Implementing Data Definition Consistency for Emergency Department Operations Benchmarking and Research

Abstract

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

Clinical operations of academic versus non-academic emergency departments: a descriptive comparison of two large emergency department operations surveys

“…The EDBA survey is administered annually to EDBA member institutions and former member institutions that submitted survey responses in prior years. EDBA membership and survey development details are available in previous publications and the EDBA website [7, 8, 10, 11]. Within the survey instrument (Additional file 2: Table S2), respondents reported clinical operations metrics attributable to the ED site level, labeling each ED as academic-affiliated (defined as participating in training EM and other residents) or not academic-affiliated.…”

“…Since no single ED data comparison effort exists to compare appropriately the clinical operations of academic versus non-academic EDs, we sought to investigate differences between these two groups by combining data from the AAAEM/AACEM and EDBA performance surveys. Both sources are characterized by high response rates, include comprehensive clinical operations metrics, have significant overlap in their definition of clinical metrics [8], and their methodologies allow for the accurate establishment of academic and non-academic cohorts, repsectively [1, 8].…”

Measuring Emergency Department Acuity

“…We asked each site to provide demographic information for their facility, percentage of patients with ESI 1 or 2, CMI 1,2 annual ED volume, 3,4,5 academic status, 6 inpatient admission rate, 6,7 percent of patient admitted under observation status, percentage of Medicare patients, patients-seen-per-attending-hour per day, and the aggregated percentage of billed charts falling into each ambulatory payment classification (APC) coding level category. 8,9,10 Each site PI agreed to accurate data collection and reporting to the EDOSG. Most frequently site-PIs are medical or clinical operations directors.…”

Measuring outcome differences associated with STEMI screening and diagnostic performance: a multicentred retrospective cohort study protocol

“…ED LOS is defined as the time from ED arrival to ED departure. 18 Change in EF is calculated as the difference between the last EF measured prior to the patients with STEMI and the first documented after hospital discharge. Hospital LOS is the time from hospital admission to hospital discharge.…”