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

Linden, Margareta; Loon, Merel van; Gaakeer, Menno; Richards, John R.; Derlet, Robert W.; Linden, Naomi van der

doi:10.1016/j.ienj.2018.05.004

Cited by 14 publications

(15 citation statements)

References 21 publications

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“…The outcome of interest was ED crowding, measured with the modified National ED OverCrowding Score (mNEDOCS), a multidimensional scale to measure patient volume and throughput in hospitals [30]. The mNEDOCS alike the NEDOCS, is subdivided into 6 categories: 0–20, not busy; 21–60, busy; 61–100, extremely busy; 101–140, crowded; 141–180, severely crowded; > 180, disaster) [31].…”

Section: Methodsmentioning

confidence: 99%

“…Variables needed to calculate the mNEDOCS include total ED beds, total hospital beds, total patients in the ED, total admits in the ED, longest admit time in hours, waiting room time in hours of the most recent patient placed in a bed in the ED, and the number of patients who are being resuscitated or assigned the highest acuity level (Table 1). The mNEDOCS has been shown to correlate well with perceived crowding in this ED [30].…”

Section: Methodsmentioning

confidence: 99%

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

Linden

Ufford²,

Linden

2019

Int J Emerg Med

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Linden

Ufford²,

Linden

2019

Int J Emerg Med

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“…Survey 64-66,80 Tertiary 11,14,34,39,66,[83][84][85][88][89][90][91] Community 86,87 All Patients 11,14,34,39,66,[83][84][85][86][87][88][89][90][91] Physicians and nurses 14,66,83,84,86,87,89 All patients 11,34,85,[88][89][90][91] Pediatric 66…”

Section: (Continued)mentioning

confidence: 99%

“…Quartiles 14,39,84,87,90 NEDOCS q10 min, AUC to predict ED diversion 11,85 <100, 100-140, >140 34,91 Retrospective 85…”

Section: Ed Nurses 39mentioning

confidence: 99%

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Measures of Emergency Department Crowding, a Systematic Review. How to Make Sense of a Long List

Badr

Nyce

Awan

et al. 2022

OAEM

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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.

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

Linden

KUNZ

Gaalen

et al. 2023

International Emergency Nursing

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

Cited by 14 publications

References 21 publications

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

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

Measures of Emergency Department Crowding, a Systematic Review. How to Make Sense of a Long List

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

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