US Emergency Department Performance on Wait Time and Length of Visit

Background 

Emergency department (ED) crowding in the United States has become so severe that the Institute of Medicine calls it a “national epidemic.”1 Across the nation, an ambulance is diverted away from a crowded ED approximately once every minute.1 Patients who do arrive in the ED have faced increasingly long average wait times and ED visit lengths over the past decade.2, 3 Most important, these increases have been most pronounced for patients with the most acute illnesses.2, 3, 4 In 2006, the average wait time for emergent patients to be treated by an ED provider was 37 minutes, well above the recommended maximum of 15 minutes.4

Importance 

Prolonged ED wait time and length of visit reduce quality of care and increase adverse events for patients with serious illnesses.5, 6, 7, 8, 9 For example, patients presenting with non-ST-elevation myocardial infarction who have an ED stay of more than 8 hours are more likely to have recurrent inhospital myocardial infarction than patients with an average ED stay.8 Prolonged wait time and length of visit also decrease patient satisfaction10, 11, 12 and increase the number of patients who leave before being seen.13, 14 Therefore, ED wait time and length of visit are important measures of the timeliness, efficiency, safety, and patient-centeredness of emergency care.

Several studies have examined wait time and visit length across the nation in aggregate,2, 3, 15, 16 including a recent comprehensive report from the Government Accountability Office.4 However, none of these studies examined hospital-level variability. The Government Accountability Office report argues that hospital-level conditions such as the availability of inpatient beds are the most important determinants of crowding and delayed care.4 Consequently, from a policy and quality improvement perspective, it is most appropriate to consider these metrics from a hospital rather than patient viewpoint.

Goals of This Investigation 

The National Quality Forum recently endorsed 10 voluntary consensus standards for emergency care quality, including measures of ED wait time, visit length for admitted patients, and visit length for discharged patients.17 If widely adopted, these measures would be reported at the hospital level. While one study has examined hospital-level performance on visit length for black patients,16 there have been no studies of variation in hospital-level performance on these metrics for all patients. National data on hospital-level performance, which would allow EDs to benchmark themselves against peers and distinguish targets for improvement, are needed.18 Accordingly, we sought to characterize the variation in ED performance in wait times and visit lengths nationally, using data from 2006 National Hospital Ambulatory Medical Care Survey (NHAMCS).

Theoretical Model of the Problem 

The central tenet of quality improvement is that “quality is a system property.”19 ED wait time and length of visit have been observed to differ systematically according to race, ethnicity, site of care, and a variety of other immutable patient-level factors.2, 15, 16 Using instead the quality improvement model, we examine ED wait time and length of visit at the system (hospital) level. Describing hospital-level rather than patient-level performance allows for benchmarking, characterization of variation, recognition of positive and negative outliers, and assessment of effective care practices, activities necessary for sustainable quality improvement.

Study Design, Setting, and Selection of Participants 

We conducted a cross-sectional study of patient visits to 364 US EDs in 2006, using NHAMCS data. The NHAMCS is a 4-stage probability sample of visits to EDs of US general and short-stay hospitals, excluding federal, military, and Veterans Administration hospitals. Each hospital abstracts data from a systematic random sample of ED visits during a randomly assigned 4-week period. Data collection, abstraction, and cleaning procedures have been fully described elsewhere.20 The data set includes weights to facilitate estimation of national results. This is a publicly available, anonymous dataset that is not direct human subjects research and does not contain any identifiable personal health information; institutional review board clearance is not necessary and was not obtained.

Methods of Measurement and Outcome Measures 

We examined median ED performance on wait time, defined as number of minutes between the time the patient arrived at the ED and the time the patient was treated by a provider, and length of visit, defined as the number of minutes between the time the patient arrived at the ED and the time the patient was discharged from the ED. These medians correspond to the National Quality Forum measures for wait time and length of visit. The data set includes a 5-level triage assessment variable: immediate (treat in 0 minutes), emergent (treat in 1 to 14 minutes), urgent (treat in 15 to 60 minutes), semiurgent (treat in 61 minutes to 2 hours), and nonurgent (treat in 121 minutes to 24 hours). Because 5-level triage systems are not yet universal in the United States,21 we collapsed the first 2 categories into one emergency category (treat in 0 to 14 minutes). For each ED, we calculated the percentage of patients who were treated within their triage target period4 and repeated this analysis, restricting the data set to acutely ill patients (those triaged as emergency or urgent). For each ED, we also calculated the percentage of patients with a visit less than 4 hours, according to United Kingdom standards,22 and the percentage with a visit less than 6 hours, according to Canadian standards.23

