A machine learning prediction model for total shoulder arthroplasty procedure duration: an evaluation of surgeon, patient, and shoulder-specific factors.

Journal: Journal of shoulder and elbow surgery
Published Date: Dec 21, 2024

Abstract

BACKGROUND: Operating room efficiency is of paramount importance for scheduling, cost efficiency, and to allow for the high operating volume required to address the growing demand for arthroplasty. The purpose of this study was to develop a machine learning predictive model for total shoulder arthroplasty (TSA) procedure duration and to identify factors which are predictive of a prolonged procedure.

Authors

  • Jay M Levin
    Department of Orthopaedic Surgery, Duke University, Durham, NC, USA.
  • Hamed Zaribafzadeh
    Department of Surgery, Duke University, Durham, North Carolina, USA.
  • Tom R Doyle
    Royal College of Surgeons in Ireland, Dublin, Ireland. tomdoyle22@rcsi.ie.
  • Kwabena Adu-Kwarteng
    Department of Orthopaedic Surgery, Duke University, Durham, NC, USA.
  • Kiera Lunn
    Department of Orthopaedic Surgery, Duke University, Durham, NC, USA.
  • Joshua K Helmkamp
    Department of Orthopaedic Surgery, Duke University, Durham, NC, USA.
  • Wendy Webster
    Department of Surgery, Duke University, Durham, North Carolina, USA.
  • Eoghan T Hurley
    Department of Orthopaedic Surgery, Duke University, Durham, NC, USA.
  • Jonathan F Dickens
    A. B. Anderson, B. K. Potter, J. F. Dickens, J. A. Forsberg, Department of Surgery, Division of Orthopaedics, Walter Reed National Military Medical Center, Bethesda, MD, USA.
  • Alison Toth
    Department of Orthopaedic Surgery, Duke University, Durham, NC, USA.
  • Oke Anakwenze
    Department of Orthopaedic Surgery, Duke University, Durham, NC, USA.
  • Christopher S Klifto
    Department of Orthopaedic Surgery, Duke University, Durham, NC, USA.

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