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Information system architectures for syndromic surveillance.

William B Lober1, L Trigg, B Karras

  • 1University of Washington, MS 357240, 1959 NE Pacific St., Seattle, Washington 98195, USA. lober@u.washington.edu

MMWR Supplements
|February 19, 2005
PubMed
Summary

Public health agencies can enhance disease surveillance by integrating advanced data architectures. Combining information technology and computer science research with Public Health Information Network (PHIN) best practices improves automated syndromic surveillance systems.

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Area of Science:

  • Public Health Informatics
  • Biomedical Data Integration
  • Computer Science Research

Background:

  • Public health agencies are building automated systems for disease surveillance.
  • Advanced biomedical data integration architectures can improve surveillance system design.
  • Data integration principles from information technology and research are crucial.

Purpose of the Study:

  • Describe essential architectural components of syndromic surveillance information systems.
  • Discuss current and potential data integration architectural approaches.
  • Inform the development of effective public health surveillance systems.

Main Methods:

  • Examined data elements, standards, extraction, transport, security, transformation, and analysis datasets.

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  • Discussed automated surveillance systems within the context of data integration research.
  • Characterized existing systems and identified future research avenues.

    • Main Results:

    • The Public Health Information Network (PHIN) outlines best practices for syndromic surveillance architecture.
    • A schema for biomedical data integration software aids in classifying current surveillance approaches and architectural variations.

    Conclusions:

    • Public health informatics and computer science research complement PHIN recommendations.
    • Research in data integration systems offers valuable insights for future public health surveillance.
    • Synergistic approaches enhance the development of robust disease surveillance capabilities.