A different crowd, a different crowding level? The predefined thresholds of crowding scales may not be optimal for all emergency departments

Improving emergency department flow by introducing four interventions simultaneously. A quality improvement project

BACKGROUND

Emergency department (ED) crowding is a widespread issue with adverse effects on patient care and outcomes.

LOCAL PROBLEM

ED crowding exacerbates wait times and compromises patient care, prompting opportunities for internal process improvement.

METHOD

Over one week, the ED flow project team implemented four interventions, including an additional triage station, to optimize patient flow. We compared triage times, length of stay, crowding levels, and patient experiences with two control periods.

RESULTS

During peak hours, waiting times to triage decreased significantly with a median of 20 min (IQR 15-30) in the project week and 26 min (IQR 18-37) in the control weeks. Self-referrals decreased, while general practitioner referrals remained unchanged. Individual patient length of stay was unaffected, but crowding reduced notably during the project week. We found no difference in patient experiences between the periods.

CONCLUSION

The interventions contributed to reduced crowding and improved patient flow. The dedication of the ED flow project team and the ED nurses was crucial to these outcomes. An additional triage station during peak hours in the ED was established as a structural change.

Emergency department crowding and mortality: an observational multicenter study in Sweden

Background

Emergency department (ED) crowding is a serious problem worldwide causing decreased quality of care. It is reasonable to assume that the negative effects of crowding are at least partially due to high staff workload, but previous crowding metrics based on high workload have not been generalisable to Swedish EDs and have not been associated with increased mortality, in contrast to, e.g., occupancy rate. We recently derived and validated the modified Skåne Emergency Department Assessment of Patient Load model (mSEAL) that measures crowding based on staff workload in Swedish EDs, but its ability to identify situations with increased mortality is unclear. In this study, we aimed to investigate the association between ED crowding measured by mSEAL model, or occupancy rate, and mortality.

Methods

All ED patients from 2017-01-01 to 2017-06-30 from two regional healthcare systems (Skåne and Östergötland Counties with a combined population of approximately 1.8 million) in Sweden were included. Exposure was ED- and hour-adjusted mSEAL or occupancy rate. Primary outcome was mortality within 7 days of ED arrival, with one-day and 30-day mortality as secondary outcomes. We used Cox regression hazard ratio (HR) adjusted for age, sex, arrival by ambulance, hospital admission and chief complaint.

Results

We included a total of 122,893 patients with 168,900 visits to the six participating EDs. Arriving at an hour with a mSEAL score above the 95th percentile for that ED and hour of day was associated with an non-significant HR for death at 7 days of 1.04 (95% CI 0.96-1.13). For one- and 30-day mortality the HR was non-significant at 1.03 (95% CI 0.9-1.18) and 1.03 (95% CI 0.97-1.09). Similarly, occupancy rate above the 95th percentile with a HR of 1.04 (95% CI 0.9-1.19), 1.03 (95%CI 0.95-1.13) and 1.04 (95% CI 0.98-1.11) for one-, 7- and 30-day mortality, respectively.

Conclusion

In this multicenter study in Sweden, ED crowding measured by mSEAL or occupancy rate was not associated with a significant increase in short-term mortality.

Effects of process changes on emergency department crowding in a changing world: an interrupted time-series analysis

BACKGROUND

During a 6-year period, several process changes were introduced at the emergency department (ED) to decrease crowding, such as the implementation of a general practitioner cooperative (GPC) and additional medical staff during peak hours. In this study, we assessed the effects of these process changes on three crowding measures: patients' length of stay (LOS), the modified National ED OverCrowding Score (mNEDOCS), and exit block while taking into account changing external circumstances, such as the COVID-19 pandemic and centralization of acute care.

METHODS

We determined time points of the various interventions and external circumstances and built an interrupted time-series (ITS) model per outcome measure. We analyzed changes in level and trend before and after the selected time points using ARIMA modeling, to account for autocorrelation in the outcome measures.

RESULTS

Longer patients' ED LOS was associated with more inpatient admissions and more urgent patients. The mNEDOCS decreased with the integration of the GPC and the expansion of the ED to 34 beds and increased with the closure of a neighboring ED and ICU. More exit blocks occurred when more patients with shortness of breath and more patients > 70 years of age presented to the ED. During the severe influenza wave of 2018-2019, patients' ED LOS and the number of exit blocks increased.

CONCLUSIONS

In the ongoing battle against ED crowding, it is pivotal to understand the effect of interventions, corrected for changing circumstances and patient and visit characteristics. In our ED, interventions which were associated with decreased crowding measures included the expansion of the ED with more beds and the integration of the GPC on the ED.

Association between Covid-19 surge and emergency department patient flow and experience

BACKGROUND

Preparations for Covid-19 in the Netherlands included hospital reconfigurations to increase capacity for the expected surge at the emergency department (ED). We describe patients' ED length of stay (LOS), crowding and experiences of patients with respiratory complaints during the first Covid-19 peak.

METHODS

Retrospective analysis of demand, ED LOS, crowding, and a patient experience survey during a 12-week period in 2020 and similar periods in 2018 and 2019. Crowding levels were calculated using the National ED OverCrowding Scale.

RESULTS

The number of patients with respiratory complaints increased significantly, while total ED numbers were unchanged. Although presentation during the Covid-19 peak and needing hospital admission were associated with a longer ED LOS in patients with respiratory complaints, significantly less crowding occurred compared with the 2018 and 2019 periods. Increased ED LOS was associated with lower patient experience scores.

CONCLUSION

Advanced warning and its associated preparation within the hospital and the community prevented significant delays in ED throughput during the first Covid-19 peak.

