Predictive Analytics for Retention in HIV Care

A Predictive Model to Identify High-Risk Individuals

Regular medical care is essential for HIV treatment and prevention, yet less than half of people living with HIV in the United States receive that care. Electronic health data can be used to create an automated predictive model to identify individuals at highest risk for falling out of care.

To help gauge feasibility of performing research in this area, CAPriCORN provided data regarding numbers of HIV-positive individuals in the network. Ultimately, the data supported a successful NIH-funded K23 grant proposal to study the use of predictive analytics to improve retention in HIV care. Now, work is underway to identify patient-level data for this project.

Collaborators of Predictive Analytics for Retention in HIV Care

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CAPriCORN provided pilot data in a timely fashion to support my successful NIH-funded K23 career development award.

‐ Jessica Ridgway, MD, Assistant Professor of Medicine, UChicago Medicine