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Objective <p>To develop and validate a multi-objective machine learning framework for welfare-optimized health insurance design in infectious disease management, explicitly modeling trade-offs among patient financial protection, provider efficiency, and insurer sustainability through empirically-derived stakeholder utilities and social welfare functions.</p> Setting <p>The national health insurance system in Iran, utilizing a total of 378,403 claims of gastroenteritis hospitalizations across public, private, and charitable hospitals in 2023.</p> Design <p>Observational study employing 5 supervised machine learning models to predict stakeholder-specific utilities—patient (health outcomes, out-of-pocket burden, affordability), provider (length-of-stay efficiency, performance quality), and insurer (coverage adequacy, subsidy management, cost containment). Utilities were aggregated using six social welfare functions (Utilitarian, Nash, Atkinson, Rawlsian, Geometric Mean, Convex Combination) with empirically-derived stakeholder weights. Pareto frontier analysis identified welfare-dominant policy configurations across insurance arrangements.</p> Results <p>Only 2.46% of observed policies achieved Pareto efficiency, indicating substantial allocative inefficiency. “Win-Win-Win” configurations (16.9% of efficient policies) dominated 54.1% of all alternatives, demonstrating simultaneous welfare gains across stakeholders without requiring zero-sum trade-offs. The equity-fiscal sustainability correlation (<i>r</i>=-0.509) was nearly twice the magnitude of the equity-efficiency correlation (<i>r</i>=-0.267), identifying fiscal capacity—not operational inefficiency—as the binding constraint on patient-centered insurance design. Nash and Geometric Mean social welfare functions achieved superior aggregate welfare (mean utilities 4.01 and 8.84 respectively) with exceptional stability (SD = 0.15 and 1.02), while provider-insurer aligned policies imposed catastrophic patient burdens (mean utility − 22.39). Patient utility was driven predominantly by out-of-pocket burden (weight = 0.80), with health gains contributing modestly (0.12).</p> Conclusion <p>Multi-stakeholder welfare optimization in health insurance is empirically feasible and does not require inherent efficiency-equity trade-offs. Fiscal constraints, rather than operational limitations, constitute the primary barrier to equitable insurance expansion, necessitating complementary revenue mobilization strategies alongside benefit design reforms to achieve sustainable universal financial protection.</p>

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Multi-objective machine learning framework for welfare-optimized health insurance design in infectious disease management (Gastroenteritis)

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月26日

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  • Shekoofeh Sadat Momahhed,
  • Atefehsadat Haghighathoseini

摘要

Objective

To develop and validate a multi-objective machine learning framework for welfare-optimized health insurance design in infectious disease management, explicitly modeling trade-offs among patient financial protection, provider efficiency, and insurer sustainability through empirically-derived stakeholder utilities and social welfare functions.

Setting

The national health insurance system in Iran, utilizing a total of 378,403 claims of gastroenteritis hospitalizations across public, private, and charitable hospitals in 2023.

Design

Observational study employing 5 supervised machine learning models to predict stakeholder-specific utilities—patient (health outcomes, out-of-pocket burden, affordability), provider (length-of-stay efficiency, performance quality), and insurer (coverage adequacy, subsidy management, cost containment). Utilities were aggregated using six social welfare functions (Utilitarian, Nash, Atkinson, Rawlsian, Geometric Mean, Convex Combination) with empirically-derived stakeholder weights. Pareto frontier analysis identified welfare-dominant policy configurations across insurance arrangements.

Results

Only 2.46% of observed policies achieved Pareto efficiency, indicating substantial allocative inefficiency. “Win-Win-Win” configurations (16.9% of efficient policies) dominated 54.1% of all alternatives, demonstrating simultaneous welfare gains across stakeholders without requiring zero-sum trade-offs. The equity-fiscal sustainability correlation (r=-0.509) was nearly twice the magnitude of the equity-efficiency correlation (r=-0.267), identifying fiscal capacity—not operational inefficiency—as the binding constraint on patient-centered insurance design. Nash and Geometric Mean social welfare functions achieved superior aggregate welfare (mean utilities 4.01 and 8.84 respectively) with exceptional stability (SD = 0.15 and 1.02), while provider-insurer aligned policies imposed catastrophic patient burdens (mean utility − 22.39). Patient utility was driven predominantly by out-of-pocket burden (weight = 0.80), with health gains contributing modestly (0.12).

Conclusion

Multi-stakeholder welfare optimization in health insurance is empirically feasible and does not require inherent efficiency-equity trade-offs. Fiscal constraints, rather than operational limitations, constitute the primary barrier to equitable insurance expansion, necessitating complementary revenue mobilization strategies alongside benefit design reforms to achieve sustainable universal financial protection.