Outpatient Scheduling For Multiple Examinations

With the development of society,the contradiction between the continual increasing demand for health and shortage of medical resource results in the issue of difficult access to quality medical services.As the forefront of the medical services,the managers in the outpatient department are usually under high pressure.Timely Examinations are important for the patients to be properly diagnosed and treated.Managers in the overcrowded hospitals are facing the problems and challenges to make advance appointments for different types of outpatients to meet their waiting time target.In addition,limited resources of examination equipment need to be fully utilized.Different urgency levels of patients,different requirement of examinations,and patients' behavioral,for example,no-show,make the patients scheduling difficult to solve.To deal with this problem,this paper starts from two different examinations,two urgency levels and patients' no-show and physicians' overtime,and proposes a discount-cost Markov Decision Process(MDP)with the objective to maximize the expected revenue from examining the patients and minimize the overtime penalty.Due to the complexity of the MDP model,it is difficult to analyze structural properties of the optimal control policies.Therefore,numerical results of MDP are solved.Based on the observed structural properties of the numerical optimal control policies,this paper proposes two parameterized heuristics for patient scheduling,where the parameters are improved by using Genetic Algorithm.Numerical experiments compare the optimal control policy,the two heuristics,and the First-Come-First-Serve(FCFS)rule.Numerical results show that the performance of the proposed heuristics are within 10% of deviation from the optimal control policy.When the workload of the system is quite high,the proposed heuristics are much better than FCFS.Finally,the above model will be extended to multiple types of examinations and multiple urgency level,with patients' no-show and overtime.A heuristic algorithm using Virtual Nesting policy is built,the upper bound of the problem is obtained by constructing a stochastic programming model to assess the heuristic algorithm.Compared with the FCFS policy,the results show the performance of the Nested Threshold Policy are much better than FCFS,especially when the workload is very high,nearly 50% lift.This study is based on the long-term cooperation with a top hospital in Shanghai.The goal is to provide theoretical guidance,which has significant academic value and real meanings for the better operation of the hospital management.