T he healthcare industry has invested heavily in electronic health records and other clinical information systems in order to improve caregivers' access to information and ability to share information with other care providers. It has been shown that these systems can readily induce in their users a state of information overload, where the volume and complexity of information overwhelms the user, leading to lower decision speed and quality. This research introduces and tests a cognitive technique called "emphasis framing" as an operational tactic to help mitigate the effects of information overload, thereby improving the quality and timeliness of clinical decision-making. Emphasis framing is the highlighting or stressing of some aspect or component of the information being exchanged in order to make it more easily processed, or more likely to be processed, by the recipient. We conducted a controlled laboratory experiment with emergency department physicians experiencing information overload to measure the effect of emphasis framing on two operational performance metrics: (1) the quality of the physician's clinical evaluation, and (2) the efficiency (timeliness) of the physician's clinical decision-making. Our findings show that the emphasis frame helped mitigate the effects of information overload and increased the quality of clinical decision-making. Contrary to expectations, however, we found decisionmaking took longer with the emphasis frame, reinforcing the need to consider the impacts of quality/speed trade-offs. Implications for theory and practice are discussed.

Hospital-based emergency departments (ED), given their high cost and major role in allocating care resources,are at the center of the debate regarding how to maximize value in delivering healthcare in the United States. In order to operate effectively and create value, EDs must be flexible: 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 upon operations research and organizational theory to identify and describe five forms of flexibility: physical, human resource, volume, behavioral, and conceptual. Each form of flexibility may be individually or in combination with others useful 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 potentially 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.

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. W ith over 130 million annual ED visits,1 a declining number of EDs to provide emergency care, 2 and lengthening wait times to see providers, 3 EDs are operating under increasingly arduous conditions. One underutilized approach to addressing problems in health care quality and value, particularly in emergency care, is through the use of computer simulation modeling. Computer simulation is a method to build dynamic models that quantitatively abstract a system, such as a facility (e.g., ED) or a process (e.g., physician-in-triage). Not unlike "highfidelity patient simulation" for training clinicians in clinical care through the use of mannequins, computer simulation provides a platform to inform decision making prior to implementation in the real world.

Study Objective 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 ten beds to a Fast Track for low-acuity patients. We estimated the effect of a Flex Track policy, which involved switching up to five 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 (ESI) score. Results A Flex Track policy using three flexible beds produced the lowest mean patient waiting of 30.9 (95% CI 30.6–31.2) minutes. The typical Fast Track approach of rigidly separating high- and low–acuity beds produced a mean patient wait time of 40.6 (95% CI 40.2–50.0) minutes, 31% higher than the three-bed Flex Track. A completely flexible ED, where all beds can accommodate any patient, produced mean wait times of 35.1 (95% CI 34.8–35.4) minutes. The results from the three-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.