Document Type : Original Article
Authors
1 General Practitioner, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
2 Associate Professor, Operation Research, Department of Management, School of Administrative and Economics, University of Isfahan AND Health Management and Economics Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
Abstract
Introduction: It is clear that if the discharge process takes less time and the patient leaves the hospital bed earlier, the length of hospitalization, and consequently, the waiting time for admission decreases. Therefore, the method of use of treatment facilities is somewhat improved. The hospital discharge process, from when the discharge authorization is issued until the patient leaves the hospital, is a bottleneck in hospital procedures. The aim of this study was to reduce the waiting time for patient discharge from hospitals in Alzahra Hospital in Isfahan, Iran. Methods: This was an empirical and descriptive-analytical research in which the data collected in the summer of 2010 was analyzed using the queueing network model. The required data consisted of the time of starting and ending each activity related to the discharge process, from 10:15 am on 10 selected days (related to 200 patients). The collected data were recorded in the related table. The required parameters for the stochastic distribution were estimated by the mean of the collected times. Statistical distribution of duration between discharge and different services were analyzed using Kolmogorov-Smirnov test in SPSS software. Evaluation of services provided in the discharge process, areas of improvement in procedures, and reduction of time spent on different procedures was conducted through analysis of the related queueing network. Results: The analysis of the discharge process procedures showed that the main factors affecting average waiting time were patients’ financial problems, unnecessary activities, and delay in writing the medical record abstract and insurance confirmation. Conclusion: Using the queuing network model, scenarios on improving the discharge process and reducing the waiting time are proposed, which are applicable in many other hospitals.
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