ISDS Conference

Automated syndromic surveillance systems often classify patients into syndromic categories based on free-text chief complaints. Chief complaints (CC) demonstrate low to moderate sensitivity in identifying syndromic cases. Emergency Department (ED) reports promise more detailed clinical information that may increase sensitivity of detection. Objective: Compare classification of patients based on chief complaints against classification from clinical data described in ED reports for identifying patients with an acute lower respiratory syndrome.

We sought to compare ambulatory care (AC) and emergency department (ED) data for the detection of clusters of lower gastrointestinal illness, using AC and ED data and AC+ED data combined, from two geographically separate health plans participating in the National Bioterrorism Syndromic Surveillance Demonstration Program [1].

To examine data from 911/Emergency Medical Services (EMS) and determine whether these data provide a useful addition to syndromic surveillance (SS) when used with emergency department (ED) chief complaint (CC) data.

Syndromic surveillance for early warning in military context needs a robust, scalable, flexible, ubiquitous, and interoperable surveillance system. A pilot project fulfilling these aims has been conceived as a collaboration of specialized web-services.

This paper describes the issues associated with the creation of a statewide emergency department syndromic surveillance system, part of the South Carolina Aberration Alerting Network (SCAAN), in a predominately rural state.

Effective anomaly detection depends on the timely, asynchronous generation of anomalies from multiple data streams using multiple algorithms. Our objective is to describe the use of a case manager tool for combining anomalies into cases, and for collaborative investigation and disposition of cases, including data visualization.

This paper develops a new Bayesian method for cluster detection, the ìBayesian spatial scan statistic,î and compares this method to the standard (frequen-tist) scan statistic approach on the task of prospective disease surveillance.

This paper describes a Bayesian network model for estimating and predicting an epicurve. Furthermore, it presents results of an experiment testing the accuracy of the predictions.

This paper describes a Bayesian algorithm for diagnosing the CDC Category A diseases, namely, anthrax, smallpox, tularemia, botulism and hemorrhagic fever, using emergency department chief complaints. The algorithm was evaluated on real data and on semi-synthetic data, and this paper summarizes the results of that evaluation.

This paper describes the value of a distributed approach to population health efforts that span clinical research, quality measurement and public health. The goal of the paper is to challenge the traditional paradigm which relies on centralized data repositories with more distributed models where data collection and analysis remains as close to local data sources as possible. We will propose that a distributed approach is desirable because it allows for information to reside more closely with those who can act upon it and it can overcome existing barriers by allowing information to be shared more rapidly and effectively while minimizing privacy risks.