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.     

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.     

Emergency Department Crowding and Decreased Quality of Pain Care

Objectives: The objective of this study was to evaluate the association of emergency department (ED) crowding factors with the quality of pain care. Methods: This was a retrospective observational study of all adult patients (≥18 years) with conditions warranting pain care seen at an academic, urban, tertiary care ED from July 1 to July 31, 2005, and December 1 to December 31, 2005. Patients were included if they presented with a chief complaint of pain and a final ED diagnosis of a painful condition. Predictor ED crowding variables studied were 1) census, 2) number of admitted patients waiting for inpatient beds (boarders), and 3) number of boarders divided by ED census (boarding burden). The outcomes of interest were process of pain care measures: documentation of clinician pain assessment, medications ordered, and times of activities (e.g., arrival, assessment, ordering of medications). Results: A total of 1,068 patient visits were reviewed. Fewer patients received analgesic medication during periods of high census (>50th percentile; parameter estimate = –0.47; 95% confidence interval [CI] = –0.80 to –0.07). There was a direct correlation with total ED census and increased time to pain assessment (Spearman r = 0.33, p < 0.0001), time to analgesic medication ordering (r = 0.22, p < 0.0001), and time to analgesic medication administration (r = 0.25, p < 0.0001). There were significant delays (>1 hour) for pain assessment and the ordering and administration of analgesic medication during periods of high ED census and number of boarders, but not with boarding burden. Conclusions: ED crowding as measured by patient volume negatively impacts patient care. Greater numbers of patients in the ED, whether as total census or number of boarders, were associated with worse pain care.     

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.     

An Independent Evaluation of Four Quantitative Emergency Department Crowding Scales

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

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.     

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.     

Ambulance Diversion as a Proxy for Emergency Department Crowding: The Effect on Pediatric Mortality in a Metropolitan Area

Objectives: The objective was to determine the prevalence of emergency department (ED) ambulance diversion among Houston pediatric hospitals and its association with mortality of pediatric patients. Methods: Hospital diversion and patient data between August 2002 and December 2004 were used to examine the impact of diversion on mortality of children under age 18 years. Patients were assumed to be exposed to ED crowding if diversion and admission or ED arrival times overlapped. Univariate and logistic regression were performed to determine if diversion was associated with mortality while controlling for age, illness severity, injury, and transfer status. Results: Mean hospital diversion hours as a percentage of operating hours were 10.58 (standard deviation [SD] ± 9). Overall, of 63,780 admissions, there were 4,095 (6.4%) children admitted during diversion. Fewer severely ill patients were admitted during diversion than nondiversion times (odds ratio [OR] = 0.72; 95% confidence interval [CI] = 0.66 to 0.78). The presence of diversion was protective for mortality (OR = 0.51; 95% CI = 0.34 to 0.77) in bivariate analysis. Mortality was associated with presence of major or extreme illness (OR = 60.7; 95% CI = 45.2 to 81.5), injury (OR=1.7; 95% CI = 1.4 to 2.1), and transfer status (OR = 6.3; 95% CI = 5.4 to 7.3). Using conditional logistic regression, major or extreme illness (OR = 50.7; 95% CI = 37.7 to 68.3), injury (OR 3.7; 95% CI = 2.9 to 4.7), and transfer (OR = 2.7; 95% CI = 2.2, 3.2) were associated with mortality, but diversion did not show any association with mortality. After combining ED and inpatient deaths, no association between diversion and mortality was observed. Conclusions: Hospital diversion due to ED crowding is common in pediatrics. The authors found no evidence of an association between diversion and ED and inpatient pediatric mortality.     

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.     

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.