Comparison of the National Emergency Department Overcrowding Scale and the Emergency Department Work Index for Quantifying Emergency Department Crowding

Background: Emergency department (ED) crowding is just beginning to be quantified. The only two scales presently available are the National Emergency Department Overcrowding Scale (NEDOCS) and the Emergency Department Work Index (EDWIN). Objectives: To assess the value of the NEDOCS and the EDWIN in predicting overcrowding. The hypothesis of this study was that the NEDOCS and the EDWIN would be equally sensitive and specific for overcrowding. Methods: The NEDOCS, the EDWIN, and an overcrowding measure (OV) were determined every two hours for a ten‐day period in December 2004. The NEDOCS is a statistically derived calculation, and the EDWIN is a formula‐based calculation. The overcrowding measure is a composite of physician and charge nurse expert opinion on the degree of overcrowding as measured on a 100‐mm visual analogue scale (VAS). The primary outcome, overcrowding, was based on the dichotomized OV VAS score at the midpoint of 50 mm (≥50, overcrowded; <50, not overcrowded). The area under the receiver operator characteristic curve (AUC) and an index of adequacy (relative prognostic content) of each measure, on the basis of the likelihood ratio chi‐square statistic, were computed to evaluate the performance of NEDOCS and EDWIN. Results: There were 130 completed sampling times over ten days. The OV indicated that the ED was overcrowded 62% of the time. The AUC for the NEDOCS was 0.83 (95% CI = 0.75 to 0.90), and the AUC for the EDWIN was 0.80 (95% CI = 0.73 to 0.88). The NEDOCS score accounts for 97% of the prognostic information provided by combining all variables used in each model into one combined model. The EDWIN score accounts for only 86% (χ² test for difference, p = 0.02). Conclusions: Both scales had high AUCs, correlated well with each other, and showed good discrimination for predicting ED overcrowding. This establishes construct validity for these scales as measures of overcrowding. Which scale is used in an ED is dependent on which set of data is most readily available, with the favored scale being the NEDOCS.     

The Effect of Emergency Department Crowding on Patient Satisfaction for Admitted Patients

Objectives: The objective was to study the association between factors related to emergency department (ED) crowding and patient satisfaction. Methods: The authors performed a retrospective cohort study of all patients admitted through the ED who completed Press‐Ganey patient satisfaction surveys over a 2‐year period at a single academic center. Ordinal and binary logistic regression was used to study the association between validated ED crowding factors (such as hallway placement, waiting times, and boarding times) and patient satisfaction with both ED care and assessment of satisfaction with the overall hospitalization. Results: A total of 1,501 hospitalizations for 1,469 patients were studied. ED hallway use was broadly predictive of a lower likelihood of recommending the ED to others, lower overall ED satisfaction, and lower overall satisfaction with the hospitalization (p < 0.05). Prolonged ED boarding times and prolonged treatment times were also predictive of lower ED satisfaction and lower satisfaction with the overall hospitalization (p < 0.05). Measures of ED crowding and ED waiting times predicted ED satisfaction (p < 0.05), but were not predictive of satisfaction with the overall hospitalization. Conclusions: A poor ED service experience as measured by ED hallway use and prolonged boarding time after admission are adversely associated with ED satisfaction and predict lower satisfaction with the entire hospitalization. Efforts to decrease ED boarding and crowding might improve patient satisfaction.     

