Deep Reinforcement Learning Based Cost-Benefit Analysis for Hospital Capacity Planning

Abstract

The stochastic nature of hospital bed demands and population growth rate in high migration areas poses significant challenges for the authorities to devise an appropriate hospital augmentation scheme. In this study, we propose a deep reinforcement learning (DRL) based model that can identify an appropriate hospital expansion plan for a particular geographical region of interest. Our proposed model analyzes the cost-benefit over a range of geographic regions and recommends the best capacity expansion area. We consider hospital bed numbers as a capacity determiner and population demographics for analyzing future demands economics in our approach. We divide a concerned geographic region into several sub-regions based on the local administrative body to recommend a sub-region where augmentation is necessary. The RL agent then works based on the age group, population growth, and current bed capacity utilizing the Advantage Actor-Critic (A2C) algorithm to minimize the cumulative cost. We also implemented our proposed approach for a case study in the Tampa Bay region, Florida, USA, to identify a hospital augmentation plan. The results from the case study verify this approach's superiority over traditional per capita-based and complaint-based policies.