Implementing an Automated Prediction Model to Improve Prescribing of HIV Preexposure Prophylaxis

Abstract

Summary Antiretroviral preexposure prophylaxis (PrEP) is nearly 100% effective at decreasing HIV acquisition but is underused in priority populations. Primary care clinicians need tools to help them identify persons likely to benefit from PrEP use and prescribe it when appropriate. The researchers developed and validated an automated decision support tool with interactive alerts in the electronic health record to increase PrEP discussions and prescribing in primary care. They piloted the tool at three federally qualified health centers and assessed feasibility, acceptance by clinicians, and preliminary impact on PrEP care. Of 33,803 patients who visited the pilot clinics from July 2022 through January 2023, providers received PrEP alerts at the point of care for 2.2% of patients, demonstrating feasibility. Although numbers of PrEP prescriptions remained low, the proportion of all patients with new PrEP prescriptions was 4.5 times higher at pilot clinics compared with matched control clinics (0.09% vs. 0.02%). Implementation of the decision support tool was associated with a statistically nonsignificant 5.5% increase in HIV tests per 100 patients. In qualitative interviews, providers said the tool facilitated PrEP discussions with patients, particularly for those patients who would not have initiated discussions because of stigma. The researchers found that acceptance, use, and impact of machine-learning models for PrEP depends on collaborating with and building trust among providers, including blending a data-driven approach to identifying patients at increased risk for HIV acquisition with providers’ traditional decision-making framework. These approaches could be useful for health care organizations seeking to implement automated prediction models across all areas of medicine.