Advanced Statistics: Developing a Formal Model of Emergency Department Census and Defining Operational Efficiency

Background: Emergency department (ED) crowding has been a frequent topic of investigation, but it is a concept without an objective definition. This has limited the scope of research and progress toward the development of consistent and meaningful operational responses.
Objectives: To develop a straightforward model of ED census that incorporates concepts of ED crowding, daily patient surge, throughput time, and operational efficiency.
Methods: Using 2005–2006 patient encounter data at a Level 1 urban trauma center, a set of three stylized facts describing daily patterns of ED census was observed. These facts guided the development of a formal, mathematical model of ED census. Using this model, a metric of ED operational efficiency and a forecast of ED census were developed.
Results: The three stylized facts of daily ED census were 1) ED census is cyclical, 2) ED census exhibits an input-output relationship, and 3) unexpected shocks have long-lasting effects. These were represented by a three-equation system. This system was solved for the following expression, Censust = A(·) + B(·) cos(vT +ε) + a(et), that captured the time path of ED census. Using nonlinear estimation, the parameters of this expression were estimated and a forecasting tool was developed.
Conclusions: The basic pattern of ED census can be represented by a straightforward expression. This expression can be quickly adapted to a variety of inquiries regarding ED crowding, daily surge, and operational efficiency.     

The Effect of Emergency Department Crowding on Analgesia in Patients with Back Pain in Two Hospitals

Objectives: The authors assessed the association between measures of emergency department (ED) crowding and treatment with analgesia and delays to analgesia in ED patients with back pain. Methods: This was a retrospective cohort study of nonpregnant patients who presented to two EDs (an academic ED and a community ED in the same health system) from July 1, 2003, to February 28, 2007, with a chief complaint of “back pain.” Each patient had four validated crowding measures assigned at triage. Main outcomes were the use of analgesia and delays in time to receiving analgesia. Delays were defined as greater than 1 hour to receive any analgesia from the triage time and from the room placement time. The Cochrane‐Armitage test for trend, the Cuzick test for trend, and relative risk (RR) regression were used to test the effects of crowding on outcomes. Results: A total of 5,616 patients with back pain presented to the two EDs over the study period (mean ± SD age = 44 ± 17 years, 57% female, 62% black or African American). Of those, 4,425 (79%) received any analgesia while in the ED. A total of 3,589 (81%) experienced a delay greater than 1 hour from triage to analgesia, and 2,985 (67%) experienced a delay more than 1 hour from room placement to analgesia. When hospitals were analyzed separately, a higher proportion of patients experienced delays at the academic site compared with the community site for triage to analgesia (87% vs. 74%) and room to analgesia (71% vs. 63%; both p < 0.001). All ED crowding measures were associated with a higher likelihood for delays in both outcomes. At the academic site, patients were more likely to receive analgesia at the highest waiting room numbers. There were no other differences in ED crowding and likelihood of receiving medications in the ED at the two sites. These associations persisted in the adjusted analysis after controlling for potential confounders of analgesia administration. Conclusions: As ED crowding increases, there is a higher likelihood of delays in administration of pain medication in patients with back pain. Analgesia administration was not related to three measures of ED crowding; however, patients were actually more likely to receive analgesics when the waiting room was at peak levels in the academic ED. ACADEMIC EMERGENCY MEDICINE 2010; 17:276–283 © 2010 by the Society for Academic Emergency Medicine     

The Association Between Emergency Department Crowding and Adverse Cardiovascular Outcomes in Patients with Chest Pain

