Case Study: Management Project in the Health

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[. . .] Also monitored by BioSense are over-the-counter drug sales and lab test results for environmental samples collected through the BioWatch effort. BioSense aims to "monitor 11 syndrome categories including fever, respiratory, gastrointestinal illness, hemorrhagic illness, localized cuteanous lesion, symphadenitis, neurologic, rash, severe illness and death, specific infection and botulism-like/botulism." (Chen, Zen, Ping, and Ping, 2010, p.10)

(2) ESSENCE

ESSENCE is reported to have been used in monitoring the health status of military healthcare beneficiaries worldwide, relying on outpatient ICD-9 diagnostic codes for outbreak detection." (Chen, Zen, Ping and Ping, 2010, p. 10) The Rapid Syndrome Validation Project (RSVP) is reported as an "Internet-based population health surveillance tool designed to facilitate rapid communications between epidemiologist and healthcare providers." (Chen, Zen, Ping, and Ping, 2010, p.10) Patients with syndrome categories are labeled and the judgment of clinician's regarding illness severity reported in order to "facilitate timely geographic and temporal analysis." (Chen, Zen, Ping, and Ping, 2010, p.11)

(3) The Early Aberration Reporting System (EARS)

The Early Aberration Reporting System (EARS) is used to monitor bioterrorism activities during events large in scale. The system monitors "emergency department visits, 911 calls, physician office data, school and work absenteeism and OTC drug sales for 42 syndrome categories." (Chen, Zen, Ping, and Ping, 2010, p.12)

(4) The National Bioterrorism Syndromic Surveillance Demonstration Program

The National Bioterrorism Syndromic Surveillance Demonstration Program monitors in excess of 20 million people and analyzes disease cases for "neurologic, upper/lower GI, upper/lower respiratory, dermatologic, sepsis/fever, bioterrorism category A agents and influenza-like illness" all derived from electronic patient-encounter records from health care organizations that are participating. (Chen, Zen, Ping, and Ping, 2010, p.12)

(5) BioAlirt

The Bio-Event Advanced Leading Indicator Recognition Technology (BioAlirt) program conduct examination of the use of spatial and other covariate information from disparate sources to improve the timeliness of outbreak detection in reaction to possible bioterrorism attacks…" as well as monitoring "military and civilians outpatient visit records with ICD-9 codes, and military prescription records for unusual ILI and GI occurrences." (Chen, Zen, Ping, and Ping, 2010, p.12)

(6) BioStorm

Biological Spatio-Temporal Outbreak Reasoning Module (BioStorm) aims at the integration of disparate data sources and deploys various analytic problem solvers focused on supporting surveillance of public health. It is reported that BioStorm is ontology-based and is comprised by "a data broker, a data mapper, a control structure, and a library of statistical and spatial problem solvers." (Chen, Zen, Ping, and Ping, 2010, p.13) BioStorm monitors such as 911 emergency calls and implements a library of statistical methods analyzing data as a single or multiple time series and knowledge-based methods that relate detected anomalies to knowledge about reportable diseases." (Chen, Zen, Ping, and Ping, 2010, p.13)

(7) BioPortal

Another biosurveillance system is that of BioPortal which makes provision of "a flexible and scalable infectious disease information sharing, alerting, analysis, and visualization platform." (Chen, Zen, Ping, and Ping, 2010, p.13)

(8) B-SAFER

Bio-Surveillance Analysis, Feedback, Evaluation, and Response (B-SAFER) is a reported web-based infectious disease monitoring system that is part of the open source OpenEMed project for use in emergency care settings that collects chief complaints, discharge diagnoses, and disposition data for detection analysis concerning a group of syndromes including respiratory, GI undifferentiated infection, lymphatic, skin, neurological, and other. This system analyzes daily on a daily basis by a first-order model that utilizes regression for fitting trends, seasonal effects, and day-of-week effects. (Chen, Zen, Ping, and Ping, 2010, p.13)

(9) INFERNO

Integrated Forecasts and EaRly eNteric Outbreak (INFERNO) incorporates infection disease epidemiology into adaptive forecasting utilizing the concept of an outbreak signature as a composite of disease epidemic curves." (Chen, Zen, Ping, and Ping, 2010, p.13)

(10) DiSTRIBuTE

The DiSTRIBuTE project is a proof-of-concept, distributed influenza surveillance system that utilizes "aggregate influenza-like illness, emergency department data from existing syndromic surveillance systems developed by state and local public health departments." (Chen, Zen, Ping, and Ping, 2010, p.18)

(11) Others

Two other global scale real-time disease even detection and tracking systems are reported as taking a differentiated approach from the systems already mentioned and those two are the Argus and HealthMap projects which monitor online media from global resources rather than disease cases reported by hospitals, clinics and other health facilities.

Summary

This study has explained the various state-of-the-art syndromic surveillance systems presently being utilized by various agencies and health care providers. The effectiveness of the use of these systems are being documented and the contribution that these systems are making to surveillance of disease and epidemic outbreaks among the population.

References

Buckeridge, DL, et al. (2005) An Evaluation Model for Syndromic Surveillance: Assessing the Performance of Temporal Algorithm. Vol. 54 MMWR Supplement.

Chen, H, Zeng, D, Ping, Y and Ping Y (2010) Infectious Disease Informatics; Syndromic Surveillance for Public Health and Biodefense. Springer Medical 2010. Retrieved from: http://books.google.com/books?id=5BdCfSxtNJMC&dq=syndromic+surveillance+system:+state+of+the+art&source=gbs_navlinks_s

Hurt-Mullen, K and Coberly, J. (2005) Syndromic Surveillance on the Epidemiologist's Desktop: Making Sense of Much Data. MMWR Supplement 26 Aug 2005. Retrieved from: http://www.cdc.gov/MMWR/preview/mmwrhtml/su5401a22.htm

Public Meaningful Use (2013) Arkansas Department of Public Health. Retrieved from: http://www.healthy.arkansas.gov/programsServices/MeaningfulUse/Pages/default.aspx

Sloane, PD, et al. (2006) Syndromic Surveillance For Emerging Infections in Office Practice Using Data. Annals of Family Medicine Vol. 4 No. 4 Jul-Aug 2006.

Stoto, M. et al. (2007) Expert Meeting on Privacy, Confidentiality, and Other Legal and Ethical Issues in Syndromic Surveillance Report from an International Society for Disease Surveillance Consultation Washington DC, October 4 -- 5, 2007. Retrieved from: http://www.isdsjournal.org/articles/6217.pdf

Syndromic Surveillance (2013) Arkansas Department of Public Health. Retrieved from: http://www.healthy.arkansas.gov/programsServices/MeaningfulUse/Pages/SyndromicSurveillance.aspx

Syndromic Surveillance (2013) Centers for Disease Control and Prevention. Retrieved from: http://www.cdc.gov/ehrmeaningfuluse/syndromic.html

Syndromic Surveillance: An Effective Tool for Detecting Bioterrorism. (2013) Centers for Disease Control and Prevention. Retrieved from: http://www.rand.org/pubs/research_briefs/RB9042/index1.html

Zhang, Y, May, L, and Stoto, MA (2011) Evaluating Syndromic surveillance systems at institutions of higher education (IHEs): A retrospective analysis of the 2009 H1N1 influenza pandemic at two universities. BMC Public Health 2011, 11:591. Retrieved from: http://www.biomedcentral.com/1471-2458/11/591 [END OF PREVIEW]

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