Data-Driven Approach to Capitation Reform in Rwanda

TL;DR

This paper presents a data-driven, transparent, and adaptable capitation payment model for Rwanda's primary healthcare, utilizing claims data to improve fairness, monitor service quality, and support continuous health system reform.

Contribution

It introduces a novel, interpretable formula for capitation payments based on detailed claims data, enabling fairer resource allocation and real-time monitoring of healthcare practices.

Findings

Payment scheme closely matches historical spending

Model promotes fairness across diverse facilities

Enables monitoring of antibiotic prescribing patterns

Abstract

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.