An independent evaluation of four quantitative emergency department crowding scales

Perceived Causes and Effects of Overcrowding Among Nurses in the Emergency Departments of Tertiary Hospitals: A Multicenter Study

Purpose

Emergency department (ED) overcrowding is a significant concern in many hospitals in Saudi Arabia, resulting in long waiting times, delays in treating patients who need urgent care, and, consequently, decreased patient satisfaction. Additionally, ED overcrowding has been linked to increased nurse turnover rates. Therefore, this study aimed to assess nurses' perceived causes and effects of overcrowding in the EDs of five tertiary hospitals in Saudi Arabia.

Methods

This study used a descriptive cross-sectional design. We surveyed 311 nurses working in the EDs of five tertiary hospitals in Saudi Arabia using the convenience sampling technique. The self-administered questionnaires used in the study were developed by the researchers. The study was conducted from October 16 to November 10, 2022. Consensus-Based Checklist for Reporting of Survey Studies was followed.

Results

The results revealed that the primary perceived causes of ED overcrowding in five tertiary hospitals were unnecessary visits due to a lack of standard procedures (mean = 2.70; SD = 0.58) and lack of inpatients beds (mean = 2.69; SD = 0.65). The perceived effect of overcrowding was stress and burnout among nurses (mean = 2.85; SD = 0.47). The perceived causes and effects of overcrowding in the ED were found to be highly significant (p <0.001) based on Pearson correlation and Spearman's rank correlation.

Conclusion

Unnecessary visits due to a lack of standard procedures lead to overcrowding. In addition, a lack of inpatient beds in the ED affects the care provided to patients seeking immediate medical attention. This may prolong patient waiting time, causing their conditions to deteriorate and prolonging hospital stay. Overcrowding leads to increased stress and burnout among nurses. The results of this study can be used to develop a comprehensive action plan to address ED overcrowding and its effects on patients, staff, and ED flow.

An Effective Methodology for Scoring to Assist Emergency Physicians in Identifying Overcrowding in an Academic Emergency Department in Thailand

BACKGROUND

Emergency Department (ED) overcrowding is a global concern, with tools like NEDOCS, READI, and Work Score used as predictors. These tools aid healthcare professionals in identifying overcrowding and preventing negative patient outcomes. However, there's no agreed-upon method to define ED overcrowding. Most studies on this topic are U.S.-based, limiting their applicability in EDs without waiting rooms or ambulance diversion roles. Additionally, the intricate calculations required for these scores, with multiple variables, make them impractical for use in developing nations.

OBJECTIVE

This study sought to examine the relationship between prevalent ED overcrowding scores such as EDWIN, occupancy rate, and Work Score, and a modified version of EDWIN newly introduced by the authors, in comparison to the real-time perspectives of emergency physicians. Additionally, the study explored the links between these overcrowding scores and adverse events related to ED code activations as secondary outcomes.

METHOD

The method described in the provided text is a correlational study. The study aims to examine the relationship between various Emergency Department (ED) overcrowding scores and the real-time perceptions of emergency physicians in every two-hour period. Additionally, it seeks to explore the associations between these scores and adverse events related to ED code activations.

RESULTS

The study analyzed 459 periods, with 5.2% having Likert scores of 5-6. EDOR had the highest correlation coefficient (0.69, p < 0.001) and an AUC of 0.864. Only EDOR significantly correlated with adverse events (p = 0.033).

CONCLUSION

EDOR shows the most robust link with 'emergency physicians' views on overcrowding. Additionally, elevated EDOR scores correlate with a rise in adverse events. Emergency physicians' perceptionof overcrowding could hint at possible adverse events. Notably, all overcrowding scores have high negative predictive values, efficiently negating the likelihood of adverse incidents.

Emergency department overcrowding and its associated factors at HARME medical emergency center in Eastern Ethiopia

Introduction

Emergency department (ED) overcrowding has become a significant concern as it can lead to compromised patient care in emergency settings. Various tools have been used to evaluate overcrowding in ED. However, there is a lack of data regarding this issue in resource-limited countries, including Ethiopia. This study aimed to validate NEDOCS, assess level of ED overcrowding and identify associated factors at HARME Medical Emergency Center, located in Hiwot Fana Comprehensive Specialized Hospital, Harar, Ethiopia.

Methods

A cross-sectional study was conducted at the HARME Medical Emergency Center, Hiwot Fana Comprehensive Specialized Hospital, involving a total of 899 patients during 120 sampling intervals. The area under the receiver operating characteristic curves (AUC) was calculated to evaluate the agreement between objective and subjective assessments of ED overcrowding. A multivariable logistic regression analysis was employed to identify factors associated with ED overcrowding and statistically significant association was declared using 95 % confidence level and a p-value < 0.05.

