AI-Driven Cross-Benefit Analytics: Optimizing Health and Pharmacy Plan Decisions for Value-Based Insurance Design
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I4P127Keywords:
AI-Driven Cross-Benefit Analytics, Value-Based Insurance Design (VBID), Reinforcement Learning In Healthcare, Medical–Pharmacy Benefit Integration, Predictive Health Insurance Analytics, Explainable Artificial Intelligence (XAI), Policy Simulation SystemsAbstract
In the evolving landscape of value-based care, health insurers face growing pressure to align benefit designs with both clinical outcomes and cost-effectiveness. Traditional benefit structures often silo medical and pharmacy data, limiting the ability to holistically evaluate a member’s health journey. This paper proposes an AI-driven Cross-Benefit Analytics (CBA) framework that integrates medical claims, pharmacy data, and social determinants of health (SDOH) to support intelligent, data-driven decisions in insurance plan optimization. Using supervised machine learning and reinforcement learning models, we develop predictive engines capable of forecasting longitudinal health outcomes and downstream costs associated with various formulary and plan design configurations. This platform simulates the trade-offs of high-cost pharmaceutical interventions versus potential reductions in emergency visits or inpatient stays. We further demonstrate how the system can recommend optimal plan structures such as high-deductible versus managed care plans tailored to individual member risk profiles. Results from a large-scale commercial dataset illustrate ~18% potential reduction in total cost of care through AI-optimized benefit adjustments. The framework supports dynamic, transparent, and equitable value-based insurance design (VBID), providing actionable insights for payers, providers, and policy makers. Ethical considerations around model fairness, explainability, and regulatory compliance are also discussed to promote responsible AI integration in health insurance systems
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