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Predicting Patient Patterns in Veterans Administration Emergency Departments

Veteran’s Affairs (VA) hospitals represent a unique patient population within the healthcare system; for example, they have few female and pediatric patients, typically do not see many trauma cases and often do not accept ambulance runs. As such, veteran-specific studies are required to understand t...

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Autores principales: Kessler, Chad S., Bhandarkar, Stephen, Casey, Paul, Tenner, Andrea
Formato: Texto
Lenguaje:English
Publicado: Department of Emergency Medicine, University of California, Irvine School of Medicine 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3099608/
https://www.ncbi.nlm.nih.gov/pubmed/21691527
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author Kessler, Chad S.
Bhandarkar, Stephen
Casey, Paul
Tenner, Andrea
author_facet Kessler, Chad S.
Bhandarkar, Stephen
Casey, Paul
Tenner, Andrea
author_sort Kessler, Chad S.
collection PubMed
description Veteran’s Affairs (VA) hospitals represent a unique patient population within the healthcare system; for example, they have few female and pediatric patients, typically do not see many trauma cases and often do not accept ambulance runs. As such, veteran-specific studies are required to understand the particular needs and stumbling blocks of VA emergency department (ED) care. The purpose of this paper is to analyze the demographics of patients served at VA EDs and compare them to the national ED population at large. Our analysis reveals that the VA population exhibits a similar set of common chief complaints to the national ED population (and in similar proportions) and yet differs from the general population in many ways. For example, the VA treats an older, predominantly male population, and encounters a much lower incidence of trauma. Perhaps most significantly, the incidence of psychiatric disease at the VA is more than double that of the general population (10% vs. 4%) and accounts for a significant proportion of admissions (23%). Furthermore, the overall admission percentage at the VA hospital is nearly three times that of the ED population at large (36% versus 13%). This paper provides valuable insight into the make-up of a veteran’s population and can guide staffing and resource allocation accordingly.
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spelling pubmed-30996082011-06-20 Predicting Patient Patterns in Veterans Administration Emergency Departments Kessler, Chad S. Bhandarkar, Stephen Casey, Paul Tenner, Andrea West J Emerg Med ED Administration Veteran’s Affairs (VA) hospitals represent a unique patient population within the healthcare system; for example, they have few female and pediatric patients, typically do not see many trauma cases and often do not accept ambulance runs. As such, veteran-specific studies are required to understand the particular needs and stumbling blocks of VA emergency department (ED) care. The purpose of this paper is to analyze the demographics of patients served at VA EDs and compare them to the national ED population at large. Our analysis reveals that the VA population exhibits a similar set of common chief complaints to the national ED population (and in similar proportions) and yet differs from the general population in many ways. For example, the VA treats an older, predominantly male population, and encounters a much lower incidence of trauma. Perhaps most significantly, the incidence of psychiatric disease at the VA is more than double that of the general population (10% vs. 4%) and accounts for a significant proportion of admissions (23%). Furthermore, the overall admission percentage at the VA hospital is nearly three times that of the ED population at large (36% versus 13%). This paper provides valuable insight into the make-up of a veteran’s population and can guide staffing and resource allocation accordingly. Department of Emergency Medicine, University of California, Irvine School of Medicine 2011-05 /pmc/articles/PMC3099608/ /pubmed/21691527 Text en Copyright © 2011 the authors. http://creativecommons.org/licenses/by-nc/4.0 This is an open access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) License. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle ED Administration
Kessler, Chad S.
Bhandarkar, Stephen
Casey, Paul
Tenner, Andrea
Predicting Patient Patterns in Veterans Administration Emergency Departments
title Predicting Patient Patterns in Veterans Administration Emergency Departments
title_full Predicting Patient Patterns in Veterans Administration Emergency Departments
title_fullStr Predicting Patient Patterns in Veterans Administration Emergency Departments
title_full_unstemmed Predicting Patient Patterns in Veterans Administration Emergency Departments
title_short Predicting Patient Patterns in Veterans Administration Emergency Departments
title_sort predicting patient patterns in veterans administration emergency departments
topic ED Administration
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3099608/
https://www.ncbi.nlm.nih.gov/pubmed/21691527
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