Results

The interrater agreement showed a strong correlation with a Cohen's kappa (κ) of 0.80. The National Emergency Department Overcrowding Study Score demonstrated a strong association with subjective assessments from residents and case team nurses, with an AUC of 0.81 and 0.79, respectively. According to residents' perceptions, ED were considered overcrowded 65.8 % of the time. Factors significantly associated with ED overcrowding included waiting time for triage (AOR: 2.24; 95 % CI: 1.54-3.27), working time (AOR: 2.23; 95 % CI: 1.52-3.26), length of stay (AOR: 2.40; 95 % CI: 1.27-4.54), saturation level (AOR: 2.35; 95 % CI: 1.31-4.20), chronic illness (AOR: 2.19; 95 % CI: 1.37-3.53), and abnormal pulse rate (AOR: 1.52; 95 % CI: 1.06-2.16).

Conclusion

The study revealed that ED were overcrowded approximately two-thirds of the time.

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

OBJECTIVE

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

Queueing Problems in Emergency Departments: A Review of Practical Approaches and Research Methodologies

Problems related to patient scheduling and queueing in emergency departments are gaining increasing attention in theory, in the fields of operations research and emergency and healthcare services, and in practice. This paper aims to provide an extensive review of studies addressing queueing-related problems explicitly related to emergency departments. We have reviewed 229 articles and books spanning seven decades and have sought to organize the information they contain in a manner that is accessible and useful to researchers seeking to gain knowledge on specific aspects of such problems. We begin by presenting a historical overview of applications of queueing theory to healthcare-related problems. We subsequently elaborate on managerial approaches used to enhance efficiency in emergency departments. These approaches include bed management, fast-track, dynamic resource allocation, grouping/prioritization of patients, and triage approaches. Finally, we discuss scientific methodologies used to analyze and optimize these approaches: algorithms, priority models, queueing models, simulation, and statistical approaches.

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.

Real-Time Measurement of Crowding in Pediatric Emergency Department: Derivation and Validation Using Consensual Perception of Crowding (SOTU-PED)

ABSTRACT

Our study aimed to develop and validate a real-time crowding composite scale for pediatric emergency department (PED). The study took place in one teaching PED for 2 months. The outcome was the perception of crowding evaluated by triage nurses and pediatricians on a 10-level Likert scale. Triage nurses evaluated crowding at each moment of a child's admission and pediatrician at each moment of a child's discharge. The outcome was the hourly mean of all evaluations of crowding (hourly crowding perception). For analysis, originally, we only selected hours during which more than 2 nurses and more than 2 pediatricians evaluated crowding and, moreover, during which evaluations were the most consensual. As predictors, we used hourly means of 10 objective crowding indicators previously selected as consensual in a published French national Delphi study and collected automatically in our software system. The model (SOTU-PED) was developed over a 1-month data set using a backward multivariable linear regression model. Then, we applied the SOTU-PED model on a 1-month validation data set. During the study period, 7341 children were admitted in the PED. The outcome was available for 1352/1392 hours, among which 639 were included in the analysis as "consensual hours." Five indicators were included in the final model, the SOTU-PED (R2 = 0.718). On the validation data set, the correlation between the outcome (perception of crowding) and the SOTU-PED was 0.824. To predict crowded hours (hourly crowding perception >5), the area under the curve was 0.957 (0.933-0.980). The positive and negative likelihood ratios were 8.16 (3.82-17.43) and 0.153 (0.111-0.223), respectively. Using a simple model, it is possible to estimate in real time how crowded a PED is.

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

BACKGROUND

Emergency 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.

METHODS

We 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.

RESULTS

A 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.

CONCLUSION

Not 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.

Does crowding influence emergency department treatment time and disposition?

OBJECTIVE

The objective of this study was to determine whether crowding influences treatment times and disposition decisions for emergency department (ED) patients.

METHODS

We conducted a retrospective cohort study at 2 hospitals from January 1, 2014, to July 1, 2014. Adult ED visits with dispositions of discharge, admission, or transfer were included. Treatment times were modeled by linear regression with log-transformation; disposition decisions (admission or transfer vs discharge) were modeled by logistic regression. Both models adjusted for chief complaint, Emergency Severity Index (ESI), and 4 crowding metrics in quartiles: waiting count, treatment count, boarding count, and National Emergency Department Overcrowding Scale.

RESULTS

We included 21,382 visits at site A (12.9% excluded) and 29,193 at site B (15.0% excluded). Respective quartiles of treatment count increased treatment times by 7.1%, 10.5%, and 13.3% at site A (P < 0.001) and by 4.0%, 6.5%, and 10.2% at site B (P < 0.001). The fourth quartile of treatment count increased estimates of treatment time for patients with chest pain and ESI level 2 from 2.5 to 2.9 hours at site A (20 minutes) and from 3.0 to 3.3 hours at site B (18 minutes). Treatment times decreased with quartiles of waiting count by 5.6%, 7.2%, and 7.3% at site B (P < 0.001). Odds of admission or transfer increased with quartiles of waiting count by 8.7%, 9.6%, and 20.3% at site A (P = 0.011) and for the third (11.7%) and fourth quartiles (27.3%) at site B (P < 0.001).

