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ISSN: 2157-7420

Journal of Health & Medical Informatics
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Review Article

Addressing Emergency Department Overcrowding Through a Systems Approach Using Big Data Research

Noreen Kamal1*, D Kelly Barnard2, James M Christenson3, Grant D Innes4, Pamela Aikman5, Eric Grafstein6 and Julian Marsden7

1Department of Clinical Neurosciences, Faculty of Medicine, University of Calgary, Calgary AB, Canada

2BC Coroners Service, BC Ministry of Justice, Victoria BC, Canada

3Department of Emergency Medicine, University of British Columbia, Vancouver BC, Canada

4Department of Family Medicine, University of Calgary, Calgary AB, Canada

5Stroke Services BC, Provincial Health Services Authority, Vancouver BC, Canada

6Department of Emergency Medicine, Providence Healthcare, Vancouver BC, Canada

7St. Paul’s Hospital, Providence Healthcare, Vancouver BC, Canada

*Corresponding Author:
Noreen Kamal
Department of Clinical Neurosciences
Faculty of Medicine, University of Calgary
Calgary AB, Canada
Tel: +1 (403) 210-8895
E-mail: [email protected]

Received Date: November 06, 2013; Accepted Date: February 12, 2014; Published Date: February 18, 2014

Citation: Kamal N, Kelly Barnard D, Christenson JM, Innes GD, Aikman P, et al. (2014) Addressing Emergency Department Overcrowding Through a Systems Approach Using Big Data Research. J Health Med Informat 5:148. doi: 10.4172/2157-7420.1000148

Copyright: © 2014 Kamal N, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


The emergency department (ED) is the canary in the coalmine for the healthcare system. The issues that EDs are facing are having adverse effects, and their causes can be better understood by viewing the ED as part of the entire health and social system. Policy decisions can be supported by sharing data across various health service areas, ministries and census data. Big Data research can support the integration and correlation of variables in large volumes of diverse datasets. Furthermore, the science of Visual Analytics can provide interactive visualization of the data to support evidence-based healthcare policy.


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