BackgroundTimely diagnosis of ST‐segment elevation myocardial infarction (STEMI) in the emergency department (ED) is made solely by ECG. Obtaining this test within 10 minutes of ED arrival is critical to achieving the best outcomes. We investigated variability in the timely identification of STEMI across institutions and whether performance variation was associated with the ED characteristics, the comprehensiveness of screening criteria, and the STEMI screening processes.Methods and ResultsWe examined STEMI screening performance in 7 EDs, with the missed case rate (MCR) as our primary end point. The MCR is the proportion of primarily screened ED patients diagnosed with STEMI who did not receive an ECG within 15 minutes of ED arrival. STEMI was defined by hospital discharge diagnosis. Relationships between the MCR and ED characteristics, screening criteria, and STEMI screening processes were assessed, along with differences in door‐to‐ECG times for captured versus missed patients. The overall MCR for all 7 EDs was 12.8%. The lowest and highest MCRs were 3.4% and 32.6%, respectively. The mean difference in door‐to‐ECG times for captured and missed patients was 31 minutes, with a range of 14 to 80 minutes of additional myocardial ischemia time for missed cases. The prevalence of primarily screened ED STEMIs was 0.09%. EDs with the greatest informedness (sensitivity+specificity−1) demonstrated superior performance across all other screening measures.ConclusionsThe 29.2% difference in MCRs between the highest and lowest performing EDs demonstrates room for improving timely STEMI identification among primarily screened ED patients. The MCR and informedness can be used to compare screening across EDs and to understand variable performance.

Objective Ultrasound guided intravenous catheter (USGIV) insertion is increasingly being used for administration of intravenous contrast for computed tomography (CT) scans. The goal of this investigation was to evaluate the risk of contrast extravasation among patients receiving contrast through USGIV catheters. Methods A retrospective observational study of adult patients who underwent a contrast-enhanced CT scan at a tertiary-care emergency department during a recent 64-month period was conducted. The unadjusted prevalence of contrast extravasation was compared between patients with an USGIV and those with a standard peripheral IV inserted without ultrasound. Then, a two-stage sampling design was used to select a subset of the population for a multivariable logistic regression model evaluating USGIVs as a risk factor for extravasation while adjusting for potential confounders. Results In total, 40,143 patients underwent a contrasted CT scan, including 364 (0.9%) who had contrast administered through an USGIV. Unadjusted prevalence of extravasation was 3.6% for contrast administration through USGIVs and 0.3% for standard IVs (relative risk: 13.9, 95% CI: 7.7 to 24.6). After adjustment for potential confounders, CT contrast administered through USGIVs was associated with extravasation (adjusted odds ratio: 8.6; 95% CI: 4.6, 16.2). No patients required surgical management for contrast extravasation; one patient in the standard IV group was admitted for observation due to extravasation. Conclusions Patients who received contrast for a CT scan through an USGIV had a higher risk of extravasation than those who received contrast through a standard peripheral IV. Clinicians should consider this extravasation risk when weighing the risks and benefits of a contrast-enhanced CT scan in a patient with USGIV vascular access.

Road traffic injuries pose a considerable public health burden in Guyana. These results suggest a pattern of high mortality in rural collisions and a disproportionate burden of injuries on vulnerable road users. The spatial distribution of collisions should be considered in order to target interventions and improve road traffic safety.

Objectives: The objective was to obtain a commitment to adopt a common set of definitions for emergency department (ED) demographic, clinical process, and performance metrics among the ED Benchmarking Alliance (EDBA), ED Operations Study Group (EDOSG), and Academy of Academic Administrators of Emergency Medicine (AAAEM) by 2017.Methods: A retrospective cross-sectional analysis of available data from three ED operations benchmarking organizations supported a negotiation to use a set of common metrics with identical definitions. During a 1.5-day meeting-structured according to social change theories of information exchange, self-interest, and interdependence-common definitions were identified and negotiated using the EDBA's published definitions as a start for discussion. Methods of process analysis theory were used in the 8 weeks following the meeting to achieve official consensus on definitions. These two lists were submitted to the organizations' leadership for implementation approval.Results: A total of 374 unique measures were identified, of which 57 (15%) were shared by at least two organizations. Fourteen (4%) were common to all three organizations. In addition to agreement on definitions for the 14 measures used by all three organizations, agreement was reached on universal definitions for 17 of the 57 measures shared by at least two organizations. The negotiation outcome was a list of 31 measures with universal definitions to be adopted by each organization by 2017. Conclusion:The use of negotiation, social change, and process analysis theories achieved the adoption of universal definitions among the EDBA, EDOSG, and AAAEM. This will impact performance benchmarking for nearly half of US EDs. It initiates a formal commitment to utilize standardized metrics, and it transitions consistency in reporting ED operations metrics from consensus to implementation. This work advances our ability to more accurately characterize variation in ED care delivery models, resource utilization, and performance. In addition, it permits future aggregation of these three data sets, thus facilitating the creation of more robust ED operations research data sets unified by a universal language. Negotiation, social change, and process analysis principles can be used to advance the adoption of additional definitions.ACADEMIC EMERGENCY MEDICINE 2016;23:796-802 © 2016 by the Society for Academic Emergency Medicine V arious sets of quality measures and definitions exist, and there are national efforts to develop health industry-wide standards for emergency medicine practice and performance metrics.1 However, implementation is the next frontier. As the demand for emergency care grows and the practice of emergency