CONCLUSIONS

Local crowding influenced ED treatment times and disposition decisions at 2 hospitals after adjusting for chief complaint and ESI.

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.

NEDOCS: is it really useful for detecting emergency department overcrowding today?

The Emergency Department (ED) overcrowding is an ongoing problem all over the world. The scoring systems are available for the detection of this problem. This study aims to test the applicability of the National Emergency Department Overcrowding Study (NEDOCS) scoring system, one of the scoring systems that evaluate the ED overcrowding.In this prospective observational study, the survey was applied on on-duty doctors, nurses, paramedics, intern doctors, and ward persons working in a University Adult Emergency Department and agreed to participate in the study, between certain hours during the day and the NEDOCS score was calculated simultaneously. The demographic characteristics of the on-duty staff, overcrowding of ED, and the number of the on-duty staff members were recorded in the questionnaires.During the study, 153 measurements were performed, and 3221 questionnaires were filled. The NEDOCS mean score was determined as 101.59 and the most reached result was "extremely busy but not overcrowded" (32%). The ED was rated mostly as "busy" (33.7%) by the on-duty staff. A significant difference was found between ED overcrowding and NEDOCS score. There is a significant difference between ED overcrowding and on-duty emergency nurse and intern doctor count.The NEDOCS score is not suitable for evaluating ED overcrowding. Accurate determination of the ED overcrowding is very important to avoid the negative consequences of the ED overcrowding. Increasing emergency nurse and intern doctor count will decrease ED overcrowding. Also, there is an urgent need to constitute local hospitals and also public health policies to satisfy the increasingly ED's presentations.

Prevalence of crowding, boarding and staffing levels in Swedish emergency departments - a National Cross Sectional Study

Background

Emergency Department (ED) crowding occurs when demand for care exceeds the available resources. Crowding has been associated with decreased quality of care and increased mortality, but the prevalence on a national level is unknown in most countries.

Method

We performed a national, cross-sectional study on staffing levels, staff workload, occupancy rate and patients waiting for an in-hospital bed (boarding) at five time points during 24 h in Swedish EDs.

Results

Complete data were collected from 37 (51% of all) EDs in Sweden. High occupancy rate indicated crowding at 12 hospitals (37.5%) at 31 out of 170 (18.2%) time points. Mean workload (measured on a scale from 1, no workload to 6, very high workload) was moderate at 2.65 (±1.25). Boarding was more prevalent in academic EDs than rural EDs (median 3 vs 0). There were an average of 2.6, 4.6 and 3.2 patients per registered nurse, enrolled nurse and physician, respectively.

Conclusion

ED crowding based on occupancy rate was prevalent on a national level in Sweden and comparable with international data. Staff workload, boarding and patient to staff ratios were generally lower than previously described.

Validation of the short form of the International Crowding Measure in Emergency Departments: an international study

OBJECTIVE

There is little consensus on the best way to measure emergency department (ED) crowding. We have previously developed a consensus-based measure, the International Crowding Measure in Emergency Departments. We aimed to externally validate a short form of the International Crowding Measure in Emergency Department (sICMED) against emergency physician's perceptions of crowding and danger.

METHODS

We performed an observational validation study in seven EDs in five different countries. We recorded sICMED observations and the most senior available emergency physician's perceptions of crowding and danger at the same time. We performed a times series regression model.

RESULTS

A total of 397 measurements were analysed. The sICMED showed moderate positive correlations with emergency physician's perceptions of crowding, r = 0.4110, P < 0.05) and safety (r = 0.4566, P < 0.05). There was considerable variation in the performance of the sICMED between different EDs. The sICMED was only slightly better than measuring occupancy or ED boarding time.

CONCLUSION

The sICMED has moderate face validity at predicting clinician's concerns about crowding and safety, but the strength of this validity varies between different EDs and different countries.

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.

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.

Evaluating the impact of emergency department crowding on disposition patterns and outcomes of discharged patients

BACKGROUND

Crowding is a major challenge faced by EDs and is associated with poor outcomes.

OBJECTIVES

Determine the effect of high ED occupancy on disposition decisions, return ED visits, and hospitalizations.

METHODS

We conducted a retrospective analysis of electronic health records of patients evaluated at an adult, urban, and academic ED over 20 months between the years 2012 and 2014. Using a logistic regression model predicting admission, we obtained estimates of the effect of high occupancy on admission disposition, adjusted for key covariates. We then stratified the analysis based on the presence or absence of high boarder patient counts.

