Comparison of the International Crowding Measure in Emergency Departments (ICMED) and the National Emergency Department Overcrowding Score (NEDOCS) to measure emergency department crowding: pilot study

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.

Development and Validation of a New Tool to Improve the Accuracy of the Hospital Mass-Casualty Incident Response Plan Activation: The PEMAAF Score

INTRODUCTION

Effective response to a mass-casualty incident (MCI) entails the activation of hospital MCI plans. Unfortunately, there are no tools available in the literature to support hospital responders in predicting the proper level of MCI plan activation. This manuscript describes the scientific-based approach used to develop, test, and validate the PEMAAF score (Proximity, Event, Multitude, Overcrowding, Temporary Ward Reduction Capacity, Time Shift Slot [Prossimità, Evento, Moltitudine, Affollamento, Accorpamento, Fascia Oraria], a tool able to predict the required level of hospital MCI plan activation and to facilitate a coordinated activation of a multi-hospital network.

METHODS

Three study phases were performed within the Metropolitan City of Milan, Italy: (1) retrospective analysis of past MCI after action reports (AARs); (2) PEMAAF score development; and (3) PEMAAF score validation. The validation phase entailed a multi-step process including two retrospective analyses of past MCIs using the score, a focus group discussion (FGD), and a prospective simulation-based study. Sensitivity and specificity of the score were analyzed using a regression model, Spearman's Rho test, and receiver operating characteristic/ROC analysis curves.

RESULTS

Results of the retrospective analysis and FGD were used to refine the PEMAAF score, which included six items-Proximity, Event, Multitude, Emergency Department (ED) Overcrowding, Temporary Ward Reduction Capacity, and Time Shift Slot-allowing for the identification of three priority levels (score of 5-6: green alert; score of 7-9: yellow alert; and score of 10-12: red alert). When prospectively analyzed, the PEMAAF score determined most frequent hospital MCI plan activation (>10) during night and holiday shifts, with a score of 11 being associated with a higher sensitivity system and a score of 12 with higher specificity.

CONCLUSIONS

The PEMAAF score allowed for a balanced and adequately distributed response in case of MCI, prompting hospital MCI plan activation according to real needs, taking into consideration the whole hospital response network.

Real-time prediction of patient disposition and the impact of reporter confidence on mid-level triage accuracies: an observational study in Israel

AIM

The emergency department (ED) is the first port-of-call for most patients receiving hospital care and as such acts as a gatekeeper to the wards, directing patient flow through the hospital. ED overcrowding is a well-researched field and negatively affects patient outcome, staff well-being and hospital reputation. An accurate, real-time model capable of predicting ED overcrowding has obvious merit in a world becoming increasingly computational, although the complicated dynamics of the department have hindered international efforts to design such a model. Triage nurses' assessments have been shown to be accurate predictors of patient disposition and could, therefore, be useful input for overcrowding and patient flow models.

METHODS

In this study, we assess the prediction capabilities of triage nurses in a level 1 urban hospital in central Israeli. ED settings included both acute and ambulatory wings. Nurses were asked to predict admission or discharge for each patient over a 3-month period as well as exact admission destination. Prediction confidence was used as an optimisation variable.

RESULT

Triage nurses accurately predicted whether the patient would be admitted or discharged in 77% of patients in the acute wing, rising to 88% when their prediction certainty was high. Accuracies were higher still for patients in the ambulatory wing. In particular, negative predictive values for admission were highly accurate at 90%, irrespective of area or certainty levels.

CONCLUSION

Nurses prediction of disposition should be considered for input for real-time ED models.

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.

Relationship of the Emergency Department Density Level with Nursing Work Stress

BACKGROUND: The imbalance between the increase in the overall volume of patients at the emergency department (ED) with the capacity of the emergency unit can lead to overcrowded conditions in the ED. Factors that cause ED density include lack of beds for inpatients, increased overall patient volume, increased non-emergency patient visits, lack of nursing staff, and lack of administrative support. Overcrowded at the ED can cause work stress on nurses. Nurses in the ED tend to experience more stress than nurses on the ward due to workload and work fatigue. AIM: This study aimed to investigate the correlation between the ED density with nursing work stress. METHODS: This study was conducted a correlational study in a hospital at Central Java in 2020. Total sampling with the criteria of nurses in the ED was the method employed to the respondents. The data analysis used descriptive statistics and rank Spearman, with a significant level of p < 0.05. RESULTS: The most frequent occurred in the ED was overcrowded, while nursing work stress was moderate stress. The result of the rank Spearman test obtained p = 0.000 with a correlation coefficient of 0.725. CONCLUSION: There was a significant correlation between the ED density levels with nursing work stress.

