Text-based syndrome case definitions published by the Center for Disease Control (CDC)1 form the basis for the syndrome queries used by the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT). Keywords within these case definitions were identified by public health epidemiologists for use as search terms with the goal of capturing symptom complexes from free-text chief complaint and triage note data for the purpose of early event detection and situational awareness.

We report here on the use of the North Carolina Bioterrorism and Emerging Infection Prevention System (NC BEIPS, www.ncbeips.org) to reverse engineer a syndrome definition of influenza for the purpose of influenza surveillance.

Of critical importance to the success of syndromic surveillance systems is the ability to collect data in a timely manner and thus ensure rapid detection of disease outbreaks. Most emergency department-based syndromic surveillance systems use information rou-tinely collected in patient care including patient chief complaints and physician diagnostic coding. These sources of data have been shown to have only limited sensitivities for the identification of cer-tain syndromes. Another potential source of information, which has not been previously studied, is the patient.