RESULTS

Disposition decisions during a high occupancy hour decreased the odds of admission (OR = 0.93, 95% CI: [0.89, 0.98]). Among those who were not admitted, high occupancy was not associated with increased odds of return in the combined (OR = 0.94, 95% CI: [0.87, 1.02]), with-boarders (OR = 0.96, 95% CI: [0.86, 1.09]), and no-boarders samples (OR = 0.93, 95% CI: [0.83, 1.04]). Among those who were not admitted and who did return within 14 days, disposition during a high occupancy hour on the initial ED visit was not associated with a significant increased odds of hospitalization in the combined (OR = 1.04, 95% CI: [0.87, 1.24]), the with-boarders (OR = 1.12, 95% CI: [0.87, 1.44]), and the no-boarders samples (OR = 0.98, 95% CI: [0.77, 1.24]).

CONCLUSION

ED crowding was associated with reduced likelihood of hospitalization without increased likelihood of 2-week return ED visit or hospitalization. Furthermore, high occupancy disposition hours with high boarder patient counts were associated with decreased likelihood of hospitalization.

Emergency Department Crowding Is Associated With Delayed Antibiotics for Sepsis

STUDY OBJECTIVE

Barriers to early antibiotic administration for sepsis remain poorly understood. We investigated the association between emergency department (ED) crowding and door-to-antibiotic time in ED sepsis.

METHODS

We conducted a retrospective cohort study of ED sepsis patients presenting to 2 community hospitals, a regional referral hospital, and a tertiary teaching hospital. The primary exposure was ED occupancy rate, defined as the ratio of registered ED patients to licensed ED beds. We defined ED overcrowding as an ED occupancy rate greater than or equal to 1. We used multivariable regression to measure the adjusted association between ED crowding and door-to-antibiotic time (elapsed time from ED arrival to first antibiotic initiation). Using Markov multistate models, we also investigated the association between ED crowding and pre-antibiotic care processes.

RESULTS

Among 3,572 eligible sepsis patients, 70% arrived when the ED occupancy rate was greater than or equal to 0.5 and 14% arrived to an overcrowded ED. Median door-to-antibiotic time was 158 minutes (interquartile range 109 to 216 minutes). When the ED was overcrowded, 46% of patients received antibiotics within 3 hours of ED arrival compared with 63% when it was not (difference 14.4%; 95% confidence interval 9.7% to 19.2%). After adjustment, each 10% increase in ED occupancy rate was associated with a 4.0-minute increase (95% confidence interval 2.8 to 5.2 minutes) in door-to-antibiotic time and a decrease in the odds of antibiotic initiation within 3 hours (odds ratio 0.90; 95% confidence interval 0.88 to 0.93). Increasing ED crowding was associated with slower initial patient assessment but not further delays after the initial assessment.

CONCLUSION

ED crowding was associated with increased sepsis antibiotic delay. Hospitals must devise strategies to optimize sepsis antibiotic administration during periods of ED crowding.

Quantifying emergency department crowding: comparison between two scores

INTRODUCTION

Emergency department (ED) crowding is a major international concern with a negative impact on both patient care and providers. Currently, there is no consensus regarding measure of crowding. Therefore, emergency physicians have to choose between numerous scoring systems, from simple to more complex. The aim of the present study was to compare the complex National Emergency Department Overcrowding Scale (NEDOCS) with the simple ED Occupancy rate (OR) determination. We further evaluated the correlation between these scores and a qualitative assessment of crowding.

METHOD

This study was conducted in two academic and one regional hospital in Liege Province, in Belgium; each accounting for an ED census of over 40,000 patient visits per year. Crowding measures were sampled four times a day, over a two-week period, in January 2016.

RESULTS

ED staff considered overcrowding as a major concern in the three ED. Median OR ranged from 68 to 100, while NEDOCS ranged from 64.5 to 76.3. A significant correlation was found in each ED between the OR and the NEDOCS (Pearson r = 0.973, 0.974 and 0.972), as well as between the OR, the NEDOCS and the subjective evaluation by the ED staff (p = 0.001).

CONCLUSION

Crowding evaluation in ED requires validated scores. Our study in three different hospitals demonstrates that simple OR appeared as accurate as more sophisticated NEDOCS. Furthermore, this measure is perfectly correlated with the feeling of ED staff.

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

INTRODUCTION

Posttraumatic 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.

METHODS

We 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).

RESULTS

EDWIN 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).

DISCUSSION

We 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.