Validation of the modified Skåne emergency department assessment of patient load (mSEAL) model for emergency department crowding and comparison with international models; an observational study

Background

Emergency Department crowding is associated with increased morbidity and mortality but no measure of crowding has been validated in Sweden. We have previously derived and internally validated the Skåne Emergency Department Assessment of Patient Load (SEAL) score as a measure of crowding in Emergency Departments (ED) in a large regional healthcare system in Sweden. Due to differences in electronic health records (EHRs) between health care systems in Sweden, all variables in the original SEAL-score could not be measured reliably nationally. We aimed to derive and validate a modified SEAL (mSEAL) model and to compare it with established international measures of crowding.

Methods

This was an observational cross sectional study at four EDs in Sweden. All clinical staff assessed their workload (1–6 where 6 is the highest workload) at 5 timepoints each day. We used linear regression with stepwise backward elimination on the original SEAL dataset to derive and internally validate the mSEAL score against staff workload assessments. We externally validated the mSEAL at four hospitals and compared it with the National Emergency Department Overcrowding Score (NEDOCS), the simplified International Crowding Measure in Emergency Department (sICMED), and Occupancy Rate. Area under the receiver operating curve (AuROC) and coefficient of determination was used to compare crowding models. Crowding was defined as an average workload of 4.5 or higher.

Results

The mSEAL score contains the variables Patient Hours and Time to physician and showed strong correlation with crowding in the derivation (r² = 0.47), internal validation (r² = 0.64 and 0.69) and in the external validation (r² = 0.48 to 0.60). AuROC scores for crowding in the external validation were 0.91, 0.90, 0.97 and 0.80 for mSEAL, Occupancy Rate, NEDOCS and sICMED respectively.

Conclusions

The mSEAL model can measure crowding based on workload in Swedish EDs with good discriminatory capacity and has the potential to systematically evaluate crowding and help policymakers and researchers target its causes and effects. In Swedish EDs, Occupancy Rate and NEDOCS are good alternatives to measure crowding based on workload.

Effectiveness of hospital emergency department regionalization and categorization policy on appropriate patient emergency care use: a nationwide observational study in Taiwan

BACKGROUND

Emergency department (ED) overcrowding is a health services issue worldwide. Modern health policy emphasizes appropriate health services utilization. However, the relationship between accessibility, capability, and appropriateness of ED use is unknown. Thus, this study aimed to examine the effect of hospital ED regionalization policy and categorization of hospital emergency capability policy (categorization policy) on patient-appropriate ED use.

METHODS

Taiwan implemented a nationwide three-tiered hospital ED regionalization and categorization of hospital emergency capability policies in 2007 and 2009, respectively. We conducted a retrospective observational study on the effect of emergency care policy intervention on patient visit. Between 2005 and 2011, the Taiwan National Health Insurance Research Database recorded 1,835,860 ED visits from 1 million random samples. ED visits were categorized using the Yang-Ming modified New York University-ED algorithm. A time series analysis was performed to examine the change in appropriate ED use rate after policy implementation.

RESULTS

From 2005 to 2011, total ED visits increased by 10.7%. After policy implementation, the average appropriate ED visit rate was 66.9%. The intervention had no significant effect on the trend of appropriate ED visit rate.

CONCLUSIONS

Although regionalization and categorization policies did increase emergency care accessibility, it had no significant effect on patient-appropriate ED use. Further research is required to improve data-driven policymaking.

Risk factors of admission in 72-h return visits to emergency department

Objective:

Return visit to emergency department (ED) is a common phenomenon and has been a clinical indicator of quality of care in ED. Most of previous articles focused on the characteristics of the patients returning within 72 h after ED discharge, while those on subsequent admission are numbered. This study's purpose is to identify risk factors for admission among 72-h return visit in the ED adult population.

