Volume-related differences in emergency department performance.

BACKGROUND: 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. METHODS: 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). RESULTS: 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. CONCLUSION: 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.

OBJECTIVE: 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has been an uptick in the field of emergency department (ED) operations research and data gathering, both published and unpublished.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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Emergency department operations dictionary: results of the second performance measures and benchmarking summit.

The public, payers, hospitals, and Centers for Medicare and Medicaid Services (CMS) are demanding that emergency departments (EDs) measure and improve performance, but this cannot be done unless we define the terms used in ED operations. On February 24, 2010, 32 stakeholders from 13 professional organizations met in Salt Lake City, Utah, to standardize ED operations metrics and definitions, which are presented in this consensus paper. Emergency medicine (EM) experts attending the Second Performance Measures and Benchmarking Summit reviewed, expanded, and updated key definitions for ED operations. Prior to the meeting, participants were provided with the definitions created at the first summit in 2006 and relevant documents from other organizations and asked to identify gaps and limitations in the original work. Those responses were used to devise a plan to revise and update the definitions. At the summit, attendees discussed and debated key terminology, and workgroups were created to draft a more comprehensive document. These results have been crafted into two reference documents, one for metrics and the operations dictionary presented here. The ED Operations Dictionary defines ED spaces, processes, patient populations, and new ED roles. Common definitions of key terms will improve the ability to compare ED operations research and practice and provide a common language for frontline practitioners, managers, and researchers.

Emergency department operational metrics, measures and definitions: results of the Second Performance Measures and Benchmarking Summit.

There is a growing mandate from the public, payers, hospitals, and Centers for Medicare & Medicaid Services (CMS) to measure and improve emergency department (ED) performance. This creates a compelling need for a standard set of definitions about the measurement of ED operational performance. This Concepts article reports the consensus of a summit of emergency medicine experts tasked with the review, expansion, and update of key definitions and metrics for ED operations. Thirty-two emergency medicine leaders convened for the Second Performance Measures and Benchmarking Summit on February 24, 2010. Before arrival, attendees were provided with the original definitions published in 2006 and were surveyed about gaps and limitations in the original work. According to survey responses, a work plan to revise and update the definitions was developed. Published definitions from key stakeholders in emergency medicine and health care were reviewed and circulated. At the summit, attendees discussed and debated key terminology and metrics and work groups were created to draft the revised document. Workgroups communicated online and by teleconference to reach consensus. When possible, definitions were aligned with performance measures and definitions put forth by the CMS, the Emergency Nurses Association Consistent Metrics Document, and the National Quality Forum. The results of this work are presented as a reference document.

A multivariate time series approach to modeling and forecasting demand in the emergency department.

STUDY OBJECTIVE: The goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models. METHODS: Hourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources. RESULTS: Descriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources. CONCLUSION: Our results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

Forecasting daily patient volumes in the emergency department.

BACKGROUND: Shifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED. OBJECTIVES: The goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method. METHODS: Daily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature. RESULTS: All time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes. CONCLUSIONS: This study confirms the widely held belief that daily demand for ED services is characterized by seasonal and weekly patterns. The authors compared several time series forecasting methods to a benchmark multiple linear regression model. The results suggest that the existing methodology proposed in the literature, multiple linear regression based on calendar variables, is a reasonable approach to forecasting daily patient volumes in the ED. However, the authors conclude that regression-based models that incorporate calendar variables, account for site-specific special-day effects, and allow for residual autocorrelation provide a more appropriate, informative, and consistently accurate approach to forecasting daily ED patient volumes.

Mapping the 24-hour emergency department cycle to improve patient flow.

