Provide a simulation model to evaluate, improve performance and reduce waiting time for patients in emergency department Khorramabad Nomadic Martyrs Educational and Medical Center

Since the emergency department is considered as a vital and important department of any hospital, so the study of service delivery processes in the emergency department has a significant role in patient satisfaction. The purpose of this study is to identify the factors affecting the performance of the emergency department and determine the existing bottlenecks and provide appropriate strategies to improve performance and reduce patient waiting time using a simulation approach.

This research is descriptive-analytical. The statistical population of this study was all patients referred to the emergency department of Khorramabad Nomadic Martyrs Educational and Medical Center in a period of three months, which was performed cross-sectionally on 200 patients. First, the processes of the emergency department were identified and then the time of patientschr('39') arrival and the time of providing services of the emergency department processes in a quarterly interval were randomly measured in three shifts of morning, evening and night. Then the current situation was designed and simulated using Arena software. By implementing the simulated model, bottlenecks in the emergency department were identified and 6 scenarios were designed and implemented to clear the bottlenecks, improve emergency performance and reduce patient waiting time, and the results were compared and analyzed.

Based on the output of the simulated model, laboratory processes, patient discharge and ultrasound-radiology processes with an average of 174, 71 and 27 minutes of waiting queue were identified as the main bottlenecks. Based on this, 6 scenarios were designed and implemented in the simulation model. According to the simulation results, scenario F with an average decrease of 1.8 hours had the highest decrease in average waiting time and scenario C with a decrease in waiting time of 0.6 hours had the lowest decrease in average patient waiting time compared to other scenarios. Also, Scenario B had the lowest and Scenario F had the highest patient output compared to other scenarios. However, scenario C, which had the least decrease in patient waiting time, has an increase of 11.67% in patient output compared to the current situation.

Scenario F, E and A have better results than other scenarios, respectively, and due to resource and cost constraints in scenarios F and E, the implementation of Scenario A (adding 2 laboratory experts) leads to a reduction in the average waiting time of patients for 1.43 hours compared to the current situation, is recommended.