Article

Relationship between the National ED Overcrowding Scale and the number of patients who leave without being seen in an academic ED

Relationship between the National ED Overcrowding Scale and the number of patients who leave without being seen in an academic ED

Steven J. Weiss MDa,*, Amy A. Ernst MDa, Robert Derlet MDb, Richard Kingb,

Aaron Bair MDb, Todd G. Nick PhDc

aDepartment of Emergency, University of New Mexico, Albuquerque, NM 87131-0001, USA

bDavis Medical Center, University of California, Sacramento, CA, USA

cCenter for Epidemiology & Biostatistics, Cincinnati Children’s Hospital Medical Center and University of Cincinnati, College of Medicine, Cincinnati, OH, USA

Received 26 January 2005; accepted 1 February 2005

Abstract

Objective: We hypothesize that the number of patients who leave without being seen is correlated with the simple-to-use National Emergency Department Overcrowding Scale (NEDOCS).

Methods: Results of a 6-item ED overcrowding scale (NEDOCS) were collected prospectively over a 17-day study period. The following additional data were extracted from records for each 2-hour study period: (1) number of registered patients, (2) number of ambulances that arrived, and (3) number of patients signed in that hour who eventually Left without being seen. Spearman Correlation coefficients were computed for the leaving without being seen (LWBS) rate with the NEDOCS score at the time of patient presentation and 2, 4, and 6 hours later.

Results: The study period represents two hundred fourteen 2-hour periods. The LWBS rate was determined for 100% of the times; NEDOCS scores were determined for a sampling of 62% of the times spread equally over all hours of the day and days of the week. Correlation between the NEDOCS score and LWBS was 0.665.

Conclusion: The NEDOCS score is well correlated with LWBS.

D 2005

Presented at Society for Academic Emergency Medicine, Orlando, Fla, May 2003.

T Corresponding author. Tel.: +1 505 272 5062; fax: +1 505 272 6503.

E-mail address: [email protected] (S.J. Weiss).

Introduction

Emergency department (ED) overcrowding is just begin- ning to be studied in ways that allow for standardized quantification of the problem [1-4]. Methods of quantifica- tion have been based on different theoretical constructs but all have emergency providers’ opinions as a starting point. The National ED Overcrowding Scale (NEDOCS) was

0735-6757/$ – see front matter D 2005 doi:10.1016/j.ajem.2005.02.034

designed and validated based on the standard of emergency providers’ opinion of overcrowding [4]. Although not the perfect gold standard, it appears to be a consistent marker for the construct of overcrowding. It is as clear as many gold standards that have been used in other research areas of emergency medicine such as pulmonary embolism studies, where the gold standard is constantly changing [5-7]. Emergency department expert opinion is also the only standard that is logical as a starting point to develop quantitative measures of overcrowding.

Individual problems that are considered to be related to overcrowding also need to be quantified so that they can be further studied. The problems that have been addressed in the literature include the following: (1) patients who leave without being seen [8-12], (2) Medical errors [13-22], (3) Ambulance diversion [23-28], (4) ED length of stay [11,29-31], (5) quality indicators in the ED such as time to a first electrocardiogram (ECG) in chest pain patients [32-35], (6) patient satisfaction [36-38], and (7) death and disability. In this study, we focused specifically on the first of these problems, those patients who leave without being seen.

This study compared the results of the NEDOCS [4], a simple overcrowding scale, and the complex variable of patients who leave without being seen. Our primary hypothesis was that the rate of leaving without being seen (LWBS) could be predicted by ED overcrowding as measured by the NEDOCS. A secondary hypothesis was that the rate of LWBS could be predicted by the NEDOCS at 2, 4, and 6 hours after patients’ presentation to the ED.

