Emergency Department Work Index Research Articles

Impact of an Emergency Department Saturation Tool on a Pediatric Hospital's Capacity Management Policy.

We sought to describe how the Emergency Department Work Index (EDWIN) saturation tool (1) correlates with PED overcrowding during a capacity management activation policy, known internally as Purple Alert and (2) compare overall hospital-wide capacity metrics on days in which the alert was instituted versus days it was not. This study was conducted between January 1, 2017 and December 31, 2019 in a 30-bed academic quaternary care, urban PED within a university hospital. The EDWIN tool was implemented in January 2019 and objectively measured the busyness of the PED. To determine correlation with overcrowding, EDWIN scores were calculated at alert initiation. Mean alert hours per month were plotted on a control chart before and after EDWIN implementation. We also compared daily numbers of PED visits, inpatient admissions, and patients left without being seen (LWBS) for days with and without alert initiation to assess whether or not Purple Alert correlated with high PED usage. During the study period, the alert was activated a total of 146 times; 43 times after EDWIN implementation. Mean EDWIN score was 2.5 (SD 0.5, min 1.5, max 3.8) at alert initiation. There were no alert occurrences for EDWIN scores less than 1.5 (not overcrowded). There was no statistically significant difference for mean alert hours per month before and after EDWIN was instituted (21.4 vs 20.2, P = 0.08). Mean numbers of PED visits, inpatient admissions, and patients left without being seen were higher on days with alert activation (P < 0.001 for all). The EDWIN score correlated with PED busyness and overcrowding during alert activation and correlated with high PED usage. Future studies could include implementing a real-time Web-based EDWIN score as a prediction tool to prevent overcrowding and verifying EDWIN generalizability at other PED sites.

Measures of Emergency Department Crowding, a Systematic Review. How to Make Sense of a Long List.

Emergency department (ED) crowding, a common and serious phenomenon in many countries, lacks standardized definition and measurement methods. This systematic review critically analyzes the most commonly studied ED crowding measures. We followed the Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guidelines. We searched PubMed/Medline Database for all studies published in English from January 1st, 1990, until December 1st, 2020. We used the National Institute of Health (NIH) Quality Assessment Tool to grade the included studies. The initial search yielded 2293 titles and abstracts, of whom we thoroughly reviewed 109 studies, then, after adding seven additional, included 90 in the final analysis. We excluded simple surveys, reviews, opinions, case reports, and letters to the editors. We included relevant papers published in English from 1990 to 2020. We did not grade any study as poor and graded 18 as fair and 72 as good. Most studies were conducted in the USA. The most studied crowding measures were the ED occupancy, the ED length of stay, and the ED volume. The most heterogeneous crowding measures were the boarding time and number of boarders. Except for the National ED Overcrowding Scale (NEDOCS) and the Emergency Department Work Index (EDWIN) scores, the studied measures are easy to calculate and communicate. Quality of care was the most studied outcome. The EDWIN and NEDOCS had no studies with the outcome mortality. The ED length of stay had no studies with the outcome perception of care. ED crowding was often associated with worse outcomes: higher mortality in 45% of the studies, worse quality of care in 75%, and a worse perception of care in 100%. The ED occupancy, ED volume, and ED length of stay are easy to measure, calculate and communicate, are homogenous in their definition, and were the most studied measures.

Overcrowding an encumbrance for an emergency health-care system: A perspective of Health-care providers from tertiary care center in Northern India.

BACKGROUND:Emergency department (ED) overcrowding is one of the leading problems of health-care organizations, discerned by ED medical staff, but it has never been measured objectively.OBJECTIVE:A 2 months prospective cross-sectional study was conducted to compare ED overcrowding measurement tools with the perceptions of ED emergency physician and ED assistant nursing superintendent (EDEP/EDANS).RESULTS:The results have shown that perceptions of ED overcrowding as noted by EDEP and EDANS, taken on a Likert scale, were 83.34% and 86.67%, respectively. Kappa values show a significant agreement between EDEP and EDANS subjective perceptions with objective values of the National Emergency Department Overcrowding Study (NEDOCS), Real-time Emergency Analysis of Demand Indicators (READI), and Emergency Department Work Index (EDWIN) scales. Furthermore, all three scales have statistically significant correlation; NEDOCS and READI had highest level of correlation coefficient (r = 0.662, P < 0.01) whereas READI and EDWIN shows least correlation coefficient value (r = 0.155, P < 0.01).CONCLUSION:Therefore, these scales may serve to quantify the subjective impressions of ED overcrowding. Evidence is clear of overcrowding harms, measures are needed to provide urgent medical care and future work up is need of the hour to systematically evaluate interventions and guide evidence-based policies.

