scholarly journals Is Inpatient Volume Or Emergency Department Crowding A Greater Driver Of Ambulance Diversion?

2018 ◽  
Vol 37 (7) ◽  
pp. 1115-1122
Author(s):  
Renee Y. Hsia ◽  
Nandita Sarkar ◽  
Yu-Chu Shen
Keyword(s):  
Emergency Department ◽  
Emergency Department Crowding ◽  
Ambulance Diversion

scholarly journals Ambulance Diversion as a Proxy for Emergency Department Crowding: The Effect on Pediatric Mortality in a Metropolitan Area

2009 ◽  
Vol 16 (2) ◽  
pp. 116-123 ◽  
Author(s):  
Rohit P. Shenoi ◽  
Long Ma ◽  
Jennifer Jones ◽  
Mary Frost ◽  
Munseok Seo ◽  
...  
Keyword(s):  
Emergency Department ◽  
Metropolitan Area ◽  
Emergency Department Crowding ◽  
Ambulance Diversion

scholarly journals Correction to: 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

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Jens Wretborn ◽  
Håkan Starkenberg ◽  
Thoralph Ruge ◽  
Daniel B. Wilhelms ◽  
Ulf Ekelund
Keyword(s):  
Emergency Department ◽  
Observational Study ◽  
Emergency Department Crowding ◽  
Patient Load

An amendment to this paper has been published and can be accessed via the original article.

scholarly journals The International Federation for Emergency Medicine report on emergency department crowding and access block: a brief summary

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
A. P. Javidan ◽  
◽  
K. Hansen ◽  
I. Higginson ◽  
P. Jones ◽  
...  
Keyword(s):  
Emergency Department ◽  
Emergency Medicine ◽  
Task Force ◽  
Video Conferencing ◽  
International Federation ◽  
Emergency Physicians ◽  
Emergency Department Crowding ◽  
Comprehensive Guidance ◽  
The World ◽  
Ed Crowding

Abstract Objective To develop comprehensive guidance that captures international impacts, causes, and solutions related to emergency department crowding and access block Methods Emergency physicians representing 15 countries from all IFEM regions composed the Task Force. Monthly meetings were held via video-conferencing software to achieve consensus for report content. The report was submitted and approved by the IFEM Board on June 1, 2020. Results A total of 14 topic dossiers, each relating to an aspect of ED crowding, were researched and completed collaboratively by members of the Task Force. Conclusions The IFEM report is a comprehensive document intended to be used in whole or by section to inform and address aspects of ED crowding and access block. Overall, ED crowding is a multifactorial issue requiring systems-wide solutions applied at local, regional, and national levels. Access block is the predominant contributor of ED crowding in most parts of the world.

scholarly journals Models Predicting Hospital Admission of Adult Patients Utilizing Prehospital Data: Systematic Review Using PROBAST and CHARMS

10.2196/30022 ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. e30022
Author(s):  
Ann Corneille Monahan ◽  
Sue S Feldman
Keyword(s):  
Emergency Department ◽  
Hospital Admission ◽  
Real Time ◽  
Prediction Models ◽  
Assessment Tool ◽  
Predictive Performance ◽  
Patient Data ◽  
Adult Patients ◽  
Emergency Department Crowding ◽  
Exit Block

Background Emergency department boarding and hospital exit block are primary causes of emergency department crowding and have been conclusively associated with poor patient outcomes and major threats to patient safety. Boarding occurs when a patient is delayed or blocked from transitioning out of the emergency department because of dysfunctional transition or bed assignment processes. Predictive models for estimating the probability of an occurrence of this type could be useful in reducing or preventing emergency department boarding and hospital exit block, to reduce emergency department crowding. Objective The aim of this study was to identify and appraise the predictive performance, predictor utility, model application, and model utility of hospital admission prediction models that utilized prehospital, adult patient data and aimed to address emergency department crowding. Methods We searched multiple databases for studies, from inception to September 30, 2019, that evaluated models predicting adult patients’ imminent hospital admission, with prehospital patient data and regression analysis. We used PROBAST (Prediction Model Risk of Bias Assessment Tool) and CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) to critically assess studies. Results Potential biases were found in most studies, which suggested that each model’s predictive performance required further investigation. We found that select prehospital patient data contribute to the identification of patients requiring hospital admission. Biomarker predictors may add superior value and advantages to models. It is, however, important to note that no models had been integrated with an information system or workflow, operated independently as electronic devices, or operated in real time within the care environment. Several models could be used at the site-of-care in real time without digital devices, which would make them suitable for low-technology or no-electricity environments. Conclusions There is incredible potential for prehospital admission prediction models to improve patient care and hospital operations. Patient data can be utilized to act as predictors and as data-driven, actionable tools to identify patients likely to require imminent hospital admission and reduce patient boarding and crowding in emergency departments. Prediction models can be used to justify earlier patient admission and care, to lower morbidity and mortality, and models that utilize biomarker predictors offer additional advantages.

Emergency department crowding affects triage processes

2016 ◽  
Vol 29 ◽  
pp. 27-31 ◽  
Author(s):  
M. Christien van der Linden ◽  
Barbara E.A.M. Meester ◽  
Naomi van der Linden
Keyword(s):  
Emergency Department ◽  
Emergency Department Crowding

The Emergency Department Occupancy Rate: A Simple Measure of Emergency Department Crowding?

2008 ◽  
Vol 51 (1) ◽  
pp. 15-24.e2 ◽  
Author(s):  
Melissa L. McCarthy ◽  
Dominik Aronsky ◽  
Ian D. Jones ◽  
James R. Miner ◽  
Roger A. Band ◽  
...  
Keyword(s):  
Emergency Department ◽  
Occupancy Rate ◽  
Emergency Department Crowding ◽  
Simple Measure
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