Model Identifies Potentially Unsafe Restaurants

Foodborne illnesses are common, costly, and land thousands of Americans in emergency rooms every year. This new technique, developed by Google, can help restaurants and local health departments find problems more quickly, before they become bigger public health problems,” said corresponding author Ashish Jha, K.T. Li Professor of Global Health at Harvard Chan School and director of the Harvard Global Health Institute. The study was published online November 6, 2018 in npj Digital Medicine. Foodborne illnesses are a persistent problem in the U.S. and current methods by restaurants and local health departments for determining an outbreak rely primarily on consumer complaints or routine inspections. These methods can be slow and cumbersome, often resulting in delayed responses and further spread of disease. To counter these shortcomings, Google researchers developed a machine-learned model and worked with Harvard to test it in Chicago and Las Vegas. The model works by first classifying search queries that can indicate foodborne illness, such as “stomach cramps” or “diarrhea.” The model then uses de-identified and aggregated location history data from the smartphones of people who have opted to save it, to determine which restaurants people searching those terms had recently visited. Health departments in each city were then given a list of restaurants that were identified by the model as being potential sources of foodborne illness. The city would then dispatch health inspectors to these restaurants, though the health inspectors did not know whether their inspection was prompted by this new model or traditional methods. During the period of the study, health departments continued to follow their usual inspection procedures as well. In Chicago, where the model was deployed between November 2016 and March 2017, the model prompted 71 inspections. The study found that the rate of unsafe restaurants among those detected by the model was 52.1% compared with 39.4% among inspections triggered by a complaint-based system. The researchers noted that Chicago has one of the most advanced monitoring programs in the nation and already employs social media mining techniques, yet this new model proved more precise in identifying restaurants that had food safety violations. In Las Vegas, the model was deployed between May and August 2016. Compared with routine inspections performed by the health department, it had a higher precision rate of identifying unsafe restaurants. When the researchers compared the model with routine inspections by health departments in Las Vegas and Chicago, they found that the overall rate across both cities of unsafe restaurants detected by the model was 52.3%, whereas the overall rate of detection of unsafe restaurants via routine inspections across the two cities was 22.7%. https://www.technologynetworks.com/tn/news/model-identifies-potentially-unsafe-restaurants-311504?utm_campaign=Newsletter_TN_BreakingScienceNews&utm_source=hs_email&utm_medium=email&utm_content=67300627&_hsenc=p2ANqtz-9jY6FcoMlplY2TGESbMFtQ412Pbyr6qTRgg0hJtaWe2ipv3npfUU3mwsqCXTpXJtJwZh9MRkLEnUSAavvhTRwb1BYZaw&_hsmi=67300627