AI-enhanced multi-method simulation for intensive care unit digital twinning

The growing prevalence of critical illness presents an urgent public health challenge, exacerbated by the aging population and associated comorbidities. This has led to an increased demand for intensive care, alongside rising complexity in managing critically ill patients and ongoing strain on the healthcare system with limited monetary and personnel resources. This research aims to develop novel multi-method simulation, hybridizing discrete event and agent-based models with AI-informed agents for supporting operational decision making in the intensive care unit (ICU) setting. We develop an AI pipeline to cluster, model, and predict clinical trajectory phenotypes that a critically ill patient should follow. We then integrate these models in the simulation environment, allowing for runtime evaluation of AI models to drive simulated agent behavior. The data-driven patient trajectory modeling and prediction enable us to capture the interplay between patients and clinicians, thereby providing a refined assessment of clinician workload and ICU resource utilization. A case study is developed based on data from one large American academic hospital system. Using the validated model, resource management under patient surge and dynamic nurse staffing was investigated through what-if analyses, demonstrating the effectiveness of this model to support ICU operational decisions.