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.     

National Emergency Department Overcrowding Study tool is not useful in an Australian emergency department

Objective: To determine the accuracy and usefulness of the National Emergency Department Overcrowding Study (NEDOCS) tool in an urban hospital ED in Australia by direct comparison with subjective assessment by senior ED staff. Method: A sample of simultaneous subjective and objective data pairs were collected six times a day for a period of 3 weeks. All senior medical staff in the ED answered a brief questionnaire along with the senior charge nurse for the ED. Simultaneously, the senior charge nurse also documented the total number of patients in the ED, the number of patients awaiting admission, the number of patients on ventilators, the longest time waited by an ED patient for ward bed, and the waiting time for the last patient from the Waiting Room placed on a trolley. The objective indicators were entered into a Web‐based NEDOCS tool and transformed scores were compared with the averaged and transformed subjective scores for each sample time. Bland–Altmann and Kappa statistics were used to test the agreement between the objective and subjective measuring methods. Results: The mean difference between the subjective and objective methods was small (3.5 [95% confidence interval ?0.875–7.878] ); however, the 95% limits of agreement was wide (?46.52–53.43). The Kappa statistic used to assess the extent of reproducibility between categorical variables was 0.31 (95% confidence interval 0.17–0.45). Conclusion: The present study suggests that NEDOCS method of processing the objective overcrowding data does not accurately reflect the subjective assessment of the senior staff working at that time in the ED. This might be because the assumptions of the original NEDOCS study are flawed.     

The impact of overcrowding on the bacterial contamination of blood cultures in the ED

Objectives

This study aims to determine the risk factors associated with the bacterial contamination of blood cultures among adults visiting the emergency department (ED).

Methods

Clinical variables and medical records of adults with bacterial growth of blood cultures in the ED as well as the degree of ED crowding, between August 2007 and July 2008, were prospectively collected.

Results

Of the 11?491 adults who underwent blood culture sampling, the medical records of 558 (4.86%) eligible patients with bacterial growth in their blood cultures were analyzed. Most patients (366, or 3.19%) had true bacteremia, whereas 192 (1.67%) were regarded as contaminated. In multivariate analyses, ED overcrowding (scoring was based on a National Emergency Department Overcrowding Study [NEDOCS] score ≥100 points) was independently associated with blood culture contamination (odds ratio [OR], 1.58; P = .04). In contrast, other medical comorbidities, such as liver cirrhosis (OR, 0.31; P = .02), thrombocytopenia (<100?000/mm³; OR, 0.28; P = .002), or high serum levels of C-reactive protein (>100 mg/L; OR, 0.24; P < .001), were negatively associated with blood culture contamination. On further analysis of the 5 crowding categories as stratified by NEDOCS scores, which included not busy and busy (0-60 points), extremely busy but not overcrowded (60-100), overcrowded (100-140), severely overcrowded (140-180), and dangerously overcrowded (180-200), there was a strong correlation between blood culture contamination rates and the degrees of ED crowding (γ = 0.99, P < .001).

Conclusions

Emergency department overcrowding may have an adverse impact on the quality of clinical care, including increasing the risk of blood culture contamination.     

The inaccuracy of determining overcrowding status by using the National ED Overcrowding Study Tool

Background

Emergency department (ED) crowding has become more common, and perceptions of crowding vary among different health care providers. The National Emergency Department Overcrowding Study (NEDOCS) tool is the most commonly used tool to estimate ED crowding but still uncertain of its reliability in different ED settings.

Objective

The objectives of this study are to determine the accuracy of using the NEDOCS tool to evaluate overcrowding in an extremely high-volume ED and assess the reliability and consistency of different providers’ perceptions of ED crowding.

Material and methods

This was a 2-phase study. In phase 1, ED crowding was determined by the NEDOCS tool. The ED length of stay and number of patients who left without being seen were analyzed. In phase 2, a survey of simulated ED census scenarios was completed by different providers. The interrater and intrarater agreements of ED crowding were tested.