Objectives: While emergency department (ED) crowding is a worldwide problem, few studies have demonstrated associations between crowding and outcomes. The authors examined whether ED crowding was associated with adverse cardiovascular outcomes in patients with chest pain syndromes (chest pain or related complaints of possible cardiac origin). Methods: A retrospective analysis was performed for patients ≥30 years of age with chest pain syndrome admitted to a tertiary care academic hospital from 1999 through 2006. The authors compared rates of inpatient adverse outcomes from ED triage to hospital discharge, defined as delayed acute myocardial infarction (AMI), heart failure, hypotension, dysrhythmias, and cardiac arrest, which occurred after ED arrival using five separate crowding measures. Results: Among 4,574 patients, 251 (4%) patients developed adverse outcomes after ED arrival; 803 (18%) had documented acute coronary syndrome (ACS), and of those, 273 (34%) had AMI. Compared to less crowded times, ACS patients experienced more adverse outcomes at the highest waiting room census (odds ratio [OR] = 3.7, 95% confidence interval [CI] = 1.3 to 11.0) and patient-hours (OR = 5.2, 95% CI = 2.0 to 13.6) and trended toward more adverse outcomes during time of high ED occupancy (OR = 3.1, 95% CI = 1.0 to 9.3). Adverse outcomes were not significantly more frequent during times with the highest number of admitted patients (OR = 1.6, 95% CI = 0.6 to 4.1) or the highest trailing mean length of stay (LOS) for admitted patients transferred to inpatient beds within 6 hours (OR = 1.5, 95% CI = 0.5 to 4.0). Patients with non-ACS chest pain experienced more adverse outcomes during the highest waiting room census (OR = 3.5, 95% CI = 1.4 to 8.4) and patient-hours (OR = 4.3, 95% CI = 2.6 to 7.3), but not occupancy (OR = 1.8, 95% CI = 0.9 to 3.3), number of admitted patients (OR = 0.6, 95% CI 0.4 to 1.1), or trailing LOS for admitted patients (OR = 1.2, 95% CI = 0.6 to 2.0). Conclusions: There was an association between some measures of ED crowding and a higher risk of adverse cardiovascular outcomes in patients with both ACS-related and non–ACS-related chest pain syndrome.     

The Association between Emergency Department Crowding and Analgesia Administration in Acute Abdominal Pain Patients

Objectives:  The authors assessed the effect of emergency department (ED) crowding on the nontreatment and delay in treatment for analgesia in patients who had acute abdominal pain.
Methods:  This was a secondary analysis of prospectively enrolled nonpregnant adult patients presenting to an urban teaching ED with abdominal pain during a 9-month period. Each patient had four validated crowding measures assigned at triage. Main outcomes were the administration of and delays in time to analgesia. A delay was defined as waiting more than 1 hour for analgesia. Relative risk (RR) regression was used to test the effects of crowding on outcomes.
Results:  A total of 976 abdominal pain patients (mean [±standard deviation] age = 41 [±16.6] years; 65% female, 62% black) were enrolled, of whom 649 (67%) received any analgesia. Of those treated, 457 (70%) experienced a delay in analgesia from triage, and 320 (49%) experienced a delay in analgesia after room placement. After adjusting for possible confounders of the ED administration of analgesia (age, sex, race, triage class, severe pain, final diagnosis of either abdominal pain not otherwise specified or gastroenteritis), increasing delays in time to analgesia from triage were independently associated with all four crowding measures, comparing the lowest to the highest quartile of crowding (total patient-care hours RR = 1.54, 95% confidence interval [CI] = 1.32 to 1.80; occupancy rate RR = 1.64, 95% CI = 1.42 to 1.91; inpatient number RR = 1.57, 95% CI = 1.36 to 1.81; and waiting room number RR = 1.53, 95% CI = 1.31 to 1.77). Crowding measures were not associated with the failure to treat with analgesia.
Conclusions:  Emergency department crowding is associated with delays in analgesic treatment from the time of triage in patients presenting with acute abdominal pain.     

Forecasting Models of Emergency Department Crowding

Objectives:  The authors investigated whether models using time series methods can generate accurate short-term forecasts of emergency department (ED) bed occupancy, using traditional historical averages models as comparison.
Methods:  From July 2005 through June 2006, retrospective hourly ED bed occupancy values were collected from three tertiary care hospitals. Three models of ED bed occupancy were developed for each site: 1) hourly historical average, 2) seasonal autoregressive integrated moving average (ARIMA), and 3) sinusoidal with an autoregression (AR)-structured error term. Goodness of fits were compared using log likelihood and Akaike's Information Criterion (AIC). The accuracies of 4- and 12-hour forecasts were evaluated by comparing model forecasts to actual observed bed occupancy with root mean square (RMS) error. Sensitivity of prediction errors to model training time was evaluated, as well.
Results:  The seasonal ARIMA outperformed the historical average in complexity adjusted goodness of fit (AIC). Both AR-based models had significantly better forecast accuracy for the 4- and the 12-hour forecasts of ED bed occupancy (analysis of variance [ANOVA] p < 0.01), compared to the historical average. The AR-based models did not differ significantly from each other in their performance. Model prediction errors did not show appreciable sensitivity to model training times greater than 7 days.
Conclusions:  Both a sinusoidal model with AR-structured error term and a seasonal ARIMA model were found to robustly forecast ED bed occupancy 4 and 12 hours in advance at three different EDs, without needing data input beyond bed occupancy in the preceding hours.     