Materials and Methods:

This retrospective cohort study was conducted at a medical center in Eastern Taiwan. The study period was from January 1, 2013, to December 31, 2013. We excluded patients who left against medical advice or without being seen, who was admitted or transferred at the index ED visit, whose medical records were incomplete, and whose age was below 18 years old. Significant variables were selected based on univariate analysis and later entered into multivariate logistic regression analysis to identify risk factors for 72-h return admission.

Results:

We identified 1575 eligible visits, and there were 1,119 visits entering into the final analysis. Male gender (odds ratio [OR] = 1.44), ambulance-transport at return visit (OR = 3.68), senior staff (OR = 1.52), work-up (OR = 3.03), and longer length of stay (LOS) were associated with higher risks of admission among ED 72-h return visits. Age, comorbidity, mode of transport at index visit, consultation, triage, type of illness, outpatient department visit between ED visits, and interval between index and return visits were not significantly associated with return admission.

Conclusion:

Gender, mode of transportation, staff experience, check-up, and LOS are associated with ED return admission.

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.

[Emergency overcrowding and hospital organization: Causes and solutions]

Emergency Department (ED) overcrowding is a silent killer. Thus, several studies in different countries have described an increase in mortality, a decrease in the quality of care and prolonged hospital stays associated with ED overcrowding. Causes are multiple: input and in particular lack of access to lab test and imaging for general practitioners, throughput and unnecessary or time-consuming tasks, and output, in particular the availability of hospital beds for unscheduled patients. The main cause of overcrowding is waiting time for available beds in hospital wards, also known as boarding. Solutions to resolve the boarding problem are mostly organisational and require the cooperation of all department and administrative levels through efficient bed management. Elderly and polypathological patients wait longer time in ED. Internal Medicine, is the ideal specialty for these complex patients who require time for observation and evaluation. A strong partnership between the ED and the internal medicine department could help to reduce ED overcrowding by improving care pathways.

A multicenter mixed-effects model for inference and prediction of 72-h return visits to the emergency department for adult patients with trauma-related diagnoses

Objective

Emergency department (ED) return visits within 72 h may be a sign of poor quality of care and entail unnecessary use of healthcare resources. In this study, we compare the performance of two leading statistical and machine learning classification algorithms, and we use the best performing approach to identify novel risk factors of ED return visits.

Methods

We analyzed 3.2 million ED encounters with at least one diagnosis under “injury, poisoning and certain other consequences of external causes” and “external causes of morbidity.” These encounters included patients 18 years or older from across 128 emergency room facilities in the USA. For each encounter, we calculated the 72-h ED return status and retrieved 57 features from demographics, diagnoses, procedures, and medications administered during the process of administration of medical care. We implemented a mixed-effects model to assess the effects of the covariates while accounting for the hierarchical structure of the data. Additionally, we investigated the predictive accuracy of the extreme gradient boosting tree ensemble approach and compared the performance of the two methods.

Results

The mixed-effects model indicates that certain blunt force and non-blunt trauma inflates the risk of a return visit. Notably, patients with trauma to the head and patients with burns and corrosions have elevated risks. This is in addition to 11 other classes of both blunt force and non-blunt force traumas. In addition, prior healthcare resource utilization, patients who have had one or more prior return visits within the last 6 months, prior ED visits, and the number of hospitalizations within the 6 months are associated with increased risk of returning to the ED after discharge. On the one hand, the area under the receiver characteristic curve (AUROC) of the mixed-effects model was 0.710 (0.707, 0.712). On the other hand, the gradient boosting tree ensemble had a lower AUROC of 0.698 CI (0.696, 0.700) on the independent test model.

Conclusions

The proposed mixed-effects model achieved the highest known AUC and resulted in the identification of novel risk factors. The model outperformed one of the leading machine learning ensemble classifiers, the extreme gradient boosting tree in terms of model performance. The risk factors we identified can assist emergency departments to decrease the number of unplanned return visits within 72 h.

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.

