The Challenge
Higher quality, more equitable primary health care (PHC) is essential to improve outcomes in Reproductive, Maternal, Newborn, Child and Adolescent Health and Nutrition (RMNCAH-N). There has been considerable progress in understanding how to measure the quality and equity of PHC, but better measurement has not translated to improved RMNCAH-N health outcomes. Why?
Traditional methods of collecting health systems data offer valuable insights, but often come with challenges that limit their frequency and timeliness. For example, routine health management information systems (HMIS) and other administrative data sources are perceived as too low quality to be useful for decision making. And large scale in-person surveys, which remain the gold standard in health systems measurement, require significant planning and resources which often prevent their frequent administration and timeliness to inform implementation and course correction. As a result, evidence is frequently too slow, fragmented, or inaccessible to inform real-time program and policy decisions — preventing Ministries of Health, health systems managers, and other stakeholders from turning data into actionable steps that can drive better health outcomes.
This gap is more concerning than ever: as fiscal space for health contracts and countries face compounding shocks — from climate change to economic instability — decision makers need faster, more integrated intelligence to prioritize, adapt and act.
The Opportunity
In search of a solution, the Global Financing Facility for Women, Children and Adolescents (GFF), in partnership with country leaders, developed the Frequent Assessments and Systems Tools for Resilience (FASTR) initiative — a rapid-cycle analytics and data use program.
FASTR supports countries with timely, rigorous and practical approaches to monitor the performance of their PHC systems with a particular focus on the needs of women, children adolescents and RMNCAH-N services. FASTR has developed four technical approaches – analysis of RMNCAH-N service use, rapid-cycle health facility phone surveys, rapid-cycle household and client surveys, and deeper follow-up analyses. These approaches are tailored to address specific data gaps and data use needs at the country level.
To date, 25 GFF partner countries have adopted FASTR’s approaches to identify the impact of shocks on health service usage, monitor the implementation of national health systems reforms, and strengthen data use at all levels of the health system. To learn more about FASTR, visit the GFF’s Data Portal.
Results for Development is partnering with the GFF to expand and scale FASTR across GFF partner countries, ensuring that FASTR’s tools, approaches, and lessons learned become country-oriented global public goods.
At the core of FASTR’s approach is an open-source, web-based analytics platform with embedded AI that transforms how countries operationalize real-time health systems intelligence. The platform ingests data directly from country systems (such as DHIS2), automates data quality diagnostics and statistical adjustments, and runs advanced analyses across priority indicators, producing high-quality visualizations and reports in minutes.
AI is embedded throughout the workflow to retrieve indicators, interpret results, synthesize findings across data sources, and generate audience-tailored briefs. Users can interact with an AI chatbot to ask learning questions, refine analyses, and tailor outputs to different audiences. Critically, AI acts as an accelerator rather than a final decision-maker: analytical outputs are deterministic and reproducible, methods are transparent, and country stakeholders retain full authority over analysis and decision-making.
R4D’s Work
As the lead implementation partner for FASTR in Ghana and Nigeria, R4D is partnering with the GFF, the World Bank, the Nigeria Federal Ministry of Health, the Ghana Health Service, and other key stakeholders to adapt FASTR approaches to meet the specific data use needs in both countries. This includes designing data-use process flows to strengthen the analyze–learn-act cycle, institutionalizing new rapid-cycle analytic approaches, and strengthening data use processes and competencies at both national and subnational levels.
In Ghana, this work is supporting the Ghana Health Service to monitor PHC performance and track progress on national health reforms at regional level, while in Nigeria, R4D is working with the Federal Ministry of Health to generate rapid-cycle evidence on RMNCAH-N service delivery reforms across states. In both countries, R4D is supporting the roll-out of the FASTR analytics platform, deploying it on country-specific infrastructure and building Ministry capacity to use AI-enabled tools for routine decision-making.
R4D is also designing and facilitating the FASTR Multi-Country Learning Exchanges in partnership with the GFF. These in-person and virtual peer learning sessions bring Ministries of Health from 24 countries together to strengthen their ability to generate, analyze, and use service delivery and facility readiness data. Country teams learn to identify service disruptions earlier, interpret results in context, and turn analysis into action. R4D helps countries prepare for participation, refine priority indicators and use cases, and review early analyses. Early FASTR adopter countries, Ghana, Nigeria, Somalia, Ethiopia, Senegal and Guinea, serve as coaches and mentors, sharing practical lessons on adapting FASTR to local contexts and helping countries newer to FASTR accelerate data-informed decision-making.
R4D brings deep expertise in coaching and mentoring, collaborative learning and evaluation and adaptive learning to support countries to not only generate more timely data, but to translate that data into decision making for improved PHC performance. Throughout this process, R4D will document and learn from the approaches being implemented, ensuring that the lessons learned are continuously improving the GFF’s FASTR initiative.
Image © Family Planning and Newborn Care by Global Financing Facility, CC BY-NC-ND 2.0
