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Proceedings of the 2013 Winter Simulation Conference R. Pasupathy, S.-H. Kim, A. Tolk, R. Hill, and M. E. Kuhl, eds MINIMIZING FLOW-TIME AND TIME-TO-FIRST-TREATMENT IN AN EMERGENCY DEPARTMENT THROUGH SIMULATION Seifu Chonde Carlos Parra Chia-Jung Chang Harold and Inge Marcus Department of Industrial and Systems Engineering The Pennsylvania State University University Park, PA 16802, USA ABSTRACT Emergency Department management is a resource constrained environment that has gained attention in recent years. An in-depth literature review was conducted and two patient flow models, Virtual Streaming (VS) and Physician Directed Queuing (PDQ), were selected to be contrasted against a FIFO-baseline model using discrete event simulation. Scenarios were constructed by assigning doctors to 4-hour shifts. Model performance was ranked by finding the minimum aggregated time to first treatment (TTFT) of admitted patients and the length of stay (LOS) of discharged patients. The benefits from PDQ were seen largely by Emergency Severity Index (ESI) 4 and 5 patients and the benefits from VS were seen largely by ESI 2 and 3 patients. Results suggest VS for the patient mix used herein when the system is near ca- pacity and the baseline when the system is not near capacity. However, trade-offs and improvements of these models are discussed. 1 INTRODUCTION AND MOTIVATION A traditional Emergency Department (ED) is one that specializes in acute care and services patients using a process of medical triage. Medical triage is a systematic way for nurses or other medical practitioners to prioritize patients. The Emergency Severity Index (ESI) is often used to systematically triage patients based on patient acuity and resource requirements in the United States (Pitts et al. 2008). In these EDs nurses determine ESI levels for patients and, subsequently, these patients are seen according to the ESI level then using a first in, first out rule (FIFO). The ESI algorithm classifies patients using a five level scale. Patients classified as ESI 1 are in a critical, life-threatening state. Patients classified as ESI 2 are in a critical, but not life-threatening state. Patients classified as ESI 3, 4, or 5 represent patients in a non- critical state stratified by the amount of resources required for treatment. The ED is the most important department in evaluating hospital performance (McClelland 2011). Pitts et al. (2008) reported that between 1996 and 2006 the number of EDs in the U.S. decreased 5% from 4,019 to 3,833 leading to overcrowding. This has led to the need for intelligent scheduling of resources. Improved resource management strategies are necessary to combat the increasing costs of healthcare and overutilization of EDs. Discrete Event Simulation (DES) has been shown to be a valuable tool in evaluat- ing the performance of healthcare management strategies (Katsaliaki and Mustafee 2011). More specifi- cally, DES has been proven to help in patient scheduling and admissions; patient flow; resource availabil-
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