PyData Chicago

Abstract: An effective method for improving health outcomes for high-risk patients in under-resourced communities is the implementation of a Community Health Worker (CHW) program. There is an urgent need to integrate emerging technologies into these programs to help CHWs better link patients with health and social service providers in the community, and better support health systems to address the social determinants of health. SCHOLAR (Smart, Connected, and Healthy Outcomes from Leveraging Assets and Research) will leverage the voices of CHWs to build the health system’s ability to use data to predict patient needs and provide better care. This approach uses data to build an information pipeline between community members and the health systems that serve them, strengthening the smart connection between them. As a result of this work, hospitals will be equipped with the right data to assign their time, talent, and resources to improve health outcomes. In the long run, this innovative sociotechnical approach will lead to more sustainable health systems in under-resourced communities by reducing Emergency Department (ED) readmission rates, which are a key metric for federal funding programs. The SCHOLAR project aims to design, develop, and evaluate an analytics dashboard that will empower CHWs to interact with and interpret data and lead to: 1) improved computational prediction modeling for Emergency Department readmission rates; 2) improved health system risk stratification and resource allocation; and, 3) strengthening of the smart connection between the health system and the communities it serves.

Audience Takeaway: We can use AI and keep a human touch!