

Authors

  • Sara E Neves
    From the Department of Anesthesiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
  • Michael J Chen
    From the Department of Anesthesiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts.
  • Cindy M Ku
    Department of Anesthesiology, Queen's Medical Center, Honolulu, Hawaii.
  • Suzanne Karan
    Department of Anesthesiology, University of Rochester Medical Center, Rochester, New York.
  • Amy N DiLorenzo
    Department of Anesthesiology, University of Kentucky College of Medicine, Lexington, Kentucky.
  • Randall M Schell
    Department of Anesthesiology, University of Kentucky College of Medicine, Lexington, Kentucky.
  • Daniel E Lee
    Department of Anesthesiology and Pediatrics, University of California, San Diego, San Diego, California.
  • Carol Ann B Diachun
    Department of Anesthesiology, University of Florida-Jacksonville, Jacksonville, Florida.
  • Stephanie B Jones
    Department of Anesthesiology, Albany Medical College, Albany, New York.
  • John D Mitchell
    From the Department of Anesthesiology, Beth Israel Deaconess Medical Center, Boston, Massachusetts.

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