Implementation of Machine Learning to Predict Cost of Care Associated with Ambulatory Single-Level Lumbar Decompression.

Journal: World neurosurgery
PMID: 36089278

Abstract

BACKGROUND: With the emergence of the concept of value-based care, efficient resource allocation has become an increasingly prominent factor in surgical decision-making. Validated machine learning (ML) models for cost prediction in outpatient spine surgery are limited. As such, we developed and internally validated a supervised ML algorithm to reliably identify cost drivers associated with ambulatory single-level lumbar decompression surgery.

Authors

  • Harold I Salmons
    Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota, USA. Electronic address: salmons.harold@mayo.edu.
  • Yining Lu
    Department of Orthopaedic Surgery, Mayo Clinic, Rochester, Minnesota, U.S.A.. Electronic address: lu.yining@mayo.edu.
  • Ryder R Reed
    Department of Orthopedic Surgery, Mayo Clinic, Rochester, Minnesota, USA.
  • Brian Forsythe
    Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, USA.
  • Arjun S Sebastian
    Mayo Clinic, Rochester, Minnesota, United States.

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