PrEP uptake remains suboptimal, particularly among sub-populations at increased risk of acquiring HIV infection (Ministry of Health, 2020).

HIV prevention services in Uganda, including PrEP, have remained largely donor-funded. With changing trends in healthcare financing, there is a need for more sustainable healthcare financing models.

Understanding the nuances in PrEP service delivery, especially targeting unserved high-risk sub-populations through innovative approaches like cost-sharing and privately purchased methods, is worth exploring.

We aim to use machine learning and AI models on both public and private datasets to build models that predict the ability to pay for HIV prevention services, particularly PrEP.

• Develop and deploy an artificial intelligence (AI) modeling technique for PrEP services market segmentation.

• Use machine learning and artificial intelligence modeling to analyze and profile sub-populations able to pay for PrEP in Kampala.

• Identify the most likely favorable pricing for PrEP.

• Validate the variables identified as predictors for PrEP payment from dataset analyses through focus group discussions.

• Test the validity and reciprocity of the developed AI model with similar datasets from other settings.