Since operational cost values in health services has been increasing in time, top management of organizations have also been focusing on efficient and instructive use of resources allocated for qualifications of systems. In this study, we have tried to determine best design of service systems with optimal servers and other related components in order to minimize waiting time of patient that are demanding efficient service from the hospital. As a powerful decision support tool, simulation with PROMODEL has used to develop better effective model. Just to obtain the best model and to determine critical factors which negatively effects waiting time of patients, many alternative scenarios have been developed. At the end of thorough analysis of alternative models, it is concluded that the limited number of beds at each facility room of hospital is the main factor.
The main objective of this study is to provide effective suggestions to achieve an efficient service system which has limited resources and to minimize waiting time of patients at health centers. The basis of this research depends on real system observations such as medical treatment time, surgical operation periods, waiting time, service time, etc. The urology section of the hospital has been chosen as an application area of the study. In order to model the service system of the hospital, all facilities, such as arrivals of patients, service times, utilization of the sub departments of clinic have been monitored and necessary data have been collected. After gathering enough data from the facilities, system parameters have been estimated and a model of real system has been developed with PROMODEL which is an object oriented simulation package program.In order to develop the simulation model of the urology clinic, necessary historical data for analysis have been gathered from the information system of hospital. The infrastructure of historical data depends on observations over 800 patients. The simulation model of the clinic has been executed many times to handle alternative behaviors and produce statistics to measure and arrange the dynamics of the real system. All executed alternative models have provided very useful outcomes, such as; utilization of service, waiting time of patients, average service time, idle time of servers, arrivals rate and departure rate of patients, and the length of queues. Some of the numerical outcomes of alternative simulation experiments have shown that the average waiting time of patients that will receive service from the clinic is about for weeks. To achieve optimum facility planning and a decrease in the waiting time of patients, intensive and progressive simulation experiments have been performed. By processing data which is collected by simulation experiments, very important results for the future of service systems have been obtained. It is found out that there may be 17% efficiency increase by reviewing and redesigning the processes in Urology Clinic. There may be 76%, 57% and 52% decrease respectively for the average service times of Medical treatment, surgery and emergency surgery patients.
It can be seen that we may achieve an 18% efficiency increase only by using the simulation model results, without any extra cost. This result is the most important outcome of this study. Another important outcome of the study is the significant decrease in the average service times of patients. Considering that 92% of the patients in the Urology Clinic are surgery patients, a decrease in the average service time would decrease hospital infections and create an important achievement. A simulation system including all sections of the hospital can provide a good source of information about the potential improvements, investments, or the changes and their effects on the efficiency of the system as a whole. Moreover, the results can provide a good basis for the development of a macro plan for determination of the relationships between different hospitals in the same region. As a result, this study shows that computer aided system simulation can be utilized to obtain critical factors that have a strong affect on service performance in healthcare.
