In the case of Chipotle's most recent outbreak in Ohio, for instance, FINDER could have sent inspectors to that precise outlet within days rather than weeks. Yet FINDER restaurants were more than three times as likely to be cited for safety violations, especially more critical ones. Inspectors in Las Vegas and Chicago looked at data from both systems blind, meaning they didn't know whether FINDER or a consumer complaint flagged the restaurant. The more customers that visit a restaurant and subsequently search phrases such as " how to relieve stomach pain" or "food poisoning symptoms," the more likely it is that the health department could be on its way. It gets smarter with time, with searches through any Google product. This technology should put restaurants of all shapes and sizes on guard. Researchers note that the sample size remains small, but the results of this initial experiment indicate that restaurants of any risk level could be a culprit. The Google and Harvard researchers call that system inefficient because in nearly 40% of reported illness cases, the second-to-last restaurant caused illness, not the most recent.įINDER scoured Google searches in the two test cities, first for terms related to foodborne illnesses and then for location data related to foodservice establishments. Yelp, for example, has blossomed into a trove of user-generated data on where and when customers feel ill after eating out.Ĭurrently consumers tend to blame the last restaurant they visited, who in turn file complaints for a venue that inspectors check to no avail. Though still in its early stages, FINDER tries to bridge another gap between government oversight and the vast troves of information available online. Google and Harvard are testing artificial intelligence (AI) to battle the growing numbers of foodborne illnesses as city governments - strapped with limited resources - struggle to keep up with health inspections. Advice to avoid such illnesses leans on home food preparation, but when going out, consumers have to trust that restaurants take all necessary precautions. That's where tech can step in. Their goal is to ultimately harness the technology to pinpoint foodborne illness faster and more accurately. "The FINDER approach is more robust than individual customer complaints, as it aggregates information from numerous people who visited the venue," the researchers noted in Nature.Chicago's existing system already mines Twitter for similar user complaints through a platform called nEmesis, but this tool outperformed it by 68%. Engadget reported that more than half of all restaurants inspected by FINDER were deemed unsafe, compared to about 23% of those visited after traditional complaints. The team tested the tool with the health departments of Las Vegas and Chicago, which used the data to initiate inspections.FINDER, short for Foodborne Illness Detector in Real-time, aggregates queries indicative of food poisoning and connects them to a user's recent search for restaurants, according to results published in the journal Nature. Google and Harvard have developed a machine-learning tool to identify "potentially unsafe" restaurants through anonymous user search data.
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