Modeling and simulation of a multi-hospital intensive care network

The Intensive Care Unit (ICU) represents a specialized sector within a hospital setting, committed to offering advanced monitoring, precise therapeutic interventions, and highly specialized nursing care for patients facing critical medical conditions that demand rigorous medical supervision and support for essential physiological functions. Nonetheless, ICUs are often constrained by a finite availability of critical resources, such as beds, specialized nursing staff, and ventilatory equipment, among others. In this thesis, I construct a simulation model to analyze the operational dynamics of a network comprising eight major ICUs in British Columbia, Canada. The focus of this thesis is development and validation of a robust discrete-event simulation model designed to estimate patient flow through individual sections of the critical care system across multiple healthcare facilities. The model includes various strategies for admitting new patients when an ICU reaches full capacity, such as utilizing overflow beds, bumping patients, or transferring patients to other hospitals. The simulation model was calibrated using real world data from the British Columbia Critical Care Database and serves as an analytical tool for planning critical care capacity in the context of endemics and pandemics such as COVID-19. This work was done in collaboration with the Ministry of Health in British Columbia.