Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study.

Journal: JMIR AI
Published Date: Sep 8, 2023

Abstract

BACKGROUND: Accurate projections of procedural case durations are complex but critical to the planning of perioperative staffing, operating room resources, and patient communication. Nonlinear prediction models using machine learning methods may provide opportunities for hospitals to improve upon current estimates of procedure duration.

Authors

  • Samir Kendale
    Department of Anesthesia, Critical Care & Pain Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States.
  • Andrew Bishara
    Department of Anesthesia and Perioperative Care, University of California, San Francisco, San Francisco, CA, United States.
  • Michael Burns
    Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States.
  • Stuart Solomon
    Department of Anesthesiology, The University of Texas Health Science Center at San Antonio, San Antonio, TX, United States.
  • Matthew Corriere
    Department of Surgery, Section of Vascular Surgery, University of Michigan Medical School, Ann Arbor, MI, United States.
  • Michael Mathis
    Department of Anesthesiology, University of Michigan Medical School, Ann Arbor, MI, United States.

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