Emergency department resilience to disaster‐level overcrowding: A component resilience framework for analysis and predictive modeling

• Author: Zachary Davis,Lara Khansa,Christopher W. Zobel,Roger E. Glick
• Date: 01 January 2020
• DOI: http://doi.org/10.1002/joom.1017
• Published date: 01 January 2020

RESEARCH ARTICLE

Emergency department resilience to disaster-level overcrowding:

A component resilience framework for analysis and

predictive modeling

Zachary Davis

1

| Christopher W. Zobel

2

| Lara Khansa

2

| Roger E. Glick

3

1

Decision Science and Information

Management, Davis College of Business,

Jacksonville University, Jacksonville,

Florida

2

Department of Business Information

Technology, Virginia Tech, Blacksburg,

Virginia

3

Departments of Pediatrics, Basic Science,

and Emergency Medicine, Carilion Clinic,

Virginia Tech Carilion School of Medicine,

Carilion Medical Center, Roanoke, Virginia

Correspondence

Zachary Davis, Decision Science and

Information Management, Davis College of

Business, Jacksonville University, 2800

University Blvd N, Jacksonville, FL 32211.

Email: zdavis1@ju.edu

Handling Editors: Lawrence Fredendall,

Anand Nair, Jeffery Smith, Anita Tucker

Abstract

Overcrowding poses a serious challenge to the operations of health care facilities, espe-

cially those with a mandate to provide emergency care. A better understanding of

emergency department (ED) performance during disaster-level overcrowding is a key

to increasing a facility's resilience, optimizing patient outcomes, and more effectively

allocating resources. With this in mind, this study quantitatively examines the extent to

which different factors contribute to the resilience of hospital EDs during disaster-level

overcrowding events. A modeling fram ework was developed in collaboration with the

Carilion Clinic ED, a level one trauma center in Virginia. The testing and analysis of

the approach is based on data from actual disaster-level overcrowding events that

occurred in the Spring of 2016. Results indicate that by considering not only the capac-

ity for resisting such events but also the capacity for recovering from them more

quickly, hospital decision makers can improve both their operational effectiveness and

the patient experience. Furthermore, by using our framework to identify precipitating

factors and predict severe overcrowding, hospital decision makers can implement

changes to improve the future resilience of their ED to such overcrowding events.

KEYWORDS

component resilience, decision support, hospital operations, multievent disasters, overcrowding,

predicted resilience

1|INTRODUCTION

Overcrowding occurs in a hospital's emergency department

(ED) when it is unable to successfully process the patients arriv-

ing within a given period of time. This often results in increased

wait times, delays in care, and the inability to treat patients with

the level of care they require. According to a 2009 report from

the United States Government Accountability Office, the

national average wait time to see a physician in the ED is about

twice the recommended length of time (U.S. Government

Accountability Office, 2009). In the United States, this corre-

sponds to average ED wait times of 4 hr (Press Ganey, 2010).

Unfortunately, the number of available EDs has been dwindling

at the same time as a dramatic increase in the number of ED

visits worldwide (Kadri, Harrou, Chaabane, & Tahon, 2014). In

Australia, for example, the number of available hospital beds

has decreased by as much as 18% despite a 3.5% increase in the

number of ED visits (Magnus & Killion, 2008; Richardson,

Kelli, & Curr, 2009). France has experienced a 64% increase in

ED visits, Germany an 8% increase, and Italy and Canada a 6%

increase, resulting in increased ED overcrowding problems in

all these countries (Pines et al., 2011).

This version of the article was corrected on 15 May 2019 following initial

publication online on 5 April 2019. The article has been amended to correct

the name of the regression technique used in the paper. A fuller description

of the change is included at the end of the article.

Received: 18 July 2017 Revised: 6 January 2019 Accepted: 5 February 2019

DOI: 10.1002/joom.1017

54 © 2019 Association for Supply Chain Management, Inc. wileyonlinelibrary.com/journal/joom J Oper Manag. 2020;66:54–66.