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Implementation of an automated, real-time public health surveillance system linking emergency departments and health units: rationale and methodology

Published online by Cambridge University Press:  21 May 2015

Kieran M. Moore ,
Bronwen L. Edgar  and
Donald McGuinness
Kieran M. Moore*
Affiliation:
Department of Emergency Medicine, Queen's University, Kingston, Ont.
Bronwen L. Edgar
Affiliation:
Emergency Department Syndromic Surveillance Team, Kingston, Frontenac, and Lennox and Addington (KFL&A) Public Health, Kingston, Ont.
Donald McGuinness
Affiliation:
Emergency Department Syndromic Surveillance Team, Kingston, Frontenac, and Lennox and Addington (KFL&A) Public Health, Kingston, Ont.
*
Queen's University Department of Emergency Medicine, c/o Kingston General Hospital, 76 Stuart St, Kingston ON K7L 2V7; moorek1@kgh.kari.net

Abstract

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In September 2004, Kingston, Frontenac, Lennox and Addington (KFL&A) Public Health, in collaboration with the Public Health Division of the Ontario Ministry of Health and Long-Term Care, Queen's University, the Public Health Agency of Canada, Kingston General Hospital and Hotel Dieu Hospital, began a 2-year pilot project to implement and evaluate an emergency department (ED) chief complaint syndromic surveillance system. Our objective was to evaluate a comprehensive and readily deployable real-time regional syndromic surveillance program and to determine its ability to detect gastrointestinal or respiratory outbreaks well in advance of traditional reporting systems. In order to implement the system, modifications were made to the University of Pittsburgh's Real-time Outbreak and Disease Surveillance (RODS) system, which has been successfully integrated into public health systems, and has enhanced communication and collaboration between them and EDs. This paper provides an overview of a RODS-based syndromic surveillance system as adapted for use at a public health unit in Kingston, Ontario. We summarize the technical specifications, privacy and security considerations, data capture, classification and management of the data streams, alerting and public health response. We hope that the modifications described here, including the addition of unique data streams, will provide a benchmark for future Canadian syndromic surveillance systems.

Keywords

syndromic surveillanceoutbreak detectionpublic health informaticsemergency departments
Type
Original Research • Recherche originale
Information
Canadian Journal of Emergency Medicine , Volume 10 , Issue 2 , March 2008 , pp. 114 - 119
Copyright
Copyright © Canadian Association of Emergency Physicians 2008

