If you take a moment to read an article that he recently wrote for the Harvard Business Review, you’ll see that the Chicago Department of Public Health has been engaging in some fascinating “disruptive innovation” of its own. Using predictive analytics in a process Dr. Choucair calls “leveraging data and history to make smarter, more targeted decisions,” the department has built predictive models that anticipate public health risks. One model determines which homes are most at risk for lead poisoning, and another successfully identifies restaurants most likely to be in violation of food safety codes. By using this information to inform their work, the Chicago Department of Public Health can better target its inspections and resources.

So what is this innovation disrupting, exactly? For decades, public health has practiced surveillance based on methods that rely on a retrospective view. But a sea change could be on the horizon for the public health agency of the future: rather than waiting for problems to emerge, epidemiologists may turn more and more to predictive tools. By putting data to work, health departments and other public health stakeholders can potentially root out public health threats before they even exist.

Here at PHII, we’re currently working on a project that could lay some of the groundwork for this shift. i³Labs is a project funded through a cooperative agreement from the CDC—the three “i”s stand for informatics, innovation and implementation. For this project, we’re identifying health departments engaging in innovative practices (much like the predictive analytics coming out of Chicago) and partnering with them as test sites to identify and document best practices.

The future of public health is being shaped by rapid changes in the health care field and how data are being used there; by scouting out innovation, we hope to help build new capabilities for the public health agency of the future. The Chicago Health Department’s “disruptive innovation” is just one example of the way public health practice may change as a result of innovations in data use, methods and technology.