Reduce Er Wait Time

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USING COMPUTER SIMULATION MODELING TO REDUCE WAITING TIMES IN EMERGENCY DEPARTMENTS
Igor Georgievskiy, Alcorn State University Zhanna Georgievskaya, Alcorn State University William Pinney, Alcorn State University ABSTRACT This paper examines the wide-spread problem of extended waiting times for health services, in the context of the Emergency Department (ED) at a regional hospital. In the first phase of the study, a field observation was conducted to document the current operation of the ED. The second phase of the study will be the building and validation of a Flexsim computer model of the ED for modeling, analysis, visualization, and optimization of the patient flow within the ED. The validity of the model will be established by
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A quantitative analysis of the wait time problem in an emergency room is dependent upon the identification of a methodology which recognizes the structure of the problem as that of a queuing system. Two modes of analysis are generally suggested by the structure of this type of problem: queuing models and discrete event simulations. Over the past thirty years, a significant amount of research has been done in the area of discrete-event simulation modeling in health care. Resent innovations in object-oriented models enable the construction of large integrated systems that become powerful tools for analysis of and innovations in health care systems (Jun, et al, 1999).

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In some studies, researchers have generated models that were able to make accurate predictions of quantities such as waiting room times and patient care times. One of such model that was developed by Rossetti et al. (1999) used the Emergency Department at the University of Virginia Medical Center in Charlottesville as a case study model. This model was used to test alternative ED attending physician staffing schedules and their impacts on patient flow and resource utilization. Shift modification was also tested in the McGuire (1997) study (Emergency Services department in a SunHealth Alliance hospital), which allows choosing a solution that reduces average length of stay for patients by up to 50 minutes. Lloyd G. Connelly and Aaron E. Bair (2006) used the Extend DES modeling package (EDSIM) to
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