Discrete Event Simulation for Healthcare Organizations: A Tool for Decision Making

EXECUTIVE SUMMARY Healthcare organizations face challenges in efficiently accommodating increased patient demand with limited resources and capacity. The modern reimbursement environment prioritizes the maximization of operational efficiency and the reduction of unnecessary costs (i.e., waste) while maintaining or improving quality. As healthcare organizations adapt, significant pressures are placed on leaders to make difficult operational and budgetary decisions. In lieu of hard data, decision makers often base these decisions on subjective information. Discrete event simulation (DES), a computerized method of imitating the operation of a real-world system (e.g., healthcare delivery facility) over time, can provide decision makers with an evidence-based tool to develop and objectively vet operational solutions prior to implementation. DES in healthcare commonly focuses on (1) improving patient flow, (2) managing bed capacity, (3) scheduling staff, (4) managing patient admission and scheduling procedures, and (5) using ancillary resources (e.g., labs, pharmacies). This article describes applicable scenarios, outlines DES concepts, and describes the steps required for development. An original DES model developed to examine crowding and patient flow for staffing decision making at an urban academic emergency department serves as a practical example. INTRODUCTION Discrete event simulation (DES) is a type of computer simulation that imitates the operation of a real-world system. This type of virtualization provides users with broad capability to examine performance measures for potential operational changes and to plan for new or changing facilities. Performance measures comprise the output of the DES model and commonly include patient throughput, timeliness of care (i.e., extent of waiting), and resource utilization (e.g., bed occupancy, magnetic resonance imaging [MRI] utilization, nurse utilization). DES allows administrators and managers to analyze the interaction of management priorities and the impact of operational decisions. For example, DES is able to demonstrate the negative impact that high rates of bed occupancy (i.e., resource utilization) may have on patient waiting and throughput measures in the emergency department (ED). The accuracy and applicability of DES design depend on the performance measures targeted for examination and improvement. DES has been shown to be effective in many healthcare settings. Some models of outpatient clinics aim to improve patient flow, reduce wait times, maximize staff utilization, and accomplish other gains in efficiency. These outpatient models are tested through changes to patient scheduling, patient routing, and internal work processes (Smith & Warner, 1971; Rising, Baron, & Averill, 1973; Bailey, 1952; Smith, Schroer, & Shannon, 1979; Williams, Covert, & Steele, 1967; Parks, Engblom, Hamrock, Satjapot, & Levin, 2011; Rohleder, Lewkonia, Bischak, Duffy, & Hendijani, 2011). Bed capacity, bed allocation, and length of stay (LOS) have been major points of focus of DES within inpatient settings (Lowery, 1992; Lowery & Martin, 1992; Dumas, 1984, 1985; Cohen, Hershey, & Weiss, 1980; Zilm, Arch, & Hollis, 1983; Bagust, Place, & Posnett, 1999; El-Darzi, Vasilakis, Chaussalet, & Millard, 1998). For example, DES was used within a hospital to determine how changes in bed allocation and discharge patterns affected hospital occupancy rates (Zhu, 2011; Dumas, 1985). DES models have also been developed to improve bed management practices and to plan for increases in admissions (e.g., surgical service expansion) (Levin et al., 2008; Levin, Dittus, Aronsky, Wienger, & France, 2011). This tool is used to develop block scheduling algorithms and examine patient flow in surgical centers (Murphy & Sigal, 1985; Fitzpatrick, Baker, & Dave, 1993). Some DES models focused on staffing typically aim to determine the impact of staffing changes on performance measures or to design staff schedules (Vemuri, 1984; McHugh, 1989; Hashimoto & Bell, 1996; Wilt & Goddin, 1989; Rossetti, Trzcinski, & Syverud, 1999; Jun, Jacobson, & Swisher, 1999). …