Utilization of Machine Learning Models to More Accurately Predict Case Duration in Primary Total Joint Arthroplasty.

Journal: The Journal of arthroplasty
PMID: 39477036

Abstract

BACKGROUND: Accurate operative scheduling is essential for the appropriation of operating room esources. We sought to implement a machine learning model to predict primary total hip arthroplasty (THA) and total knee arthroplasty (TKA) case time.

Authors

  • Gennaro DelliCarpini
    Department of Orthopedic Surgery, NYU Langone, Long Island, New York.
  • Brandon Passano
    Department of Orthopedic Surgery, NYU Langone, Long Island, New York.
  • Jie Yang
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.
  • Sallie M Yassin
    Department of Population Health, New York University School of Medicine, New York, New York.
  • Jacob C Becker
    Department of Orthopedic Surgery, NYU Langone, Long Island, New York.
  • Yindalon Aphinyanaphongs
    Department of Population Health, New York University, New York.
  • James D Capozzi
    Department of Orthopedic Surgery, NYU Langone, Long Island, New York.

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