This research will assess the validity of patient-level computable social factor phenotypes used to predict a patient’s risk of increased healthcare utilization and costs.
This research will compare the use of predictive modeling versus traditional questionnaires to identify those with unmet social needs, use the superior method to inform the development of a clinical decision support tool, and evaluate the tool’s impact on referrals to social providers.
This research demonstrated primary care providers’ complementary use of “push” and “pull” health information exchange technologies to meet their information needs and provides evidence that “pull” exchange reduces potentially avoidable healthcare utilization.
This project examined the concept of geographical area that is fundamental to health information exchange activity, and explored the practical and policy implications of how exchange service areas are defined and measured.