Methods

The study was conducted at an academic medical center with an ED census of more than 60 000 adult patient visits per year. This was a prospective observa- tional study. Samplings were taken over an 18-day period in July 2003. Leaving without being seen was determined for 214 consecutive 2-hour periods during this time. National ED Overcrowding Scale scores were determined for a sampling of 62% of the periods. The Institutional Review Board approved this study as exempt.

The NEDOCS scale

The criteria for NEDOCS variables were the following:

(1) represented a bsnapshotQ in time of the ED, (2) repre- sented an aspect of ED patient management (eg, triage, treatment, and disposition), (3) readily available, (4) defin- able such that results were reproducible between observers, and (5) consistently defined between institutions. Variables such as patient acuity, the definition of which varies greatly between institutions, were not used.

The NEDOCS was created in a stepwise fashion. First, a Composite outcome variable representing staff opinions of the degree of overcrowding was selected and tested.

Then, 19 variables that fit the above criteria and reflected overcrowding were chosen for the full model. Next, this full model was compared with the composite outcome variable. Finally, a reduced model of 5 variables was developed. The full model had an R2 of 0.49 when compared with the composite outcome variable. The reduced model represented 92% of the variation in the full model [1,4].

The reduced model of overcrowding includes the following 5 items: (1) ED patients (indexed to ED beds),

(2) number of ventilators in use in the ED, (3) longest admission time, (4) waiting room wait time for the last patient called to a bed, and (5) indexed admissions in the ED (indexed to Hospital beds). The results of entering these items into the developed algorithm are to yield a score between 1 and 200 with less than 100 considered not overcrowded and more than 100 overcrowded. Within this spectrum, 6 categories exist from bnot busyQ to bdangerously overcrowded.Q

Data collection

Trained observers in the ED collected and time stamped the 5 variables required to obtain a NEDOCS score. In addition, total census, diversion status, number of ambu- lance arrivals, and time to get an ECG for patients presenting with chest pain (ECG time) were recorded from computerized ED flow records. Electrocardiogram data were collected only if a patient presented acutely during the 2-hour sampling time.

The primary investigator extracted all ED log data for time patients signed in, whether or not they left without being seen, and primary diagnosis. Patients who leave without being seen were defined as patients who registered and were triaged at a given time but were never seen by a physician. The LWBS status was determined retrospectively from the ED logs as patients who had registered at a cer- tain time and had not been seen by a physician more than 72 hours later.

Table 1 Results of NEDOCS score and LWBS for sampling times of the day over a 2-week period

Time

Average NEDOCS F

SD (no. of samples)

Average LWBS F

SD (no. of samples)

2 am

77.6 F 11.6 (11)

0.5 F 0.1 (18)

4 am

88.5 F 11.7 (7)

0.9 F 0.2 (17)

6 am

79.7 F 19.9 (6)

0.7 F 0.2 (17)

8 am

90.9 F 32.9 (11)

0.8 F 0.2 (18)

10 am

75.1 F 28.1 (11)

1.5 F 0.4 (18)

Noon

92.2 F 24.0 (14)

2.4 F 0.6 (18)

2 pm

103.8 F 26.7 (14)

2.0 F 0.5 (18)

4 pm

107.4 F 28.4 (14)

1.8 F 0.4 (18)

6 pm

103.9 F 23.3 (13)

2.4 F 0.6 (18)

8 pm

108.0 F 24.1 (12)

1.8 F 0.4 (18)

10 pm

108.1 F 23.7 (11)

1.8 F 0.4 (18)

Midnight

82.3 F 31.5 (11)

1.4 F 0.3 (18)

Data are based on averages for all samplings based on 2-hour periods.

T P b .05.

TT P b .01.

0.15

0.60T

0.36

0.28

0.86TT

0.36

0.27

0.80TT

0.73TT 0.36

ECG

divert ambulances times

0.47 0.77TT 0.54 0.68T

scores

0.66T

Average

LWBS

Average NEDOCS

score

% of time on divert

Average no. of

ambulances Average

census

Table 2 spearman correlation coefficients for LWBS,

NEDOCS score, time on diversion, number of ambulances, census, and ECG times

Average % of Average Average Average NEDOCS time on no. of census

Fig. 1 A, Average number of patients LWBS for every 2-hour period during the day. Error bars represent SD. B, Average NEDOCS score for every 2-hour period during the day. Error bars represent SD.

