Performance Improvement 2011-2012. What Are The Different Approaches Taken By Different Health Departments For The BioSense Program?

01/01/2012

Researchers analyzed approaches taken by different health departments to the BioSense program, and evaluated the structural capability of several state departments of health to respond to a public health incident. In early 2008, the John Hopkins team evaluated the BioSense programs for Indiana, Wisconsin and Minnesota. The teams developed a coordinated continuum of BioSense, including both centralized and decentralized public health, and assessed the continuum's strengths, weaknesses, related costs, barriers and successes. The team interviewed the states' departments of health and, based on the findings, conducted case studies to explore the states' use of BioSense.

Syndromic surveillance data, specifically BioSense, can be used as a proxy for detecting and monitoring outbreak events such as influenza-like illness, H1N1, and chickenpox, as a proxy for smallpox, where rapid testing is not available. Additional work assessed the use of natural language processing (NLP) for parsing clinicians' free text notes in relation to influenza like illness and gastrointestinal illness. Researchers found that natural language processing: 1) Increases statistical performance over detection by ICD9 codes alone; 2) Increases statistical performance when combined with ICD9 codes; 3) Can lower the delay and workload requirements needed to detect an influenza like illness outbreak; 4) Increases the sensitivity of influenza like illness detection when compared to using such information as chief complaint, and emergency department and triage notes; 5) Can be of greater assistance to public health investigations as compared to structured data sources; 6) Can increase sensitivity when combined with analysis using chief complaint, and emergency department and triage notes; 7) Improves detection of patients with febrile illness within a study group; and, 8) Using natural language processing that detects negative statements in these notes lowers false positives. An assessment of BioSense's Cumulative Sum algorithm also showed it to provide a shorter detection delay than SatScan.

Report Title: BioSense Evaluation Cooperative Agreement: The Mayo and Hopkins Evaluations

Agency Sponsor: CDC, Centers for Disease Control and Prevention
Federal Contact: Taha Kass-Hout, 404-498-2014
Performer: Mount Sinai Medical Center
Record ID: 9412 (Report issued January 1, 2010)

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