Literature DB >> 16177687

BioSense: implementation of a National Early Event Detection and Situational Awareness System.

Colleen A Bradley1, H Rolka, D Walker, J Loonsk.   

Abstract

BioSense is a CDC initiative to support enhanced early detection, quantification, and localization of possible biologic terrorism attacks and other events of public health concern on a national level. The goals of the BioSense initiative are to advance early detection by providing the standards, infrastructure, and data acquisition for near real-time reporting, analytic evaluation and implementation, and early event detection support for state and local public health officials. BioSense collects and analyzes Department of Defense and Department of Veterans Affairs ambulatory clinical diagnoses and procedures and Laboratory Corporation of America laboratory-test orders. The application summarizes and presents analytical results and data visualizations by source, day, and syndrome for each ZIP code, state, and metropolitan area through maps, graphs, and tables. An initial proof of a concept evaluation project was conducted before the system was made available to state and local users in April 2004. User recruitment involved identifying and training BioSense administrators and users from state and local health departments. User support has been an essential component of the implementation and enhancement process. CDC initiated the BioIntelligence Center (BIC) in June 2004 to conduct internal monitoring of BioSense national data daily. BIC staff have supported state and local system monitoring, conducted data anomaly inquiries, and communicated with state and local public health officials. Substantial investments will be made in providing regional, state, and local data for early event detection and situational awareness, test beds for data and algorithm evaluation, detection algorithm development, and data management technologies, while maintaining the focus on state and local public health needs.

Entities:  

Mesh:

Year:  2005        PMID: 16177687

Source DB:  PubMed          Journal:  MMWR Suppl        ISSN: 2380-8942


  48 in total

1.  Syndromic surveillance in an ICD-10 world.

Authors:  Achala Jayatilleke; Jeffrey Kriseman; Lisa H Bastin; Umed Ajani; Peter Hicks
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

2.  Linking surveillance to action: incorporation of real-time regional data into a medical decision rule.

Authors:  Andrew M Fine; Lise E Nigrovic; Ben Y Reis; E Francis Cook; Kenneth D Mandl
Journal:  J Am Med Inform Assoc       Date:  2007-01-09       Impact factor: 4.497

3.  The Indiana Public Health Emergency Surveillance System: ongoing progress, early findings, and future directions.

Authors:  Shaun Grannis; Michael Wade; Joseph Gibson; J Marc Overhage
Journal:  AMIA Annu Symp Proc       Date:  2006

4.  AEGIS: a robust and scalable real-time public health surveillance system.

Authors:  Ben Y Reis; Chaim Kirby; Lucy E Hadden; Karen Olson; Andrew J McMurry; James B Daniel; Kenneth D Mandl
Journal:  J Am Med Inform Assoc       Date:  2007-06-28       Impact factor: 4.497

5.  Electronic Support for Public Health: validated case finding and reporting for notifiable diseases using electronic medical data.

Authors:  Ross Lazarus; Michael Klompas; Francis X Campion; Scott J N McNabb; Xuanlin Hou; James Daniel; Gillian Haney; Alfred DeMaria; Leslie Lenert; Richard Platt
Journal:  J Am Med Inform Assoc       Date:  2008-10-24       Impact factor: 4.497

6.  Electronic medical record (EMR) utilization for public health surveillance.

Authors:  Zaruhi R Mnatsakanyan; Daniel J Mollura; John R Ticehurst; Mohammad R Hashemian; Lang M Hung
Journal:  AMIA Annu Symp Proc       Date:  2008-11-06

7.  A model for expanded public health reporting in the context of HIPAA.

Authors:  Soumitra Sengupta; Neil S Calman; George Hripcsak
Journal:  J Am Med Inform Assoc       Date:  2008-06-25       Impact factor: 4.497

8.  Bayesian information fusion networks for biosurveillance applications.

Authors:  Zaruhi R Mnatsakanyan; Howard S Burkom; Jacqueline S Coberly; Joseph S Lombardo
Journal:  J Am Med Inform Assoc       Date:  2009-08-28       Impact factor: 4.497

9.  Comparison of machine learning classifiers for influenza detection from emergency department free-text reports.

Authors:  Arturo López Pineda; Ye Ye; Shyam Visweswaran; Gregory F Cooper; Michael M Wagner; Fuchiang Rich Tsui
Journal:  J Biomed Inform       Date:  2015-09-16       Impact factor: 6.317

10.  Telephone survey to assess influenza-like illness, United States, 2006.

Authors:  Joseph L Malone; Mohammad Madjid; S Ward Casscells
Journal:  Emerg Infect Dis       Date:  2008-01       Impact factor: 6.883

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