The Application of GSCM in Eliminating Healthcare Waste: Hospital EDC as an Example.

Eliminating unnecessary healthcare waste in hospitals and providing better healthcare quality are the core issues of green supply chain management (GSCM). Hence, this study used a hospital’s emergency department crowding (EDC) problem to illustrate how to establish an emergency medicine service (EMS) simulation system to obtain a robust parameters setting for solving hospitals’ EDC and waste problems, thereby increasing healthcare quality. Inappropriate resource allocation results in more serious EDC; more serious EDC results in increasing operating costs. Therefore, in the healthcare system, waste includes inappropriate costs and inappropriate resource allocation. The EMS of a medical center in central Taiwan was the object of the study. In this study, the dynamic Taguchi method was used to set the signal factor, noise factor, and control factors to simulate the EMS system to obtain the optimal parameters setting. The performance was set to Emergency Department Work Index (EDWINC) and system time (waiting time and service time) per patient. The signal factor was set to the number of physicians; the noise factor was set to patient arrival rate; the control factors included persuading Triage 4 and Triage 5 outpatients, checkup process, bed occupation rate in the emergency department (ED), and medical checkup sequence for Triage 4 and Triage 5 patients. This study makes two significant contributions. First, the study introduces the GSCM concept to the healthcare setting to bring green innovation to hospitals. Hospital administrators may hence design better GSCM activities to facilitate healthcare processes to provide better healthcare outcomes. Second, the study applied the dynamic Taguchi method to the EMS and neural network (NN) to construct a computational model revealing the cause (factors) and effect (performances) relationship. In addition, the genetic algorithm (GA), a solution method, was used to obtain the optimal parameters setting of the EDC in Taiwan. Hence, after obtaining the solutions, the unnecessary waste in EDC—inappropriate costs and inappropriate resource allocation—is reduced.

Optimal Measurement Interval for Emergency Department Crowding Estimation Tools

Emergency department (ED) crowding is a barrier to timely care. Several crowding estimation tools have been developed to facilitate early identification of and intervention for crowding. Nevertheless, the ideal frequency is unclear for measuring ED crowding by using these tools. Short intervals may be resource intensive, whereas long ones may not be suitable for early identification. Therefore, we aim to assess whether outcomes vary by measurement interval for 4 crowding estimation tools. Our eligible population included all patients between July 1, 2015, and June 30, 2016, who were admitted to the JPS Health Network ED, which serves an urban population. We generated 1-, 2-, 3-, and 4-hour ED crowding scores for each patient, using 4 crowding estimation tools (National Emergency Department Overcrowding Scale [NEDOCS], Severely Overcrowded, Overcrowded, and Not Overcrowded Estimation Tool [SONET], Emergency Department Work Index [EDWIN], and ED Occupancy Rate). Our outcomes of interest included ED length of stay (minutes) and left without being seen or eloped within 4 hours. We used accelerated failure time models to estimate interval-specific time ratios and corresponding 95% confidence limits for length of stay, in which the 1-hour interval was the reference. In addition, we used binomial regression with a log link to estimate risk ratios (RRs) and corresponding confidence limit for left without being seen. Our study population comprised 117,442 patients. The time ratios for length of stay were similar across intervals for each crowding estimation tool (time ratio=1.37 to 1.30 for NEDOCS, 1.44 to 1.37 for SONET, 1.32 to 1.27 for EDWIN, and 1.28 to 1.23 for ED Occupancy Rate). The RRs of left without being seen differences were also similar across intervals for each tool (RR=2.92 to 2.56 for NEDOCS, 3.61 to 3.36 for SONET, 2.65 to 2.40 for EDWIN, and 2.44 to 2.14 for ED Occupancy Rate). Our findings suggest limited variation in length of stay or left without being seen between intervals (1 to 4 hours) regardless of which of the 4 crowding estimation tools were used. Consequently, 4 hours may be a reasonable interval for assessing crowding with these tools, which could substantially reduce the burden on ED personnel by requiring less frequent assessment of crowding.