Our independent variables included visit characteristics, patient sociodemographic factors, and hospital characteristics. Visit characteristics included triage category (emergent, urgent, semiurgent, nonurgent), level of pain at presentation, admission to intensive care, number of medications given in ED (0, 1 to 3, ≥3), season of visit, and day of visit. Because research suggests that ED tests lengthen ED stays,15 we included binary dummy variables for presence of any laboratory test, any plain radiograph, any ultrasonography, any computed tomography scan, any magnetic resonance imaging scan, or any ECG for the length of visit analyses. We also created 2 dummy variables for procedures: one for any emergency procedure (cardiopulmonary resuscitation, endotracheal intubation, or thrombolysis) and one for any nonemergency procedure (all others).

Patient sociodemographic factors included age, self-reported race and ethnicity (non-Hispanic white, non-Hispanic black, Hispanic, other), sex, method of payment, arrival by ambulance, quartile of poverty rate in the patient's zip code (<5%, 5% to 9.99%, 10% to 19.99%, ≥20%), quartile of median household income in the patient's zip code (<$32,793, $37,794 to $40,626, $40,627 to $52,387, ≥$52,388), and quartile of bachelor's degree or higher in the patient's zip code (<12.8%, 12.8% to 19.7%, 19.7% to 31.7%, ≥31.7%).

Hospital characteristics included urban status (mode of patients' zip code locations: large central metro, large fringe metro, medium metro, small metro, nonmetro), ownership, proportion of uninsured patients (<10%, 10% to 19.9%, 20% to 29.9%, ≥30%), geographic region (Northeast, Midwest, South, West), teaching status (any patients treated by a trainee versus none), and use of electronic medical records (all electronic; part paper, part electronic; all paper).

Primary Data Analysis 

We used standard descriptive statistics to characterize the sample of patients and hospitals. To describe EDs' median performance on wait time and length of visit, we reported the median of ED medians, with interquartile ranges (IQRs). We also constructed histograms of EDs' median results for wait time by triage category and length of visit for admitted and for nonadmitted patients. We then calculated the weighted percentage of EDs treating a given percentage of their patients within triage targets or within length of visit goals (eg, half their patients, 75% of their patients, 90% of their patients).

We estimated linear models with a separate random intercept for each ED to identify the percentage of variation in log-transformed wait time and in log-transformed length of visit that was present within hospitals and between hospitals. Next, we estimated hospital random-effects regressions with a separate random intercept for each hospital, with one of the hospitals excluded as the reference group, and reported the percentage of overall variation explained by the model (R2).24, 25 This approach allowed us to account for the clustering of patients within hospitals while including hospital-level predictors of wait time performance. Multivariate analyses were conducted using Stata (version 10.0; StataCorp, College Station, TX), and all other analyses were conducted using SAS (version 9.1.2; SAS Institute, Cary, NC). To account for the complex sampling design and survey weights, procedures surveyfreq and surveymeans were used in SAS. All statistical tests were 2-tailed, and we used a P value of .05 to determine statistical significance.

Characteristics of Study Subjects 

The 2006 NHAMCS data set comprised data about 35,849 patient visits at 364 EDs, weighted to represent 119,191,528 visits to 4,654 EDs. The wait time analysis included 24,889 patient visits to 354 EDs; the length of visit analyses included 33,339 patient visits to 363 EDs. Descriptive statistics for the sample of patients and hospitals are shown in Table 1.

Table 1. Weighted characteristics of study sample.