The impact of prehospital blood sampling on the emergency department process of patients with chest pain: a pragmatic non-randomized controlled trial

BACKGROUND

In patients with chest pain who arrive at the emergency department (ED) by ambulance, venous access is frequently established prehospital, and could be utilized to sample blood. Prehospital blood sampling may save time in the diagnostic process. In this study, the association of prehospital blood draw with blood sample arrival times, troponin turnaround times, and ED length of stay (LOS), number of blood sample mix-ups and blood sample quality were assessed.

METHODS

The study was conducted from October 1, 2019 to February 29, 2020. In patients who were transported to the ED with acute chest pain with low suspicion for acute coronary syndrome (ACS), outcomes were compared between cases, in whom prehospital blood draw was performed, and controls, in whom blood was drawn at the ED. Regression analyses were used to assess the association of prehospital blood draw with the time intervals.

RESULTS

Prehospital blood draw was performed in 100 patients. In 406 patients, blood draw was performed at the ED. Prehospital blood draw was independently associated with shorter blood sample arrival times, shorter troponin turnaround times and decreased LOS (P<0.001). No differences in the number of blood sample mix-ups and quality were observed (P>0.05).

CONCLUSION

For patients with acute chest pain with low suspicion for ACS, prehospital blood sampling is associated with shorter time intervals, while there were no significant differences between the two groups in the validity of the blood samples.

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.

Telephone follow-up to reduce unplanned hospital returns for older emergency department patients: A randomized trial

BACKGROUND/OBJECTIVES

Telephone follow-up calls could optimize the transition from the emergency department (ED) to home for older patients. However, the effects on hospital return rates are not clear. We investigated whether telephone follow-up reduces unplanned hospitalizations and/or unplanned ED return visits within 30 days of ED discharge.

DESIGN

Pragmatic randomized controlled trial with allocation by month; odd months intervention group, even months control group.

SETTING

Two ED locations of a non-academic teaching hospital in The Netherlands.

PARTICIPANTS

Community-dwelling adults aged ≥70 years, discharged home from the ED were randomized to the intervention group (N = 4732) or control group (N = 5104).

INTERVENTION

Intervention group patients: semi-scripted telephone call from an ED nurse within 24 h after discharge to identify post-discharge problems and review discharge instructions. Control group patients: scripted satisfaction survey telephone call.

MEASUREMENTS

Primary outcome: total number of unplanned hospitalizations and/or ED return visits within 30 days of ED discharge.

SECONDARY OUTCOMES

separate numbers of unplanned hospitalizations and ED return visits. Subgroup analysis by age, sex, living condition, and degree of crowding in the ED at discharge.

RESULTS

Overall, 42% were males, and median age was 78 years. In the intervention group, 1516 of 4732 patients (32%) consented, and in the control group 1659 of 5104 (33%) patients. Unplanned 30-day hospitalization and/or ED return visit was found in 16% of intervention group patients and 14% of control group patients (odds ratio 1.16; 95% confidence interval: 0.96-1.42). Also, no statistically significant differences were found in secondary outcome measures. Within the subgroups, the intervention did not have beneficial effects for the intervention group.

CONCLUSION

Telephone follow-up after ED discharge in older patients did not result in reduction of unplanned hospital admissions and/or ED return visits within 30 days. These results raise the question of whether other outcomes could be improved by post-discharge ED telephone follow-up.

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.

The impact of a multimodal intervention on emergency department crowding and patient flow

Objective

The objective of this study is to assess the impact of a multimodal intervention on emergency department (ED) crowding and patient flow in a Dutch level 1 trauma center.

Methods

In this cross-sectional study, we compare ED crowding and patient flow between a 9-month pre-intervention period and a 9-month intervention period, during peak hours and overall (24/7). The multimodal intervention included (1) adding an emergency nurse practitioner (ENP) and (2) five medical specialists during peak hours to the 24/7 available emergency physicians (EPs), (3) a Lean programme to improve radiology turnaround times, and (4) extending the admission offices’ openings hours.

Crowding is measured with the modified National ED OverCrowding Score (mNEDOCS). Furthermore, radiology turnaround times, patients’ length of stay (LOS), proportion of patients leaving without being seen (LWBS) by a medical provider, and unscheduled representations are assessed.

Results

The number of ED visits were grossly similar in the two periods during peak hours (15,558 ED visits in the pre-intervention period and 15,550 in the intervention period) and overall (31,891 ED visits in the pre-intervention period vs. 32,121 in the intervention period). During peak hours, ED crowding fell from 18.6% (pre-intervention period) to 3.5% (intervention period), radiology turnaround times decreased from an average of 91 min (interquartile range 45–256 min) to 50 min (IQR 30–106 min., p < 0.001) and LOS reduced with 13 min per patient from 167 to 154 min (p < 0.001). For surgery, neurology and cardiology patients, LOS reduced significantly (with 17 min, 25 min, and 8 min. respectively), while not changing for internal medicine patients. Overall, crowding, radiology turnaround times and LOS also decreased. Less patients LWBS in the intervention period (270 patients vs. 348 patients, p < 0.001) and less patients represented unscheduled within 1 week after the initial ED visit: 864 (2.7%) in the pre-intervention period vs. 645 (2.0%) patients in the intervention period, p < 0.001.

Conclusions

In this hospital, a multimodal intervention successfully reduces crowding, radiology turnaround times, patients’ LOS, number of patients LWBS and the number of unscheduled return visits, suggesting improved ED processes. Further research is required on total costs of care and long-term effects.