arXiv subjects

Eunchan Daniel Cha

Publications and source records attributed to Eunchan Daniel Cha.

4 recordsLinked to original sources

arXiv2026

Modeling ROI in chronic disease management: a simulation-based framework integrating patient adherence and policy timing

Background: Chronic diseases impose a sustained burden on healthcare systems through progressive deterioration and long-term costs. Although adherence-enhancing interventions are widely promoted, their return on investment (ROI) remains uncertain, particularly under heterogeneous patient behavior and socioeconomic variation. Methods: We developed a simulation-based framework integrating disease progression, time-varying adherence, and policy timing. Cumulative healthcare costs were modeled over a 10-year horizon using continuous-time stochastic formulations calibrated with Medical Expenditure Panel Survey (MEPS) data stratified by income. ROI was estimated across adherence gains (delta) and policy costs (gamma). Results: Early and adaptive interventions yielded the highest ROI by sustaining adherence and slowing progression. ROI exceeded 20 percent when delta >= 0.20 and gamma <= 1.5, whereas low-impact or high-cost policies failed to break even. Subgroup analyses showed a 32 percent ROI gap between the lowest and highest income strata, with projected savings of 312 USD per patient versus baseline. Sensitivity tests confirmed robustness under stochastic adherence and inflation variability. Conclusions: The framework provides a transparent and adaptable tool for evaluating cost-effective adherence strategies. By linking behavioral effectiveness with fiscal feasibility, it supports the design of robust and equitable chronic disease policies. Reported ROI values represent conservative lower bounds, and extensions incorporating DALYs and QALYs illustrate scalability toward full health outcome integration.

q-fin.GN↗

arXiv2026

Mechanism design and equilibrium analysis of smart contract-mediated resource allocation

Decentralized coordination and digital contracting are becoming essential in complex industrial systems, yet existing approaches often rely on ad-hoc heuristics or purely technical blockchain implementations without a rigorous economic foundation. This study developed a mechanism-design framework for smart contract-mediated resource allocation that jointly embeds efficiency, fairness, and resilience in decentralized coordination. We modeled agent interactions as a contract-clearing game under shared capacity constraints, established the existence and uniqueness of equilibrium, and proposed a decentralized price-adjustment algorithm with provable convergence suitable for real-time operation. Performance was evaluated through extensive synthetic simulations and validated using a representative real-world dataset. In addition to controlled experiments, a long-horizon empirical analysis using financial and macroeconomic data from 2006 to 2025 examined the mechanism under major economic regimes and shock conditions. Results showed that the proposed mechanism consistently reduces inequality and cost while maintaining near-optimal efficiency and rapid recovery following shocks, demonstrating dynamic stability beyond steady state.

cs.GT↗

arXiv2025

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

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.

q-fin.MF↗

arXiv2025

FOSSIL: Regret-Minimizing Curriculum Learning for Metadata-Free and Low-Data Mpox Diagnosis

Deep learning in small and imbalanced biomedical datasets remains fundamentally constrained by unstable optimization and poor generalization. We present the first biomedical implementation of FOSSIL (Flexible Optimization via Sample-Sensitive Importance Learning), a regret-minimizing weighting framework that adaptively balances training emphasis according to sample difficulty. Using softmax-based uncertainty as a continuous measure of difficulty, we construct a four-stage curriculum (Easy-Very Hard) and integrate FOSSIL into both convolutional and transformer-based architectures for Mpox skin lesion diagnosis. Across all settings, FOSSIL substantially improves discrimination (AUC = 0.9573), calibration (ECE = 0.053), and robustness under real-world perturbations, outperforming conventional baselines without metadata, manual curation, or synthetic augmentation. The results position FOSSIL as a generalizable, data-efficient, and interpretable framework for difficulty-aware learning in medical imaging under data scarcity.

cs.LG↗