Bundled Care for Hip Fractures: A Machine-Learning Approach to an Untenable Patient-Specific Payment Model.

Journal: Journal of orthopaedic trauma
PMID: 30730360

Abstract

OBJECTIVES: With the transition to a value-based model of care delivery, bundled payment models have been implemented with demonstrated success in elective lower extremity joint arthroplasty. Yet, hip fracture outcomes are dependent on patient-level factors that may not be optimized preoperatively due to acuity of care. The objectives of this study are to (1) develop a supervised naive Bayes machine-learning algorithm using preoperative patient data to predict length of stay and cost after hip fracture and (2) propose a patient-specific payment model to project reimbursements based on patient comorbidities.

Authors

  • 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.
  • Damien G Billow
    Department of Orthopedic Surgery, Cleveland Clinic, Cleveland, OH.
  • Viktor E Krebs
    Department of Orthopedic Surgery, Cleveland Clinic, Cleveland, OH.
  • Prem N Ramkumar
    Department of Orthopedic Surgery, Cleveland Clinic, Cleveland, OH.

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