Development and Validation of a Machine Learning Algorithm After Primary Total Hip Arthroplasty: Applications to Length of Stay and Payment Models.

Journal: The Journal of arthroplasty
Published Date: Dec 27, 2018

Abstract

BACKGROUND: Value-based payment programs in orthopedics, specifically primary total hip arthroplasty (THA), present opportunities to apply forecasting machine learning techniques to adjust payment models to a specific patient or population. The objective of this study is to (1) develop and validate a machine learning algorithm using preoperative big data to predict length of stay (LOS) and patient-specific inpatient payments after primary THA and (2) propose a risk-adjusted patient-specific payment model (PSPM) that considers patient comorbidity.

Authors

  • Prem N Ramkumar
    Department of Orthopedic Surgery, Cleveland Clinic, Cleveland, OH.
  • Sergio M Navarro
    Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, Said Business School, University of Oxford, Oxford, United Kingdom.
  • Heather S Haeberle
    Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, TX.
  • Jaret M Karnuta
    Department of Orthopedic Surgery, Cleveland Clinic, Cleveland, OH.
  • Michael A Mont
    Department of Orthopaedic Surgery, Lenox Hill Hospital of Northwell Health, New York, NY.
  • Joseph P Iannotti
    Department of Orthopedic Surgery, Cleveland Clinic, Cleveland, OH.
  • Brendan M Patterson
    Department of Orthopedic Surgery, Cleveland Clinic, Cleveland, OH.
  • Viktor E Krebs
    Department of Orthopedic Surgery, Cleveland Clinic, Cleveland, OH.

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