Estimating the Degree of Emergency Department Overcrowding in Academic Medical Centers: Results of the National ED Overcrowding Study (NEDOCS)

OBJECTIVES: No single universal definition of emergency department (ED) overcrowding exists. The authors hypothesize that a previously developed site-sampling form for academic ED overcrowding is a valid model to quantify overcrowding in academic institutions and can be used to develop a validated short form that correlates with overcrowding. METHODS: A 23-question site-sampling form was designed based on input from academic physicians at eight medical schools representative of academic EDs nationwide. A total of 336 site-samplings at eight academic medical centers were conducted at 42 computer-generated random times over a three-week period by independent observers at each site. These sampling times ranged from very slow to severely overcrowded. The outcome variable was the degree of overcrowding as assessed by the charge nurse and ED physicians. The full model consisted of objective data that were obtained by counting the number of patients, determining patients' waiting times, and obtaining information from registration, triage, and ancillary services. Specific objective data were indexed to site-specific demographics. The outcome and objective data were compared using a multiple linear regression to determine predictive validity of the full model. A five-question reduced model was calculated using a backward stepdown procedure. Predictive validity and relationships between the outcome and objective data were assessed using a mixed-effects linear regression model, treating center as random effect. RESULTS: Overcrowding occurred 12% to 73% of the time (mean, 35%), with two hospitals being overcrowded more than 50% of the time. Comparison of objective and outcome data resulted in an R(2) of 0.49 (p < 0.001), indicating a good degree of predictive validity. A reduced five-question model predicted the full model with 88% accuracy. CONCLUSIONS: Overcrowding varied widely between academic centers during the study period. Results of a five-question reduced model are valid and accurate in predicting the degree of overcrowding in academic centers.     

Enhancing Hospital-Wide Patient Flow to Reduce Emergency Department Crowding and Boarding

IntroductionED overcrowding and boarding is a global phenomenon that negatively affects patients, hospital staff, and hospital-wide operations. Poor patient flow has been identified as a major contributing factor to ED overcrowding and boarding, which is directly linked to negative patient outcomes. This project implemented a multidisciplinary rounding team that addressed barriers to patient flow in real time. By reducing the inpatient length of stay bed capacity will improve, which in turn will help alleviate ED boarding and overcrowding.MethodsThis before-and-after process improvement project took place on a 30-bed, inpatient medicine floor of a level-I trauma, tertiary, regional transfer center. Multidisciplinary rounding was used to improve care team communication and collaboration. Concepts from a Real-Time Demand Capacity model were used in this project to help develop a plan for capacity issues regarding bed supply and demand. Outcome variables included inpatient length of stay and ED boarding hours.ResultsImplementation of multidisciplinary rounding resulted in a statistically significant reduction of 0.83 days in the length of stay for patients on this floor. By increasing inpatient bed capacity, ED boarding hours for patients targeted to the 3000-medicine floor was reduced by an average of 8.83 hours per month, a reduction > 50% from baseline.DiscussionIncreasing inpatient bed capacity helps decrease ED access block, and contributes to reducing ED overcrowding. Implementing a daily multidisciplinary rounding structure on the inpatient floor helped hospital throughput by expediting discharges, which in turn created inpatient bed capacity.     