Describing the categories of people that contribute to an Emergency Centre crowd at Khayelitsha hospital, Western Cape, South Africa

INTRODUCTION

Emergency Centre (EC) crowding has globally been recognised to adversely affect patients, staff and visitors. Anecdotally, local ECs are perceived to be fairly crowded, however, not much is known about the size of this crowd and what constitutes it. Although more reliable, resource restrictions render more detailed flow studies less achievable. This study describes the EC crowd at Khayelitsha hospital in Cape Town, South Africa as the number and different categories of people, at predefined times during the day over a four-week period.

METHODS

A prospective, cross-sectional design was used. Headcounts were made by predefined groups at 09h00, 14h00, and 21h00 every day for four weeks. Predefined groups included doctors, nurses, visitors, patients, and other allied health staff. Summary statistics were used to describe the data. Precision was described using the 95% confidence interval.

RESULTS

A total of 16,353 people were counted during the study period. On average, 6370 (39%) of the groups were staff, 5231 (32%) were patients and 4752 (29%) were visitors. Of the staff, 586 (3.6%) were EC doctors, 733 (4.4%) were non-EC doctors, 1488 (9%) were EC nurses, and 445 (3%) were non-EC nurses. Although patient numbers in the EC remained constant, visitors and non-EC staff varied significantly with visitors peaking in the afternoon and non-EC staff drastically reducing in the evening. The EC was consistently crowded - average occupancy: 130%.

CONCLUSION

Staff levels fluctuated predictably, reducing at night and over weekends, while patient levels remained constant. Non-EC doctors more than doubled during the day on week shifts, in significantly more numbers than EC doctors, suggesting that many of the patients in the EC were likely to be admissions boarding in the EC. Visitor numbers were substantial during visiting hours and further aggravated crowding. Resource-light studies involving flow are important to explore crowding in low- and middle income settings.

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

Background

Emergency 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.

Objectives

To 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.

Methods

A 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 75^th percentile of mEDWIN per hour, which was ≥0.28.

Results

In 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).

Conclusion

The 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.

Door-to-Imaging Time for Acute Stroke Patients Is Adversely Affected by Emergency Department Crowding

Background and Purpose—

National guidelines call for door-to-imaging time (DIT) within 25 minutes for suspected acute stroke patients. Studies examining factors that affect DIT have focused primarily on stroke-specific care processes and patient-specific factors. We hypothesized that emergency department (ED) crowding is associated with longer DIT.

Methods—

We conducted a retrospective investigation of 1 year of consecutive patients in our prospective Code Stroke registry, which included all ED stroke team activations. The registry and electronic health records were abstracted for 27 potential predictors of DIT, including patient, stroke care process, and ED operational factors. We fit a multivariate logistic regression model and calculated odds ratios and 95% confidence intervals. Second, we constructed a random forest recursive partitioning model to cross-validate our findings and explore the proportional importance of each category of predictor. Our primary outcome was the binary variable of DIT within the 25-minute goal.

Results—

A total of 463 patients met inclusion criteria. In the regression model, ED occupancy rate emerged as a predictor of DIT, with odds ratio of 0.83 (95% confidence interval, 0.75–0.91) of DIT within 25 minutes per 10% absolute increase in ED occupancy rate. The secondary analysis estimated that ED operational factors accounted for nearly 14% of the algorithm’s prediction of DIT.

Conclusions—

ED crowding is associated with reduced odds of meeting DIT goals for acute stroke. In addition to improving stroke-specific processes of care, efforts to reduce ED overcrowding should be considered central to optimizing the timeliness of acute stroke care.

Comparison of emergency department crowding scores: a discrete-event simulation approach

According to American College of Emergency Physicians, emergency department (ED) crowding occurs when the identified need for emergency services exceeds available resources for patient care in the ED, hospital, or both. ED crowding is a widely reported problem and several crowding scores are proposed to quantify crowding using hospital and patient data as inputs for assisting healthcare professionals in anticipating imminent crowding problems. Using data from a large academic hospital in North Carolina, we evaluate three crowding scores, namely, EDWIN, NEDOCS, and READI by assessing strengths and weaknesses of each score, particularly their predictive power. We perform these evaluations by first building a discrete-event simulation model of the ED, validating the results of the simulation model against observations at the ED under consideration, and utilizing the model results to investigate each of the three ED crowding scores under normal operating conditions and under two simulated outbreak scenarios in the ED. We conclude that, for this hospital, both EDWIN and NEDOCS prove to be helpful measures of current ED crowdedness, and both scores demonstrate the ability to anticipate impending crowdedness. Utilizing both EDWIN and NEDOCS scores in combination with the threshold values proposed in this work could provide a real-time alert for clinicians to anticipate impending crowding, which could lead to better preparation and eventually better patient care outcomes.

A generic method for evaluating crowding in the emergency department

Background

Crowding in the emergency department (ED) has been studied intensively using complicated non-generic methods that may prove difficult to implement in a clinical setting. This study sought to develop a generic method to describe and analyse crowding from measurements readily available in the ED and to test the developed method empirically in a clinical setting.

