Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
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Review Comparative Study
Review of modeling approaches for emergency department patient flow and crowding research.
Emergency department (ED) crowding is an international phenomenon that continues to challenge operational efficiency. Many statistical modeling approaches have been offered to describe, and at times predict, ED patient load and crowding. A number of formula-based equations, regression models, time-series analyses, queuing theory-based models, and discrete-event (or process) simulation (DES) models have been proposed. In this review, we compare and contrast these modeling methodologies, describe the fundamental assumptions each makes, and outline the potential applications and limitations for each with regard to usability in ED operations and in ED operations and crowding research.
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Review Comparative Study
Consensus-based recommendations for research priorities related to interventions to safeguard patient safety in the crowded emergency department.
This article describes the results of the Interventions to Safeguard Safety breakout session of the 2011 Academic Emergency Medicine (AEM) consensus conference entitled "Interventions to Assure Quality in the Crowded Emergency Department." Using a multistep nominal group technique, experts in emergency department (ED) crowding, patient safety, and systems engineering defined knowledge gaps and priority research questions related to the maintenance of safety in the crowded ED. Consensus was reached for seven research priorities related to interventions to maintain safety in the setting of a crowded ED. Included among these are: 1) How do routine corrective processes and compensating mechanism change during crowding? 2) What metrics should be used to determine ED safety? 3) How can checklists ensure safer care and what factors contribute to their success or failure? 4) What constitutes safe staffing levels/ratios? 5) How can we align emergency medicine (EM)-specific patient safety issues with national patient safety issues? 6) How can we develop metrics and skills to recognize when an ED is getting close to catastrophic overload conditions? and 7) What can EM learn from experts and modeling from fields outside of medicine to develop innovative solutions? These priorities have the potential to inform future clinical and human factors research and extramural funding decisions related to this important topic.
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Review Comparative Study
Comparison of methods for measuring crowding and its effects on length of stay in the emergency department.
This consensus conference presentation article focuses on methods of measuring crowding. The authors compare daily versus hourly measures, static versus dynamic measures, and the use of linear or logistic regression models versus survival analysis models to estimate the effect of crowding on an outcome. ⋯ Crowding measured at the daily level will mask much of the variation in crowding that occurs within a 24-hour period. ED census at arrival demonstrated similar variation in crowding exposure as time-varying ED census. Discrete time survival analysis is a more appropriate approach for estimating the effect of crowding on an outcome.
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Review
System dynamics and dysfunctionalities: levers for overcoming emergency department overcrowding.
Overcrowding of U. S. emergency departments (EDs) is a widely recognized and growing problem. ⋯ It posits that ED overcrowding is actually a symptom of 10 more fundamental problems in U. S. health care and EDs: variations/supply-demand mismatch; primary care provider shortfalls; limited after-hours access; admission throughput challenges; clinical challenges related to discontinuity patients; clinical challenges related to those with special needs; interruptions; testing logistical challenges; suboptimal information systems; and fragmented/dysfunctional health insurance system, leaving many un- and underinsured.
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The effect of emergency department (ED) crowding on equitable care is the least studied of the domains of quality as defined by the Institute of Medicine (IOM). Inequities in access and treatment throughout the health care system are well documented in all fields of medicine. ⋯ To design successful interventions, however, it is important to first understand how crowding can result in disparities and base interventions on these mechanisms. A research agenda is proposed to understand mechanisms that may threaten equity during periods of crowding and design and test potential interventions that may ensure the equitable aspect of quality of care.