Evaluation of Overcrowding of Emergency Department in Imam Reza Hospital in 2015 by Implementing 2 Scales: NEDOCS and EDWIN

Background: The reason behind patients’ overcrowding in emergency departments (ED) may be due to several factors such as great number of referring patients or inaccessibility of facilities or hospital beds. Objectives: The present study aimed at evaluating the overcrowding of ED in Tabriz Imam Reza hospital in 2015 using 2 scales: National Emergency Overcrowding Scale (NEDOCS), and Emergency Department Work Index (EDWIN). Methods: This was a cross-sectional descriptive study conducted in the ED of Imam Reza hospital affiliated to Tabriz University of Medical Sciences, Iran, in a one-year period (Jan- Dec 2015). Convenience sampling method was used for data collection. During the study, the researchers randomly selected 10 days out of every month and started to collect index variables 4 times a day at 6 AM, 12 noon, 6 PM, and 12 midnight. In total, 488 samples were gathered and both NEDOCS and EDWIN were implemented for each sample. Then, the frequency of overcrowding in the emergency department was reported based on the obtained results. Results: Based on NEDOCS, ED was extremely busy, but not overcrowded in 51 cases (10.5%), it was overcrowded in 298 (61.1%) cases, severely overcrowded in 138 cases (28.3%), and it was busy only in one case (0.2%). However, based on the EDWIN scale, the ED was active but manageable in 91 cases (18.6%), very busy in 36 cases (7.4%), and extremely busy in the remaining 361 cases (74%). Conclusions: EDWIN scale depicted August, July, December, June, and April to be, respectively, the most overcrowded months. This finding revealed EDWIN scale to perform better when trying to have an overall assessment of ED during the whole year, which helps us have clear- cut results for analysis and policy making in managing EDs.

Depressive Symptoms and Perceptions of ED Care in Patients Evaluated for Acute Coronary Syndrome

IntroductionPosttraumatic stress disorder (PTSD) develops in 1 out of 8 survivors of acute coronary syndrome (ACS) events, and these persons have a doubling of risk for recurrent ACS and mortality. Overcrowding in the emergency department during ACS evaluation has been associated with increased risk for PTSD, and depressed patients have been found to be particularly vulnerable. Little is known about the mechanisms by which overcrowding increases PTSD risk in depressed patients. Our aim was to evaluate one possible mechanism, patient perception of crowding and care, in depressed and nondepressed ED patients evaluated for ACS. MethodsWe enrolled 912 participants in the REactions to Acute Care and Hospitalization study, an ongoing observational cohort study assessing patients evaluated for ACS. Participants completed the Emergency Department Perceptions questionnaire. Depressive symptoms were screened using the Personal Health Questionnaire Depression Scale. Objective ED crowding was calculated using the Emergency Department Work Index (EDWIN). ResultsEDWIN scores did not significantly differ between groups. Although perceptions of ED crowding did not differ between groups, depressed patients perceived the emergency department as more stressful [t = 4.45, P < .001] and perceived poorer care [t = 3.03, P = .003]. Multiple regression modeling found a significant interaction between EDWIN scores and depression, predicting participants’ perception of stress in the emergency department (F[7,904] = 7.93, P < .001). DiscussionWe found that depressed patients experienced the emergency department as more stressful as objectively measured crowding increased. Our study highlights the complex interplay between cardiovascular disease and mental health in impacting patient health outcomes in the emergency department.Image 1

Applicability of the modified Emergency Department Work Index (mEDWIN) at a Dutch emergency department.

BackgroundEmergency department (ED) crowding leads to prolonged emergency department length of stay (ED-LOS) and adverse patient outcomes. No uniform definition of ED crowding exists. Several scores have been developed to quantify ED crowding; the best known is the Emergency Department Work Index (EDWIN). Research on the EDWIN is often applied to limited settings and conducted over a short period of time.ObjectivesTo explore whether the EDWIN as a measure can track occupancy at a Dutch ED over the course of one year and to identify fluctuations in ED occupancy per hour, day, and month. Secondary objective is to investigate the discriminatory value of the EDWIN in detecting crowding, as compared with the occupancy rate and prolonged ED-LOS.MethodsA retrospective cohort study of all ED visits during the period from September 2010 to August 2011 was performed in one hospital in the Netherlands. The EDWIN incorporates the number of patients per triage level, physicians, treatment beds and admitted patients to quantify ED crowding. The EDWIN was adjusted to emergency care in the Netherlands: modified EDWIN (mEDWIN). ED crowding was defined as the 75th percentile of mEDWIN per hour, which was ≥0.28.ResultsIn total, 28,220 ED visits were included in the analysis. The median mEDWIN per hour was 0.15 (Interquartile range (IQR) 0.05–0.28); median mEDWIN per patient was 0.25 (IQR 0.15–0.39). The EDWIN was higher on Wednesday (0.16) than on other days (0.14–0.16, p<0.001), and a peak in both mEDWIN (0.30–0.33) and ED crowding (52.9–63.4%) was found between 13:00–18:00 h. A comparison of the mEDWIN with the occupancy rate revealed an area under the curve (AUC) of 0.86 (95%CI 0.85–0.87). The AUC of mEDWIN compared with a prolonged ED-LOS (≥4 hours) was 0.50 (95%CI 0.40–0.60).ConclusionThe mEDWIN was applicable at a Dutch ED. The mEDWIN was able to identify fluctuations in ED occupancy. In addition, the mEDWIN had high discriminatory power for identification of a busy ED, when compared with the occupancy rate.

