Data-Driven Approach to Capitation Reform in Rwanda

TL;DR

This paper presents a data-driven capitation reform for Rwanda's CBHI, using IHBS claims data to design a transparent, interpretable payment formula that accounts for intra- and inter-catchment patient flows. The capitation equation Capitation = $A_i \cdot U \cdot M + B \cdot I$ couples catchment utilization, population size, and inflow with a fixed inflow parameter, calibrated via regression to historical costs and updated quarterly to reflect seasonal demand. The framework emphasizes simplicity, behavioral neutrality, and budget alignment, and includes a robust monitoring system with priority indicators to safeguard care quality. Beyond payments, the analysis demonstrates IHBS’s potential for behavioral insights, notably identifying widespread pediatric antibiotic overprescription and guiding stewardship, procurement optimization, and system-wide reforms to advance universal health coverage in Rwanda. The approach lays the groundwork for a learning health financing system by linking digital infrastructure, funding, and service quality to enable continuous policy feedback and iterative improvements, with prospects for broader integration with EMRs and the RMS supply chain.

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 report 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.

Figures (16)

• Figure 1: Schematic representation of Rwanda's health system, aligned with its administrative structure. Average catchment populations and number of facilities are indicated at each level.
• Figure 2: Health Centers connected to the nearest hospital across sectors. The Health Centers (blue dots) and the nearest Hospitals (red dots), along with the line connecting the health centers to the nearest hospital (red lines)
• Figure 3: The geographical locations of facilities across their sectors are shown, indicating Health Centers (in blue) and private Health Posts (in yellow). The size of each facility is proportional to the volume of visits
• Figure 4: Average cost per visit across non-medicalized Health Centers.
• Figure 5: Average monthly cost per visit. Top plot represents the average over all HCs while the bottom plot shows its distribution across facilities.