用强化学习优化母婴健康服务资源分配，提升长期参与度。
Decisions and Deployment: The Five-Year SAHELI Project (2020-2025) on Restless Multi-Armed Bandits for Improving Maternal and Child Health
- 采用决策导向学习替代传统预测再优化流程，直接优化长期参与效果。
- 大规模随机试验显示，参与度下降减少31%，显著优于现行标准方案。
- 成果可推广至其他资源受限的公共卫生项目，适合政策制定者参考。
全球范围内，孕产妇和儿童健康仍是重大挑战。在许多健康干预项目中，医疗工作者资源有限，难以持续个性化地接触脆弱人群。因此，如何最优调度有限的人力资源以最大化长期参与度至关重要。为应对这一核心问题，2020至2025年的多期SAHELI项目（与非政府组织ARMMAN合作），利用人工智能在印度一项母婴健康项目中实现资源优化分配。该系统采用无休止多臂老虎机（Restless Multi-Armed Bandit, RMAB）框架解决序列化资源分配问题。关键方法创新在于从传统的“预测-再优化”两阶段模式转向决策导向学习（Decision-Focused Learning, DFL），使模型学习目标与最终最大化受益人参与度的目标直接对齐。通过大规模随机对照试验验证，DFL策略相比当前标准护理，累计参与度下降减少了31%，显著优于两阶段模型。尤为重要的是，研究证实这种参与度提升直接转化为现实健康行为的显著改善，尤其是新母亲持续服用关键铁钙补充剂的行为。最终，SAHELI项目为将序列决策型AI应用于健康项目资源优化提供了可扩展的范本。
原文摘要 · Abstract (English)
Maternal and child health is a critical concern around the world. In many global health programs disseminating preventive care and health information, limited healthcare worker resources prevent continuous, personalised engagement with vulnerable beneficiaries. In such scenarios, it becomes crucial to optimally schedule limited live-service resources to maximise long-term engagement. To address this fundamental challenge, the multi-year SAHELI project (2020-2025), in collaboration with partner NGO ARMMAN, leverages AI to allocate scarce resources in a maternal and child health program in India. The SAHELI system solves this sequential resource allocation problem using a Restless Multi-Armed Bandit (RMAB) framework. A key methodological innovation is the transition from a traditional Two-Stage "predict-then-optimize" approach to Decision-Focused Learning (DFL), which directly aligns the framework's learning method with the ultimate goal of maximizing beneficiary engagement. Empirical evaluation through large-scale randomized controlled trials demonstrates that the DFL policy reduced cumulative engagement drops by 31% relative to the current standard of care, significantly outperforming the Two-Stage model. Crucially, the studies also confirmed that this increased program engagement translates directly into statistically significant improvements in real-world health behaviors, notably the continued consumption of vital iron and calcium supplements by new mothers. Ultimately, the SAHELI project provides a scalable blueprint for applying sequential decision-making AI to optimize resource allocation in health programs.
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