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.

Machine learning based forecast for the prediction of inpatient bed demand.

Background: Overcrowding is a serious problem that impacts the ability to provide optimal level of care in a timely manner. High patient volume is known to increase the boarding time at the emergency department (ED), as well as at post-anesthesia care unit (PACU). Furthermore, the same high volume increases inpatient bed transfer times, which causes delays in elective surgeries, increases the probability of near misses, patient safety incidents, and adverse events.