Online Social Network Use by Health Care Providers in a High Traffic Patient Care Environment

BackgroundThe majority of workers, regardless of age or occupational status, report engaging in personal Internet use in the workplace. There is little understanding of the impact that personal Internet use may have on patient care in acute clinical settings.ObjectiveThe objective of this study was to investigate the volume of one form of personal Internet use—online social networking (Facebook)—generated by workstations in the emergency department (ED) in contrast to measures of clinical volume and severity.MethodsThe research team analyzed anonymous network utilization records for 68 workstations located in the emergency medicine department within one academic medical center for 15 consecutive days (12/29/2009 to 1/12/2010). This data was compared to ED work index (EDWIN) data derived by the hospital information systems.ResultsHealth care workers spent an accumulated 4349 minutes (72.5 hours) browsing Facebook, staff cumulatively visited Facebook 9369 times and spent, on average, 12.0 minutes per hour browsing Facebook. There was a statistically significant difference in the time spent on Facebook according to time of day (19.8 minutes per hour versus 4.3 minutes per hour, P<.001). There was a significant, positive correlation between EDWIN scores and time spent on Facebook (r=.266, P<.001).ConclusionsFacebook use constituted a substantive percentage of staff time during the 15-day observation period. Facebook use increased with increased patient volume and severity within the ED.

Emergency Department Crowding is Associated with Reduced Satisfaction Scores in Patients Discharged from the Emergency Department

IntroductionEmergency department (ED) crowding has been shown to negatively impact patient outcomes. Few studies have addressed the effect of ED crowding on patient satisfaction. Our objective was to evaluate the impact of ED crowding on patient satisfaction in patients discharged from the ED.Methods:We measured patient satisfaction using Press-Ganey surveys returned by patients that visited our ED between August 1, 2007 and March 31, 2008. We recorded all mean satisfaction scores and obtained mean ED occupancy rate, mean emergency department work index (EDWIN) score and hospital diversion status over each 8-hour shift from data archived in our electronic tracking board. Univariate and multivariate logistic regression analysis was calculated to determine the effect of ED crowding and hospital diversion status on the odds of achieving a mean satisfaction score ≥ 85, which was the patient satisfaction goal set forth by our ED administration.Results:A total of 1591 surveys were returned over the study period. Mean satisfaction score was 77.6 (standard deviation [SD] ±16) and mean occupancy rate was 1.23 (SD ± 0.31). The likelihood of failure to meet patient satisfaction goals was associated with an increase in average ED occupancy rate (odds ratio [OR] 0.32, 95% confidence interval [CI] 0.17 to 0.59, P < 0.001) and an increase in EDWIN score (OR 0.05, 95% CI 0.004 to 0.55, P = 0.015). Hospital diversion resulted in lower mean satisfaction scores, but this was not statistically significant (OR 0.62, 95% CI 0.36 to 1.05). In multivariable analysis controlling for hospital diversion status and time of shift, ED occupancy rate remained a significant predictor of failure to meet patient satisfaction goals (OR 0.34, 95% CI 0.18 to 0.66, P = 0.001).Conclusion:Increased crowding, as measured by ED occupancy rate and EDWIN score, was significantly associated with reduced patient satisfaction. Although causative attribution was limited, our study suggested yet another negative impact resulting from ED crowding.