References

1.Walker, D. For the public’s health: initial report of the Ontario Expert Panel on SARS and Infectious Disease Control. Toronto (ON): Ministry of Health and Long-Term Care; 2003.Google Scholar
2.The National Advisory Committee on SARS and Public Health. Learning from SARS: renewal of public health in Canada. Ottawa (ON): Health Canada; 2003.Google Scholar
3.Campbell, A. The SARS Commission — spring of fear — final report. Toronto (ON): Ontario Ministry of Health and Long-Term Care; 2006. Available: http://www.health.gov.on.ca/english/public/pub/ministry_reports/campbell06/campbell06.html (accessed 2007 Feb 20).Google Scholar
4.Ministry of Health and Long-Term Care. Operation health protection: an action plan to prevent threats to our health and to promote a healthy Ontario. 2004. Available: http://www.health.gov.on.ca/english/providers/project/ohp/ohp_mn.html (accessed 2007 Feb 20).Google Scholar
5.Buehler, JW, Hopkins, RS, Overhage, JM, et al.; CDC Working Group. Framework for evaluating public health surveillance systems for early detection of outbreaks: recommendations from the CDC Working Group. MMWR Recomm Rep 2004;53 (RR-5):1–11.Google Scholar
6.Espino, JU, Wagner, C, Szczepaniak, F-C, et al. Removing a barrier to computer-based outbreak and disease surveillance — the RODS Open Source Project. MMWR Morb Mortal Wkly Rep 2004;53:32–9.Google Scholar
7.Ontario Ministry of Health and Long-Term Care. Emergency department chief complaint syndromic surveillance system pilot project: technical evaluation. Kingston (ON): The Ministry; 2006.Google Scholar
8.Canadian Association of Emergency Physicians. Implementation guidelines for the Canadian Emergency Department Triage and Acuity Scale (CTAS). Version: CTAS16 December 16, 1998. Available: http://www.caep.ca/CMS/get_file.asp?id=d09ba8ebe 7b649338e35c69dacc5c1f8&ext=.pdf&name=ctased16.pdf (accessed 2007 Feb 20).Google Scholar
9.Murray, M, Bullard, M, Grafstein, E; CTAS and CEDrosoph Inf Serv National Working Groups. Revisions to the Canadian Emergency Department Triage and Acuity Scale Implementation Guidelines. CJEM 2004;6:421–7.CrossRefGoogle Scholar
10.Jimenez, JG, Murray, MJ, Beveridge, R, et al. Implementation of the Canadian Emergency Department Triage and Acuity Scale in the Principality of Andorra: Can triage parameters serve as emergency department quality indicators? CJEM 2003;5:315–22.Google ScholarPubMed
11.Yoon, P, Steiner, I, Reinhardt, G. Analysis of factors influencing length of stay in the emergency department. CJEM 2003;5:155–62.Google Scholar
12.Innes, GD, Stenstrom, R, Grafstein, E, et al. Prospective time study derivation of emergency physician workload predictors. CJEM 2005;7:299–308.Google Scholar
13.Ontario Ministry of Health and Long-Term Care. Preventing respiratory illnesses protecting patients and staff: infection control and surveillance standards for febrile respiratory illness (FRI) in non-outbreak conditions in acute care hospitals. 2003. Available: http://www.health.gov.on.ca/english/providers/program /pubhealth/sars/docs/docs3/dir_infec_control_010604.pdf (accessed 2007 Feb 16).Google Scholar
14.O’Connor, K, Rimmer, M, Moore, K, et al. Maximum entropy models in chief complaint classification. Adv Dis Surv 2007;2:23.Google Scholar
15.Chapman, WW, Dowling, JN, Wagner, MM. Classification of emergency department chief complaints into 7 syndromes: a retrospective analysis of 527,228 patients. Ann Emerg Med 2005;46:445–55.Google Scholar
16.Chapman, WW, Dowling, JN, Ivanov, O, et al. Evaluating natural language processing applications applied to outbreak and disease surveillance. Paper presented at: Proc 36th Symposium on the Interface: Computing Science and Statistics; 26 May, 2004; Baltimore, Maryland.Google Scholar
17.Hutwagner, L, Browne, T, Seeman, GM, et al. Comparing aberration detection methods with simulated data. Emerg Infect Dis 2005;11:314–6.CrossRefGoogle ScholarPubMed
18.ESRI ArcIMS. California: Environmental Systems Research Institute, Inc. (ESRI). Available: http://www.esri.com/software/arcgis /arcims/index.html (accessed 2007 Apr 10).Google Scholar
19.Moore, K, Rimmer, M, O’Connor, K, et al. Integration of hospital admissions, febrile respiratory illness screening and triage acuity score into an emergency department syndromic surveillance system. Adv Dis Surv 2006;1:51.Google Scholar
20.Ontario Ministry of Health and Long-Term Care Provincial Infectious Disease Advisory Committee. Preventing respiratory illnesses; protecting patients and staff. Best practices in surveillance and infection prevention and control for febrile respiratory illness (FRI), excluding tuberculosis, for all Ontario health care settings (revised edition). Toronto (ON): The Ministry; 2006.Google Scholar
21.Moore, K, Rimmer, M, O’Connor, K, et al. Multi-sectored approach to evaluation of a syndromic surveillance system. Adv Dis Surv 2006;1:52.Google Scholar