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

par Akuze, Joseph;Ngatia, Bancy;Amare, Samson Yohannes;Wanduru, Phillip;Otieno, Grieven P;Kananura, Rornald RM;Kirakoya, Fati ;Amouzou, Agbessi;Estifanos, Abiy Seifu;Ohuma, Eric
Référence BMJ open, 14, 12, page (e091883)
Publication Publié, 2024-12

Article révisé par les pairs

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Résumé :

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.