Pressures are steadily increasing, and have become even more critical with the COVID-19 pandemic, for healthcare delivery to provide high quality, high value, patient-centric care while simultaneously improving access and costs. Concurrently, aging and active populations’ demand for services like knee arthroplasty continue to rise. Tools and techniques including machine learning and artificial intelligence (ML/AI) using past clinical data primarily replicates existing cause-to-effect actions is no longer sufficient to forecast outcomes, costs, resource utilization and complications when radical process re-engineering like COVID-inspired telemedicine occurs (Gehlot, V., et al. 2021). To predict episodes of care for innovative arthroplasty patient journeys, a sophisticated integrated knowledge network must model optimal novel care pathways. A key first step of the patient journey is shared surgical decision making. Patient engagement is critical to successful outcomes, yet existing methods cannot model the impact of specific decision variables like interactive clinician/caregiver/patient participation in pre- and post-operative rehabilitation, or other factors like comorbidities. We demonstrate coupling of simulation and AI/ML (Greasley, A. and Edwards, J. S. 2021). for augmented intelligence musculoskeletal virtual care decisions for knee arthroplasty. This novel coupled-solution integrates critical data and information with tacit clinician knowledge (Wickramasinghe, N., Gehlot, V., and Sloane, E. B. 2020). We use coupled simulation and AI/ML modeling with a use case of musculoskeletal virtual care (MSKVC) which is integrated with knee arthroplasty. In this way we are able to show how it is possible to yield real-time integration of critical data and information, while simultaneously capturing tacit knowledge that can enhance optimal clinical outcomes and patient experience. To successfully effect such coupling we propose a taxonomy and an approach for coupling simulation models with AI/ML and data analytics models for a better clinical informatics (Figure 1) to illustrate the various facets of this couplings that can co-exist (Gehlot, V., et al. 2022).
Sloane, Elliot; Gehlot, Vijay; Wickramasinghe, Nilmini; Schaffer, Jonathan; and King, Dominic, "Better Clinical Informatics Using Coupled Simulation and AI/ML Modeling" (2022). AMCIS 2022 TREOs. 48.