Opportunities for machine learning to improve surgical ward safety.

Journal: American journal of surgery
Published Date: Oct 1, 2020

Abstract

BACKGROUND: Delayed recognition of decompensation and failure-to-rescue on surgical wards are major sources of preventable harm. This review assimilates and critically evaluates available evidence and identifies opportunities to improve surgical ward safety.

Authors

  • Tyler J Loftus
    Department of Surgery, University of Florida Health, Gainesville, FL. Electronic address: tyler.loftus@surgery.ufl.edu.
  • Patrick J Tighe
    Department of Anesthesiology, University of Florida College of Medicine, Gainesville, Florida, USA.
  • Amanda C Filiberto
    Department of Surgery, University of Florida Health, Gainesville, FL, USA.
  • Jeremy Balch
    Department of Surgery, University of Florida Health, Gainesville, FL, USA.
  • Gilbert R Upchurch
    TCV Division, Department of Surgery, University of Virginia Medical Center, Charlottesville, Virginia.
  • Parisa Rashidi
    Department of Biomedical Engineering, University of Florida, Gainesville, FL USA.
  • Azra Bihorac
    Department of Medicine, University of Florida, Gainesville, FL USA.

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