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Developing syndrome definitions based on consensus and current use

  • Wendy W. Chapman
  • , John N. Dowling
  • , Atar Baer
  • , David L. Buckeridge
  • , Dennis Cochrane
  • , Michael A. Conway
  • , Peter Elkin
  • , Jeremy Espino
  • , Julia E. Gunn
  • , Craig M. Hales
  • , Lori Hutwagner
  • , Mikaela Keller
  • , Catherine Larson
  • , Rebecca Noe
  • , Anya Okhmatovskaia
  • , Karen Olson
  • , Marc Paladini
  • , Matthew Scholer
  • , Carol Sniegoski
  • , David Thompson
  • Bill Lober
  • University of Pittsburgh
  • Division of Biomedical Informatics
  • Public Health - Seattle and King County
  • McGill University
  • Emergency Medical Associates
  • Commonwealth of Massachusetts
  • Centers for Disease Control and Prevention
  • Boston Children's Hospital
  • University of Arizona
  • Harvard University
  • New York City Department of Health and Mental Hygiene
  • University of North Carolina at Chapel Hill
  • Johns Hopkins University Applied Physics Laboratory
  • Northwestern University
  • University of Washington

Research output: Contribution to journal › Article › peer-review

23 Scopus citations

Abstract

Objective: Standardized surveillance syndromes do not exist but would facilitate sharing data among surveillance systems and comparing the accuracy of existing systems. The objective of this study was to create reference syndrome definitions from a consensus of investigators who currently have or are building syndromic surveillance systems. Design: Clinical condition - syndrome pairs were catalogued for 10 surveillance systems across the United States and the representatives of these systems were brought together for a workshop to discuss consensus syndrome definitions. Results: Consensus syndrome definitions were generated for the four syndromes monitored by the majority of the 10 participating surveillance systems: Respiratory, gastrointestinal, constitutional, and influenza-like illness (ILI). An important element in coming to consensus quickly was the development of a sensitive and specific definition for respiratory and gastrointestinal syndromes. After the workshop, the definitions were refined and supplemented with keywords and regular expressions, the keywords were mapped to standard vocabularies, and a web ontology language (OWL) ontology was created. Limitations: The consensus definitions have not yet been validated through implementation. Conclusion: The consensus definitions provide an explicit description of the current state-of-the-art syndromes used in automated surveillance, which can subsequently be systematically evaluated against real data to improve the definitions. The method for creating consensus definitions could be applied to other domains that have diverse existing definitions.

Original languageEnglish
Pages (from-to)595-601
Number of pages7
JournalJournal of the American Medical Informatics Association
Volume17
Issue number5
DOIs
StatePublished - Sep 2010

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