Forecasting hourly emergency department arrival using time series analysis

Background/aims

The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients.

Methods

Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals.

Results

The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained.

Conclusions

The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.

Stakeholder opinion on the proposal to introduce 'treat and referral' into the Irish emergency medical service

BACKGROUND

The Irish ambulance services have traditionally transported all patients following an emergency (112/999) call, regardless of acuity, to an emergency department (ED). A proposal to introduce Treat and Referral, an established care pathway in some jurisdictions, is under active consideration in Ireland. This will present a significant change. Stakeholder engagement is recognised as an essential component of management of such change. This study has conducted a multicentre, cross-sectional survey exploring opinions on the introduction of Treat and Referral among key Irish stakeholders; consultants in emergency medicine, paramedics and advanced paramedics.

METHODS

Public-sector consultants in emergency medicine (EM), registered paramedics and advanced paramedics, in Ireland at the time of the study, were invited to complete an on-line survey.

RESULTS

A significant finding was that 90% of both cohorts (EM consultants and registered paramedic practitioners) support written after-care instructions being given to referred patients, that > 83% agree that Treat and Referral will reduce unnecessary ambulance journeys and that 70% are in favour of their own family member being offered Treat and Referral. Consensus was reached between respondents that Treat and Referral would improve care and increase clinical judgement of practitioners. Differences were identified in relation to the increased availability of ambulances locally, that only adults should be included, and that research was required to extend Treat and Referral beyond the index conditions. There was no consensus on whether general practitioners (GPs) should be directly informed.

CONCLUSIONS

This study identified that the Irish healthcare practitioners surveyed are supportive of the introduction of Treat and Referral into Ireland. It also affords healthcare policymakers the opportunity to address the concerns raised, in particular the clinical level which will be targeted for inclusion in this extended scope of practice.

The impact of delayed transfers of care on emergency departments: common sense arguments, evidence and confounding

OBJECTIVES

There have been claims that Delayed Transfers of Care (DTOCs) of inpatients to home or a less acute setting are related to Emergency Department (ED) crowding. In particular DTOCs were associated with breaches of the UK 4-hour waiting time target in a previously published analysis. However, the analysis has major limitations by not adjusting for the longitudinal trend of the data. The aim of this work is to investigate whether the proposition that DTOCs impact the 4-hour target requires further research.

METHOD

Estimation of an association between two or more variables that are measured over time requires specialised statistical methods. In this study, we performed two separate analyses. First, we created two sets of artificial data with no correlation. We then added an upward trend over time and again assessed for correlation. Second, we reproduced the simple linear regression of the original study using NHS England open data of English trusts between 2010 and 2016, assessing correlation of numbers of DTOCs and ED breaches of the 4-hour target. We then reanalysed the same data using standard time series methods to remove the trend before estimating an association.

RESULTS

After introducing upward trends into the uncorrelated artificial data the correlation between the two data sets increased (R²=0.00 to 0.51 respectively). We found strong evidence of longitudinal trends within the NHS data of ED breaches and DTOCs. After removal of the trends the R² reduced from 0.50 to 0.01.

CONCLUSION

Our reanalysis found weak correlation between numbers of DTOCs and ED 4-hour target breaches. Our study does not indicate that there is no relationship between 4-hour target and DTOCs, it highlights that statistically robust evidence for this relationship does not currently exist. Further work is required to understand the relationship between breaches of the 4-hour target and numbers of DTOCs.

Do health care professionals' perceptions help to measure the degree of overcrowding in the emergency department? A pilot study in an Italian University hospital

BACKGROUND

Overcrowding in emergency departments (EDs) is internationally recognized as one of the greatest challenges to healthcare provision. Numerous studies have highlighted the ill-effects of overcrowding, including increased length of stay, mortality and cost per admission. This study measures overcrowding in EDs through health care professionals' perceptions of it, comparing the results with the NEDOCS score, an objectively validated measurement tool and describing meaningful tools and strategies used to manage ED overcrowding.

METHODS

This single-centre prospective, observational, pilot study was conducted from February 19th to March 7th, 2018 at the ED in the University Hospital of Ferrara, Italy to measure the agreement of the NEDOCS, comparing objective scores with healthcare professionals' perception of overcrowding, using the kappa statistic assessing linear weights according to Cohen's method. The tools and strategies used to manage ED overcrowding are described.

