Inverse Behavioral Optimization of QALY-Based Incentive Systems Quantifying the System Impact of Adaptive Health Programs

2025-10-26

AI Summary

This study examines how QALY-oriented health insurance policies balance efficiency, equity, and sustainability. Method: We propose a reverse behavioral optimization framework integrating QALY-based health outcomes, ROI-driven incentives, and adaptive learning; introduce the System Impact Index (SII) to quantify macro-level policy effects; and apply behavioral elasticity analysis within the FOSSIL paradigm—using sample-sensitive importance weighting and regret-minimizing estimation—validated via OECD-WHO panel data simulations and sensitivity analyses. Contribution/Results: We demonstrate that the healthcare system operates near an efficiency saturation frontier, where minor behavioral parameter adjustments trigger nonlinear shifts in resilience, equity, and ROI. Empirically, equity optimization exhibits diminishing returns under saturation but substantially enhances systemic stability.

Technology Category

• Search and Optimization: Learning to Search
• Game Theory and Economic Paradigms: Behavioral Game Theory
• Constraint Satisfaction and Optimization: Satisfiability

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

This study introduces an inverse behavioral optimization framework that integrates QALY-based health outcomes, ROI-driven incentives, and adaptive behavioral learning to quantify how policy design shapes national healthcare performance. Building on the FOSSIL (Flexible Optimization via Sample-Sensitive Importance Learning) paradigm, the model embeds a regret-minimizing behavioral weighting mechanism that enables dynamic learning from heterogeneous policy environments. It recovers latent behavioral sensitivities (efficiency, fairness, and temporal responsiveness T) from observed QALY-ROI trade-offs, providing an analytical bridge between individual incentive responses and aggregate system productivity. We formalize this mapping through the proposed System Impact Index (SII), which links behavioral elasticity to measurable macro-level efficiency and equity outcomes. Using OECD-WHO panel data, the framework empirically demonstrates that modern health systems operate near an efficiency-saturated frontier, where incremental fairness adjustments yield stabilizing but diminishing returns. Simulation and sensitivity analyses further show how small changes in behavioral parameters propagate into measurable shifts in systemic resilience, equity, and ROI efficiency. The results establish a quantitative foundation for designing adaptive, data-driven health incentive programs that dynamically balance efficiency, fairness, and long-run sustainability in national healthcare systems.

Problem

• Quantify how policy design affects national healthcare performance
• Recover latent behavioral sensitivities from QALY-ROI trade-offs
• Link behavioral elasticity to system efficiency and equity outcomes

Innovation

• Inverse optimization integrates QALY outcomes and ROI incentives
• Model embeds regret-minimizing behavioral weighting mechanism
• Framework links behavioral elasticity to system efficiency outcomes