Skip to main content

Expanding the Functionality of Syndromic Surveillance Systems: Data Mining and Query Development


The Indiana Public Health Emergency Surveillance System (PHESS) currently receives approximately 5,000 near real-time chief complaint messages from 55 hospital emergency departments daily.  The ISDH partners with the Regenstrief Institute to process, batch, and transmit data every three hours.  The Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) tool is utilized to analyze these chief complaint data and visualize generated alerts.1   


The ISDH syndromic surveillance team discovered that certain chief complaints of interest were coded into the “other” syndrome and not visible in typical daily alert data.  Staff determined that even a single chief complaint containing keywords related to specific reportable diseases could be of significant public health value and should be made available to investigating epidemiologists.2 


In addition, data quality is critical to the success of the program and must be evaluated to ensure optimal system performance.  Metrics related to data flow and completeness were identified to serve as indicators of hospital connectivity or coding problems.  These measures included the percent change in daily admits and the proportion of chief complaints missing the patient address.


This paper describes the development of targeted query tools and processes designed to maximize the extraction of information from, and improve the quality of, the hospital emergency department chief complaint data stream utilized by the Indiana State Department of Health (ISDH) for syndromic surveillance.

Submitted by elamb on