Statistics

Samplings were combined to represent time of day (2-hour periods). Mean values and SEs were calculated

Fig. 2 Average number of patients LWBS vs NEDOCS score.

and plotted for twelve 2-hour periods for LWBS and NEDOCS. Spearman correlation coefficients were used to compare LWBS with the NEDOCS score at the time of registration, average time on divert, number of ambulan- ces, total census, and ECG times. Spearman correlation coefficients were also used to compare LWBS with NEDOCS scores at 2, 4, and 6 hours after presentation. Results were significant if the P value was less than .05. Multivariate analysis was performed excluding variables for sparseness of data and multicolinearity. All statistical computations were performed using SPSS version 12.0 (SPSS Inc, Chicago Ill, 2003). Relative risks of LWBS were calculated based on presentation diagnosis using confidence interval analysis (CIA 2.1, Southampton,

Table 3 Spearman correlation coefficients for LWBS and NEDOCS score at 0, 2, 4,and 6 hours after patient presentation

Average NEDOCS

Average NEDOCS

Average NEDOCS

Average NEDOCS

2 hours

4 hours

6 hours

later

later

later

Average LWBS

0.66T

0.67T

0.67T

0.19

Average

0.43

0.21

–0.70

NEDOCS

Average

0.43

0.21

NEDOCS

2 hours later

Average

0.43

NEDOCS

4 hours later

Data are based on averages for all samplings based on 2-hour periods.

T P b .05.

in group

in group

Gynecological

33

1.9

12

3.8

45

2.1 (1.20-3.58)T

Musculoskeletal

86

4.8

28

9.0

114

1.9 (1.3-2.8)T

Neurological

140

7.9

36

11.5

176

1.5 (1.1-2.0)T

Gastrointestinal/genitourinary

291

16.3

66

21.1

357

1.3 (1.1-1.6)T

Minor nonemergent complaints

127

7.1

28

9.0

155

1.3 (0.9-1.8)

Skin/soft tissue

137

7.7

25

8.0

162

1.1 (0.7-1.5)

Eye, ear, nose, throat

134

7.5

23

7.4

157

1.0 (0.7-1.4)

Toxicological

19

1.1

3

1.0

22

0.9 (0.3-2.5)

Trauma

493

27.7

67

21.4

560

0.8 (0.6-0.9)T

Fever

63

3.5

7

2.2

70

0.6 (0.3-1.2)

Endocrine

22

1.2

2

0.6

24

0.5 (0.2-1.7)

Pulmonary

95

5.3

7

2.2

102

0.4 (0.2-0.8)T

Cardiac

138

8.4

8

2.6

146

0.3 (0.2-0.6)T

Unknown

3

0.2

0

0.0

3

Total

1781

312

2093

London, 2001). The study was approved by the Institu- tional Review Board.

Table 4 Comparison of diagnoses among patients who were treated vs those who left without being seen

Disease category

Treated

n

Left without being seen

% of total patients

n

% of total patients

Total patients Relative risk

registered (95% CI)

Results are ordered from diagnoses in patients most to least likely to leave without being seen.

T P b .05.

Results

Of 214 sampling times, 132 NEDOCS scores were captured for a sampling of 62%. Over the course of the study, the NEDOCS scores represented samples from all times of day. In total, 58 of the 132 (44%) samplings had NEDOCS scores indicating an overcrowded status (ie, a score N100). Table 1 illustrates the mean values, SDs, and number of NEDOCS and LWBS samplings with respect to time, where each time listed represents the end of a 2-hour time interval.

