Emergency Department Benchmarking Alliance Research Articles

Extremes of Emergency Department Boarding are Associated With Poorer Financial Performance Among Hospitals.

Boarding emergency department (ED) patients is associated with reductions in quality of care, patient safety and experience, and ED operational efficiency. However, ED boarding is ultimately reflective of inefficiencies in hospital capacity management. The ability of a hospital to accommodate variability in patient flow presumably affects its financial performance, but this relationship is not well studied. We investigated the relationship between ED boarding and hospital financial performance measures. Our objective was to see if there was an association between key financial measures of business performance and limitations in patient progression efficiency, as evidenced by ED boarding. Cross-sectional ED operational data were collected from the Emergency Department Benchmarking Alliance, a voluntarily self-reporting operational database that includes 54% of EDs in the United States. Freestanding EDs, pediatric EDs and EDs with missing boarding data were excluded. The key operational outcome variable was boarding time. We reviewed the financial information of these nonprofit institutions by accessing their Internal Revenue Service Form 990. We examined standard measures of financial performance, including return on equity, total margin, total asset turnover, and equity multiplier (EM). We studied these associations using quantile regressions of added ED volume, ED admission percentage, urban versus nonurban ED site location, trauma status, and percentage of the population receiving Medicare and Medicaid as covariates in the regression models. Operational data were available for 892 EDs from 31 states. Of those, 127 reported a Form 990 in the year corresponding to the ED boarding measures. Median boarding time across EDs was 148min (interquartile range [IQR]: 100-216). A significant relationship exists between boarding and the EM, along with a negative association with the hospital's total profit margin in the highest-performing hospitals (by profit margin percentage). After adjusting for the covariates in the regression model, we found that for every 10min above 90min of boarding, the mean EM for the top quartile increased from 245.8% to 249.5% (p <.001). In hospitals in the top 90th percentile of total margin, every 10min beyond the median ED boarding interval led to a decrease in total margin of 0.24%. Using the largest available national registry of ED operational data and concordant nonprofit financial reports, higher boarding among the highest-profitability hospitals (i.e., top 10%) is associated with a drag on profit margin, while hospitals with the highest boarding are associated with the highest leverage (i.e., indicated by the EM). These relationships suggest an association between a key ED indicator of hospital capacity management and overall institutional financial performance.

Operations Factors Associated with Emergency Department Length of Stay: Analysis of a National Operations Database.

Prolonged emergency department (ED) length of stay (LOS) has been shown to adversely affect patient care. We sought to determine factors associated with ED LOS via analysis of a large, national, ED operations database. We performed retrospective, multivariable, linear regression modeling using the 2019 Emergency Department Benchmarking Alliance survey results to identify associated factors of ED LOS for admitted and discharged patients. A total of 1,052 general and adult-only EDs responded to the survey. Median annual volume was 40,946. The median admit and discharge LOS were 289 minutes and 147 minutes, respectively. R-squared values for the admit and discharge models were 0.63 and 0.56 with out-of-sample R-squared values of 0.54 and 0.59, respectively. Both admit and discharge LOS were associated with academic designation, trauma level designation, annual volume, proportion of ED arrivals occurring via emergency medical services, median boarding, and use of a fast track. Additionally, admit LOS was associated with transfer-out percentage, and discharge LOS was associated with percentage of high Current Procedural Terminology, percentage of patients <18 years old, use of radiographs and computed tomography, and use of an intake physician. Models derived from a large, nationally representative cohort identified diverse associated factors of ED length of stay, several of which were not previously reported. Dominant within the LOS modeling were patient population characteristics and other factors extrinsic to ED operations, including boarding of admitted patients, which was associated with both admitted and discharged LOS. The results of the modeling have significant implications for ED process improvement and appropriate benchmarking.

Estimates of throughput and utilization at freestanding compared to low-volume hospital-based emergency departments.

