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Treatment of Ankle Osteoarthritis with Total Ankle Replacement Through a Lateral Transfibular Approach
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Advanced decision-making using patient-reported outcome measures in total joint replacement.

Prakash Jayakumar1, Kevin J Bozic1

  • 1Department of Surgery and Perioperative Care, Dell Medical School, University of Texas at Austin, Austin, Texas.

Journal of Orthopaedic Research : Official Publication of the Orthopaedic Research Society
|January 30, 2020
PubMed
Summary
This summary is machine-generated.

A new tool uses patient data and AI to predict risks and benefits for total joint replacement (TJR), improving shared decision-making and patient outcomes. This technology aims to reduce inappropriate procedures and enhance patient satisfaction.

Keywords:
ArthroplastyArtificial intelligenceHip/Knee osteoarthritisPROMSShared decision making

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Area of Science:

  • Orthopedic surgery
  • Health informatics
  • Decision science

Background:

  • A significant portion of total joint replacement (TJR) procedures may be unnecessary, leading to patient dissatisfaction.
  • Patient-reported outcome measures (PROMs) are crucial for understanding patient well-being.
  • Leveraging patient data with advanced analytics can improve decision-making in healthcare.

Purpose of the Study:

  • To introduce a novel shared decision-making tool for TJR.
  • To assess the impact of this tool on decision quality, shared decision-making, and patient outcomes via a randomized controlled trial.
  • To explore the integration of patient-reported data and artificial intelligence for enhanced clinical decision support.

Main Methods:

  • Development of a shared decision-making tool integrating PROMs, clinical, and demographic data.
  • Protocol for a randomized controlled trial to evaluate the tool's effectiveness.
  • Analysis of personalized risk-benefit predictions for TJR.

Main Results:

  • The study protocol is designed to measure improvements in decision quality and shared decision-making.
  • The tool aims to provide personalized predictions to inform TJR procedures.
  • The trial will assess various patient and process outcomes related to TJR.

Conclusions:

  • Shared decision-making is vital in TJR to improve patient satisfaction and reduce inappropriate procedures.
  • Integrating patient-reported outcomes and artificial intelligence offers a promising avenue for decision support.
  • This research protocol outlines a rigorous evaluation of a novel decision-making tool in TJR.