	Characteristic	Percentage of Total Sample⁎
	Visit-level characteristics
	Arrival by ambulance	16.0
	Triage assessment
	Nonurgent (treat in >2-24 h)	12.1
	Semiurgent (treat in >1-2 h)	22.0
	Urgent (treat in 15-60 min)	36.6
	Emergent (treat in 0-14 min)	15.9
	No/unknown triage	13.4
	Pain
	No pain	19.6
	Mild pain	13.9
	Moderate pain	25.9
	Severe pain	21.1
	Unknown pain	19.5
	Any blood test or ECG	41.0
	Any radiology	44.2
	Any emergent procedure	0.5
	Any nonemergent procedure	47.5
	Number of medications given in ED
	0	44.6
	1-3	49.3
	>3	6.0
	Admitted to hospital	12.8
	Season
	Winter	26.4
	Spring	24.0
	Summer	25.6
	Autumn	24.0
	Day of week
	Weekend	28.9
	Age, y, mean	36.8
	Female	54.5
	Race/ethnicity
	White, non-Hispanic	60.3
	Black, non-Hispanic	23.6
	Hispanic Other	12.5 3.7
	Payment type
	Private insurance	35.3
	Medicare	14.8
	Medicaid/SCHIP	26.7
	Self-pay	18.5
	Other pay type	4.7
	Percentage with bachelor's degree or higher in patient's zip code
	<12.84	32.9
	12.84-19.66	26.6
	19.67-31.68	22.2
	≥31.69	18.3
	Median household income of patient's zip code, $
	<32,793	33.7
	32,794-40,626	27.0
	40,627-52,387	21.3
	≥52,388	18.1
	Median poverty rate of patient's zip code, %
	<5	13.2
	5-9.99	25.8
	10-19.99	37.4
	≥20	23.6
	Median wait time, min (IQR)	33(15-69)
	Median wait time, by triage category, min (IQR)
	Emergent (treat within 15 min)	15(6-38)
	Urgent (treat in 16-60 min)	32(17-60)
	Semiurgent (treat in 60-120 min)	48(23-90)
	Nonurgent (treat in 2-24 h)	44(20-95)
	No triage or unknown	33(14-74.5)
	Median length of visit, patients ultimately admitted, h (IQR)	4.4(2.8-6.7)
	Median length of visit, patients ultimately discharged, h (IQR)	2.3(1.3-3.8)
	Hospital-level characteristics
	Region
	Northeast	13.9
	Midwest	28.7
	South	38.9
	West	18.5
	Urban status
	Large central metro	17.9
	Large fringe metro	15.7
	Medium metro	20.0
	Small metro	13.3
	Nonmetro	33.2
	Ownership
	Voluntary nonprofit	67.6
	Government, nonfederal	22.0
	Proprietary	10.4
	Teaching status
	Teaching hospital	47.7
	Proportion of uninsured patients, %
	0-9.9	32.1
	10-19.9	34.0
	20-29.9	25.1
	≥30	8.9
	Electronic medical records
	All electronic	16.1
	Part paper, part electronic	30.4
	None electronic	53.5

Weighted visit N=119,191,528. Weighted ED N=4,654. SCHIP, State Children’s Health Insurance Program; IQR, interquartile range.

⁎ Unless otherwise specified in the table.

Main Results 

Median ED wait times are shown in Table 2. Among acutely ill (emergent and urgent) patients, the median ED wait time was 27.5 minutes, with wide variability across hospitals (IQR 19 min to 40 min). Variation in EDs' median wait times was apparent at every level of triage urgency (Table 2; Figure 1).

Table 2. Hospital performance on wait time and length of ED visit.

	Performance Measure	Mean of Hospital Means (SE)	Median of Hospital Medians (IQR)	Median Hospital Proportion Within Target, % (IQR)
	Wait time, all patients, min	52.4(7.3)	34.0(22.5-47.8)	78.3(63.2-89.5)
	Wait time by triage category, min
	Immediate and emergent (treat within 15 min)	31.8(4.6)	16.0(9.5-28.0)	48.4(23.1-66.7)
	Urgent (treat in 16-60 min)	45.2(3.0)	32.0(23.0-45.0)	80.0(63.3-92.2)
	Semiurgent (treat in 60-120 min)	58.6(3.4)	45.0(30.0-75.0)	91.7(77.4-100.0)
	Nonurgent (treat in 2-24 h)	68.6(6.7)	45.0(27.0-83.0)	100(100-100)
	No triage or unknown	60.9(13.2)	36.0(21.0-63.0)	N/A
	Length of visit, patients ultimately admitted, h	4.93(0.65)	4.3(3.3-5.6)	76.3(54.4-93.9)⁎ 60.0(35.3-87.5)†
	Length of visit, patients ultimately discharged, h	3.0(0.45)	2.3(1.9-2.9)	93.0(87.1-97.3)⁎ 86.8(75.5-94.3)†

SE, Standard error.

