As part of Rwanda's transition toward universal health coverage, the national Community-Based Health Insurance (CBHI) scheme is moving from retrospective fee-for-service reimbursements to prospective capitation payments for public primary healthcare providers. This work outlines a data-driven approach to designing, calibrating, and monitoring the capitation model using individual-level claims data from the Intelligent Health Benefits System (IHBS). We introduce a transparent, interpretable formula for allocating payments to Health Centers and their affiliated Health Posts. The formula is based on catchment population, service utilization patterns, and patient inflows, with parameters estimated via regression models calibrated on national claims data. Repeated validation exercises show the payment scheme closely aligns with historical spending while promoting fairness and adaptability across diverse facilities. In addition to payment design, the same dataset enables actionable behavioral insights. We highlight the use case of monitoring antibiotic prescribing patterns, particularly in pediatric care, to flag potential overuse and guideline deviations. Together, these capabilities lay the groundwork for a learning health financing system: one that connects digital infrastructure, resource allocation, and service quality to support continuous improvement and evidence-informed policy reform.

翻译：作为卢旺达向全民健康覆盖转型的一部分，国家社区健康保险计划正从按服务项目后付费模式转向针对公立初级医疗机构的按人头预付模式。本研究提出了一种数据驱动的方法，利用智能健康福利系统的个人级理赔数据来设计、校准和监测按人头付费模型。我们引入了一个透明、可解释的支付分配公式，用于向健康中心及其附属健康站分配资金。该公式基于服务人口、医疗服务利用模式和患者流动情况构建，其参数通过基于全国理赔数据校准的回归模型进行估计。重复验证结果表明，该支付方案在促进不同机构间公平性与适应性的同时，与历史支出高度吻合。除支付设计外，同一数据集还可用于获取可操作的行为洞察。我们重点展示了监测抗生素处方模式（尤其是儿科诊疗中）的应用案例，以识别潜在过度用药和指南偏离现象。这些能力共同为建立学习型健康筹资体系奠定了基础：该体系通过连接数字基础设施、资源配置与服务质量，支持持续改进和循证政策改革。