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Improving Resource Utilization for Arthroplasty Care by Leveraging Machine Learning and Optimization: A Systematic

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Machine learning (ML) models can predict outcomes for total joint arthroplasty (TJA), while optimization strategies improve surgical scheduling. These AI-driven approaches offer enhanced efficiency and cost savings in TJA care delivery.

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Area of Science:

  • Orthopedic Surgery
  • Health Informatics
  • Operations Research

Background:

  • Increasing demand for total joint arthroplasty (TJA) necessitates improved healthcare efficiency.
  • Machine learning (ML), mathematical optimization, and computer simulation offer potential solutions.
  • Current TJA care delivery faces challenges in efficiency and resource utilization.

Purpose of the Study:

  • To evaluate advanced analytics and computational modeling strategies for TJA.
  • To identify methods for improving TJA planning and overall care efficiency.
  • To assess the impact of ML and optimization on TJA outcomes and scheduling.

Main Methods:

  • Systematic review of ML models for TJA length of stay, surgery duration, and readmission prediction (MEDLINE, Embase, IEEE Xplore).
  • Scoping review of optimization strategies for elective surgical scheduling.
  • Inclusion of 20 studies for ML evaluation and 17 for scheduling optimization.

Main Results:

  • ML models, particularly neural networks, demonstrated high performance in predicting TJA outcomes.
  • Mathematical and simulation strategies significantly improved operational efficiency compared to traditional scheduling.
  • Control models generally underperformed compared to ML models.

Conclusions:

  • Advanced ML models are effective for TJA outcome prediction.
  • Mathematical optimization strategies enhance elective surgical scheduling efficiency.
  • Leveraging AI in TJA offers substantial opportunities for resource optimization and cost reduction.