Public health agencies have long used surveillance—the systematic monitoring of health conditions of importance within populations—to measure the incidence of disease, identify outbreaks, and evaluate the impact of prevention programs (Buehler et al., 2004). Active surveillance using traditional methods such as postcards, telephone lines, faxed forms, and even e-mail is erratic because clinicians may forget to report a case when they see one or assume someone else is doing so. This can be true whether the condition involves an infectious disease such as tuberculosis or a high-impact injury, such as a gunshot wound (Kellermann et al., 2001). Electronic monitoring of key triage complaints and/or discharge diagnoses would greatly facilitate ED compliance with this traditional public health obligation.

A relatively recent development in population health monitoring is the notion of syndromic surveillance (Mandl et al., 2004)—methods relying on the detection of individual and population health indicators that are discernible before confirmed diagnoses are made. Before there is laboratory confirmation of an infectious disease, ill persons may behave according to identifiable patterns or have symptoms, signs, or laboratory findings that can be tracked through mining of data sources, including ED chief complaints (Fleischauer et al., 2004), International Classification of Diseases (ICD)-9 codes (Espino and Wagner, 2001), laboratory data, and pharmaceutical data (Tsui et al., 2003).

The goal of outbreak detection is to generate an alert whenever observed data depart sufficiently from an expected baseline. To this end, the system must be able to detect a signal (i.e., disease outbreak) against background noise (i.e., normally varying baseline disease in the region). A number of syndromic surveillance systems are currently being developed regionally as well as nationally. These include the Automated Epidemiologic Geotemporal Integrated Surveillance System (AEGIS) in Massachusetts (Mandl et al., 2004; Children’s Hospital Informatics Program, 2005), the Real Time Outbreak Disease Surveillance System (RODS) in Pittsburgh (Tsui et al., 2003), the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) system in the National Capital Area (Lombardo et al., 2003), and the national BioSense system being developed by the Centers for Disease Control and Prevention (Loonsk and CDC, 2004).

As such systems become more advanced, the need for standard protocols for alerting appropriate personnel of abnormal conditions becomes more pressing. One model may be AEGIS, which fully automates population health monitoring from end to end and interfaces with a statewide health alert network. This network, a comprehensive communication and alert messaging switch that provides message content and routing, is an example of a communications technology that helps unite clinical and public health entities.