ObjectiveOur investigation compared throughput metrics and utilization measures for freestanding emergency departments (FSEDs) versus hospital‐based emergency departments (HBEDs) of similar volumes in the United States.MethodsThis study is a cross sectional survey of 183 FSEDs and 317 HBEDs located across the United States using the Emergency Department Benchmarking Alliance (EDBA) Database. We measured common emergency department (ED) throughput metrics. Primary outcomes included overall length of stay, length of stay for admitted, and length of stay for treated and released patients. Outcomes were weighted based on the proportion of ED volume per facility as per a prior pilot study. Multiple linear regression analysis was used to adjust for measured differences between FSEDs and HBEDs. The variables that were controlled for in regression analysis included geographic location of the ED (urban, suburban, and rural), percent of high acuity capacity, ED volume, percentage of patients arriving via emergency medical services (EMS), and percentage of pediatric patients.ResultsNationally, the median length of stay in minutes (104.2 vs 140.0), length of stay for treated and released patients (98.6 vs 122.9), door‐to‐bed (4.0 vs 8.0), door‐to‐doctor (11.0 vs 16.0), percentage of patients admitted through the ED (4.0 vs 11.0), and percentage of patients leaving the ED without being seen (LWBS) (0.9 vs 1.5), were significantly lower at FSEDs compared to HBEDs (P < 0.0001 for all comparisons). Length of stay for admitted patients (265.9 vs 241.8) and median boarding time (96.8 vs. 71.3) were significantly lower in HBEDs compared to FSEDs. X‐ray, computed tomography, and ECG utilization per 100 patients was significantly lower at the FSEDs compared to HBEDs. Multiple linear regression analysis demonstrated that the length of stay for treated and released patients was 8.67 minutes shorter for FSEDs as compared to HBEDs (95% confidence interval [CI] = −1.4 to −16.0). The length of stay for admitted patients was 44 minutes longer for FSEDs as compared to HBEDs (95% CI = 25.5 to 63.0).ConclusionsIn this study of similarly sized EDs in the United States, throughput metrics for FSEDs tended to be significantly shorter from the arrival of the patient until their departure, except for patients requiring hospital admission. For measures favoring FSEDs, throughput times range from 20%–50% shorter than HBEDs.

316 Emergency Department Benchmarking Alliance Survey: Anatomy and Physiology of an Emergency Department Observation Unit

Health care entities can deliver observation services to determine whether or not a patient needs an inpatient admission. Many studies have shown that relative to care in a traditional inpatient bed, managing observation patients in an emergency department observation unit (EDOU), using condition-specific protocols, is associated with shorter hospital length of stays, lower costs, and decreased inpatient admission rates. Because of these advantages, health care entities have been opening these units more frequently. A 2003, survey found that 1/3 of hospitals in the US had EDOUs, and more were planning on building them. This statistic has been the standard talking point in the growth of observation for more than a decade. Since this original survey, there has been very little published data on the number of EDOUs as well as their operating characteristics. We surveyed the members of the Emergency Department Benchmarking Alliance (EDBA) as well as the American College of Emergency Physicians (ACEP) Observation Services Section as to the presence of an Observation Unit at their hospitals. Surveys were sent via email which contained a SurveyMonkey link to the questions. The account is securely maintained by EDBA. The responses were de-identified and entered into an excel spreadsheet for analysis. Respondents: Ninety sites responded to the survey representing 28 states plus the District of Columbia. Five responses answered only the state of practice and an additional 2 respondents answered only who admits to the OU. Unit Characteristics: Most of the OUs in this cohort were closed units. The median number of beds (IQR) for the respondents of the survey was 12 (10-18). Only 7 units reported not using patient care protocols. Unit Staffing: Emergency medicine admitted patients to 61 (67%) of the OUs, Hospitalist/Internal medicine were the admitting physicians in 5 units and Cardiology in 1 unit. Two sites reported they had hybrid units where EM or IM could admit to the OU. Fifteen of the units (16%) were “open,” were any service could admit to the OU. Advanced Practice Providers (APPs) are utilized in the vast majority of sites (69 as opposed to 13 that don’t). In terms of physician rounding in the unit, 49 of the sites (54%) utilize an emergency physician who is concurrently working a shift in the ED. Eighteen sites have a dedicated OU physician. One site reported that their NPs function independently with no physician oversight. The most common RN: Patient ratio is 1:5, used in 42 (46%) of the surveyed sites. The highest and lowest RN:Patient ratios are 1:2 and 1:8, respectively. These were each reported by one site. Unit Throughput Median annual volume (IQR) for this cohort is 4,015 (2,676 - 5,475) patients. Median length of stay in the unit (IQR) is 20 hours (17-23). Average conversion to inpatient status is 16%. Patient characteristics: The most common chief complaint seen in OUs in this cohort is chest pain. Other common reasons for observation include abdominal pain, TIA, syncope, asthma and COPD. Observation units as surveyed in this cohort are mainly emergency department-based and ED managed. Future challenges for these units will be maintaining occupancy in the face of potentially decreasing chest pain cases. Further study needs to be done as to the impact of APPs and role of EM/IM collaboration. Ultimately, a national database of Observation Units and observation patient stays is needed.