Results

In phase 1, the subject ED was determined to be overcrowded more than 75% of the time in which nearly 50% was rated as severely overcrowded by the NEDOCS tool. No statistically significant difference was found in terms of the average length of stay and the number of left without being seen patients under different crowding categories. In phase 2, 88 surveys were completed. A moderate level of agreement between health care providers was reached (κ = 0.5402, P < .0001). Test-retest reliability among providers was high (r = 0.8833, P = .0007). The strength of agreement between study groups and the NEDOCS was weak (κ = 0.3695, P < .001).

Conclusion

Using the NEDOCS tool to determine ED crowding might be inaccurate in an extremely high-volume ED setting.     

Markers of Overcrowding in a Pediatric Emergency Department

Objectives: The objective of this study was to identify markers of overcrowding in pediatric emergency departments (PEDs) according to expert opinion and then to use statistical methods to further explore the underlying construct of overcrowding. Methods: A cross‐sectional survey of all PED directors (n = 12) and pediatric emergency medicine fellowship program directors (n = 10) across Canada was conducted to elicit expert opinion on relevant markers of emergency department (ED) crowding. The list of markers was reduced to those specific to the ED for which data could be extracted from one tertiary care PED from an existing computerized patient tracking system. Data representing 2,190 consecutive shifts and 138,361 patient visits were collected between April 2005 and March 2007. Common factor analysis (CFA) was used to determine the underlying factors that best represented overcrowding as determined by markers identified by experts in pediatric emergency medicine Results: The main markers of overcrowding identified by the survey included measures of patient volume (25%), ED operational processes (55%), and delays in transferring patients to inpatient beds (13%). Data collected on 41 markers were retained for the CFA. The results of the CFA indicated that the largest portion of variation in the data (48%) was accounted for by markers describing patient volumes and flow through the ED. Measures of admission delays accounted for a smaller proportion of variability (9%). Conclusions: The results suggest that for this tertiary PED, markers of ED operational processes and patient volume may be more relevant for determination of overcrowding than markers reflecting delays in transferring patients to inpatient beds. This study provides a foundation for further research on markers of overcrowding specific to the pediatric setting. ACADEMIC EMERGENCY MEDICINE 2010; 17:151–156 © 2010 by the Society for Academic Emergency Medicine     

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.     

Entropy: A Conceptual Approach to Measuring Situation-level Workload Within Emergency Care and its Relationship to Emergency Department Crowding

Background

Emergency department (ED) crowding correlates with patient safety. Difficulties quantifying crowding and providing solutions were highlighted in the recent Institute of Medicine (IOM) report calling for the application of advanced industrial engineering (IE) research techniques to evaluate ED crowding. ED personnel workload is a related concept, with potential reciprocal effects between the two. Collaboration between emergency medicine and IE is needed to address crowding and ED personnel workload.

Objective

We review ED crowding and workload literature, relationships between workload and ED crowding, and the potential application of information theory as implemented in IE frameworks entitled “entropy” in evaluating both topics.

Discussion

IE techniques have applications for emergency medicine and have been successful in helping improve ED operations. Lean and Six Sigma applications are some of these techniques. Existing ED workload measures don't account for all aspects of work in the ED (acuity, efficiency, tasks, etc.) Crowding scales, such as NEDOCS (National ED Overcrowding Study) and EDWIN (ED Work Index), fail to predict ED crowding. A new measurement “entropy” may provide a more comprehensive evaluation of ED workload and may predict work overload seen with crowding. Entropy measures task-based work and the information flow involved. By assigning an entropy value to patient type-specific tasks, we might predict when the ED is overwhelmed, and crowded.

Conclusions

IE techniques provide solutions to the ED crowding problem and improve ED workload. We propose a technique novel to medicine: “Entropy,” derived from information theory, which may provide insight into ED personnel workload, its potential for measuring ED crowding, and possibly, in predicting an overwhelming situation.     

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

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.     

