Artificial Intelligence and Counterfactually Actionable Responses to End HIV (AI-CARE-HIV)

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ABSTRACT Florida has the highest incidence of Human Immunodeficiency Virus (HIV) infections in the US, with marked social and racial disparities. About 40% of people with HIV in Florida do not reach undetectable viral load, and Black African Americans are the most affected. Besides well-known sociodemographic factors contributing to unfavorable outcomes and disparities, part of such remains unexplained and cannot be actioned upon. Advances in artificial intelligence (AI) and increasing availability of large real-world data (RWD) databases, e.g., electronic health records (EHRs) and administrative claims data, are ideal for developing models for precision health. However, the full capabilities of AI are still hampered by the fact that EHRs are not well integrated with other relevant data sources, containing information on social and behavioral determinants of health (SDoH), especially important for HIV care access and outcomes. Further, a strong determinant of HIV outcomes is stigma, which is not captured in structured fields of EHRs, but can be identified in clinical notes via natural language processing. In fact, many other contextual- and individual-level SDoH can be extracted from clinical narratives in EHRs. Another critical problem with AI built on RWD is that, due to inherent bias in observational data like EHRs, the AI models might identify wrong effects for interventions. Thus, alternative predictions (i.e., counterfactuals) of naïve AI systems might be mistaken, potentially leading to harm. Causal inference methods are being increasingly coupled with AI to address such bias. The overarching goal of this project is to develop “AI-CARE- HIV,” an actionable counterfactual RWD AI framework to improve HIV outcomes in Florida, in particular reducing disparity through addressing SDoH. We hypothesize that a portion of the unexplained systemic disparity can be elucidated by combining causal inference and AI models that exploit complex interactions between individual- and contextual-level SDoH. This framework can then be used to develop an unbiased (under certain assumptions), actionable model usable for planning and implementing clinical and public health interventions. We will develop the project through the OneFlorida+ Clinical Research Consortium, which collates RWD data from >16.8M Floridians, and specifically the OneFlorida+ HIV cohort (now N=71,363). Our project aims to: (1) Enhance the cohort by incorporating large-scale SDoH (9,000+) and prospectively validate new SDoH, including stigma, using NLP; (2) Create polysocial risk scores from SDoH, identify population-level causal effects of SDOH-conditioned interventions on to HIV outcomes, and develop individualized counterfactual AI models for HIV outcomes, calibrated to reduce disparity; (3) Plan –with healthcare providers, State officials, citizen scientists– targeted clinical and public health interventions anchored on our counterfactual AI models, using implementation science, standardized protocols (e.g., CONSORT-AI). Our team includes multidisciplinary (methodological, clinical, qualitative) expertise supported by OneFlorida+, Fl Dept of Health, and minority- serving entities. We expect impact at multiple levels, from infrastructure enhancement to public health benefit.