Managing and Measuring Emergency Department Care: Results of the Fourth Emergency Department Benchmarking Definitions Summit.

A shared language and vocabulary are essential for managing emergency department (ED) operations. This Fourth Emergency Department Benchmarking Alliance (EDBA) Summit brought together experts in the field to review, update, and add to key definitions and metrics of ED operations. Summit objectives were to review and revise existing definitions, define and characterize new practices related to ED operations, and introduce financial and regulatory definitions affecting ED reimbursement. Forty-six ED operations, data management, and benchmarking experts were invited to participate in the EDBA summit. Before arrival, experts were provided with documents from the three prior summits and assigned to update the terminology. Materials and publications related to standards of ED operations were considered and discussed. Each group submitted a revised set of definitions prior to the summit. Significantly revised, topical, or controversial recommendations were discussed among all summit participants. The goal of the in-person discussion was to reach consensus on definitions. Work group leaders made changes to reflect the discussion, which was revised with public and stakeholder feedback. The entire EDBA dictionary was updated and expanded. This article focuses on an update and discussion of definitions related to specific topics that changed since the last summit, specifically ED intake, boarding, diversion, and observation care. In addition, an extensive new glossary of financial and regulatory terminology germane to the practice of emergency medicine is included. A complete and precise set of operational definitions, time intervals, and utilization measures is necessary for timely and effective ED care. A common language of financial and regulatory definitions that affect ED operations is included for the first time. This article and its companion dictionary should serve as a resource to ED leadership, researchers, informatics and health policy leaders, and regulatory bodies.

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

BackgroundAcademic and non-academic emergency departments (EDs) are regularly compared in clinical operations benchmarking despite suggestion that the two groups may differ in their clinical operations characteristics. and outcomes. We sought to describe and compare clinical operations characteristics of academic versus non-academic EDs.MethodsWe performed a descriptive, comparative analysis of academic and non-academic adult and general EDs with 40,000+ annual encounters, using the Academy of Academic Administrators of Emergency Medicine (AAAEM)/Association of Academic Chairs of Emergency Medicine (AACEM) and Emergency Department Benchmarking Alliance (EDBA) survey results. We defined academic EDs as primary teaching sites for emergency medicine (EM) residencies and non-academic EDs as sites with minimal resident involvement. We constructed the academic and non-academic cohorts from the AAAEM/AACEM and EDBA surveys, respectively, and analyzed metrics common to both surveys.ResultsEighty and 454 EDs met inclusion criteria for academic and non-academic EDs, respectively. Academic EDs had more median annual patient encounters (73,001 vs 54,393), lower median proportion of pediatric patients (6.3% vs 14.5%), higher median proportion of EMS patients (27% vs 19%), and were more commonly designated as Level I or II Trauma Centers (94% vs 24%). Median patient arrival-to-provider times did not differ (26 vs 25 min). Median length-of-stay was longer (277 vs 190 min) for academic EDs, and left-before-treatment-complete was higher (5.7% vs 2.9%). MRI utilization was higher for academic EDs (2.2% patients with at least one MRI vs 1.0 MRIs performed per 100 patients). Patients-per-hour of provider coverage was lower for academic EDs with and without consideration for advanced practice providers and residents.ConclusionsDemographic and operational performance measures differ between academic and non-academic EDs, suggesting that the two groups may be inappropriate operational performance comparators. Causes for the differences remain unclear but the differences appear not to be attributed solely to the academic mission.

Resource Utilization in Non-Academic Emergency Departments with Advanced Practice Providers.

