Modeling ROI in Chronic Disease Management, A Simulation-Based Framework Integrating Patient Adherence and Policy Timing

Paper: arXiv 2510.06379

Authors: Jinho Cha, Eunchan D. Cha, Emily Yoo, Hyoshin Song

Abstract

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.

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 6.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper presents a clear, simulation-based framework applied to healthcare cost economics using differential cost components and empirical calibration via MEPS/NHANES data. While the mathematical formulations are straightforward continuous-time additions rather than complex stochastic calculus, the empirical grounding and clear scenario analysis provide solid practical insights.

Research Flowchart

  flowchart TD
    A["Research Goal:<br>Evaluate Chronic Disease Intervention ROI"] --> B["Data Inputs:<br>MEPS Income-Stratified Data"]
    B --> C["Simulation Modeling:<br>Continuous-Time Stochastic Framework<br><i>(Adherence Delta & Policy Cost Gamma)</i>"]
    C --> D["Cost Analysis:<br>10-Year Cumulative Cost & Sensitivity Testing"]
    D --> E["Key Outcomes:<br>Early Policy ROI > 20%<br><i>(when Delta ≥ 0.20, Gamma ≤ 1.5)</i>"]
    D --> F["Equity Insights:<br>32% ROI Gap Across Income Strata<br><i>($312/patient savings vs baseline)</i>"]

Cited References (50)

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2. Trends in out-of-pocket medical expenditures among US adults with diabetes: 2001–2011 (2017) — link
3. Economic costs of diabetes in the U.S. in 2012 (2013) — link
4. Modeling healthcare policy impacts using agent-based simulation: a case study of chronic disease care coordination (2019) — link
5. Integrating Blockchain and Machine Learning for Chronic Disease Management: A Real-Time Data Sharing Framework (2025) — link
6. Systematic review of the application of the plan–do–study–act method to improve quality in healthcare (2013) — link
7. Preparing for responsible public health decision making: a framework for integrating the economic evaluation of vaccination programs (2009) — link
8. Why behavioral and environmental interventions are needed to improve health at lower cost (2011) — link

0 of 50 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.