Paper: arXiv 2510.22518

Authors: Jinho Cha, Justin Yu, Junyeol Ryu, Eunchan Daniel Cha, Hyeyoung Hwang

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.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 6.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents an innovative application of machine learning (the FOSSIL framework) and inverse optimization to health economics and incentive design. It combines formal utility maximization and inverse estimation equations with empirical calibration on OECD-WHO panel data.

Research Flowchart

  flowchart TD
    A["Research Goal: Quantify System Impact (SII) of Adaptive QALY-Based Incentives"] --> B["Data Inputs: OECD-WHO Panel Data & Policy Environments"]
    B --> C["Methodology: FOSSIL Inverse Behavioral Optimization"]
    C --> D["Computational Engine: Regret-Minimizing Learning & Latent Parameter Recovery"]
    D --> E["Key Finding 1: Modern Health Systems Operate Near Efficiency-Saturated Frontier"]
    D --> F["Key Finding 2: Incremental Fairness Adjustments Yield Stabilizing, Diminishing Returns"]

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and 49 more
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0 of 57 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.