Development and validation of an artificial intelligence system for surgical case length prediction.

Journal: Surgery
PMID: 39613655

Abstract

BACKGROUND: Accurate case length estimation is a vital part of optimizing operating room use; however, significant inaccuracies exist with current solutions. The purpose of this study was to develop and validate an artificial intelligence system for improved surgical case length prediction by applying natural language processing and machine-learning methods.

Authors

  • Adhitya Ramamurthi
    Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
  • Bhabishya Neupane
    Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI.
  • Priya Deshpande
    Department of Electrical and Computer Engineering, Opus College of Engineering, Marquette University, Milwaukee, WI, USA.
  • Ryan Hanson
    Froedtert Hospital, Milwaukee, WI.
  • Kellie R Brown
    Division of Vascular Surgery, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI.
  • Kathleen K Christians
    Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI.
  • Douglas B Evans
    Division of Surgical Oncology, Department of Surgery, Medical College of Wisconsin, Milwaukee, WI.
  • Anai N Kothari
    Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.

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