RESULTS

Seventy-two healthcare professionals (66.1% of 109 eligible subjects) were included in the analyses. The study obtained a total of 262 surveys from 23 ED physicians (31.9%), 31 nurses (43.1%) and 18 nursing assistants (25.0%) and a total of 262 NEDOCS scores. The agreement between the NEDOCS and the subjective scales was poor (k = 0.381, 95% CI 0.313-0.450).

CONCLUSIONS

The subjective health care professionals' perceptions did not provide an adequate real-time measure of the current demands and capacity of the ED. A more objective measure is needed to make quality decisions about health care professional needs and the ability to manage patients to ensure the provision of proper care.

Emergency Medicine Resident Efficiency and Emergency Department Crowding

OBJECTIVES

Provider efficiency has been reported in the literature but there is a lack of efficiency analysis among emergency medicine (EM) residents. We aim to compare efficiency of EM residents of different training levels and determine if EM resident efficiency is affected by emergency department (ED) crowding.

METHODS

We conducted a single-center retrospective observation study from July 1, 2014, to June 30, 2017. The number of new patients per resident per hour and provider-to-disposition (PTD) time of each patient were used as resident efficiency markers. A crowding score was assigned to each patient upon the patient's arrival to the ED. We compared efficiency among EM residents of different training levels under different ED crowding statuses. Dynamic efficiency changes were compared monthly through the entire academic year (July to next June).

RESULTS

The study enrolled a total of 150,920 patients. A mean of 1.9 patients/hour was seen by PGY-1 EM residents in comparison to 2.6 patients/hour by PGY-2 and -3 EM residents. Median PTD was 2.8 hours in PGY-1 EM residents versus 2.6 hours in PGY-2 and -3 EM residents. There were no significant differences in acuity across all patients seen by EM residents. When crowded conditions existed, residency efficiency increased, but such changes were minimized when the ED became overcrowded. A linear increase of resident efficiency was observed only in PGY-1 EM residents throughout the entire academic year.

CONCLUSION

Resident efficiency improved significantly only during their first year of EM training. This efficiency can be affected by ED crowding.

Modelling Waiting Times in an Emergency Department in Greece During the Economic Crisis

Overcrowding is one of the most common phenomenon at the emergency departments of the hospitals across the world. The aforementioned phenomenon causes many problems such as long waiting times for patients, increasing length of stay, patient dissatisfaction, ambulance diversions in some cities, prolonged pain and suffering, violence and miscommunication between the medical staff and the patients, patients leaving the emergency department without been seen and decreased physician productivity. This article analyses the problem of the increased waiting times in a Greek emergency department during the economic crisis. We use statistical models in order to reveal the factors that can lead to a decrease in waiting times. Our findings suggest that Greece’s Department of Health should standardize an uppermost waiting time for emergency departments which could help improve health care.

The characteristics of pediatric emergency department visits in Korea: An observational study analyzing Korea Health Panel data

Objective

We investigated the characteristics of pediatric emergency department (ED) patients in Korea and determined factors associated with hospital admission after ED treatment.

Methods

Korea Health Panel data from 2008 through 2013 were analyzed retrospectively; we included patients under 18 years old who visited the ED at least once. We collected patient and household epidemiologic data such as sex, age group, region of residence, disability, chronic disease, household income quintile, national health insurance type, use of private insurance, and annual frequency of ED visits. We also examined data related to each ED visit, such as reason for visit, medical service provided, and hospital size/ownership. We then investigated which factors were correlated with case disposition (discharge home or hospital admission) after ED treatment.

Results

In total, 3,160 pediatric ED visits occurred during the six-year period. Males (57.5%) and children aged 0–5 years (47.7%) made more visits than females and older children, respectively. The proportion of ED visits for disease (67.7%) was much higher than for injury or poisoning (32.2%), and 452 cases (14.3%) required hospital admission. For hospital admission, the odds ratio (OR) of females was 0.73 compared to males, and the OR of children aged 6–11 was 0.68 compared to children aged 0–5. The OR of capital residents was 0.69 compared to province residents, and the OR of the highest income quintile was 0.51 compared to the lowest quintile. The OR of children with private insurance coverage was 0.49 compared to those lacking private insurance, and the OR of ED visits due to disease was 1.82 compared to visits due to injury/poisoning.

