Rwanda's health system has made remarkable strides over the past two decades — falling child mortality rates, near-universal health insurance coverage through Mutuelle de Santé, and a community health worker network that has become a global reference. Yet beneath these headline achievements lies a persistent and often underappreciated challenge: the gap between health data collection and its practical application at the facility, district, and national level. Aimecol

The Data Abundance Problem

Rwanda collects an impressive volume of health data. The District Health Information Software 2 (DHIS2) platform aggregates reporting from thousands of health facilities across the country. Community health workers submit monthly reports on maternal health, child nutrition, malaria, and a range of other indicators. National surveys — the DHS, MICS, and various programme evaluations — add further layers of evidence to an already substantial dataset.

The problem is not a lack of data. The problem is that much of this data is collected, compiled, and then largely sits — underutilised by the very decision-makers it was intended to support.

Studies across sub-Saharan Africa consistently show that less than 40% of routinely collected health data is ever reviewed at the facility level — and an even smaller proportion influences operational decisions at the district or national level.

Why the Gap Exists

Understanding why decision-makers don't use available data requires looking beyond the obvious explanations — poor connectivity, limited digital literacy, or insufficient equipment. While these factors matter, the more fundamental barriers are institutional and human.

Capacity and Confidence

Many district health officers and facility managers lack confidence in their ability to interpret complex datasets. Presenting a facility manager with a dashboard of 40 indicators and expecting them to identify actionable priorities is unrealistic without adequate analytical support and training. Data literacy is not simply about knowing how to use a system — it is about developing the judgement to distinguish signal from noise.

Incentive Structures

Reporting compliance is often incentivised; data use rarely is. Health workers are evaluated on whether they submit reports, not on whether those reports informed better decisions. Until data use becomes a visible and valued part of performance evaluation — at every level of the system — it will remain secondary to data collection.

Watch: Workflow assessment and record digitisation inside a district health facility in Kigali.

The Feedback Loop Problem

In many health systems, data flows upward but rarely flows back down. A community health worker submits her monthly report to the health centre. The health centre submits to the district. The district submits to the Ministry. But the CHW rarely receives feedback on what her data revealed, and almost never sees how it influenced a decision. Without a functioning feedback loop, data collection feels like an administrative burden rather than a meaningful contribution.

The goal of a health information system is not to generate reports — it is to enable action. Data without decisions is just noise.

— World Health Organization, Health Information Systems Framework, 2024

What Evidence-Based Decision-Making Looks Like in Practice

The good news is that Rwanda has several structural advantages that position it to close this gap faster than many comparable health systems. A strong decentralisation framework, a capable cadre of district health teams, and a culture of performance accountability through imihigo (performance contracts) all create conditions where data use can be meaningfully embedded.

In our consulting work with health sector clients, we have identified three practical approaches that consistently make a difference:

• Simplify the data dashboard. Identifying the five to seven indicators that most directly correspond to a decision-maker's responsibilities — and presenting only those — dramatically increases the likelihood of engagement and use.
• Build data review into existing routines. Rather than creating new meetings or processes, effective programmes embed structured data review into the monthly coordination meetings that already exist at facility and district level.
• Close the feedback loop deliberately. Even a brief written summary shared back with community health workers — acknowledging what their data showed and what decision it supported — meaningfully increases reporting quality and engagement over time.

Ireme's Role in Strengthening Health Data Use

At Ireme, our public health consulting engagements frequently centre on exactly this challenge — helping clients move from data collection toward genuine data-driven decision-making. This means working alongside district health teams to build analytical capacity, supporting programme evaluations that are designed from the outset to produce actionable recommendations, and advising on health information system design that puts the end-user's decision-making needs first.

Rwanda's health system has demonstrated again and again that it can achieve ambitious goals when evidence and political will align. Closing the gap between data and decisions is the next frontier — and it is one where rigorous, context-sensitive consulting can make a tangible difference.