EHR-Based Prediction Models to Improve PrEP Use in Community Health Centers

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PROJECT SUMMARY Rates of new HIV infections are disproportionately high, and uptake of preexposure prophylaxis (PrEP) low, in Black, Latino, and underinsured individuals in the U.S. Healthcare providers at safety net community health centers (CHCs) provide care to racially diverse populations with high rates of underinsurance. However, providers cite barriers to PrEP prescribing, including lack of tools to identify candidates for PrEP. Without practical tools to help providers identify patients at risk for HIV infection and prescribe PrEP when appropriate, the population-level benefits of PrEP are unlikely to be realized. Electronic clinical decision support using data embedded in patients’ electronic health records (EHRs) has the potential to fulfill this need. EHRs contain rich data that can help identify patients at high risk of HIV acquisition, including demographics, diagnoses, testing patterns, prescriptions, and social determinants of health. In our prior work, we developed and validated prediction models using EHR data from two large healthcare systems in Massachusetts and California, with patient populations of 1.1 and 4.3 million, to identify patients at high risk for incident HIV. These machine learning models had strong predictive performance, with C-statistics up to 0.91. The objective of this proposal is to test the hypothesis that a clinical decision support tool that incorporates an HIV risk prediction model can help providers identify patients at high risk for HIV infection and improve PrEP prescribing. Our study setting is a national network of CHCs with 2.8 million patients (OCHIN). We will first tailor our HIV prediction models to this clinic network, and then conduct formative work with providers to inform our development of alerts and additional PrEP decision support tools that will be effective and welcomed. The study team includes experts in HIV, PrEP implementation, predictive analytics, and healthcare delivery in CHCs. Our specific aims are to 1) optimize prediction models that use EHR data to identify potential PrEP candidates in racially, socioeconomically, and geographically diverse patient populations; 2) explore providers’ perspectives on barriers to PrEP prescribing, and their preferences for PrEP decision support, to inform development of an EHR-based decision support tool for CHCs; and 3) conduct a pilot trial to assess the feasibility, acceptability, and preliminary impact of an EHR-based clinical decision support intervention on PrEP-related care in CHCs. We will assess impact on metrics across the PrEP care continuum, including prescriptions, persistence, clinical monitoring, and tests and diagnoses of HIV and other sexually transmitted infections. This proposal is innovative in its use of predictive analytics and clinical decision support to optimize PrEP. The project is significant because our intervention will be scalable across CHCs nationally and to other healthcare systems with EHRs, and because it addresses the federal initiative to end the HIV epidemic by scaling up PrEP in high- incidence settings. The expected outcome is the foundation for a cluster randomized trial to test whether EHR- based decision support for PrEP can prevent new HIV infections in a national network of CHCs.