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HEalth Record Optimization for Identifying Candidates for HIV PRe-Exposure Prophylaxis: A Community-Informed Approach

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Summary

Electronic health record (EHR) models can identify more people for pre-exposure prophylaxis (PrEP) than traditional scores. Local input from community advocates and clinicians is crucial for optimizing these models and improving PrEP uptake.

Keywords:
HIV preventioncliniciancommunitycommunity machine learningcommunity-based participatory researchelectronic health recordsmachine learning

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Area of Science:

  • Public Health
  • Infectious Disease Epidemiology
  • Health Informatics

Background:

  • Electronic health record (EHR)-based models show promise in identifying candidates for pre-exposure prophylaxis (PrEP), potentially improving upon traditional risk scores.
  • Local adaptation of these models is essential due to regional variations in HIV (Human Immunodeficiency Virus) epidemiology within the United States.

Purpose of the Study:

  • To inform the development and implementation of EHR-based PrEP identification models in New Orleans and Baton Rouge, Louisiana.
  • To gather insights from community advocates and clinicians on optimizing model performance and implementation strategies for PrEP.

Main Methods:

  • Focus group discussions (FGDs) with 18 community advocates and in-depth interviews (IDIs) with 12 clinicians were conducted from January to March 2023.
  • Thematic analysis was used to analyze audio recordings of FGDs and IDIs.
  • Participants provided perspectives on HIV epidemiology, PrEP uptake, and suggested locally relevant variables for model optimization.

Main Results:

  • Community advocates reported that PrEP marketing lacked inclusivity, hindering perceived impact on local HIV incidence.
  • Clinicians identified a need for improved access to education, PrEP providers, and affordable medication to boost PrEP uptake.
  • Stakeholders suggested specific variables for EHR models, including sexual assault history, pregnancies, hepatitis B, sexually transmitted infection (STI) treatment, incarceration history, and opiate use.

Conclusions:

  • EHR-based models are supported by evidence for identifying PrEP candidates.
  • Incorporating insights from local stakeholders, such as community advocates and clinicians, is vital for enhancing the performance and successful implementation of these models.
  • Addressing community concerns and clinician-identified barriers is key to optimizing PrEP accessibility and uptake.