Q-Learning Based Adaptive Scheduling Method for Hospital Outpatient Clinics

Proper selection of the number of Service Providers (SPs) such as doctors, registration windows, and examination equipments in outpatient clinics can improve the efficiency of services and promote the sharing and effective use of medical resources. In this paper an adaptive scheduling model for hospital outpatient clinics on the number of SPs while minimizing total cost is proposed. Firstly, the M/G/K model of the outpatient queuing process is constructed based on queuing theory, where M denotes that the Poisson process of patient arrivals, the service time follows the general distribution defined as G, and K is the number of SPs. Secondly, the objective function of minimizing cost such as waiting, setup, SP usage is established. The optimal number of SP is solved with the Q-learning (QL) algorithm in reinforcement learning (RL). Finally, the simulation verifies that as the cost of service gradually increases, the system will favor fewer SPs to perform the service to reduce the total cost. This scheduling model can not only adjust the scheduling scheme according to the different service costs to maximize the economic efficiency of the hospital, but also can be used to manage the hospital staffing.