BACKGROUND: Intermountain Healthcare (Salt Lake City), in conjunction with emergency department (ED) staff at LDS Hospital, designed an integrated patient tracking system (PTS) and a specialized data repository (ED Data Mart) that was part of an overall enterprisewide data warehouse. After two years of internal beta testing the PTS and its associated data captures, an analysis of various ED operations by time of day was undertaken. METHODS: Real-time data, concurrent with individual ED patient encounters from July 1, 2004 through June 30, 2005 were included in a retrospective analysis. RESULTS: A number of patterns were revealed that provide a starting point for understanding ED processes and flow. In particular, ED census, acuity, operations, and throughput vary with the time of day. For example, patients seen during low-census times, in the middle of the night, appear to have a higher acuity. Radiology and laboratory utilization were highly correlated with ED arrivals, and the higher the acuity, the greater the utilization. DISCUSSION: Although it is unclear whether or not these patterns will be applicable to other hospitals in and out of the cohort of tertiary care hospitals, ED cycle data can help all facilities anticipate the resources needed and the services required for efficient patient flow. For example, the fact that scheduling of most service departments falls off after 5:00 P.M., just when the ED is most in need of those services, illustrates a fundamental mismatch between service capacity and demand.

An independent evaluation of four quantitative emergency department crowding scales.

BACKGROUND: Emergency department (ED) overcrowding has become a frequent topic of investigation. Despite a significant body of research, there is no standard definition or measurement of ED crowding. Four quantitative scales for ED crowding have been proposed in the literature: the Real-time Emergency Analysis of Demand Indicators (READI), the Emergency Department Work Index (EDWIN), the National Emergency Department Overcrowding Study (NEDOCS) scale, and the Emergency Department Crowding Scale (EDCS). These four scales have yet to be independently evaluated and compared. OBJECTIVES: The goals of this study were to formally compare four existing quantitative ED crowding scales by measuring their ability to detect instances of perceived ED crowding and to determine whether any of these scales provide a generalizable solution for measuring ED crowding. METHODS: Data were collected at two-hour intervals over 135 consecutive sampling instances. Physician and nurse agreement was assessed using weighted kappa statistics. The crowding scales were compared via correlation statistics and their ability to predict perceived instances of ED crowding. Sensitivity, specificity, and positive predictive values were calculated at site-specific cut points and at the recommended thresholds. RESULTS: All four of the crowding scales were significantly correlated, but their predictive abilities varied widely. NEDOCS had the highest area under the receiver operating characteristic curve (AROC) (0.92), while EDCS had the lowest (0.64). The recommended thresholds for the crowding scales were rarely exceeded; therefore, the scales were adjusted to site-specific cut points. At a site-specific cut point of 37.19, NEDOCS had the highest sensitivity (0.81), specificity (0.87), and positive predictive value (0.62). CONCLUSIONS: At the study site, the suggested thresholds of the published crowding scales did not agree with providers' perceptions of ED crowding. Even after adjusting the scales to site-specific thresholds, a relatively low prevalence of ED crowding resulted in unacceptably low positive predictive values for each scale. These results indicate that these crowding scales lack scalability and do not perform as designed in EDs where crowding is not the norm. However, two of the crowding scales, EDWIN and NEDOCS, and one of the READI subscales, bed ratio, yielded good predictive power (AROC >0.80) of perceived ED crowding, suggesting that they could be used effectively after a period of site-specific calibration at EDs where crowding is a frequent occurrence.

Data-driven quality improvement in the Emergency Department at a level one trauma and tertiary care hospital.

To demonstrate how a comprehensive and internally driven Continuous Quality Improvement (CQI) program was designed and implemented in our Emergency Department (ED) in 1999. This program involved monthly data collection and analysis, data-driven process change, staff education in the core concepts of quality, and data reanalysis. Data components collected during the program included census data, physician profiling, and focused clinical audits. CQI measures collected at the beginning of the program and quarterly included: (1) CQI metric data (turnaround times [TAT] and rates of left against medical advice [AMA] or left without being seen [LWOBS]), (2) rates and nature of patient complaints, and (3) results of patient satisfaction surveys performed by an outside consulting firm contracted by hospital administration. During the 4 years since its implementation the program demonstrated improvement in all measured areas. Despite an increase in patient volume of 32% to nearly 37,000 visits/year, and only minimal staffing adjustments, the mean quarterly TAT decreased from 183 min to 165 min (9.8% decrease), the rate of complaints dropped by 56.1% (2.1 per 1000 patients to 0.92), and patients leaving AMA or LWOBS decreased 66.7% from 2.7% to 0.9%. Overall, 44.8% of ED patients rated their care as "excellent." In summary, we demonstrate how a comprehensive quality improvement program was structured and implemented at a tertiary care center and how such a program demonstrated improvement in specific CQI parameters.