Dynamic Allocation of Medical Examination Resources in Hospitals with Varying Appointment Preferences

Appointment systems have been widely utilized across various domains, particularly in healthcare systems. However, the dynamic allocation of appointment resources has not yet been fully studied. Dynamic resource allocation refers to the process of continuously reallocating future resources based on the evolving appointment status. This study addresses the dynamic allocation of examination resources between outpatient and inpatient departments in large general hospitals, where imbalanced resource distribution often leads to prolonged appointment lead times and inefficiencies. By incorporating diverse patient preferences, this study formulates the problem as a Markov Decision Process (MDP) and solves it using a reinforcement learning approach, specifically the Proximal Policy Optimization (PPO) algorithm. The proposed dynamic strategy dynamically adapts resource allocation based on real-time patient appointment status, optimizing the appointment lead time. Numerical experiments demonstrate the superiority of the dynamic resource allocation strategy over static allocation strategy and random allocation strategy in different scenarios. Particularly under high patient volumes and complex decision spaces, reinforcement learning-based dynamic resource allocation strategy exhibits excellent effectiveness and strong robustness.