Crowding is the strongest predictor of left without being seen risk in a pediatric emergency department

BackgroundEmergency Department (ED) patients who leave without being seen (LWBS) are associated with adverse safety and medico-legal consequences. While LWBS risk has been previously tied to demographic and acuity related factors, there is limited research examining crowding-related risk in the pediatric setting. The primary objective of this study was to determine the association between LWBS risk and crowding, using the National Emergency Department Overcrowding Score (NEDOCS) and occupancy rate as crowding metrics.MethodsWe performed a retrospective observational study on electronic health record (EHR) data from the ED of a quaternary care children's hospital and trauma center during the 14-month study period. NEDOCS and occupancy rate were calculated for 15-min windows and matched to patient arrival time. We leveraged multiple logistic regression analyses to demonstrate the relationship between patientlevel LWBS risk and each crowding metric, controlling for characteristics drawn from the pre-arrival state. We performed a chi-squared test to determine whether a difference existed between the receiver operating characteristic (ROC) curves in the two models. Finally, we executed a dominance analysis using McFadden's pseudo-R 2 to determine the relative importance of each crowding metric in the models.ResultsA total of 54,890 patient encounters were studied, 1.22% of whom LWBS. The odds ratio for LWBS risk was 1.30 (95% CI 1.27–1.33) per 10-point increase in NEDOCS and 1.23 (95% CI 1.21–1.25). per 10% increase in occupancy rate. Area under the curve (AUC) was 86.9% for the NEDOCS model and 86.7% for the occupancy rate model. There was no statistically significant difference between the AUCs of the two models (p-value 0.27). Dominance analysis revealed that in each model, the most important variable studied was its respective crowding metric; NEDOCS accounted for 55.6% and occupancy rate accounted for 53.9% of predicted variance in LWBS.ConclusionNot only was ED overcrowding positively and significantly associated with individual LWBS risk, but it was the single most important factor that determined a patient's likelihood of LWBS in the pediatric ED. Because occupancy rate and NEDOCS are available in real time, each could serve as a monitor for individual LWBS risk in the pediatric ED.     

EMDOC (Emergency Department overcrowding) Internet-based safety net research

Emergency Department (ED) overcrowding is a national crisis with few prospective data to document its occurrence. The objective of this study was to prospectively collect data on variables involved in Emergency Department overcrowding (EMDOC) using an Internet-based data entry model. A prospective observational Internet-based study involving 18 hospitals over a 13-month period was designed. Investigators input data into the EmDOC Internet site at 10:00 p.m. on 7 random days each month. The study found that the primary reason for ED overcrowding was lack of inpatient beds. Important means were: patient-to-nurse ratio = 2.85, diversion was 7.4 h/24 h, and hospital census was 83%. From ED waiting room to an ED bed took a mean time of 209 min. The mean number of makeshift beds was 3.1. There was no single variable that was noted to define or predict overcrowding. Documentation of factors involved in ED overcrowding found that overcrowding was not just an ED problem, but a problem that occurs due to overcrowding in the entire institution.     

An Interpretative Study on Nurses' Perspectives of Working in an Overcrowded Emergency Department in Taiwan

Purpose

This study aims to gain in-depth understanding of nurses' perspectives of working in an overcrowded emergency.

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

Background

Recently developed crowding measures, such as the Emergency Department (ED) Work Index (EDWIN) score, provide a quantifiable measurement of overcrowding in the ED.

Aims

We 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).

Methods

We 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?Results Seventeen 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.

Conclusion

Overcrowding appears to be associated with a decreased likelihood of timely treatment for acute AMI in our ED.     

Emergency Department overcrowding and ambulance diversion: the impact and potential solutions of extended boarding of admitted patients in the Emergency Department

Emergency Department (ED) crowding and ambulance diversion have been increasingly significant national problems for more than a decade. Surveys of hospital directors have reported overcrowding in almost every state and 91% of hospital ED directors report overcrowding as a problem. The problem has developed because of multiple factors in the past 20 years, including a steady downsizing in hospital capacity, closures of a significant number of EDs, increased ED volume, growing numbers of uninsured, and deceased reimbursement for uncompensated care. Initial position statements from major organizations, including JCAHO and the General Accounting Office, suggested the problem of overcrowding was due to inappropriate use of emergency services by those with no urgent conditions, probably cyclical, and needed no specific policy response. More recently, the same and other organizations have more forcefully highlighted the problem of overcrowding and focused on the inability to transfer emergency patients to inpatient beds as the single most important factor contributing to ED overcrowding. This point has been further solidified by initial overcrowding research. This article will review how overcrowding occurred with a focus on the significance and potential remedies of extended boarding of admitted patients in the Emergency Department.     

