Unlocking the transformative potential of data science in improving maternal, newborn and child health in Africa: A scoping review protocol
Authors: Joseph Akuze , Ngatia Bancy , Amare Samson Yohannes , Wanduru Phillip , Otieno Grieven P. , Kananura Rornald Muhumuza , Kirakoya-Samadoulougou Fati , Amouzou Agbessi , Estifanos Abiy Seifu , Ohuma Eric O. 
Published: August 2024

ABSTRACTIntroductionApplication 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 is crucial for holding countries accountable, tracking progress towards realisation of SDG3 targets on MNCH, and guiding interventions. Data science can improve data availability, quality, healthcare provision, and decision-making for MNCH programs. We aim to map and synthesise use cases of data science in MNCH across Africa.Methods and AnalysisWe will develop a conceptual framework encompassing seven domains: Infrastructure and Systemic Challenges, Data Acquisition, Data Quality, Governance, Regulatory Dynamics and Policy, Technological Innovations and Digital Health, Capacity Development, Human Capital, Collaborative and Strategic Frameworks, data analysis, visualization, dissemination and Recommendations for Implementation and Scaling.A scoping review methodology will be used including literature searches in seven databases, grey literature sources and data extraction from the Digital Health Initiatives database. 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.Ethics and disseminationThis scoping review require no formal ethics, because no primary data will be collected. Findings will showcase gaps, opportunities, advances, innovations, implementation, areas needing additional research and propose next steps for integration of data science in MNCH programs in Africa. The findings’ implications will be examined in relation to possible methods for enhancing data science in MNCH settings, 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 in Africa.ARTICLE SUMMARYStrengths and limitations of this study➣This scoping review is the first to examine the role and potential of data science applications in maternal, newborn and child health (MNCH) in Africa, with assessments on healthcare infrastructure, data quality improvement, innovative data collection and analyses, policy formulation, data-driven interventions, technologies for healthcare delivery, and capacity building.➣We will conduct systematic searches across multiple databases (PubMed, Scopus, Web of Science, Google Scholar, CINAHL, EMBASE, and Ovid) and grey literature.➣Focusing on studies that have used data science we will synthesise our findings with detailed explanations, informative charts, graphs, and tables.➣The study will deliver actionable recommendations for stakeholders engaged in MNCH policy formulation, strategic planning, academia, funders and donors, and clinicians aimed at improving MNCH outcomes in Africa.➣Our scoping review will primarily rely on published literature in English, therefore, will omit valuable insights that may have been published for non-anglophone and francophone regions of Africa.