Optimizing hospital outpatient services: A comparative study of backward induction and Q-learning techniques

Abstract

This study addresses the critical issue of optimizing outpatient services in high-capacity hospitals, focusing on developing cost-effective management strategies. Utilizing a simulated model of outpatient services, this research incorporates real data from the National Health Service (NHS) to tackle practical challenges in hospital management. The methodology encompasses the application of backward induction, Q-learning, and Deep Q-Network (DQN) algorithms to formulate solutions. The findings indicate that backward induction effectively resolves simpler scenarios within the assumed conditions. In contrast, Q-learning offers a viable approach, with DQN demonstrating superior performance in addressing more complex, realistic problems. The conclusion drawn from this study is that each algorithm exhibits unique strengths in its respective operational environment. While direct comparison between the models based on output analysis is not feasible due to the variation in environmental settings, it is evident that all three algorithms significantly contribute to resolving the targeted issues in outpatient service management. This research not only provides valuable insights into hospital outpatient service optimization but also opens avenues for further exploration in the application of advanced computational techniques in healthcare management.