Conclusion

This analysis of clinical and demographic characteristics of pediatric ED visits and hospital admissions can serve as the foundation of future prospective studies required for establishing appropriate policies for the Korean pediatric emergency medical system.

Emergency department overcrowding: Quality improvement in a Taiwan Medical Center

BACKGROUND/PURPOSE

Overcrowding of hospital emergency departments (ED) is a worldwide health problem. The Taiwan Joint Commission on Hospital Accreditation has stressed the importance of finding solutions to overcrowding, including, reducing the number of patients with >48 h stay in the ED. Moreover, the Ministry of Health and Welfare aims at transferring non-critical patients to district or regional hospitals. We report the results of our Quality Improvement Project (QIP) on ED overcrowding, especially focusing on reducing length of stay (LOS) in ED.

METHODS

For QIP, the following 3 action plans were initiated: 1) Changing the choice architecture of patients' willingness to transfer from opt-in to opt-out; 2) increasing the turnover rate of beds and daily monitoring of the number of free beds for boarding ED patients; 3) reevaluation of patients with a LOS of >32 h after the morning shift.

RESULTS

Transfer rates increased minimally after implementation of this project, but the sample size was too small to achieve statistical significance. No significant increase was observed in the number of free medical beds, but discharge rates after 12 pm decreased significantly (p < 0.001). The proportion of over 48 h LOSs decreased from 4.9% to 3.7% before and after QIP implementation, respectively (p < 0.001).

CONCLUSION

Patients with LOS of >32 h were reevaluated first. After QIP, the proportion of LOSs of >48 h dropped significantly. Changing the choice architecture may require further systemic effort and a longer observation duration. Higher-level administrators will need to formulate a more comprehensive bed management plan to speed up the turnover rate of free inpatient beds.

Impact of emergency department surge and end of shift on patient workup and treatment prior to referral to internal medicine: a health records review

BACKGROUND

The goal of this study was to determine if ED surge and end-of-shift assessment of patients affect the extent of diagnostic tests, therapeutic interventions and accuracy of diagnosis prior to referral to internal medicine.

METHODS

This study was a health records review of consecutive patients referred to the internal medicine service with an ED diagnosis of heart failure, chronic obstructive pulmonary disease (COPD) or sepsis starting 1 December 2013 until 100 cases for each condition had been obtained. We developed a scoring system in consultation with emergency and internal medicine physicians to uniformly assess the completeness of treatments and investigations performed. These scores, expressed as percentage of possible points, were compared at high and low surge levels and at middle and end of shift at time of patient referral. End of shift was defined as 7:30-8:30, 15:30-16:30 and 23:30-00:30 as our shift changes occur at 8:00, 16:00 and 24:00. Rate of admission, diversion to other services and diagnosis disagreements were also assessed.

RESULTS

We included 308 patients (101 heart failure, 101 COPD, 106 sepsis) with a mean age of 74.7. Comparing middle of shift to end of shift, the mean scores were 91.9% versus 91.8% (difference 0.1% (95% CI -2.4 to 3.0)) for investigations and 73.0% versus 70.4% (difference 2.6% (95% CI -1.8 to 7.4)) for treatments. Comparing low to high surge times, the mean scores were 92.1% versus 91.7% (difference 0.4% (95% CI -1.2 to 2.4)) for investigations and 71.4% versus 73.6% (difference -2.2% (95% CI -5.6 to 1.3)) for treatments. We found low rates of diversion to alternate services (8.9% heart failure, 0% COPD, 6.6% sepsis) and low rates of diagnosis disagreement (4.0% heart failure, 10.9% COPD, 8.5% sepsis).

CONCLUSIONS

We found no evidence that surge levels and end of shift impact the extent of investigations and treatments provided to patients diagnosed in the ED with heart failure, COPD or sepsis and referred to internal medicine.