Emergency department overcrowding: the Emergency Department Cardiac Analogy Model (EDCAM)

Increasing patient numbers, changing demographics and altered patient expectations have all contributed to the current problem with 'overcrowding' in emergency departments (EDs). The problem has reached crisis level in a number of countries, with significant implications for patient safety, quality of care, staff 'burnout' and patient and staff satisfaction. There is no single, clear definition of the cause of overcrowding, nor a simple means of addressing the problem. For some hospitals, the option of ambulance diversion has become a necessity, as overcrowded waiting rooms and 'bed-block' force emergency staff to turn patients away. But what are the options when ambulance diversion is not possible? Christchurch Hospital, New Zealand is a tertiary level facility with an emergency department that sees on average 65,000 patients per year. There are no other EDs to whom patients can be diverted, and so despite admission rates from the ED of up to 48%, other options need to be examined. In order to develop a series of unified responses, which acknowledge the multifactorial nature of the problem, the Emergency Department Cardiac Analogy model of ED flow, was developed. This model highlights the need to intervene at each of three key points, in order to address the issue of overcrowding and its associated problems.     

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.     

Screening Adolescents in the Emergency Department for Weapon Carriage

Objectives: The objective was to describe the prevalence and correlates of past‐year weapon involvement among adolescents seeking care in an inner‐city emergency department (ED). Methods: This cross‐sectional study administered a computerized survey to all eligible adolescents (age 14–18 years), 7 days a week, who were seeking care over an 18‐month period at an inner‐city Level 1 ED. Validated measures were administered, including measures of demographics, sexual activity, substance use, injury, violent behavior, weapon carriage, and/or weapon use. Zero‐inflated Poisson (ZIP) regression models were used to identify correlates of the occurrence and past‐year frequency of these weapons variables. Results: Adolescents (n = 2069, 86% response rate) completed the computerized survey. Fifty‐five percent were female; 56.5% were African American. In the past year, 20% of adolescents reported knife or razor carriage, 7% reported gun carriage, and 6% pulled a knife or gun on someone. Although gun carriage was more frequent among males, females were as likely to carry a knife or pull a weapon in the past year. Conclusions: One‐fifth of all adolescents seeking care in this inner‐city ED have carried a weapon. Understanding weapon carriage among teens seeking ED care is a critical first step to future ED‐based injury prevention initiatives. ACADEMIC EMERGENCY MEDICINE 2010; 17:168–176 © 2010 by the Society for Academic Emergency Medicine     

Risk Stratification of the Potentially Septic Patient in the Emergency Department: The Mortality in the Emergency Department Sepsis (MEDS) Score

Background: The prompt recognition and management of septic patients remains a challenge within the busy Emergency Department (ED). Prognostic screening aids have traditionally required time-delayed laboratory measurements not validated upon the emergency medicine population. Recently, a brief prognostic tool has been derived and subsequently validated in heterogeneous ED populations. Clinical Question: Can a risk-stratification tool predict 1-month mortality in ED patients with suspected infection? Evidence Review: Six studies evaluating the Mortality in the Emergency Department Sepsis (MEDS) score were identified and evaluated. Results: Higher MEDS scores are associated with increasing mortality. MEDS score's short- and long-term prognostic accuracy is superior to other sepsis scales as well as isolated biomarkers C-reactive protein and procalcitonin. MEDS' prognostic accuracy in severe sepsis is inferior to undifferentiated systemic inflammatory response syndrome (SIRS) patients. Conclusion: The MEDS score is an accurate and reliable prognostic tool for 28-day mortality in ED SIRS patients, but may not be optimal for those with severe sepsis.     