IntroductionAdvanced practice providers (APP), including physicians’ assistants and nurse practitioners, have been increasingly incorporated into emergency department (ED) staffing over the past decade. There is scant literature examining resource utilization and the cost benefit of having APPs in the ED. The objectives of this study were to compare resource utilization in EDs that use APPs in their staffing model with those that do not and to estimate costs associated with the utilized resources.MethodsIn this five-year retrospective secondary data analysis of the Emergency Department Benchmarking Alliance (EDBA), we compared resource utilization rates in EDs with and without APPs in non-academic EDs. Primary outcomes were hospital admission and use of computed tomography (CT), radiography, ultrasound, and magnetic resonance imaging (MRI). Costs were estimated using the 2014 physician fee schedule and inpatient payments from the Centers for Medicare and Medicaid Services. We measured outcomes as rates per 100 visits. Data were analyzed using a mixed linear model with repeated measures, adjusted for annual volume, patient acuity, and attending hours. We used the adjusted net difference to project utilization costs between the two groups per 1000 visits.ResultsOf the 1054 EDs included in this study, 79% employed APPs. Relative to EDs without APPs, EDs staffing APPs had higher resource utilization rates (use per 100 visits): 3.0 more admissions (95% confidence interval [CI], 2.0–4.1), 1.7 more CTs (95% CI, 0.2–3.1), 4.5 more radiographs (95% CI, 2.2–6.9), and 1.0 more ultrasound (95% CI, 0.3–1.7) but comparable MRI use 0.1 (95% CI, −0.2–0.3). Projected costs of these differences varied among the resource utilized. Compared to EDs without APPs, EDs with APPs were estimated to have 30.4 more admissions per 1000 visits, which could accrue $414,717 in utilization costs.ConclusionEDs staffing APPs were associated with modest increases in resource utilization as measured by admissions and imaging studies.

Implementing Data Definition Consistency for Emergency Department Operations Benchmarking and Research.

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. 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. 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. 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.

Drivers of ED efficiency: a statistical and cluster analysis of volume, staffing, and operations

Study objectiveThe percentage of patients leaving before treatment is completed (LBTC) is an important indicator of emergency department performance. The objective of this study is to identify characteristics of hospital operations that correlate with LBTC rates. MethodsThe Emergency Department Benchmarking Alliance 2012 and 2013 cross-sectional national data sets were analyzed using multiple regression and k-means clustering. Significant operational variables affecting LBTC including annual patient volume, percentage of high-acuity patients, percentage of patients admitted to the hospital, number of beds, academic status, waiting times to see a physician, length of stay (LOS), registered nurse (RN) staffing, and physician staffing were identified. LBTC was regressed onto these variables. Because of the strong correlation between waiting times measured as door to first provider (DTFP), we regressed DTFP onto the remaining predictors. Cluster analysis was applied to the data sets to further analyze the impact of individual predictors on LBTC and DTFP. ResultsLOS and the time from DTFP were both strongly associated with LBTC rate (P<.001). Patient volume is not significantly associated with LBTC rate (P=.16). Cluster analysis demonstrates that physician and RN staffing ratios correlate with shorter DTFP and lower LBTC. ConclusionVolume is not the main driver of LBTC. DTFP and LOS are much more strongly associated. We show that operational factors including LOS and physician and RN staffing decisions, factors under the control of hospital and physician executives, correlate with waiting time and, thus, in determining the LBTC rate.

Association among Emergency Department Volume Changes, Length of Stay, and Leaving Before Treatment Complete

The authors examined the association between the size of an emergency department (ED), volume increases over time, length of stay (LOS), and left before treatment complete (LBTC). EDs participating in the Emergency Department Benchmarking Alliance providing at least two years of data from 2004 to 2011 were included in the analysis. The impact of volume on LOS and LBTC varied depending on annual ED volume. Based on this, EDs can anticipate better how changes in volume will impact patient throughput in the future.

Risk-Adjusted Variation of Publicly Reported Emergency Department Timeliness Measures

The Centers for Medicare & Medicaid Services (CMS) recently published emergency department (ED) timeliness measures. These data show substantial variation in hospital performance and suggest the need for process improvement initiatives. However, the CMS measures are not risk adjusted and may provide misleading information about hospital performance and variation. We hypothesize that substantial hospital-level variation will persist after risk adjustment. This cross-sectional study included hospitals that participated in the Emergency Department Benchmarking Alliance and CMS ED measure reporting in 2012. Outcomes included the CMS measures corresponding to median annual boarding time, length of stay of admitted patients, length of stay of discharged patients, and waiting time of discharged patients. Covariates included hospital structural characteristics and case-mix information from the American Hospital Association Survey, CMS cost reports, and the Emergency Department Benchmarking Alliance. We used a γregression with a log link to model the skewed outcomes. We used indirect standardization to create risk-adjusted measures. We defined "substantial" variation as coefficient of variation greater than 0.15. The study cohort included 723 hospitals. Risk-adjusted performance on the CMS measures varied substantially across hospitals, with coefficient of variation greater than 0.15 for all measures. Ratios between the 10th and 90th percentiles of performance ranged from 1.5-fold for length of stay of discharged patients to 3-fold for waiting time of discharged patients. Policy-relevant variations in publicly reported CMS ED timeliness measures persist after risk adjustment for nonmodifiable hospital and case-mix characteristics. Future "positive deviance" studies should identify modifiable process measures associated with high performance.