Fig. 1A shows 2-hour time intervals vs the average number of patients LWBS. There is an increase in the number of patients LWBS that begins in the late morning hours. In Fig. 1B, 2-hour time intervals are plotted vs the NEDOCS score. A NEDOCS score of 100 is considered the cutoff for overcrowding. This graph also displays a similar rising trend in crowding beginning in the late morning hours. It appears that overcrowding begins between noon and 2 pm and that the ED remains overcrowded the rest of the day.

There is a strong positive Spearman correlation between the data in the graphs. In Fig. 2, the NEDOCS scores are plotted on the x-axis and the number of patients LWBS is plotted on the y-axis. The Spearman correlation between LWBS rates and NEDOCS scores is 0.665 ( P b .05).

Table 2 is a correlation matrix of LWBS rates, NEDOCS scores, and other variables obtained during the sampling times. All variables were obtained simultaneously with the NEDOCS score. Chest pain patients (for ECG times) were

available for 38 (18%) sampling times. Overcrowding (NEDOCS) is extremely well correlated with LWBS, diversion status, and census. Leaving without being seen was well correlated with overcrowding but also with number of ambulances and ECG times. For a multivariate analysis, ECG times were removed because of the sparse- ness of data. Diversions and census were removed because of their high correlation with each other. The multivariate analysis showed that NEDOCS by itself accounted for an R2 of 0.43 ( P b .02). With the number of ambulances included, the R2 was 0.76 (change of 0.35; P b .02). The addition of census resulted in a nonsignificant improvement in R2 to

0.78 (change of 0.02; P = .40). The unadjusted effect for the NEDOCS was 0.03 (95% CI, 0.01-0.06). Adjusted for the NEDOCS score, a multivariable model showed a value of

0.62 (95% CI, 0.21-1.02) for the number of ambulances and

0.02 (95% CI, –0.02-0.05) for census.

When the LWBS data are compared with NEDOCS scores at time points after patient registration, the correlation remains strong. The Spearman correlation is 0.67 at 2 hours and 0.67 likewise at 4 hours. At 6 hours, the correlation drops off to 0.19. These are shown in Table 3.

The Risk of LWBS was not the same for all diagnostic groups. The relative risk of leaving ranged from 2.1 for patients with obstetrics and gynecology complaints to 0.3 for those with cardiac complaints. These are summarized in Table 4.

Discussion

We found that ED overcrowding was an issue in 44% of the periods analyzed for this study. This was similar to the

35% previously reported in the multicenter NEDOCS trial [4]. Although NEDOCS scores were collected on 14 of the 17 days within the study period, 13 of the 14 days (93%) were overcrowded during at least one 2-hour period. We were able to show a strong correlation between the easy-to-obtain overcrowding scale and the number of patients LWBS. We found that the correlation was consistent over the 2 to 4 hours after presentation. Leaving without being seen can be predicted with a good degree of certainty with the NEDOCS. In fact, the overcrowding scores at 2 and 4 hours after registration are equally good predictors of LWBS. This suggests that patients LWBS wait between 4 and 6 hours before leaving. Baker et al [8] found that their patient population waited 6.2 hours before LWBS. It is clear that reasons for LWBS are multifactorial. In one study, reasons given for leaving included brecent family/ friend deaths, alcoholism, financial problems, suicidal thoughts/behavior, court appearances, pregnancies/miscar- riages, new people in the home, relatives with illnesses, and other psychological and social factorsQ [10]. Problems that have been identified as causes of LWBS are long waiting times [9], length of stay [11], total ED patients, and total admissions to the hospital [12]. All of these are taken into account in the present model.

