Optimizing Teleconsultation Scheduling with a Two-Level Approach Based on Reinforcement Learning

Using advanced communication and information technologies, teleconsultation can provide high-quality healthcare services to remote areas. To enhance service efficiency, this study develops a two-level dynamic scheduling model for teleconsultation, which prioritizes optimizing service frequency and incorporates downstream room utilization and overtime risk as considerations. The first-level model is a data-driven framework that optimizes the frequency by adjusting service start times. Based on the solutions of the first-level model, a second-level model is built to assign teleconsultation rooms to departments with demands and reduce the total overtime risk and and room opening cost. For solving, an integer programming (IP) solver is embedded in a deep reinforcement learning (DRL) approach. A presorting mechanism of interval constraints is proposed to improve the quality of solutions. For verification, actual teleconsultation data are used as samples. The experimental results demonstrate the effectiveness of the proposed two-level model, the embedded solving algorithm, and the interval constraint presorting mechanism. Compared with real schedules, the two-level model can reduce four service scheduling performance criteria, including demand average waiting time, number of services, risk of overtime, and number of rooms used. As a result, the efficiency of teleconsultation is improved to promote its development.