⁎ Proportion within 6-hour target. † Proportion within 4-hour target.

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Figure 1. Distribution of hospital median wait time, by triage assessment.

The median ED evaluated 78.3% of its patients within the triage target time (IQR 63.2% to 89.5%). Performance for acutely ill patients was lower. The median ED evaluated 66.9% of its acutely ill patients within the triage target time (IQR 52.0% to 81.9%). Although 80.3% of EDs evaluated at least half of their acutely ill patients within the triage target time, only 13.8% achieved this target for at least 90% of their patients (Table 3). Overall, 82.7% of the variation in wait time was due to within-hospital (patient-level) variation, and 17.3% of the variation was due to between-hospital (hospital-level) variation. The multivariate model explained 15.0% of the total variation (R2=0.15).

Table 3. Weighted percentage of hospitals meeting performance measure for a given proportion of their patients.

		Wait Time Within Triage Target		Length of Visit Less Than 6 Hours		Length of Visit Less Than 4 Hours
		All Patients	Immediate, Emergent, and Urgent Patients*	Patients Discharged Home	Patients Admitted	Patients Discharged Home	Patients Admitted
	Percent of Hospitals	(Weighted Hospital N=4,402)	(Weighted Hospital N=4,380)	(Weighted Hospital N=4,653)	(Weighted Hospital N= 4,015)	(Weighted Hospital N= 4,653)	(Weighted Hospital N= 4,015)
	At least half of patients, % (SE)	91.7(2.0)	80.3(2.9)	98.6(1.3)	90.3(2.0)	96.1(1.5)	62.7(5.0)
	At least 75% of patients, % (SE)	64.9(4.2)	37.1(3.9)	95.7(1.6)	69.6(4.5)	75.5(3.8)	41.6(5.7)
	At least 90% of patients, % (SE)	30.5(4.3)	13.8(3.3)	79.0(3.3)	47.7(5.1)	41.1(5.2)	24.5(5.5)

The median ED length of visit was 4.3 hours (IQR 3.3 to 5.6 hours) for admitted patients and 2.3 hours (IQR 1.9 to 2.9 hours) for discharged patients (Table 2). Figure 2 illustrates ED variation in length of visit by showing the distribution of EDs' median lengths of visit for admitted and discharged patients, separately.

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Figure 2. Distribution of hospital median length of visit, by discharge disposition.

The median ED admitted 76.3% (IQR 54.4% to 93.9%) of its patients within 6 hours and 60.0% (IQR 35.3% to 87.5%) of its patients within 4 hours. This outcome, too, varied substantially among EDs: 90.3% of EDs admitted at least 50% of their patients within 6 hours, whereas 47.7% of EDs admitted at least 90% of their patients within 6 hours (Table 3). Similar variation was found for 4-hour visit lengths (Table 3). Overall, 66.8% of the variation in visit length for admitted patients was within hospitals, and 33.2% of the variation was between hospitals. The multivariate model explained 6.9% of the total variation (R2=0.069).

The median ED discharged 93.0% (IQR 87.1% to 97.3%) of its patients within 6 hours and 86.8% (75.5% to 94.3%) of its patients within 4 hours. As shown in Table 3, EDs had better performance and were more consistent on this outcome for their discharged patients. A total of 98.6% of EDs discharged at least 50% of their patients within 6 hours, and 96.1% of EDs discharged at least 50% of their patients within 4 hours. A total of 79.0% of EDs discharged at least 90% of their patients within 6 hours, but only 41.1% of EDs discharged at least 90% of their patients within 4 hours. Overall, 82.6% of the variation in visit length for discharged patients was within hospitals, and 17.4% of the variation was between hospitals. The multivariate model explained 28.7% of the total variation (R2=0.287).