Optimization-simulation framework to optimize hospital bed allocation in academic medical centers

Congestion, overcrowding, and increasing patient wait times are major challenges that many large, academic centers currently face. To address these challenges, hospitals must effectively utilize available beds through proper strategic bed allocation and robust operational day-to-day bed assignment policies. Since patient daily demand for beds is highly variable, it is frequent that the physical capacity allocated to a given clinical service is not sufficient to accommodate all of the patients who belong to that service. This situation could lead to extensive wait time of patients in various locations in the hospital (e.g., the emergency department), as well as clinically and operationally undesirable misplacements of patients in hospital floors/beds that are managed by other clinical services than the ones to which the patients belong. In this thesis, we develop an optimization-simulation framework to optimize the bed allocation at Mass General Hospital. Detailed, data-driven simulation suggests that the newly proposed bed allocation would lead to significant reduction in patient intra-day wait time in the emergency department and other hospital locations, as well as a major reduction in the misplacements of patients in the Medicine service, which is the largest service in the hospital. We employ a two-pronged approach. First, we developed a detailed simulation setting of the entire hospital that could be used to assess the effectiveness of day-to-day operational bed assignment policies given a specific bed allocation. However, the simulation does not allow tractable optimization that seeks to find the best bed allocation among all possible allocations. This motivates the development of a network-flow/network design inspired mixed integer program that approximates the operational performance of bed allocations and allows us to effectively search for approximately the best allocation. The mixed integer program can be solved via a scenario sampling approach to provide candidate bed allocations. These are then tested and evaluated via the simulation setting. These tools facilitate expert discussions on how to modify the existing bed allocation at MGH to improve the day-to-day performance of the bed assignment process.