Boarding Inpatients in the Emergency Department Increases Discharged Patient Length of Stay

Background

Boarding of inpatients in the Emergency Department (ED) has been widely recognized as a major contributor to ED crowding and a cause of adverse outcomes. We hypothesize that these deleterious effects extend to those patients who are discharged from the ED by increasing their length of stay (LOS).

Study Objectives

This study investigates the impact of boarding inpatients on the ED LOS of discharged patients.

Methods

This retrospective, observational, cohort study investigated the association between ED boarder burden and discharged patient LOS over a 3-year period in an urban, academic tertiary care ED. Median ED LOS of 179,840 discharged patients was calculated for each quartile of the boarder burden at time of arrival, and Spearman correlation coefficients were used to summarize the relationship. Subgroup analyses were conducted, stratified by patient acuity defined by triage designation, and hour of arrival.

Results

Overall median discharged patient ED LOS increased by boarder burden quartile (205 [95% confidence interval (CI) 203–207], 215 [95% CI 214–217], 221 [95% CI 219–223], and 221 [95% CI 219–223] min, respectively), with a Spearman correlation of 0.25 between daily total boarder burden hours and median LOS. When stratified by patient acuity and hour of arrival (11:00 a.m.–11:00 p.m.), LOS of medium-acuity patients increased significantly by boarder burden quartile (252 [95% CI 247–255], 271 [95% CI 267–275], 285 [95% CI 95% CI 278–289], and 309 [95% CI 305–315] min, respectively) with a Spearman correlation of 0.18.

Conclusion

In this retrospective study, increasing boarder burden was associated with increasing LOS of patients discharged from the ED, with the greatest effect between 11:00 a.m. and 11:00 p.m. on medium-acuity patients. This relationship between LOS and ED capacity limitation by inpatient boarders has important implications, as ED and hospital leadership increasingly focus on ED LOS as a measure of efficiency and throughput.     

Development and Validation of a New Index to Measure Emergency Department Crowding

OBJECTIVES: To develop a quantitative measure of emergency department (ED) crowding and busyness. METHODS: A five-week study in spring 2002 in an urban teaching ED compared a new index (the Emergency Department Work Index [EDWIN]) with attending physician and nurse ratings of crowding. EDWIN is defined as summation operator n(i)t(i)/N(a)(B(T)-B(A)), where n(i) = number of patients in the ED in triage category i, t(i) = triage category, N(a) = number of attending physicians on duty, B(T) = number of treatment bays, and B(A) = number of admitted patients in the ED. The triage system used is the Emergency Severity Index (ESI), which was modified by reversing the ranking of triage categories; that is, an ESI score of 1 represented the least acute patient and 5 the sickest. EDWIN was calculated every two hours in a convenience sample of 60 eight-hour shifts. With each measurement, the charge attending physician and nurse estimated how busy/crowded the ED was, using a Likert scale. Nurse and physician assessments were averaged and compared with EDWIN scores. Data were analyzed with SPSS 10.0 (SPSS Inc., Chicago, IL). RESULTS: A total of 2,647 patients aged 18 years and older were assessed at 225 time points over 35 consecutive days. Nurses and physicians showed good interrater agreement of crowding assessment (weighted kappa 0.61, 95% confidence interval = 0.53 to 0.69). Median EDWIN scores and interquartile ranges (IQRs) when the ED was rated as not busy, average, and very busy were 1.07 (IQR = 0.80 to 1.55), 1.55 (IQR = 1.16 to 1.93), and 1.83 (IQR = 1.42 to 2.45) (p < 0.001). The ED was on diversion for 17 time blocks (6.5% of all blocks), with a median EDWIN of 2.77 (IQR = 1.83 to 3.63), compared with an EDWIN of 1.45 (IQR = 1.05 to 2.00) when not on diversion (p < 0.001). EDWIN scores correlated weakly with various process-of-care measures chosen as secondary end points. CONCLUSIONS: EDWIN correlated well with staff assessment of ED crowding and diversion. The index can be programmed into tracking software for use as a "dashboard" to alert staff when the ED is approaching crisis. If validated across other sites, EDWIN may provide a tool to compare crowding levels among different EDs.     