Methods

We conceptualised a model with ED patient flow divided into separate queues identified by timestamps for predetermined events. With temporal resolution of 30 min, queue lengths were computed as Q(t + 1) = Q(t) + A(t) – D(t), with A(t) = number of arrivals, D(t) = number of departures and t = time interval. Maximum queue lengths for each shift of each day were found and risks of crowding computed. All tests were performed using non-parametric methods. The method was applied in the ED of Aarhus University Hospital, Denmark utilising an open cohort design with prospectively collected data from a one-year observation period.

Results

By employing the timestamps already assigned to the patients while in the ED, a generic queuing model can be computed from which crowding can be described and analysed in detail. Depending on availability of data, the model can be extended to include several queues increasing the level of information. When applying the method empirically, 41,693 patients were included. The studied ED had a high risk of bed occupancy rising above 100 % during day and evening shift, especially on weekdays. Further, a ‘carry over’ effect was shown between shifts and days.

Conclusions

The presented method offers an easy and generic way to get detailed insight into the dynamics of crowding in an ED.

A New Model in Reducing Emergency Department Crowding: The Electronic Blockage System

Objectives

Emergency department (ED) crowding is a growing problem across the world. Hospitals need to identify the situation using emergency department crowding scoring systems and to produce appropriate solutions.

Methods

A new program (Electronic Blockage System, EBS) was written supplementary to the Hospital Information System. It was planned that the number of empty beds in the hospital should primarily be used for patients awaiting admission to a hospital bed at the ED. In the presence of patients awaiting admission at the ED, non-urgent admissions to other departments were blocked. ED overcrowded was measured in the period before initiation of EBS, the early post-EBS period and the late post-EBS period, of one-week's duration each, using NEDOCS scoring.

Results

NEDOCS values were significantly lower in the early post-EBS period compared to the other periods (p≤0.0001). Although outpatient numbers applying to the ED and existing patient numbers at time of measurement remained unchanged in all three periods, the number of patients awaiting admission in the early post-EBS period was significantly lower than in the pre-EBS and late post-EBS periods (p=0.0001, p=0.001).

Conclusions

EBS is a form of triage system aimed at preventing crowding and ensuring the priority admission of emergency patients over that of polyclinic patients. In hospitals with an insufficient number of total beds it can be used to reduce ED crowding and accelerate admissions to hospital from the ED.

Skåne Emergency Department Assessment of Patient Load (SEAL)-A Model to Estimate Crowding Based on Workload in Swedish Emergency Departments

Objectives

Emergency department (ED) crowding is an increasing problem in many countries. The purpose of this study was to develop a quantitative model that estimates the degree of crowding based on workload in Swedish EDs.

Methods

At five different EDs, the head nurse and physician assessed the workload on a scale from 1 to 6 at randomized time points during a three week period in 2013. Based on these assessments, a regression model was created using data from the computerized patient log system to estimate the level of crowding based on workload. The final model was prospectively validated at the two EDs with the largest census.

Results

Workload assessments and data on 14 variables in the patient log system were collected at 233 time points. The variables Patient hours, Occupancy, Time waiting for the physician and Fraction of high priority (acuity) patients all correlated significantly with the workload assessments. A regression model based on these four variables correlated well with the assessed workload in the initial dataset (r2 = 0.509, p < 0.001) and with the assessments in both EDs during validation (r2 = 0.641; p < 0.001 and r2 = 0.624; p < 0.001).

Conclusions

It is possible to estimate the level of crowding based on workload in Swedish EDs using data from the patient log system. Our model may be applicable to EDs with different sizes and characteristics, and may be used for continuous monitoring of ED workload. Before widespread use, additional validation of the model is needed.

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.

Measuring emergency department crowding in an inner city hospital in The Netherlands

Background

Overcrowding in the emergency department (ED) is an increasing problem worldwide. In The Netherlands overcrowding is not a major issue, although some urban hospitals struggle with increased throughput. In 2004, Weiss et al. created the NEDOCS tool (National Emergency Department Over Crowding Study), a web-based instrument to measure objective overcrowding with scores between 0 (not busy at all) to above 181 (disaster). In this study we tried to validate the accuracy of the NEDOCS tool by comparing this with the subjective feelings of the ED nurse and emergency physician (EP) in an inner city hospital in The Netherlands.

Methods

In a 4-week period, data of a total of 147 time samplings were collected. The subjective feelings of being overcrowded and feeling rushed by the ED nurse and EP were scored on a survey using a 6-point Likert scale on answering the question of how busy they would say the ED is right now. NEDOCS tool scores were calculated, and these were compared with the subjective feelings using the kappa statistic assessing linear weights according to Cohen’s method.

