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

TL;DR

This paper develops an inverse optimization framework integrating QALY outcomes and adaptive learning to quantify how health policy design influences system performance, emphasizing the importance of behavioral sensitivities and systemic resilience.

Contribution

It introduces a novel inverse behavioral optimization model that links individual incentives to macro-level healthcare efficiency and equity outcomes, incorporating adaptive learning and behavioral sensitivities.

Findings

Modern health systems operate near an efficiency-saturated frontier.

Small behavioral changes can significantly impact systemic resilience and equity.

The framework effectively recovers latent behavioral sensitivities from observed data.

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.