The Effect of Emergency Department Crowding on Clinically Oriented Outcomes

Background:  An Institute of Medicine (IOM) report defines six domains of quality of care: safety, patient-centeredness, timeliness, efficiency, effectiveness, and equity. The effect of emergency department (ED) crowding on these domains of quality has not been comprehensively evaluated.
Objectives:  The objective was to review the medical literature addressing the effects of ED crowding on clinically oriented outcomes (COOs).
Methods:  We reviewed the English-language literature for the years 1989–2007 for case series, cohort studies, and clinical trials addressing crowding's effects on COOs. Keywords searched included "ED crowding,""ED overcrowding,""mortality,""time to treatment,""patient satisfaction,""quality of care," and others.
Results:  A total of 369 articles were identified, of which 41 were kept for inclusion. Study quality was modest; most articles reflected observational work performed at a single institution. There were no randomized controlled trials. ED crowding is associated with an increased risk of in-hospital mortality, longer times to treatment for patients with pneumonia or acute pain, and a higher probability of leaving the ED against medical advice or without being seen. Crowding is not associated with delays in reperfusion for patients with ST-elevation myocardial infarction. Insufficient data were available to draw conclusions on crowding's effects on patient satisfaction and other quality endpoints.
Conclusions:  A growing body of data suggests that ED crowding is associated both with objective clinical endpoints, such as mortality, as well as clinically important processes of care, such as time to treatment for patients with time-sensitive conditions such as pneumonia. At least two domains of quality of care, safety and timeliness, are compromised by ED crowding.     

Supplemented Triage and Rapid Treatment (START) Improves Performance Measures in the Emergency Department

Background: Emergency Department (ED) crowding is well recognized, and multiple studies have demonstrated its negative effect on patient care. Study Objectives: This study aimed to assess the effect of an intervention, Supplemented Triage and Rapid Treatment (START), on standard ED performance measures. The START program complemented standard ED triage with a team of clinicians who initiated the diagnostic work-up and selectively accelerated disposition in a subset of patients. Methods: This retrospective before–after study compared performance measures over two 3-month periods (September–November 2007 and 2008) in an urban, academic tertiary care ED. Data from an electronic patient tracking system were queried over 12,936 patients pre-intervention, and 14,220 patients post-intervention. Primary outcomes included: 1) overall length of stay (LOS), 2) LOS for discharged and admitted patients, and 3) the percentage of patients who left without complete assessment (LWCA). Results: In the post-intervention period, patient volume increased 9% and boarder hours decreased by 1.3%. Median overall ED LOS decreased by 29 min (8%, 361 min pre-intervention, 332 min post-intervention; p < 0.001). Median LOS for discharged patients decreased by 23 min (7%, 318 min pre-intervention, 295 min post-intervention; p < 0.001), and by 31 min (7%, 431 min pre-intervention, 400 min post-intervention) for admitted patients. LWCA was decreased by 1.7% (4.1% pre-intervention, 2.4% post-intervention; p < 0.001). Conclusions: In this study, a comprehensive screening and clinical care program was associated with a significant decrease in overall ED LOS, LOS for discharged and admitted patients, and rate of LWCA, despite an increase in ED patient volume.     

The Effect of Emergency Department Crowding on Education: Blessing or Curse?

