Reconfiguration of inpatient services to reduce bed pressure in hospitals

Abstract

Healthcare systems around the world are facing an inpatient bed crisis. This was highlighted more than ever during the recent COVID-19 pandemic. The consequences of bed shortage are substantial for both patients and staff. Finding innovative ways to improve the utilization of the existing bed base is therefore of significant importance. We focus on reconfiguration of inpatient services as a cost-effective solution to bed pressure in hospitals, and propose a comprehensive methodology for finding a low-cost configuration given a total number of beds, a set of specialties, and a finite or infinite waiting time threshold for patients. This involves developing novel approximations for performance evaluation of overflow delay and abandonment systems, and embedding them within heuristic search algorithms. We apply our reconfiguration methodology on inpatient data from a large UK hospital. Simulation experiments show that the configurations proposed by our methodology can result in significant savings compared to the existing configuration, and that a clustered overflow configuration is likely to produce the best results in many scenarios.