Overcrowding and Its Association With Patient Outcomes in a Median-Low Volume Emergency Department

Background

Crowding occurs commonly in high volume emergency departments (ED) and has been associated with negative patient care outcomes. We aim to assess ED crowding in a median-low volume setting and evaluate associations with patient care outcomes.

Methods

This was a prospective single-center study from November 14, 2016 until December 14, 2016. ED crowding was measured every 2 h by three different estimation tools: National Emergency Department Overcrowding Score (NEDOCS); Community Emergency Department Overcrowding Score (CEDOCS); and Severely-overcrowding Overcrowding and Not-overcrowding Estimation Tool (SONET) categorized under six different levels of crowding (not busy, busy, extremely busy, overcrowded, severely overcrowded, and dangerously overcrowded). Crowding scores were assigned to each patient upon ED arrival. We evaluated the distributions of crowding and patient ED length of stay (ED LOS) across estimation tools. Accelerated failure time models were utilized to estimate time ratios and their corresponding 95% confidence intervals comparing median LOS across levels of crowding within each estimation tool.

Results

This study comprised 2,557 patients whose median ED LOS was 150 min. Approximately 2% of patients arrived during 2 h time intervals deemed overcrowded regardless of the crowding tool used. Median ED LOS increased with the increased level of ED crowding and prolonged median ED LOS (> 150 min) occurred at ED of extremely busy status. Time ratios ranged from 1.09 to 1.48 for NEDOCS, 1.25 - 1.56 for CEDOCS, and 1.26 - 1.72 for SONET.

Conclusion

Overcrowding rarely occurred in study ED with median-low annual volume and might not be a valuable marker for ED crowding report. Though similar patterns of prolonged ED LOS occurred with increased levels of ED crowding, it seems crowding alerts should be initiated during extremely busy status in this ED setting.

Frequent users of the pediatric emergency department

OBJECTIVES

Emergency department (ED) crowding is associated with increased morbidity and mortality. Its etiology is multifactorial, and frequent ED use (defined as more or equal to five visits per year) is a major contributor to high patient volumes. Our primary objective is to characterize the frequent user population. Our secondary objective is to examine risk factors for frequent emergency use.

METHODS

We conducted a retrospective cohort study of pediatric emergency department (PED) visits at the Montreal Children's Hospital using the Système Informatique Urgence (SIURGE), electronic medical record database. We analysed the relation between patient's characteristics and the number of PED visits over a 1-year period following the index visit.

RESULTS

Patients totalling 52,088 accounted for 94,155 visits. Of those, 2,474 (4.7%) patients had five and more recurrent visits and accounted for 16.6% (15,612 visits) of the total PED visits. Lower level of acuity at index visit (odds ratio [OR] 0.85) was associated with a lower number of recurrent visits. Lower socioeconomic status (social deprivation index OR 1.09, material deprivation index OR 1.08) was associated with a higher number of recurrent visits. Asthma (OR 1.57); infectious ear, nose, and sinus disorders (OR 1.33); and other respiratory disorders (OR 1.56) were independently associated with a higher incidence of a recurrent visit within the year following the first visit.

CONCLUSION

Our study is the first Canadian study to assess risk factors of frequent pediatric emergency use. The identified risk factors and diagnoses highlight the need for future evidence-based, targeted innovative research evaluating strategies to minimize ED crowding, to improve health outcomes and to improve patient satisfaction.

What do emergency physicians in charge do? A qualitative observational study

INTRODUCTION

The emergency physician in charge role has developed in many large EDs to assist with patient flow. We aimed to describe and classify the problem-solving actions that this role requires.

METHODS

We interviewed senior emergency physicians and performed iterative, qualitative observations, using continuous reflective inquiry, in a single centre. We reviewed and classified these approaches by consensus.

RESULTS

Nine different problem-solving approaches were identified. These are deflecting, front loading, placing, plucking, flooding, targeting, chasing, guiding and juggling. These are useful for training and developing our understanding of how to manage an ED.

CONCLUSIONS

Emergency physicians in charge have a number of problem-solving approaches that can be readily defined. We have described and categorised these. These results are potentially useful for developing decision support software.