Results

Of all the time samplings, approximately 80% of the surveys were completed. The ED was rated as overcrowded 9% of the time by the ED nurses and 11% of the time by the EPs. The median NEDOCS score was 37 (0 to 120) and scored as overcrowded in 3%. There was a good intrarater agreement for the ED nurse and EP for the feeling of overcrowding and feeling of being rushed (κ = 0.79 and 0.73, respectively); the interrater agreement was moderate (κ = 0.53 and 0.43, respectively). The agreement between the NEDOCS and the subjective variables was moderate (κ = 0.50 and 0.53, respectively). A composite variable was created as the average of both the scores of feeling overcrowded of the nurse and the EP and the score of the EP of feeling rushed. The agreement between this and the NEDOCS was κ = 0.53.

Conclusions

The NEDOCS tool is a reasonably good tool to quantify the subjective feelings of overcrowding. When overcrowding is encountered and immediately recognised, specific measures can be taken to guarantee the timely provision of necessary medical care to the patients in the ED at that time. However, possibly more accurate agreements could be obtained as approximately 20% of the surveys were not completed because of perceived crowdedness. An important limitation is that only 3% of the NEDOCS is scored as overcrowded, so no conclusions can be drawn about the agreement for higher categories of overcrowding. It is suggested to repeat the study in a busier period. As the triage category was not taken into account in the formula, a high workload with only a few patients giving high scores in subjective overcrowding in spite of a low NEDOCS score could have led to lower agreements. Incorporating the triage category in the NEDOCS tool possibly will lead to better agreement, but further research is needed to assess this idea.

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

INTRODUCTION

Emergency 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.

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.

CrowdED: crowding metrics and data visualization in the emergency department

OBJECTIVES

Emergency department (ED) crowding metrics were validated in our facility and a new technique of data visualization is proposed.

DESIGN

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.

SETTING/PARTICIPANTS

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.

MAIN OUTCOME MEASURE(S)

The key outcome variables were patient reneging (number of patients who left before being seen by a physician) and ambulance diversion status.

RESULTS

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.

CONCLUSIONS

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.

Emergency department crowding: time for interventions and policy evaluations

This paper summarises the consequences of emergency department crowding. It provides a comparison of the scales used to measure emergency department crowding. We discuss the multiple causes of crowding and present an up-to-date literature review of the interventions that reduce the adverse consequences of crowding. We consider interventions at the level of an individual hospital and a policy level.

Factors related to missed diagnosis of incidental scabies infestations in patients admitted through the emergency department to inpatient services

OBJECTIVES

Scabies is highly contagious and requires prompt diagnosis and implementation of infection control measures to prevent transmission and outbreaks. This study investigated the clinical and administrative correlates associated with missed diagnosis of scabies in an emergency department (ED).

METHODS

This was a retrospective study of patients with incidental scabies infestations who were admitted to a university hospital via the ED during a 4-year period.

RESULTS

A total of 135 inpatients were identified as having scabies; among them, 111 patients (82%) had visited the ED. Scabies were diagnosed during the ED stay in 39 of 111 patients (35%), while the diagnosis was missed in the ED in 72 patients (65%). Although no geographic clusters suggestive of nosocomial scabies transmission were registered, 160 medical workers and one hospitalized patient received prophylactic treatment due to direct skin-to-skin contact with inpatient scabies cases during the study period. Overcrowding (odds ratio [OR] = 8.4; 95% confidence interval [CI] = 1.9 to 38.0) and time constraints (OR = 8.2; 95% CI = 1.9 to 34.7) in the ED were associated with a missed diagnosis of scabies during ED stay. Patients with lower illness severity scores were at higher risk for failure to diagnose and to treat scabies prior to hospital admission (OR = 5.7; 95% CI = 1.6 to 20.9).

CONCLUSIONS

Missed diagnoses of scabies during ED stay may result in nosocomial spread and increase the unnecessary use of prophylactic treatments. ED overcrowding, time constraints, and less severe illness compromise ED recognition of scabies. Health care workers should be especially alert for signs of scabies infestations under these conditions.

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 rho = 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.

Forecasting emergency department crowding: an external, multicenter evaluation

STUDY OBJECTIVE

We apply a previously described tool to forecast emergency department (ED) crowding at multiple institutions and assess its generalizability for predicting the near-future waiting count, occupancy level, and boarding count.

METHODS

The ForecastED tool was validated with historical data from 5 institutions external to the development site. A sliding-window design separated the data for parameter estimation and forecast validation. Observations were sampled at consecutive 10-minute intervals during 12 months (n=52,560) at 4 sites and 10 months (n=44,064) at the fifth. Three outcome measures-the waiting count, occupancy level, and boarding count-were forecast 2, 4, 6, and 8 hours beyond each observation, and forecasts were compared with observed data at corresponding times. The reliability and calibration were measured following previously described methods. After linear calibration, the forecasting accuracy was measured with the median absolute error.