A complex web of interrelated issues causes ED over- crowding. About 8 to 10 years ago, overcrowding in EDs was described in a few metropolitan academic centers. In the early 1990s, a number of articles in the lay press and academic journals documented the problems related to providing adequate or even basic care to patients [8,34,39-45]. In 1990, Time Magazine focused on overcrowded EDs in a detailed cover story [46]. Documentation showed that patients suffered undue prolonged pain, inconvenience, and poor outcomes as a result of delays in emergency care. Few of these issues have changed over the past decade as illustrated by the fact that, once again in 2000, Time Magazine focused on the problem of overcrowding [47]. One of the key expectations of EDs is the ability to provide immediate access and stabilization for those patients who have an emergency medical condition [41,43,45,48-51]. When the hospital is overcrowded, the ED is likely to be overcrowded [52,53] and then emergency medical services become overburdened with longer transport times due to ED diversions [23,25,26]. Overcrowding diminishes the capability of the entire health system to manage emergencies effectively.

Solving overcrowding is still a difficult issue. recent developments have led to the resurrection of studies from

10 years ago suggesting the btriaging outQ of some less urgent ED cases of patients after the completion of a federally mandated screening triage examination [50,54,55]. This is still an unsettling approach for most ED physicians. Other suggestions include increases in ED physician staffing [30], ED cardiac risk stratification [56], and assessment of patient flow [57]. Diverting Frequent ED users to outpatient clinics does not alleviate the problem because of the severity, acuity,

and case mix of the patients involved [58]. True solutions can only be determined by efforts to further understand and quantify these complex issues.

The NEDOCS score provides an overcrowding tool for ED use. Up until the development of the NEDOCS score, a single, universally acceptable definition of overcrowding has not existed. In contrast to bovercrowdingQ at public places such as supermarkets, international airports, and national parks, the precise scientific definition and threshold for ED capacity are subjects of debate. Until recently, there was no quantifiable, generalizable way of measuring and reporting overcrowding. Therefore, no 2 hospitals could consistently and reliably relate their experiences with overcrowding. A standardized scale was needed to address this issue. Two such scales have recently been developed and validated [2,4]. Each uses a slightly different algorithm to mirror the opinions of ED personnel on the degree of overcrowding. In this way, EDs can respond to administrative questions about the degree of ED overcrowding in a standardized manner.

The Bernstein model of overcrowding includes the following 5 variables: number of patients, patient acuity, number of attending medical doctors, number of treatment bays, and number of admitted patients [2]. All of these were taken into account in the NEDOCS score as well except for patient acuity, which was deemed to be too site dependent and too difficult to quantify to be an acceptable variable for the NEDOCS model. We cannot make correlation compar- isons because none were published in the original article although good correlation with staff opinions was suggested. As with the Bernstein model, we had good correlations with ambulance diversion status. We believe that our model is simpler to complete and more generalizable.

In summary, the NEDOCS score is well correlated with LWBS at the time a patient registers and at 2 and 4 hours later. In a multivariate analysis, the NEDOCS score, in combination with the average number of ambulance arrivals, could be used to explain 80% of the variability in LWBS.

Limitations and future directions

This study was performed at a single academic site. It represents a cross-section of a busy inner-city ED but may not be applicable to other systems or private EDs. In the future, we need to quantitate the problem in smaller community EDs that make up the great majority of the EDs in the United States.

Future overCrowding research needs to evaluate other difficult input variables that did not fit the constraints of the model such as department census, severity of illness, and ambulance diversion issues. With computerized registration programs, census is becoming easier to obtain.

Overcrowding research needs to address other outcome variables related to overcrowding such as number of Medical errors and patient satisfaction. Both of these serious problems may be consequences of overcrowding. We are interested in

determining if they are correlated with overcrowding as reflected by the NEDOCS score. Conceivably, by showing that there is a direct relationship between the NEDOCS score and these 2 variables, it should be possible to quantify risks of poor outcome relative to varying levels of ED crowding.

Conclusion

Overcrowding occurred in 44% of our sampling times on 93% of the sampling days. There was a significant correlation between LWBS and the NEDOCS score. That correlation was highest for the NEDOCS score from the 2- to 4-hour period after patients registered.

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