Preoperative Prediction of Value Metrics and a Patient-Specific Payment Model for Primary Total Hip Arthroplasty: Development and Validation of a Deep Learning Model.

Journal: The Journal of arthroplasty
PMID: 31122849

Abstract

BACKGROUND: The primary objective was to develop and test an artificial neural network (ANN) that learns and predicts length of stay (LOS), inpatient charges, and discharge disposition for total hip arthroplasty. The secondary objective was to create a patient-specific payment model (PSPM) accounting for patient complexity.

Authors

  • Prem N Ramkumar
    Department of Orthopedic Surgery, Cleveland Clinic, Cleveland, OH.
  • Jaret M Karnuta
    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.
  • Richard Iorio
    Department of Orthopaedic Surgery, Brigham & Women's Hospital, Boston, MA.
  • Michael A Mont
    Department of Orthopaedic Surgery, Lenox Hill Hospital of Northwell Health, New York, NY.
  • 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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