RESULTS

The tool was successfully used for 5 different sites. Its forecasts were more reliable, better calibrated, and more accurate at 2 hours than at 8 hours. The reliability and calibration of the tool were similar between the original development site and external sites; the boarding count was an exception, which was less reliable at 4 of 5 sites. Some variability in accuracy existed among institutions; when forecasting 4 hours into the future, the median absolute error of the waiting count ranged between 0.6 and 3.1 patients, the median absolute error of the occupancy level ranged between 9.0% and 14.5% of beds, and the median absolute error of the boarding count ranged between 0.9 and 2.8 patients.

CONCLUSION

The ForecastED tool generated potentially useful forecasts of input and throughput measures of ED crowding at 5 external sites, without modifying the underlying assumptions. Noting the limitation that this was not a real-time validation, ongoing research will focus on integrating the tool with ED information systems.

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.

The impact of inpatient boarding on ED efficiency: a discrete-event simulation study

In this study, a discrete-event simulation approach was used to model Emergency Department's (ED) patient flow to investigate the effect of inpatient boarding on the ED efficiency in terms of the National Emergency Department Crowding Scale (NEDOCS) score and the rate of patients who leave without being seen (LWBS). The decision variable in this model was the boarder-released-ratio defined as the ratio of admitted patients whose boarding time is zero to all admitted patients. Our analysis shows that the Overcrowded(+) (a NEDOCS score over 100) ratio decreased from 88.4% to 50.4%, and the rate of LWBS patients decreased from 10.8% to 8.4% when the boarder-released-ratio changed from 0% to 100%. These results show that inpatient boarding significantly impacts both the NEDOCS score and the rate of LWBS patient and this analysis provides a quantification of the impact of boarding on emergency department patient crowding.

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.

Forecasting emergency department crowding: a prospective, real-time evaluation

OBJECTIVE

Emergency department crowding threatens quality and access to health care, and a method of accurately forecasting near-future crowding should enable novel ways to alleviate the problem. The authors sought to implement and validate the previously developed ForecastED discrete event simulation for real-time forecasting of emergency department crowding.

DESIGN AND MEASUREMENTS

The authors conducted a prospective observational study during a three-month period (5/1/07-8/1/07) in the adult emergency department of a tertiary care medical center. The authors connected the forecasting tool to existing information systems to obtain real-time forecasts of operational data, updated every 10 minutes. The outcome measures included the emergency department waiting count, waiting time, occupancy level, length of stay, boarding count, boarding time, and ambulance diversion; each forecast 2, 4, 6, and 8 hours into the future.

RESULTS

The authors obtained crowding forecasts at 13,239 10-minute intervals, out of 13,248 possible (99.9%). The R(2) values for predicting operational data 8 hours into the future, with 95% confidence intervals, were 0.27 (0.26, 0.29) for waiting count, 0.11 (0.10, 0.12) for waiting time, 0.57 (0.55, 0.58) for occupancy level, 0.69 (0.68, 0.70) for length of stay, 0.61 (0.59, 0.62) for boarding count, and 0.53 (0.51, 0.54) for boarding time. The area under the receiver operating characteristic curve for predicting ambulance diversion 8 hours into the future, with 95% confidence intervals, was 0.85 (0.84, 0.86).

CONCLUSIONS

The ForecastED tool provides accurate forecasts of several input, throughput, and output measures of crowding up to 8 hours into the future. The real-time deployment of the system should be feasible at other emergency departments that have six patient-level variables available through information systems.

Pediatric emergency department overcrowding and impact on patient flow outcomes

BACKGROUND

Understanding the impact of overcrowding in pediatric emergency departments (PEDs) on quality of care is a growing concern. Boarding admitted patients in the PED and increasing emergency department (ED) visits are two potentially significant factors affecting quality of care.

OBJECTIVES

The objective was to describe the impact ED boarding time and daily census have on the timeliness of care in a PED.

METHODS

Pediatric ED boarding time and daily census were determined each day from July 2003 to July 2007. Outcome measures included mean length of stay (LOS), time to triage, time to physician, and patient elopement during a 24-hour period.

RESULTS

For every 50 patients seen above the average daily volume of 250, LOS increased 14.8 minutes, time to triage increased 6.6 minutes, time to physician increased 18.2 minutes, and number of patient elopements increased by three. For each increment of 24 hours to total ED boarding time, LOS increased 7.6 minutes, time to triage increased 0.6 minutes, time to physician increased 3 minutes, and number of patient elopements increased by 0.6 patients.

CONCLUSIONS

ED boarding time and ED daily census show independent associations with increasing overall LOS, time to triage, time to physician, and number of patient elopements in a PED.

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.