This Technology is Successfully Predicting Foodborne Illness Outbreaks in Chicago (and Maybe Your Town Soon)
Sitting down for a meal in a restaurant is always a gamble; coming away with a potentially life-threatening foodborne illness is, in today’s world, a legitimate risk. But in Chicago, that risk just got a little less threatening thanks to some tech-minded folks.
Chicago’s Department of Innovation and Technology created an alogrithm designed to help food safety inspectors better predict which of the city’s more than 16,000 restaurants posed the greatest risk of violating health codes, putting the general public at risk.
“The program generated a ranked list of which establishments the inspectors should look at first,” reports CityLab. “The project is notable not just because it worked—the algorithm identified violations significantly earlier than business as usual did—but because the team made it as easy as possible for other cities to replicate the approach.”
Prior to using the algorithm, Chicago operated like most every other city in the U.S., going restaurant by restaurant for safety inspection visits—with only three-dozen inspectors serving the whole city. But the inspectors didn’t necessarily visit the establishments with prior safety violations or other risk factors first.
“And speed matters in this case,” reports CityLab. “Every day that unsanitary vendors serve food is a new chance for diners to get violently ill, paying in time, pain, and medical expenses.”
The algorithm looks at public data available on the restaurants and specifically what type of prior violations they incurred. It ranks the restaurants so the inspectors can look at the riskiest ones first.
And, it worked. “The project is notable not just because it worked—the algorithm identified violations significantly earlier than business as usual did,” explains CityLab, “but because the team made it as easy as possible for other cities to replicate the approach.”
And while the system isn’t perfect, or a guarantee of safety, it does work. Chicago officials found nine violations after using the algorithm, which helped inspectors find violations more than a week faster than without using it.
“That trial gave us enough confidence that we were able to roll it out to drive day-to-day decisions,” Chicago’s chief data officer Tom Schenk told CityLab.
But to date, only one other city has taken up using the code–Washington D.C.
Of course, restaurant violations aren’t the only factor in foodborne illness outbreaks; often it’s the ingredients coming into kitchens that are contaminated with e. coli or salmonella, for example. And that contamination can start at the farm or slaughterhouse.
In the case of Chipotle’s recent outbreaks, there could have been contaminations at a central location, not necessarily individual restaurants. The same was true for Fig & Olive, which traced a salmonella outbreak to its central kitchen in Long Island, New York. And while central kitchens are also inspected, violations there would not necessarily mark one of the restaurant locations as high risk.
So, while predictive technology isn’t perfect, it is only going to get better and more accurate. And we can hope, so will protecting against foodborne illnesses where they often start, in the plants we grow and animals we raise for food.
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restaurant image via Shutterstock
