measurement and analysis of strategies to reduce referral’s waiting time to public health centers based on simulation

normal 0 false false false en-us x-none ar-sa microsoftinternetexplorer4 background: urban living and transition of illnesses, today has led hygienic centers for dedicating services to serious challenge management of an outpatient center is very difficult. worse is that the patient advice and service time is much less than waiting time. many people to avoid long waiting go to private treatment centers. this study was done to determine ways to reduce client waiting time based on discrete event simulation. methods: this was a cross-sectional study and data were collected by questionnaire and check list and survey form whose validity was confirmed by the experts. data were completed by the relevant parts responsible. statistical data analysis was done by spss software and determining the probability distribution function of service time and their entry into the system at various places was done by professional easy fit software. findings: the total number of the subjects was 400. time to access first service varied from 1 to 250 minutes and the average was 59 minutes. average time of general physician services to people was 4 minutes and total service time varies from 1 to 20 minutes and the average was 7.5 minutes. delay in physician entrance was 15 minutes. the probability distribution function of patients’ arrival time was exponential and probability distribution function of service time was a gamma distribution function. conclusion: much of the waiting time was related to pre-starting the work of system, and also admission and physician visit. 65% of the patients waited more than 30 minutes to receive the first service and given the patient charter, it is not acceptable that 75% of the patients should have less than 30 minutes to reach their first service. to reduce the waiting time, seven scenarios were suggested for using parameters variation experiment in anylogic simulation software; these scenarios were simulated in the system. among these scenarios, the mixed scenario which changes the start time of system and admits 30% of patients through book appointment, yielded the best results .