Research On Optimization Of Appointment Rules For Large-Scale Medical Diagnostic Equipment

In the post-pandemic era,the Novel Coronavirus has not completely disappeared.Outbreaks will occur on a large scale at any time,so the service capacity of large medical diagnostic equipment which is the main tool for diagnosis is often in short supply.To avoid cross-infection and improve the efficiency of equipments,most hospitals have improved their management methods by introducing appointment systems to rationalize patient diagnostic time.As appointment checking has become the focus of healthcare reform,setting up appointment rules in a scientific and reasonable manner is of utmost importance.There are irrationalities in the current appointment,including the appointment interval rules which does not conform to the patient arrival patterns and the service rule which does not consider the differences of patients.It causes so many problems such as low utilization of overloaded equipments,long waiting-time of patients and serious overtime of medical and nursing staff.This thesis learns about the current status of diagnostic appointment system through field research and divides the appointment rules into two aspects.Firstly,the scheduling rule of the medical system are optimized to obtain the optimal scheduling rule while finding the optimal appointment interval schedule.Secondly,the system is allocated to reserve a part of the examination capacity for emergency patients and the remaining part is allocated to the different types of regular appointed patients.The details of the study are as follows:To address the problem of unreasonable appointment rule which leads to low utilization of large-scale medical equipment,this thesis use the time-cost of hospital and the time cost of patient waiting represent respectively the economic and social benefits of hospitals.The objective of this thesis is striving for the lowest hospital time-cost and the highest rate of patient satisfaction.By constructing a discrete-time simulation model and solving it with a multi-objective differential evolutionary algorithm,this thesis investigates the impact of the proposed new appointment interval rules on the time cost and discusses the anti-interference of different rules under different no-show rates.After the comparison,we obtain the optimal scheduling rule is "DOME",which means longer appointment intervals in the peak period than the rest.Aiming at the process of optimizing the service rules,this thesis divides patients into two categories:emergency patients and regular appointed patients.In order to avoid upcoming emergency patients can’t access to diagnostic services in time and reduce the interference of random arrivals to regular appointed patients’ visits,a part of diagnostic capacity is reserved for emergency patients with the objective of minimizing the weighted sum of lag time and overtime time.As for the sequencing rule of different types of regular appointed patients including outpatients,inpatients and physical examination patients,a bi-objective dynamic programming is constructed.The programming takes into account the differences in examination time and arrival rate of patients.And the objective is to maximize the economic benefits and examination capacity of the hospital.It emphasizes its social responsibility based on the previous consideration of economic benefits,so as to enhance the patient experiences and improve patient satisfaction.Finally,the optimal appointment interval schduling is obtained after simulation verification and analysis of calculation results.The conclusion is that the length of the appointment interval during the peak period should be higher than the length of the rest of the time.As for the sequencing rule,it found that lag time decreases as set-aside increases but overtime increases as set-aside increases.Besides that the optimal reservation scheme is"Combination of centralization and decentralization" and it is also proved that the proposed service rules are more reasonable and effective than the first-come-first-served strategy for different types of regular patients.The proposed admission control strategy is more reasonable and effective than the FCFS strategy.