Abstract

Background: The diagnostic services such as Magnetic Resonance Imaging (MRI) require advanced and costly technologies; and making the right managerial decisions to reduce patients’ waiting time and increase employees’ productivity in providing such services is essential. Objectives: The current study aims at optimizing the performance of the mentioned MRI department using queuing theory and simulation to increase its productivity and patients’ satisfaction. Methods: It was an applied and cross-sectional study conducted in 2015 in the MRI department of a military hospital affiliated to Baqiyatallah University of Medical Sciences, Iran, in day and night shifts in which the referral rate and the time spent in different workstations were recorded for a sample of 264 patients by Quota Sampling. After the initial evaluation of data using Excel 2013, arrival time of patients and the average time of service delivery, associated with the queuing network, in both shifts were calculated. Then, to provide practical solutions, various scenarios were modeled using Arena14.5 and the results were compared. Results: The current study results showed that the highest average of patients’ waiting time was related to turn-taking until admission (56 days) while the average time spent from admission to leaving the studiedMRIdepartment was 124 minutes. The department productivity on average was 52.5 indicating the high system capacity, which had not been used. There was dramatically reduced waiting time and increased required services in the 4th scenario indicating that the waiting time could be significantly reduced by making small changes in the human resources (changes in the working hours and the number of personnel) and the number of machines (adding an MRI machine). Conclusions: In the 4th scenario, the department and its personnel’s productivity improved and the patients’ waiting time for turn-taking until admission was eliminated by adding an MRI machine and an MRI technologist. Therefore, the implementation of this scenario is proposed, after performing cost-benefit analyses. © 2017, Shiraz University of Medical Sciences.