HEalth Record Optimization for Identifying Candidates for HIV PRe-Exposure Prophylaxis: A Community-Informed Approach to Model Development.
Journal:
AIDS patient care and STDs
Published Date:
Jun 27, 2025
Abstract
Electronic health record (EHR)-based models to identify individuals who may benefit from pre-exposure prophylaxis (PrEP) outperform traditional risk scores and may alleviate challenges associated with PrEP initiation. Pre-implementation work is critical to ensure algorithms are optimized for the local context, particularly given regional differences in the US HIV epidemic. To inform the derivation and implementation of EHR-based models within health systems in New Orleans and Baton Rouge, Louisiana, we conducted focus group discussions (FGDs) with community advocates and in-depth interviews (IDIs) with emergency department, primary care, and HIV-trained clinicians. We asked about their perspectives on HIV epidemiology and PrEP uptake and sought suggestions for locally relevant variables to optimize model performance. FGDs and IDIs were audio-recorded and analyzed using thematic analysis. From January to March 2023, FGDs were conducted with 18 community advocates and IDIs with 12 clinicians. Community advocates did not believe that PrEP had reduced local HIV incidence, primarily due to a lack of inclusive marketing. Clinicians noted that improving PrEP uptake would require better access to education, PrEP providers, and affordable medication. Community advocates suggested adding sexual assault history and number of pregnancies to the model; clinicians suggested adding hepatitis B, more sexually transmitted infection treatment modalities, incarceration history, and opiate use. To optimize model implementation, community advocates emphasized the need to convey model output respectfully and compassionately, and clinicians suggested involving ancillary staff in PrEP discussions. Although evidence supports the use of EHR-based models to identify PrEP candidates, local stakeholders can provide unique insight into optimizing model performance and implementation.
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