Insights from increasingly rich and varied sources of data are informing highly targeted population health interventions for disease prevention. Yet there are complex challenges to overcome before the potential of data and technology to improve population health management can be fulfilled. This session will explore the use of predictive analytics and big data to manage population health initiatives and the challenges around interoperability, data analytics and technology deployment. Experts will discuss the topic from the perspective of the triple aim of reducing cost, improving quality, and improving overall population health with a particular focus on predicting and managing communicable diseases. We will also examine the ability of AI to supplement traditional disease surveillance by using novel data sources to extract meaningful public health information from unstructured data sources. Implemented examples will be presented of data driven population health management from the EU.

Learning objectives:

• Understand the use of data analytics to predict and manage new diseases, with a focus on communicable disease 
• Discuss AI’s ability to supplement traditional disease surveillance by extracting meaningful public health information from unstructured data sources.
• Learn about implemented examples of data driven population health management from across the EU.