Improving Operating Room Efficiency: Machine Learning Approach to Predict Case-Time Duration.

Journal: Journal of the American College of Surgeons
Published Date: Oct 1, 2019

Abstract

BACKGROUND: Accurate estimation of operative case-time duration is critical for optimizing operating room use. Current estimates are inaccurate and earlier models include data not available at the time of scheduling. Our objective was to develop statistical models in a large retrospective data set to improve estimation of case-time duration relative to current standards.

Authors

  • Matthew A Bartek
    Department of General Surgery, University of Washington, Seattle, WA. Electronic address: bartek@uw.edu.
  • Rajeev C Saxena
    Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA.
  • Stuart Solomon
    Department of Anesthesiology, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States.
  • Christine T Fong
    Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA.
  • Lakshmana D Behara
    Perimatics LLC, Bellevue, WA.
  • Ravitheja Venigandla
    Perimatics LLC, Bellevue, WA.
  • Kalyani Velagapudi
    Perimatics LLC, Bellevue, WA.
  • John D Lang
    Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA.
  • Bala G Nair
    Department of Anesthesiology and Pain Medicine, University of Washington, Seattle, WA.

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