Optimizing surgical efficiency: predicting case duration of common general surgery procedures using machine learning.

Journal: Surgical endoscopy
Published Date: Aug 1, 2025

Abstract

BACKGROUND: Accurate prediction of surgical duration is critical to optimizing use of operating room resources. Currently, cases are scheduled using subjective estimates of length by surgeons, relying heavily on prior experience. This study aims to develop and compare various predictive models-from conventional statistics to machine learning-based algorithms-to accurately and objectively predict case duration for common elective general surgical procedures.

Authors

  • Michelle Kwong
    Department of Anesthesiology and Pain Medicine, University of Alberta, Edmonton, Canada.
  • Mohammad Noorchenarboo
    Department of Electrical and Computer Engineering, Western University, London, Canada.
  • Katarina Grolinger
  • Jeff Hawel
    Department of Surgery, Western University, London, Canada.
  • Christopher M Schlachta
    London Health Sciences Centre, Canadian Surgical Technologies & Advanced Robotics, London, Ontario, Canada.
  • Ahmad Elnahas
    Department of Surgery, Western University, London, Canada. Ahmad.Elnahas@lhsc.on.ca.

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