Comparison of the National Emergency Department Overcrowding Scale and the Emergency Department Work Index for Quantifying Emergency Department Crowding

Abstract

Both scales had high AUCs, correlated well with each other, and showed good discrimination for predicting ED overcrowding. This establishes construct validity for these scales as measures of overcrowding. Which scale is used in an ED is dependent on which set of data is most readily available, with the favored scale being the NEDOCS.

Systems Factors in Obstetric Care

“…All cutoffs are arbitrary to some extent, and our understanding of relative daily volume would be improved by examining alternative definitions, as is seen in the literature regarding emergency department crowding. 22,30 This study provides evidence that relative high daily volume is associated with changes in obstetric practice and outcomes on weekends in California hospitals. Specifically, higher asphyxia rates and lower nulliparous, term, singleton, vertex cesarean delivery rates were observed on these days.…”

Evaluation of Overcrowding of Emergency Department in Imam Reza Hospital in 2015 by Implementing 2 Scales: NEDOCS and EDWIN

“…Weiss et al in their study, concluded that both scales acquire a high level of accuracy in prediction of crowdedness level in ED and that both scales can be implemented in ED (7). In all countries, health services are delivered by the existing health systems (11) that are comprehensive and complicated in nature.…”

“…Seven factors were used to measure the index, which are as follow: number of ED and hospital beds, total patients in ED, number of respirators in ED, longest admit delay in first visit per hour, longest admit time, and total admits in ED. Another index was emergency department work index (EDWIN) that assumed the number of patients in each triage as its base and disregarded the number or activities of training nurses or care providers (6,7).…”

Emergency Department Visit Forecasting and Dynamic Nursing Staff Allocation Using Machine Learning Techniques With Readily Available Open-Source Software

“…Prediction models were successively applied every N weeks (N = 2, 4, 8,12,16,20,24). Within each iteration, algorithms were retrained with all the information from January 2008 to the reference date, and a new forecast of N weeks ahead was performed, and predictions were stored in a macro test set ( Figure 2).…”

“…Real-time crowding assessment tools have been devised, [12][13][14] but their practical usefulness has been contested. 15 Short-term (up to 12 hours ahead) crowding forecasting models have also been developed.…”