Author/Authors :
Akbari Haghighinejad، Hourvash نويسنده Student Research Committee, Department of Community Medicine, Shiraz University of Medical Sciences, Shiraz, Iran; , , Kharazmi، Erfan نويسنده Department of Health Administration, School of Management and Information Sciences, Shiraz University of Medical Sciences, Shiraz, Iran; , , Hatam، Nahid نويسنده School of Health Administration and Medical Information, Shiraz University of Medical Sciences, Shiraz, Iran , , Yousefi، Sedigheh نويسنده Research and Development Committee, Shiraz University of Medical Sciences, Shiraz, Iran; , , Hesami، Seyed Ali نويسنده Student Research Committee, Department of Community Medicine, Shiraz University of Medical Sciences, Shiraz, Iran; , , Danaei، Mina نويسنده Shiraz University of Medical Sciences, Shiraz , , Askarian، Mehrdad نويسنده ,
Abstract :
Background: Hospital emergencies have an essential role in health care systems. In the last decade, developed countries have paid great attention to overcrowding crisis in emergency departments. Simulation analysis of complex models for which conditions will change over time is much more effective than analytical solutions and emergency department (ED) is one of the most complex models for analysis. This study aimed to determine the number of patients who are waiting and waiting time in emergency department services in an Iranian hospital ED and to propose scenarios to reduce its queue and waiting time.
Methods: This is a cross-sectional study in which simulation software (Arena, version 14) was used. The input information was extracted from the hospital database as well as through sampling. The objective was to evaluate the response variables of waiting time, number waiting and utilization of each server and test the three scenarios to improve them.
Results: Running the models for 30 days revealed that a total of 4088 patients left the ED after being served and 1238 patients waited in the queue for admission in the ED bed area at end of the run (actually these patients received services out of their defined capacity). The first scenario result in the number of beds had to be increased from 81 to179 in order that the number waiting of the “bed area” server become almost zero. The second scenario which attempted to limit hospitalization time in the ED bed area to the third quartile of the serving time distribution could decrease the numberwaiting to 586 patients.
Conclusion: Doubling the bed capacity in the emergency department and consequently other resources and capacity appropriately can solve the problem. This includes bed capacity requirement for both critically ill and less critically ill patients. Classification of ED internal sections based on severity of illness instead of medical specialty is another solution.
