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

Measuring the crowding of emergency departments: an assessment of the NEDOCS in Lombardy, Italy, and the development of a new objective indicator based on the waiting time for the first clinical assessment

BACKGROUND

There is no ubiquitous definition of Emergency Department (ED) crowding and several indicators have been proposed to measure it. The National ED Overcrowding Study (NEDOCS) score is among the most popular, even though it has been severely criticised. We used the waiting time for the physician's initial assessment to evaluate the performance of the NEDOCS and proposed a new crowding indicator based on this objective measure.

METHODS

To evaluate the NEDOCS, we used the 2022 data of all the Lombardy EDs and compared the distribution of waiting times across the five levels of the NEDOCS at ED arrival. To construct the new indicator, we estimated the centre-specific relationship between the total number of ED patients and the waiting time of those with minor or deferrable urgency. We defined seven classes of waiting times and calculated how many patients corresponded to an average waiting time in the classes. These centre-specific cutoffs were used to define the 7-level crowding indicator. The indicator was then compared to the NEDOCS score and validated on the first six months of 2023 data.

RESULTS

Patients' waiting time did not increase at the increase of the NEDOCS score, suggesting the absence of a relationship between this score and the effect of ED crowding on the ED capacity of evaluating new patients. The indicator we propose is easy to estimate in real-time and based on centre-specific cutoffs, which depend on the volume of yearly accesses. We observed minimal agreement between the proposed indicator and the NEDOCS in most EDs, both in the development and validation datasets.

CONCLUSIONS

We proposed to quantify ED crowding using the waiting time for physician's initial assessment of patients with minor or deferrable urgency, which increases in crowding situations due to the prioritization of urgent patients. The centre-specific cutoffs avoid the problem of the heterogeneity of the volume of accesses and organization among EDs, while enabling a fair comparison between centres.

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.

Validation of the National Emergency Department Overcrowding Score (NEDOCS) in a UK non-specialist emergency department

INTRODUCTION

Emergency department (ED) crowding has significant adverse consequences, however, there is no widely accepted tool to measure it. This study validated the National Emergency Department Overcrowding score (NEDOCS) (range 0-200 points), which uses routinely collected ED data.

METHODS

This prospective single-centre study sampled data during four periods of 2018. The outcome against which NEDOCS performance was assessed was a composite of clinician opinion of crowding (physician and nurse in charge). Area under the receiver operating characteristic curves (AUROCs) and calibration plots were produced. Six-hour stratified sampling was added to adjust for temporal correlation of clinician opinion. Staff inter-rater agreement and NEDOCS association with opinion of risk, safety and staffing levels were collected.

RESULTS

From 905 sampled hours, 448 paired observations were obtained, with the ED deemed crowded 18.5% of the time. Inter-rater agreement between staff was moderate (weighted kappa 0.57 (95% CI 0.56 to 0.60)). AUROC for NEDOCS was 0.81 (95% CI 0.77 to 0.86). Adjusted for temporal correlation, AUROC was 0.80 (95% CI 0.73 to 0.88). At a cut-off of 100 points sensitivity was 75.9% (95% CI 65.3% to 84.6%), specificity 72.1% (95% CI 67.1% to 76.6%), positive predictive value 38.2% (95% CI 30.7% to 46.1%) and negative predictive value 92.9% (95% CI 89.3% to 95.6%). NEDOCS underpredicted clinical opinion on Calibration assessment, only partially correcting with intercept updating. For perceived risk of harm, safety and insufficient staffing, NEDOCS AUROCs were 0.71 (95% CI 0.61 to 0.82), 0.71 (95% CI 0.63 to 0.80) and 0.70 (95% CI 0.64 to 0.76), respectively.

CONCLUSIONS

NEDOCS demonstrated good discriminatory power for clinical perception of crowding. Prior to implementation, determining individual unit ED cut-off point(s) would be important as published thresholds may not be generalisable. Future studies could explore refinement of existing variables or addition of new variables, including acute physiological data, which may improve performance.

Estrategias para la medición y el manejo de la sobreocupación de los servicios de urgencias de adultos en instituciones de alta complejidad con altos volúmenes de consulta. Revisión de la literatura

La sobreocupación de los servicios de urgencias es un problema global que cada vez afecta más las instituciones de salud que atienden pacientes de mediana y alta complejidad, haciendo que estos permanezcan más tiempo en una sala de espera con la consiguiente demora en los tiempos de atención, bajo nivel de satisfacción de los usuarios, retraso en la toma de ayudas diagnósticas, retrasos al definir altas del servicio y favorecimiento de complicaciones médicas, entre otros. Para mejorar esta situación se han desarrollado estrategias como la creación de unidades de observación, unidades fast track o asignación de citas prioritarias para los pacientes que no requieren una atención urgente, de modo adicional el triaje, los exámenes point of care y la vinculación de especialistas en medicina de urgencias. Todo esto con el fin de mejorar la calidad de la atención de los pacientes, evitar que se presenten eventos adversos durante su proceso y disminuir la sobreocupación del servicio.

