Can a Large Language Model Interpret Data in the Electronic Health Record to Infer Minimum Clinically Important Difference Achievement of Knee Osteoarthritis Outcome Score-Joint Replacement Score Following Total Knee Arthroplasty?

Journal: The Journal of arthroplasty
Published Date: Mar 24, 2025

Abstract

BACKGROUND: Obtaining total knee arthroplasty patient-reported outcomes for quality assessment is costly and difficult. We asked whether a large language model (LLM) could interpret electronic health record notes to differentiate patients attaining a 1-year minimum clinically important difference (MCID) for the Knee Osteoarthritis Outcome Score-Joint Replacement (KOOS-JR) from those who did not. We also investigated whether sufficient information to infer MCID achievement exists in the chart by having a blinded orthopaedic surgeon make the same determination.

Authors

  • Abdul K Zalikha
    Department of Orthopedic Surgery, Stanford University and VA Palo Alto Health Care System, Palo Alto, California.
  • Thomas S Hong
    Department of Orthopedic Surgery, Stanford University and VA Palo Alto Health Care System, Palo Alto, California.
  • Easton A Small
    Department of Orthopedic Surgery, Stanford University and VA Palo Alto Health Care System, Palo Alto, California.
  • Michael Constant
    Department of Orthopedic Surgery, Stanford University and VA Palo Alto Health Care System, Palo Alto, California.
  • Alex H S Harris
    Department of Orthopedic Surgery, Stanford University and VA Palo Alto Health Care System, Palo Alto, California.
  • Nicholas J Giori
    Department of Orthopedic Surgery, Stanford University and VA Palo Alto Health Care System, Palo Alto, California.

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