Emergency department crowding and risk of preventable medical errors

The objective of the study is to determine the association between emergency department (ED) crowding and preventable medical errors (PME). This was a retrospective cohort study of 533 ED patients enrolled in the National ED Safety Study (NEDSS) in four Massachusetts EDs. Individual patients' average exposure to ED crowding during their ED visit was compared with the occurrence of a PME (yes/no) for the three diagnostic categories in NEDSS: acute myocardial infarction, asthma exacerbation, and dislocation requiring procedural sedation. To accommodate site-to-site differences in available administrative data, ED crowding was measured using one of three previously validated crowding metrics (ED Work Index, ED Workscore, and ED Occupancy). At each site, the continuous measure was placed into site-specific quartiles, and these quartiles then were combined across sites. We found that 46 (8.6%; 95% confidence interval, 6.4-11.3%) of the 533 patients experienced a PME. For those seen during higher levels of ED crowding (quartile 4 vs. quartile 1), the occurrence of PMEs was more than twofold higher, both on unadjusted analysis and adjusting for two potential confounders (diagnosis, site). The association appeared non-linear, with most PMEs occurring at the highest crowding level. We identified a direct association between high levels of ED crowding and risk of preventable medical errors. Further study is needed to determine the generalizability of these results. Should such research confirm our findings, we would suggest that mitigating ED crowding may reduce the occurrence of preventable medical errors.

CrowdED

Emergency department (ED) crowding metrics were validated in our facility and a new technique of data visualization is proposed. A sequential cross-sectional study was conducted in our ED during October 2007. Data were collected every 2 hours by a research assistant and included patient arrivals and acuity levels, available inpatient and ED beds, ambulance diversion status, staff present, and patient reneging. The charge nurse and an attending physician also completed a single-question crowding instrument. Pearson correlation coefficients were calculated and logistic regression were performed to test the usefulness of the crowding score and test significance of the data visualization trends. Our ED is an adult, level-III, veterans administration ED in urban southern California. It is open 24 hours per day, has 15 treatment beds with 4 cardiac monitors, and typically sees about 30 000 patients per year. The key outcome variables were patient reneging (number of patients who left before being seen by a physician) and ambulance diversion status. Average response rate was 72% (n = 227) of sampling times. Emergency Department Work Index, demand value, lack of inpatient beds, census, patients seen in alternate locations, and patient reneging correlated significantly (P < .01) with the crowding instrument. Staff workload ranks predicted patient reneging (odds ratio 6.0, 95% confidence interval 2.3-15.4). The data visualization focused on common ED overcrowding metrics and was supported by logistic regression modeling. The demand value, ED Work Index, and patient reneging are valid measures of crowding in the studied ED, with staff workload rank being an easy, 1-question response. Data visualization may provide the site-specific crowding component analysis needed to guide quality improvement projects to reduce ED crowding and its impact on patient outcome measures.

ED overcrowding is associated with an increased frequency of medication errors

Objectives Despite the growing problems of emergency department (ED) crowding, the potential impact on the frequency of medication errors occurring in the ED is uncertain. Using a metric to measure ED crowding in real time (the Emergency Department Work Index, or EDWIN, score), we sought to prospectively measure the correlation between the degree of crowding and the frequency of medication errors occurring in our ED as detected by our ED pharmacists. Methods We performed a prospective, observational study in a large, community hospital ED of all patients whose medication orders were evaluated by our ED pharmacists for a 3-month period. Our ED pharmacists review the orders of all patients in the ED critical care section and the Chest Pain unit, and all admitted patients boarding in the ED. We measured the Spearman correlation between average daily EDWIN score and number of medication errors detected and determined the score's predictive performance with receiver operating characteristic (ROC) curves. Results A total of 283 medication errors were identified by the ED pharmacists over the study period. Errors included giving medications at incorrect doses, frequencies, durations, or routes and giving contraindicated medications. Error frequency showed a positive correlation with daily average EDWIN score (Spearman's ρ = 0.33; P = .001). The area under the ROC curve was 0.67 (95% confidence interval, 0.56-0.78) with failure defined as greater than 1 medication error per day. Conclusions We identified an increased frequency of medication errors in our ED with increased crowding as measured with a real-time modified EDWIN score.

Overcrowding is associated with delays in percutaneous coronary intervention for acute myocardial infarction.