Use of the SONET Score to Evaluate High Volume Emergency Department Overcrowding: A Prospective Derivation and Validation Study

Background. The accuracy and utility of current Emergency Department (ED) crowding estimation tools remain uncertain in EDs with high annual volumes. We aimed at deriving a more accurate tool to evaluate overcrowding in a high volume ED setting and determine the association between ED overcrowding and patient care outcomes. Methods. A novel scoring tool (SONET: Severely overcrowded-Overcrowded-Not overcrowded Estimation Tool) was developed and validated in two EDs with both annual volumes exceeding 100,000. Patient care outcomes including the number of left without being seen (LWBS) patients, average length of ED stay, ED 72-hour returns, and mortality were compared under the different crowding statuses. Results. The total number of ED patients, the number of mechanically ventilated patients, and patient acuity levels were independent risk factors affecting ED overcrowding. SONET was derived and found to better differentiate severely overcrowded, overcrowded, and not overcrowded statuses with similar results validated externally. In addition, SONET scores correlated with increased length of ED stay, number of LWBS patients, and ED 72-hour returns. Conclusions. SONET might be a better fit to determine high volume ED overcrowding. ED overcrowding negatively impacts patient care operations and often produces poor patient perceptions of standardized care delivery.

Is emergency department crowding associated with increased "bounceback" admissions?

OBJECTIVE

Emergency department (ED) crowding is linked with poor quality of care and worse outcomes, including higher mortality. With the growing emphasis on hospital performance measures, there is additional concern whether inadequate care during crowded periods increases a patient's likelihood of subsequent inpatient admission. We sought to determine if ED crowding during the index visit was associated with these "bounceback" admissions.

METHODS

We used comprehensive, nonpublic, statewide ED and inpatient discharge data from the California Office of Statewide Health Planning and Development from 2007 to identify index outpatient ED visits and bounceback admissions within 7 days. We further used ambulance diversion data collected from California local emergency medical services agencies to identify crowded days using intrahospital daily diversion hour quartiles. Using a hierarchical logistic regression model, we then determined if patients visiting on crowded days were more likely to have a subsequent bounceback admission.

RESULTS

We analyzed 3,368,527 index visits across 202 hospitals, of which 596,471 (17.7%) observations were on crowded days. We found no association between ED crowding and bounceback admissions. This lack of relationship persisted in both a discrete (high/low) model (OR, 1.01; 95% CI, 0.99, 1.02) and a secondary model using ambulance diversion hours as a continuous predictor (OR, 1.00; 95% CI, 1.00, 1.00).

CONCLUSIONS

Crowding as measured by ambulance diversion does not have an association with hospitalization within 7 days of an ED visit discharge. Therefore, bounceback admission may be a poor measure of delayed or worsened quality of care due to crowding.

California hospitals serving large minority populations were more likely than others to employ ambulance diversion

It is well documented that racial and ethnic minority populations disproportionately use hospital emergency departments for safety-net care. But what is not known is whether emergency department crowding is disproportionately affecting minority populations and potentially aggravating existing health care disparities, including poorer outcomes for minorities. We examined ambulance diversion, a proxy measure for crowding, at 202 California hospitals. We found that hospitals serving large minority populations were more likely to divert ambulances than were hospitals with a lower proportion of minorities, even when controlling for hospital ownership, emergency department capacity, and other hospital demographic and structural factors. These findings suggest that establishing more-uniform criteria to regulate diversion may help reduce disparities in access to emergency care.

Measures of crowding in the emergency department: a systematic review

OBJECTIVES

Despite consensus regarding the conceptual foundation of crowding, and increasing research on factors and outcomes associated with crowding, there is no criterion standard measure of crowding. The objective was to conduct a systematic review of crowding measures and compare them in conceptual foundation and validity.

METHODS

This was a systematic, comprehensive review of four medical and health care citation databases to identify studies related to crowding in the emergency department (ED). Publications that "describe the theory, development, implementation, evaluation, or any other aspect of a 'crowding measurement/definition' instrument (qualitative or quantitative)" were included. A "measurement/definition" instrument is anything that assigns a value to the phenomenon of crowding in the ED. Data collected from papers meeting inclusion criteria were: study design, objective, crowding measure, and evidence of validity. All measures were categorized into five measure types (clinician opinion, input factors, throughput factors, output factors, and multidimensional scales). All measures were then indexed to six validation criteria (clinician opinion, ambulance diversion, left without being seen (LWBS), times to care, forecasting or predictions of future crowding, and other).

RESULTS

There were 2,660 papers identified by databases; 46 of these papers met inclusion criteria, were original research studies, and were abstracted by reviewers. A total of 71 unique crowding measures were identified. The least commonly used type of crowding measure was clinician opinion, and the most commonly used were numerical counts (number or percentage) of patients and process times associated with patient care. Many measures had moderate to good correlation with validation criteria.

