Biosurveillance systems typically receive free- text chief complaint and coded diagnosis data, however this data has limited specificity for notifiable disease surveillance. The Biosense System receives chief complaint and/or diagnosis data from over 360 hospitals and laboratory results from 24 hospitals in 7 states using the Public Health Information Network Messaging System (PHINMS) and HL7 standards. BioSense also receives final diagnosis from Veterans’ Affairs and Department of Defense outpatient clinics, but these clinics do not currently report laboratory findings.

To recognize outbreaks so that early interventions can be applied, BioSense uses a modification of the EARS C2 method, stratifying days used to calculate the expected value by weekend vs weekday, and including a rate-based method that accounts for total visits. These modifications produce lower residuals (observed minus expected counts), but their effect on sensitivity has not been studied.

Objective

To evaluate several variations of a commonlyused control chart method for detecting injected signals in 2 BioSense System datasets.

The purposes of this study are to validate a keyword-based text parsing algorithm for identifying fractures and compare radiology results with chief complaint and ICD-9 final diagnoses.

Analysis of time series data requires accurate calculation of a predicted value. Non-regression methods such as the Early Aberration Reporting System CuSum are computationally simple, but most do not adjust for day of week or holiday. Alternately, regression methods require larger counts, more computer resources, and possibly longer baseline periods of data. As increasing volumes of data are reported and analyzed, the predictive accuracy of simpler methods should be assessed and optimized.

Objective