Optimizing Bed Utilization for COVID-19 Patient Flow Protocols in a Behavioral Health Inpatient Facility using Discrete Event Simulation.

In this paper, we propose utilizing a discrete event simulation model as a decision-support tool to optimize bed capacity and configuration of Geisinger's inpatient drug and alcohol treatment facility. During the COVID-19 pandemic patient flows and processes needed to adapt to new safety protocols. The existing bed configurations are not designed for social distancing and other COVID protocols. The data for this study was collected post implementation of COVID-19 protocols on patient arrivals, and process flows by level of care. The baseline model was validated and verified against retrospective data to ensure the model assumptions were reasonable. The model showed that current bed capacity can be reduced by approximately 14% and bed configurations can be modified without impacting patient flow and wait times. These results help stakeholders make data-driven decisions to reduce redundancies and realize efficiency gains while improving their ability to plan for the growth of the facility.