CONCLUSIONS

Time intervals and patient counts are emerging as the most promising tools for measuring flow and nonflow (i.e., crowding), respectively. Standardized definitions of time intervals (flow) and numerical counts (nonflow) will assist with validation of these metrics across multiple sites and clarify which options emerge as the metrics of choice in this "crowded" field of measures.

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 < 1.5) and high crowding (EDWIN > or = 1.5) conditions.

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.

Myths versus facts in emergency department overcrowding and hospital access block

Overcrowding occurs when emergency department (ED) function is impeded, primarily by overwhelming of ED staff resources and physical capacity by excessive numbers of patients needing or receiving care. Access block occurs when there is excessive delay in access to appropriate inpatient beds (> 8 hours total time in the ED). Access block for admitted patients is the principal cause of overcrowding, and is mainly the result of a systemic lack of capacity throughout health systems, and not of inappropriate presentations by patients who should have attended a general practitioner. Overcrowding is most strongly associated with excessive numbers of admitted patients being kept in the ED. Excessive numbers of admitted patients in the ED are associated with diminished quality of care and poor patient outcomes. These include (but are not limited to) adverse events, errors, delayed time-critical care, increased morbidity and excess deaths (estimated as at least 1500 per annum in Australia). There is no evidence that telephone advice lines or collocated after-hours GP services assist in reducing ED workloads. Changes to ED structure and function do not address the underlying causes or major adverse effects of overcrowding. They are also rapidly overwhelmed by increasing access block. The causes of overcrowding, and hence the solutions, lie outside the ED. Solutions will mainly be found in managing hospital bedstock and systemic capacity (including the use of step-down and community resources) so that appropriate inpatient beds remain available for acutely sick patients.

Systematic review of emergency department crowding: causes, effects, and solutions

Emergency department (ED) crowding represents an international crisis that may affect the quality and access of health care. We conducted a comprehensive PubMed search to identify articles that (1) studied causes, effects, or solutions of ED crowding; (2) described data collection and analysis methodology; (3) occurred in a general ED setting; and (4) focused on everyday crowding. Two independent reviewers identified the relevant articles by consensus. We applied a 5-level quality assessment tool to grade the methodology of each study. From 4,271 abstracts and 188 full-text articles, the reviewers identified 93 articles meeting the inclusion criteria. A total of 33 articles studied causes, 27 articles studied effects, and 40 articles studied solutions of ED crowding. Commonly studied causes of crowding included nonurgent visits, "frequent-flyer" patients, influenza season, inadequate staffing, inpatient boarding, and hospital bed shortages. Commonly studied effects of crowding included patient mortality, transport delays, treatment delays, ambulance diversion, patient elopement, and financial effect. Commonly studied solutions of crowding included additional personnel, observation units, hospital bed access, nonurgent referrals, ambulance diversion, destination control, crowding measures, and queuing theory. The results illustrated the complex, multifaceted characteristics of the ED crowding problem. Additional high-quality studies may provide valuable contributions toward better understanding and alleviating the daily crisis. This structured overview of the literature may help to identify future directions for the crowding research agenda.

ED crowding is associated with variable perceptions of care compromise

OBJECTIVES

The authors measured the association between emergency department (ED) crowding and patient and provider perceptions about whether patient care was compromised.

METHODS

This was a cross-sectional study of patients admitted from the ED and their providers. Surveys of patients, nurses, and resident physicians were linked. The primary outcome was agreement or strong agreement on a five-item scale assessing whether ED crowding compromised care. Logistic regression was used to determine the association between the primary outcome and measures of ED crowding.

RESULTS

Of 741 patients approached, 644 patients consented (87%); 703 resident physician surveys (95%) and 716 nursing surveys (97%) were completed. A total of 106 patients (16%), 86 residents (12%), and 173 nurses (24%) reported that care was compromised by ED crowding. In 252 cases (35%), one or more respondents reported that care was compromised. There was poor agreement over whose care was compromised. For patients, independent predictors of compromised care were waiting room time (odds ratio [OR], 1.05 for each additional 10-minute wait [95% confidence interval {CI} = 1.02 to 1.09]) and being surveyed in a hallway bed (OR, 2.02 [95% CI = 1.12 to 3.68]). Predictors of compromised care for nurses included waiting room time (OR, 1.05 for each additional 10-minute wait [95% CI = 1.01 to 1.08]), number of patients in the waiting room (OR, 1.05 for each additional patient waiting [95% CI = 1.02 to 1.07]), and number of admitted patients waiting for an inpatient bed (OR, 1.08 for each additional patient [95% CI = 1.03 to 1.12]). For residents, predictors of compromised care were patient/nurse ratio (OR, 1.39 for a one-unit increase [95% CI = 1.09 to 1.20]) and number of admitted patients waiting for an inpatient bed (OR, 1.14 for each additional patient [95% CI = 1.10 to 1.75]).

CONCLUSIONS

ED crowding is associated with perceptions of compromised emergency care. There is considerable variability among nurses, patients, and resident physicians over which factors are associated with compromised care, whose care was compromised, and how care was compromised.

Measuring and forecasting emergency department crowding in real time

STUDY OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.