Emergency department performance measures updates: proceedings of the 2014 emergency department benchmarking alliance consensus summit.

The objective was to review and update key definitions and metrics for emergency department (ED) performance and operations. Forty-five emergency medicine leaders convened for the Third Performance Measures and Benchmarking Summit held in Las Vegas, February 21-22, 2014. Prior to arrival, attendees were assigned to workgroups to review, revise, and update the definitions and vocabulary being used to communicate about ED performance and operations. They were provided with the prior definitions of those consensus summits that were published in 2006 and 2010. Other published definitions from key stakeholders in emergency medicine and health care were also reviewed and circulated. At the summit, key terminology and metrics were discussed and debated. Workgroups communicated online, via teleconference, and finally in a face-to-face meeting to reach consensus regarding their recommendations. Recommendations were then posted and open to a 30-day comment period. Participants then reanalyzed the recommendations, and modifications were made based on consensus. A comprehensive dictionary of ED terminology related to ED performance and operation was developed. This article includes definitions of operating characteristics and internal and external factors relevant to the stratification and categorization of EDs. Time stamps, time intervals, and measures of utilization were defined. Definitions of processes and staffing measures are also presented. Definitions were harmonized with performance measures put forth by the Centers for Medicare and Medicaid Services (CMS) for consistency. Standardized definitions are necessary to improve the comparability of EDs nationally for operations research and practice. More importantly, clear precise definitions describing ED operations are needed for incentive-based pay-for-performance models like those developed by CMS. This document provides a common language for front-line practitioners, managers, health policymakers, and researchers.

Overall ED efficiency is associated with decreased time to percutaneous coronary intervention for ST-segment elevation myocardial infarction

BackgroundPerformance of percutaneous coronary intervention (PCI) within 90 minutes of hospital arrival for ST-segment elevation myocardial infarction patients is a commonly cited clinical quality measure. The Centers for Medicare and Medicaid Services use this measure to adjust hospital reimbursement via the Value-Based Purchasing Program. This study investigated the relationship between hospital performance on this quality measure and emergency department (ED) operational efficiency. MethodsHospital-level data from Centers for Medicare and Medicaid Services on PCI quality measure performance was linked to information on operational performance from 272 US EDs obtained from the Emergency Department Benchmarking Alliance annual operations survey. Standard metrics of ED size, acuity, and efficiency were compared across hospitals grouped by performance on the door-to-balloon time quality measure. ResultsMean hospital performance on the 90-minute arrival to PCI measure was 94.0% (range, 42-100). Among hospitals failing to achieve the door-to-balloon time performance standard, median ED length of stay was 209 minutes, compared with 173 minutes among those hospitals meeting the benchmark standard (P < .001). Similarly, median time from ED patient arrival to physician evaluation was 39 minutes for hospitals below the performance standard and 23 minutes for hospitals at the benchmark standard (P < .001). Markers of ED size and acuity, including annual patient volume, admission rate, and the percentage of patients arriving via ambulance did not vary with door-to-balloon time. ConclusionBetter performance on measures associated with ED efficiency is associated with more timely PCI performance.

Volume-Related Differences in Emergency Department Performance

Emergency departments (EDs) are an important source of care for a large segment of the population of the United States. In 2009 there were more than 136 million visits to the ED each year, and more than half of hospital admissions begin in the ED. Measurement and monitoring of emergency department performance has been prompted by The Joint Commission's patient flow standards. A study was conducted to attempt to correlate ED volume and other operating characteristics with performance on metrics. A retrospective analysis of the Emergency Department Benchmarking Alliance annual ED survey data for the most recent year for which data were available (2009) was performed to explore observed patterns in ED performance relative to size and operating characteristics. The survey was based on 14.6 million ED visits in 358 hospitals across the United States, with an ED size representation (sampling) approximating that of the Emergency Medicine Network (EM Net). Larger EDs (with higher annual volumes) had longer lengths of stay (p < .0001), higher left without being seen rates (p < .0001), and longer door-to-physician times (p < .0001), all suggesting poorer operational performance. Operating characteristics indicative of higher acuity were associated with worsened performance on metrics and lower acuity characteristics with improved performance. ED volume, which also correlates with many operating characteristics, is the strongest predictor of operational performance on metrics and can be used to categorize EDs for comparative analysis. Operating characteristics indicative of acuity also influence performance. The findings suggest that ED performance measures should take ED volume, acuity, and other characteristics into account and that these features have important implications for ED design, operations, and policy decisions.

