AI-Optimized Formulary Designs Addressing Social Determinants of Health
Keywords:
Formulary design; social determinants of health; AI; pharmacoequity; health equity; predictive modelling; benefit designAbstract
Formulary design the way payers, PBMs, and health systems choose which drugs to cover and the terms under which they cover them has recently been recognized as a contributor to both pharmacoequity and health equity. While the social determinants of health (SDoHs) like socioeconomic status, neighbourhood deprivation, housing, transportation, and access barriers all play a major role in health outcomes as well as access to and use of medications. This paper presents a conceptual and methodological framework to meet the challenges of integrating artificial intelligence (AI) with formulary design in consideration of the social determinants of health, to improve formulary design in order to reduce inequities in coverage policies, access pathways, and benefit designs. And summarize the literature on social determinants of health and formulary policy, providing a checklist of major AI methods machine learning, predictive analytics, optimization, and decision support before presenting a multi-phase framework: (1) data aggregation and SDoH construction, (2) predictive analytics of medication access/outcome by SDoH strata, (3) formulary optimization with equity constraints, and (4) execution and evaluation. also provide examples of hypothetical cases (ex. diabetes medications, newly introduced biologics) and identify some operational, ethical, legal, and methodological challenges. And provide suggestions for future work and discuss what changes to policy and practice would stem from it.
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