Optimizing Responsive Hospital Operations: A New Process for Simulating Uncertain Treatment Demand

Hospitals face growing challenges in optimizing resource allocation to meet diverse stakeholder demands under conditions of uncertainty and variability. A pioneering approach to address this issue was recently introduced by Suo et al. (2024b), which, despite its contributions, relied on a limited and predefined set of demand scenarios due to the difficulty of realistically generating a demand model that accurately reflects the highly dynamic nature of real hospital demand. To overcome this challenge, this study presents a novel process that utilizes the Monte Carlo method to model and forecast hospital treatment demand. The process is demonstrated through the simulation of surgical demand in a fictitious yet realistic animal hospital. Using hypothetical data spanning a one-year period, the variability in demand across various surgeries and animal species was modeled, showcasing the method’s ability to simulate realistic scenarios within a one-month timeframe. The findings highlight the process’s potential to enhance decision-making and optimize resource allocation strategies in healthcare settings. The insights gained from this study provide a solid foundation for expanding probabilistic modeling to broader healthcare contexts, offering valuable guidance for future research in this field.