Understanding Emergency Care Delivery Through Computer Simulation Modeling.

In 2017, Academic Emergency Medicine convened a consensus conference entitled, "Catalyzing System Change through Health Care Simulation: Systems, Competency, and Outcomes." This article, a product of the breakout session on "understanding complex interactions through systems modeling," explores the role that computer simulation modeling can and should play in research and development of emergency care delivery systems. This article discusses areas central to the use of computer simulation modeling in emergency care research. The four central approaches to computer simulation modeling are described (Monte Carlo simulation, system dynamics modeling, discrete-event simulation, and agent-based simulation), along with problems amenable to their use and relevant examples to emergency care. Also discussed is an introduction to available software modeling platforms and how to explore their use for research, along with a research agenda for computer simulation modeling. Through this article, our goal is to enhance adoption of computer simulation, a set of methods that hold great promise in addressing emergency care organization and design challenges.

The nature and necessity of operational flexibility in the emergency department.

Hospital-based emergency departments (EDs), given their high cost and major role in allocating care resources, are at the center of the debate about how to maximize value in delivering health care in the United States. To operate effectively and create value, EDs must be flexible, having the ability to rapidly adapt to the highly variable needs of patients. The concept of flexibility has not been well described in the ED literature. We introduce the concept, outline its potential benefits, and provide some illustrative examples to facilitate incorporating flexibility into ED management. We draw on operations research and organizational theory to identify and describe 5 forms of flexibility: physical, human resource, volume, behavioral, and conceptual. Each form of flexibility may be useful individually or in combination with other forms in improving ED performance and enhancing value. We also offer suggestions for measuring operational flexibility in the ED. A better understanding of operational flexibility and its application to the ED may help us move away from reactive approaches of managing variable demand to a more systematic approach. We also address the tension between cost and flexibility and outline how "partial flexibility" may help resolve some challenges. Applying concepts of flexibility from other disciplines may help clinicians and administrators think differently about their workflow and provide new insights into managing issues of cost, flow, and quality in the ED.

The flex track: flexible partitioning between low- and high-acuity areas of an emergency department.

STUDY OBJECTIVE: Emergency departments (EDs) with both low- and high-acuity treatment areas often have fixed allocation of resources, regardless of demand. We demonstrate the utility of discrete-event simulation to evaluate flexible partitioning between low- and high-acuity ED areas to identify the best operational strategy for subsequent implementation. METHODS: A discrete-event simulation was used to model patient flow through a 50-bed, urban, teaching ED that handles 85,000 patient visits annually. The ED has historically allocated 10 beds to a fast track for low-acuity patients. We estimated the effect of a flex track policy, which involved switching up to 5 of these fast track beds to serving both low- and high-acuity patients, on patient waiting times. When the high-acuity beds were not at capacity, low-acuity patients were given priority access to flexible beds. Otherwise, high-acuity patients were given priority access to flexible beds. Wait times were estimated for patients by disposition and Emergency Severity Index score. RESULTS: A flex track policy using 3 flexible beds produced the lowest mean patient waiting time of 30.9 minutes (95% confidence interval [CI] 30.6 to 31.2 minutes). The typical fast track approach of rigidly separating high- and low-acuity beds produced a mean patient wait time of 40.6 minutes (95% CI 40.2 to 50.0 minutes), 31% higher than that of the 3-bed flex track. A completely flexible ED, in which all beds can accommodate any patient, produced mean wait times of 35.1 minutes (95% CI 34.8 to 35.4 minutes). The results from the 3-bed flex track scenario were robust, performing well across a range of scenarios involving higher and lower patient volumes and care durations. CONCLUSION: Using discrete-event simulation, we have shown that adding some flexibility into bed allocation between low and high acuity can provide substantial reductions in overall patient waiting and a more efficient ED.