From clean water supplies to the polio vaccine, the most effective public health interventions are typically preventive interventions and policies that help stop a crisis before it starts. However, predicting the next public health crisis has historically been a challenge. Thanks to new technologies, we innovate age-old public health workflows and methods of analysis. We have moved from one time programmatic interventions to sustainable policy, system, and environmental level innovations that have had scalable and meaningful impact.
First unveiled in August 2011 by Mayor Emanuel and the Chicago Department of Public Health (CDPH), Healthy Chicago is an ambitious public health agenda that serves as blueprint to changing individual behavior and most importantly changing the behavior of a city. Predictive analytics is one way we are achieving progress across several of our public health priorities while improving public health infrastructure.
So, what is predictive analytics? It has been used for years by companies like Amazon and in other retail industries. Predictive analytics is a novel approach to employ an array of public health data to predict who is most susceptible to preventable illness or when an outbreak might occur. With this information, data then becomes actionable. Public health officials can respond before the issue manifests itself – providing the right prenatal treatments to mitigate birth complications, providing diet recommendations to overcome a chronic disease or distributing vaccines early to ensure an outbreak is contained. This is transforming how government operates, how resources are allocated and improving public’s health.
Recently, CDPH partnered with development and coding groups to identify various data related to food establishments and their locations – building environments and building code violations, sourcing of food, registered complaints, lighting in the alley behind the food establishment, near-by construction, social media reports, sanitation code violations, neighborhood population density, complaint histories of other establishments with the same owner and more.
Our food inspectors will use these data to determine establishments most at risk for health code violations, evaluating several hundred establishments to test the model. Based on their results and additional stakeholder input, we will evaluate the system and make adjustments as needed. Once it is proven successful, we will expand the program to help identify more health code violations more quickly, helping to make our food supply even safer and prevent more cases of food poisoning before they occur.
To be clear, this new system will not replace our current program. We will still inspect every food establishment following our current schedule, ensuring the entire food supply remains safe and healthy for our residents and tourists. But predictive analytics allows us to better concentrate our efforts on those establishments more likely to have challenges. This system is helping us work more closely with restaurateurs so they can improve their business and decrease complaints. In short, businesses and their customers will both be happier and healthier.
Furthermore, our predictive analytics pilot builds on innovative work of the agency in food protection. CDPH and its partners launched a new application using Twitter last year, www.foodbornechicago.org, where the agency monitors public tweets from Chicago that mention food poisoning. Whenever a resident tweets about a case of food poisoning in the City, CDPH responds and invites the individual to provide additional information so our inspectors can follow up. And it’s working.
Public institutions and the health system will increasingly employ technologies like those above to help advance their efforts to protect the health of their residents. Furthermore, the emergence of Big Data allows pattern recognition from diverse data sources and types leading to relevant potential action. For the Chicago Department of Public Health, big data is not the future, it is already here.