Syndrome and outbreak detection using chief-complaint data--experience of the Real-Time Outbreak and Disease Surveillance project.

This paper summarizes the experience of the Real-Time Outbreak and Disease Surveillance (RODS) project in collecting and analyzing free-text emergency department (ED) chief complaints. The technical approach involves real-time transmission of chief-complaint data as Health Level 7 messages from hospitals to a regional data center, where a Bayesian text classifier assigns each chief complaint to one of eight syndrome categories. Time-series algorithms analyze the syndrome data and generate alerts. Authorized public health users review the syndrome data by using Internet interfaces with timelines and maps. Deployments in Pennsylvania, Utah, Atlantic City, and Ohio have demonstrated feasibility of real-time collection of chief complaints. Retrospective experiments that measured case-classification accuracy demonstrated that the Bayesian classifier can discriminate between different syndrome presentations. Retrospective experiments that measured outbreak-detection accuracy determined that the classifier's performance was adequate to support accurate and timely detection of seasonal disease outbreaks. Prospective evaluation revealed that a cluster of carbon monoxide exposures was detected by RODS within 4 hours of the presentation of the first case to an emergency department.