Unlocking the transformative potential of data science in improving maternal, newborn and child health in Africa: a...

Application of data science in maternal, newborn, and child health (MNCH) across Africa is variable with limited documentation. Despite efforts to reduce preventable MNCH morbidity and mortality, progress remains slow. Accurate data are crucial for holding countries accountable for tracking progress towards achieving the Sustainable Development Goal 3 targets on MNCH. Data science can improve data availability, quality, healthcare provision and decision-making for MNCH programmes. We aim to map and synthesise data science use cases in MNCH across Africa.

We will develop a conceptual framework encompassing seven domains: (1) infrastructure and systemic challenges, (2) data quality, (3) data governance, regulatory dynamics and policy, (4) technological innovations and digital health, (5) capacity development, human capital and opportunity, (6) collaborative and strategic frameworks and (7) recommendations for implementation and scaling.We will use a scoping review methodology involving literature searches in seven databases, grey literature sources and data extraction from the Digital Health Atlas. Three reviewers will screen articles and extract data. We will synthesise and present data narratively and use tables, figures and maps. Our structured search strategy across academic databases and grey literature sources will find relevant studies on data science in MNCH in Africa.

This scoping review does not require formal ethical review and approval because it will not involve collecting primary data. The findings will showcase gaps, opportunities, advances, innovations, implementation and areas needing additional research. They will also propose next steps for integrating data science in MNCH programmes in Africa. The implications of our findings will be examined in relation to possible methods for enhancing data science in MNCH, such as community and clinical settings, monitoring and evaluation. This study will illuminate data science applications in addressing MNCH issues and provide a holistic view of areas where gaps exist and where there are opportunities to leverage and tap into what already exists. The work will be relevant for stakeholders, policymakers and researchers in the MNCH field to inform planning. Findings will be disseminated through peer-reviewed journals, conferences, policy briefs, blogs and social media platforms.