BackgroundRecently developed crowding measures, such as the Emergency Department (ED) Work Index (EDWIN) score, provide a quantifiable measurement of overcrowding in the ED.AimsWe sought to determine the association between overcrowding, measured with the EDWIN score, and the time required to initiate percutaneous coronary intervention (PCI) for patients presenting to the ED with acute myocardial infarction (AMI).MethodsWe retrospectively reviewed the performance improvement (PI) data on all patients presenting to the ED over a 2-month period with chest pain and whose subsequent ECG showed AMI requiring PCI. We recorded the time from patient presentation to the (1) time to first ECG, (2) time to patient arrival in the catheterization laboratory, and (3) time to catheter balloon inflation. We calculated EDWIN scores using data archived from our electronic tracking board and compared the proportion of patients who achieved our established ED goal times between patients presenting during low crowding (EDWIN < 1.5) and high crowding (EDWIN ≥ 1.5) conditions.ResultsSeventeen patients underwent emergent PCI in the study period. Patients presenting to the ED during periods of low crowding had shorter times to balloon inflation (median of 84 min, IQR 80 to 93 min) than patients presenting to the ED during periods of high crowding (median of 107 min, IQR 94 to 122 min), P = 0.008. Times to first ECG and to arrival in the catheterization laboratory were not significantly different between patients presenting during low and high crowding conditions.ConclusionOvercrowding appears to be associated with a decreased likelihood of timely treatment for acute AMI in our ED.

The Emergency Department Occupancy Rate: A Simple Measure of Emergency Department Crowding?

We examine the validity of the emergency department (ED) occupancy rate as a measure of crowding by comparing it to the Emergency Department Work Index Score (EDWIN), a previously validated scale. A multicenter validation study was conducted according to ED visit data from 6 academic EDs for a 3-month period in 2005. Hourly ED occupancy rate (ie, total number of patients in ED divided by total number of licensed beds) and EDWIN scores were calculated. The correlation between the scales was determined and their validity evaluated by their ability to discriminate between hours when 1 or more patients left without being seen and hours when the ED was on ambulance diversion, using area under the curve (AUC) statistics estimated from the bootstrap method. We calculated the ED occupancy rate and EDWIN for 2,208 consecutive hours at each of the 6 EDs. The overall correlation between the 2 scales was 0.58 (95% confidence interval [CI] 0.56 to 0.60). The ED occupancy rate (AUC=0.73; 95% CI 0.65 to 0.80) and the EDWIN (AUC=0.65; 95% CI 0.58 to 0.72) did not differ significantly in correctly identifying hours when patients left without being seen. The ED occupancy rate (AUC=0.78; 95% CI 0.75 to 0.80) and the EDWIN (AUC=0.70; 95% CI 0.59 to 0.81) performed similarly for ED diversion hours. The ED occupancy rate and the EDWIN classified leaving without being seen and ambulance diversion hours with moderate accuracy. Although the ED occupancy rate is not ideal, its simplicity makes real-time assessment of crowding feasible for more EDs nationwide.

Measuring and Forecasting Emergency Department Crowding in Real Time

We quantified the potential for monitoring current and near-future emergency department (ED) crowding by using 4 measures: the Emergency Department Work Index (EDWIN), the National Emergency Department Overcrowding Scale (NEDOCS), the Demand Value of the Real-time Emergency Analysis of Demand Indicators (READI), and the Work Score. We calculated the 4 measures at 10-minute intervals during an 8-week study period (June 21, 2006, to August 16, 2006). Ambulance diversion status was the outcome variable for crowding, and occupancy level was the performance baseline measure. We evaluated discriminatory power for current crowding by the area under the receiver operating characteristic curve (AUC). To assess forecasting power, we applied activity monitoring operating characteristic curves, which measure the timeliness of early warnings at various false alarm rates. We recorded 7,948 observations during the study period. The ED was on ambulance diversion during 30% of the observations. The AUC was 0.81 for the EDWIN, 0.88 for the NEDOCS, 0.65 for the READI Demand Value, 0.90 for the Work Score, and 0.90 for occupancy level. In the activity monitoring operating characteristic analysis, only the occupancy level provided more than an hour of advance warning (median 1 hour 7 minutes) before crowding, with 1 false alarm per week. The EDWIN, the NEDOCS, and the Work Score monitor current ED crowding with high discriminatory power, although none of them exceeded the performance of occupancy level across the range of operating points. None of the measures provided substantial advance warning before crowding at low rates of false alarms.

An Independent Evaluation of Four Quantitative Emergency Department Crowding Scales

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. 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. 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. 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). 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.

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

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). 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. 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 (> or =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. 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% (chi2 test for difference, p = 0.02). 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.

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

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

Validation of the Emergency Department Work Index at Six Academic Emergency Departments

Validation of the Emergency Department Work Index at Six Academic Emergency Departments