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This study introduces a novel approach combining simulation and artificial intelligence (AI) to optimize knee arthroplasty patient journeys. It enhances decision-making for personalized virtual care, improving patient outcomes and healthcare value.

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Area of Science:

  • Orthopedic Surgery
  • Health Informatics
  • Artificial Intelligence

Background:

  • Rising demand for knee arthroplasty necessitates innovative care models.
  • Current machine learning/AI models struggle to predict outcomes during radical care pathway changes.
  • Patient engagement is crucial but existing methods lack the ability to model its impact on decision variables.

Purpose of the Study:

  • To develop a sophisticated integrated knowledge network for predicting optimal, novel care pathways in knee arthroplasty.
  • To model the impact of patient engagement and decision variables on surgical outcomes.
  • To demonstrate a coupled simulation and AI/ML solution for augmented intelligence in musculoskeletal virtual care.

Main Methods:

  • Coupling simulation with AI/ML to create an augmented intelligence system.
  • Integrating critical data and tacit clinician knowledge into a unified network.
  • Focusing on the shared surgical decision-making phase of the patient journey.

Main Results:

  • Demonstrated a novel coupled-solution for augmented intelligence in virtual care decisions.
  • Integrated diverse data sources and clinician expertise for enhanced predictive capabilities.
  • Laid the groundwork for modeling innovative arthroplasty patient journeys.

Conclusions:

  • The coupled simulation and AI/ML approach offers a sophisticated method for optimizing knee arthroplasty patient journeys.
  • This integrated knowledge network can improve prediction of outcomes, costs, and resource utilization.
  • This technology supports patient-centric, high-value care in the face of increasing healthcare demands.