Predictive Analytics for Retention in Care in an Urban HIV Clinic

Consistent medical care among people living with HIV is essential for both individual and public health. HIV-positive individuals who are ‘retained in care’ are more likely to be prescribed antiretroviral medication and achieve HIV viral suppression, effectively eliminating the risk of transmitting HIV to others. However, in the United States, less than half of HIV-positive individuals are retained in care. Interventions to improve retention in care are resource intensive, and there is currently no systematic way to identify patients at risk for falling out of care who would benefit from these interventions. This machine learning model was developed to identify patients at risk for dropping out of care in an urban HIV care clinic using electronic medical records and geospatial data.