Using Data to Drive Emergency Department Design: A Metasynthesis

There has been an uptick in the field of emergency department (ED) operations research and data gathering, both published and unpublished. This new information has implications for ED design. The specialty suffers from an inability to have these innovations reach frontline practitioners, let alone design professionals and architects. This paper is an attempt to synthesize for design professionals the growing data regarding ED operations. The following sources were used to capture and summarize the research and data collections available regarding ED operations: the Emergency Department Benchmarking Alliance database; a literature search using both PubMed and Google Scholar search engines; and data presented at conferences and proceedings. Critical information that affects ED design strategies is summarized, organized, and presented. Data suggest an optimal size for ED functional units. The now-recognized arrival and census curves for the ED suggest a department that expands and contracts in response to changing census. Operational improvements have been dearly identified and are grouped into three categories: input, throughput, and outflow. Applications of this information are suggested. The sentinel premise of this meta-synthesis is that data derived from improvement work in the area of ED operations has applications for ED design. EDs can optimize their functioning by marrying good processes and operations to good design. This review paper is an attempt to bring this new information to the attention of the multidisciplinary team of architects, designers, and clinicians.

Viewpoint: Canʼt Get No Satisfaction? The Real Truth Behind Patient Satisfaction Surveys

Viewpoint: Canʼt Get No Satisfaction? The Real Truth Behind Patient Satisfaction Surveys

181: Following Volume Trends Throughout the Country Using the Emergency Department Benchmarking Alliance Database

To compare emergency department (ED) admission percentage, mean ED length of stay (LOS), and percentage of patients who leave before treatment is complete (LBTC) among various volume EDs using data from the Emergency Department Benchmarking Alliance (EDBA) database. A retrospective analytic cohort study examining selected data points from the EDBA database was conducted. The EDBA consists of 86 hospitals in 2005, 84 in 2006, 85 in 2007, and 86 in 2008, with EDs ranging in size from under 25,000 to over 100,000 visits per year. The EDBA database contains operational data submitted by member hospitals on a yearly basis. Data from 2004-2008 were included in this study. EDs were grouped into 3 categories by volume: <25,000 visits/yr (low volume), 25,000-60,000 visits/yr (medium volume), and > 60,000 visits/yr (high volume). ED admission percentage, mean ED LOS, and % LBTC were compared among the 3 groups using analysis of variance (ANOVA). A p-value ≤ 0.05 was considered significant. ED admission % was higher in the high volume EDs than in the medium and low volume EDs (24.2% ± 6.4 versus 17.6% ± 6.4 versus 10.6%± 7, p<0.01). Mean ED LOS for admitted patients was higher in the high volume EDs (385 min ± 106 versus 314 min ± 95 versus 223 min ± 52, p<0.01). Mean ED LOS for discharged patients was also higher for high volume EDs (202 min ± 45 versus 161 min ± 47 versus 108 min ± 32, p<0.01). Finally, the % LBTC was higher in the high volume EDs than in the medium and low volume EDs (3.1% ± 1.7 versus 2.9%± 1.9 versus 1.5% ± 1.1, p<0.01). ED admission percentage, ED LOS, and % LBTC were higher in high volume EDs when compared with medium and low volume EDs. A possible explanation being since high volume EDs have a higher admission percentages - suggesting higher acuity - the mean length of stay can be expected to be longer. That longer length of stay can therefore lead to a higher %LBTC. Going forward, continued research must be done to further elucidate the causes of these trends.

Exploring New Intake Models for the Emergency Department

The objective of this article was to explore new intake models for processing patients into the emergency department (ED) and disseminate these new ideas. In the fall of 2008, the Board of Directors of the Emergency Department Benchmarking Alliance (EDBA) identified intake as an area of focus and asked its members to submit new intake strategies alternative to traditional triage. All EDBA members were invited to participate via an e-mail survey. New models could be of their own design or developed by another organization and presented with permission. In all, 25 departments provided information on intake innovations. These submissions were collated into a document that outlines some of the new models. Collaborative methodology promoted the diffusion of innovation in this organization. The results of the project are presented here as an original article that outlines some of the new and mostly unpublished work occurring to improve the intake process into the ED.