Healthcare Optimization and Augmented Intelligence by Coupling Simulation & Modeling: An Ideal AI/ML Partnership for a Better Clinical Informatics

Healthcare must deliver high quality, high value, patient-centric care while improving access and costs even as aging and active populations increase demand for services like knee arthroplasty. Machine learning and artificial intelligence (ML/AI) using past clinical data primarily replicates existing cause-to-effect actions. This is insufficient to forecast outcomes, costs, resource utilization and complications when radical process re-engineering like COVID- inspired telemedicine occurs. To predict episodes of care for innovative arthroplasty patient journeys, a sophisticated integrated knowledge network must model optimal novel care pathways. We focus on the first step of the patient journey: shared surgical decision making. Patient engagement is critical to successful outcomes, yet existing methods cannot model impact of specific decision variables like interactive clinician/caregiver/patient participation in pre- and post-operative rehabilitation, and other factors like comorbidities. We demonstrate coupling of simulation and AI/ML for augmented intelligence musculoskeletal virtual care decisions for knee arthroplasty. This novel coupled-solution integrates critical data and information with tacit clinician knowledge.