Automated detection of influenza-like illness using clinical surveillance markers at a Department of Veterans Affairs Medical Center.

Published online

Journal Article

BACKGROUND: Using demographic and clinical measures from emergency department evaluations, we developed an automated surveillance system for influenza-like illness (ILI). METHODS: We selected a random sample of patients who were seen at the Durham, NC Veterans Affairs Medical Center between May 2002 and October 2009 with fever or a respiratory ICD-9 diagnosis code and divided this into subsets for system development and validation. Comprehensive chart reviews identified patients who met a standard case definition for ILI. Logistic regression models predicting ILI were fit in the development sample. We applied the parameter estimates from these models to the validation sample and evaluated their utility using receiver-operator characteristic analysis. RESULTS: The models discriminated ILI very well in the validation sample; the C-statistics were >0.89. CONCLUSIONS: Risk estimates based on statistical models can be incorporated into electronic medical records systems to assist clinicians and could be used in real-time surveillance for disease outbreaks.

Full Text

Duke Authors

Cited Authors

  • Park, LP; Rao, S; Nabity, SA; Abbott, D; Frederick, J; Woods, CW

Published Date

  • April 20, 2011

Published In

Volume / Issue

  • 4 /

Start / End Page

  • 7108 -

PubMed ID

  • 24149026

Pubmed Central ID

  • 24149026

International Standard Serial Number (ISSN)

  • 1752-8550

Digital Object Identifier (DOI)

  • 10.3402/ehtj.v4i0.7108


  • eng

Conference Location

  • Sweden