The Effect of Emergency Department Expansion on Emergency Department Overcrowding

Objectives: To examine the effects of emergency department (ED) expansion on ambulance diversion at an urban, academic Level 1 trauma center. Methods: This was a pre‐post study performed using administrative data from the ED and hospital electronic information systems. On April 19, 2005, the adult ED expanded from 28 to 53 licensed beds. Data from a five‐month pre‐expansion period (November 1, 2004, to March 1, 2005) and a five‐month postexpansion period (June 1, 2005, to October 31, 2005) were included for this analysis. ED and waiting room statistics as well as diversion status were obtained. Total ED length of stay (LOS) was defined as the time from patient registration to the time leaving the ED. Admission hold LOS was defined as the time from the inpatient bed request to the time leaving the ED for admitted patients. Mean differences (95% confidence interval [CI]) in total time spent on ambulance diversion per month, diversion episodes per month, and duration per diversion episode were calculated. An accelerated failure time model was performed to test if ED expansion was associated with a reduction in ambulance diversion while adjusting for potential confounders. Results: From pre‐expansion to postexpansion, daily patient volume increased but ED occupancy decreased. There was no significant change in the time spent on ambulance diversion per month (mean difference, 10.9 hours; 95% CI =?74.0 to 95.8), ambulance diversion episodes per month (two episodes per month; 95% CI =?4.2 to 8.2), and duration of ambulance diversion per episode (0.3 hours; 95% CI =?4.0 to 3.5). Mean (±SD) total LOS increased from 4.6 (±1.9) to 5.6 (±2.3) hours, and mean (±SD) admission hold LOS also increased from 3.0 (±0.2) to 4.1 (±0.2) hours. The proportion of patients who left without being seen was 3.5% and 2.7% (p = 0.06) in the pre‐expansion and postexpansion periods, respectively. In the accelerated failure time model, ED expansion did not affect the time to the next ambulance diversion episode. Conclusions: An increase in ED bed capacity did not affect ambulance diversion. Instead, total and admission hold LOS increased. As a result, ED expansion appears to be an insufficient solution to improve diversion without addressing other bottlenecks in the hospital.     

The Effect of Hospital Occupancy on Emergency Department Length of Stay and Patient Disposition

Emergency department (ED) overcrowding is a common problem. Despite a widespread belief that low hospital bed availability contributes to ED overcrowding, there are few data demonstrating this effect. OBJECTIVES: To identify the effect of hospital occupancy on ED length of stay for admitted patients and patient disposition. METHODS: This was an observational study design using administrative data at a 500-bed acute care teaching hospital. All patients presenting to the ED between April 1993 and June 1999 were included in the study. The predictor variable was daily hospital occupancy. Outcome measures included daily ED length of stay for admitted patients, daily consultation rate, and daily admission rate. The models controlled for the average daily age of ED patients and the average daily "arrival density" index, which adjusts for patient volume and clustering of patient arrivals. RESULTS: The average hospital occupancy was 89.7%. On average 155 patients visited the ED daily; 21% were referred to hospital physicians and 19% were admitted. The median ED length of stay for admitted patients was 5 hours 54 minutes (interquartile range 5 hr 12 min to 6 hr 42 min). Daily ED length of stay for admitted patients increased 18 minutes (95% CI = 12 to 24) when there was an absolute increase in occupancy of 10%. The ED length of stay appeared to increase extensively when hospital occupancy exceeded a threshold of 90%. Consultation and admission rates were not influenced by hospital occupancy. CONCLUSIONS: Increased hospital occupancy is strongly associated with ED length of stay for admitted patients. Increasing hospital bed availability might reduce ED overcrowding.     

A Research Agenda for Gender and Substance Use Disorders in the Emergency Department

For many years, gender differences have been recognized as important factors in the etiology, pathophysiology, comorbidities, and treatment needs and outcomes associated with the use of alcohol, drugs, and tobacco. However, little is known about how these gender‐specific differences affect ED utilization; responses to ED‐based interventions; needs for substance use treatment and barriers to accessing care among patients in the ED; or outcomes after an alcohol‐, drug‐, or tobacco‐related visit. As part of the 2014 Academic Emergency Medicine consensus conference on “Gender‐Specific Research in Emergency Care: Investigate, Understand and Translate How Gender Affects Patient Outcomes,” a breakout group convened to generate a research agenda on priority questions related to substance use disorders.