“There is plotting or mapping software that can help a lay person see at a glance, conceptually, what is going on in their area and whether they may be fitting into a demographic or may be an isolated case,” said Bill Stephens, manager of the Southwest Center for Advanced Public Health Practice in Tarrant County. “They can ask themselves, using this data, ‘Is my situation fitting into this category or should I be going to the doctor because I'm the only one who seems to have this problem?’ People can actually look at, and make use of, syndromic surveillance data in making their own health decisions.”

The new BioSense architecture, and the upcoming wellspring of data spawned by meaningful use requirements, is meant for far more than letting prudent laypeople gauge whether their sniffle might be a cold or something more sinister. David Buckeridge, associate professor of epidemiology, biostatistics and occupational health at McGill University in Montreal and a member of the BioSense redesign's expert advisory panel, said the new approach signals a diametric change from the origins of mandated biosurveillance in that the new user-centered development process and the federated IT concept may help public health officials who have been hesitant to share data change their minds.

“Historically, the way syndromic surveillance as an example of automated surveillance came into being in public health has left a bad taste in the mouth of a lot of people in public health,” Buckeridge said, “because it came mainly out of bio-terror funding. And it was sort of dedicated funding when health departments felt they had other more pressing issues they couldn't fund – ‘For crying out loud, I can't fund my preventive child health program, but you’re telling me to do bio-terror surveillance’ – so they think of syndromic surveillance as bio-terror surveillance.”

The paucity of surveillance data related to bio-terror attacks has made justifying the emphasis on that vector quite difficult, if not controversial. The 2001 anthrax attacks, which directly resulted in the deaths of five people and sickened 22 to 68 others (that figure is in dispute) are an example. Such a small cohort falls well outside an area of great potential for a richer environment of surveillance data: The employment of statistical methodologies to try to predict a disease’s virulence and travel vectors.

“There’s no reason to expect a statistical algorithm has any chance of catching that,” said Naval Postgraduate School researcher Ron Fricker, co-author of the EARS evaluation paper on which Hanni worked and the author of “Some Methodological Issues in Biosurveillance,” published earlier this year in Statistics in Medicine.

The EARS paper, Fricker added, could serve as an example of the potential of local health departments sharing data with academic researchers to improve reporting tools' performance for a specific disease, no matter where it may emerge.