ABSTRACT With the highest HIV incidence rates observed in the US, Florida strives to develop effective and sustainable HIV prevention strategies. HIV screening and pre-exposure prophylaxis (PrEP) are proven interventions to prevent transmission and reduce new HIV infections. However, uptake of these HIV prevention services is low among women relative to their needs and male counterparts. One of the key barriers to promoting HIV testing and PrEP among women is the challenge of identifying women at risk of HIV acquisition by healthcare providers. Researchers demonstrated potential HIV risk prediction models using electronic health records (EHRs) among men. Unfortunately, they failed to identify HIV risk among women due to having fewer women HIV incident cases in their datasets and a lack of risk factors tailored for women. To fill the gap, we propose to develop an HIV risk prediction model specifically tailored for women and integrate the prediction model into an EHR system as a clinical decision support prototype to assess its feasibility, acceptability, and usability with primary care providers. In Aim 1, we will develop an HIV risk prediction model specific for women to identify potential candidates for HIV testing and PrEP. Leveraging patients’ structured EHRs, ZIP code-linked community-level factors and social determinants of health, and factors extracted from clinical notes via a state- of-the-art natural language processing (NLP) algorithm, we will use AI/machine learning to develop and validate an HIV risk prediction model specifically developed for women (ACTION-HIV algorithm). The results will be used to design a clinical decision support (CDS) prototype to help providers better identify women in need of HIV testing and PrEP. In Aim 2, using a user-centered design approach guided by the five “rights” of the CDS intervention framework, we will conduct 6 focus groups with providers to design and prototype a women-specific HIV risk prediction CDS tool (ACTION-HIV CDS). In Aim 3, using think-aloud protocols and surveys, we will assess the feasibility, acceptability, and usability of the ACTION-HIV algorithm and CDS in a simulated EHR environment with 20 primary care providers at the UF Health outpatient clinics. We will integrate our ACTION-HIV algorithm into UF Health’s EHR system (Epic) to produce the CDS alerts for HIV testing and pilot test ACTION-HIV CDS in a simulated Epic environment presenting synthetic patient data. Our proposed research is highly innovative as it expands past HIV risk prediction models and pioneers in designing and prototyping HIV testing and PrEP-related CDS in primary care settings. This project will provide valuable insights into a future clinical trial which we will investigate the efficacy (including patient outcomes such as rates of HIV testing and rates of PrEP prescription) of a women-specific HIV risk prediction CDS tool within the real-time EHR production in real-world settings.