Emergency department (ED) crowding is a national crisis that contributes to medical error and system inefficiencies. There is a natural concern that crowding may also adversely affect undergraduate and graduate emergency medicine (EM) education. ED crowding stems from a myriad of factors, and individually these factors can present both challenges and opportunities for education. Review of the medical literature demonstrates a small body of evidence that education can flourish in difficult clinical environments where faculty have a high clinical load and to date does not support a direct deleterious effect of crowding on education. To provide a theoretical framework for discussing the impact of crowding on education, the authors present a conceptual model of the effect of ED crowding on education and review possible positive and negative effects on each of the six recognized Accreditation Council for Graduate Medical Education (ACGME) core competencies.     

Emergency Department Patients Who Stay More Than 6 Hours Contribute to Crowding

Background: Admitted and discharged patients with prolonged emergency department (ED) stays may contribute to crowding by utilizing beds and staff time that would otherwise be used for new patients. Objectives: To describe patients who stay > 6 h in the ED and determine their association with measures of crowding. Methods: This was a retrospective, observational study carried out over 1 year at a single, urban, academic ED. Results: Of the 96,562 patients seen, 16,017 (17%) stayed > 6 h (51% admitted). When there was at least one patient staying > 6 h, 60% of the time there was at least one additional patient in the waiting room who could not be placed in an ED bed because none was open. The walk-out rate was 0.34 patients/hour when there were no patients staying in the ED > 6 h, vs. 0.77 patients/hour walking out when there were patients staying > 6 h in the ED (p < 0.001). When the ED contained more than 3 patients staying > 6 h, a trend was noted between increasing numbers of patients staying in the ED > 6 h and the percentage of time the ED was on ambulance diversion (p = 0.011). Conclusion: In our ED, having both admitted and discharged patients staying > 6 h is associated with crowding.     

Racial Disparities in Emergency Department Length of Stay for Admitted Patients in the United States

Objectives: Recent studies have demonstrated the adverse effects of prolonged emergency department (ED) boarding times on outcomes. The authors sought to examine racial disparities across U.S. hospitals in ED length of stay (LOS) for admitted patients, which may serve as a proxy for boarding time in data sets where the actual time of admission is unavailable. Specifically, the study estimated both the within‐ and among‐hospital effects of black versus non–black race on LOS for admitted patients. Methods: The authors studied 14,516 intensive care unit (ICU) and non‐ICU admissions in 408 EDs in the National Hospital Ambulatory Medical Care Survey (NHAMCS; 2003–2005). The main outcomes were ED LOS (triage to transfer to inpatient bed) and proportion of patients with prolonged LOS (>6 hours). The effects of black versus non–black race on LOS were decomposed to distinguish racial disparities between patients at the same hospital (within‐hospital component) and between hospitals that serve higher proportions of black patients (among‐hospital component). Results: In the unadjusted analyses, ED LOS was significantly longer for black patients admitted to ICU beds (367 minutes vs. 290 minutes) and non‐ICU beds (397 minutes vs. 345 minutes). For admissions to ICU beds, the within‐hospital estimates suggested that blacks were at higher risk for ED LOS of >6 hours (odds ratio [OR] = 1.42, 95% confidence interval [CI] = 1.01 to 2.01), while the among‐hospital differences were not significant (OR = 1.08 for each 10% increase in the proportion of black patients, 95% CI = 0.96 to 1.23). By contrast, for non‐ICU admissions, the within‐hospital racial disparities were not significant (OR = 1.12, 95% CI = 0.94 to 1.23), but the among‐hospital differences were significant (OR = 1.13, 95% CI = 1.04 to 1.22) per 10% point increase in the percentage of blacks admitted to a hospital. Conclusions: Black patients who are admitted to the hospital through the ED have longer ED LOS compared to non–blacks, indicating that racial disparities may exist across U.S. hospitals. The disparity for non‐ICU patients might be accounted for by among‐hospital differences, where hospitals with a higher proportion of blacks have longer waits. The disparity for ICU patients is better explained by within‐hospital differences, where blacks have longer wait times than non–blacks in the same hospital. However, there may be additional unmeasured clinical or socioeconomic factors that explain these results.