Big Data in decision-making processes relating to sexual and reproductive health: practical case studies of midwives in the Democratic Republic of the Congo

Abstract

Sexual and reproductive health is a major public health issue in the Democratic Republic of the Congo (DRC), characterised by high maternal mortality rates and inequalities in access to care. In this context, midwives play a central role in the care of women and newborns. However, their practices remain poorly modelled and insufficiently utilised to support decision-making.

This article explores the contribution of Big Data to improving decision-making in reproductive health through the modelling of midwives’ practices in the DRC. Using a mixed-methods approach combining big data analysis, field surveys and predictive modelling, the study highlights the potential of analytical technologies to improve the quality of care.

The results show that integrating clinical, demographic and behavioural data enables the prediction of obstetric risks, the optimisation of midwives’ interventions and the strengthening of health planning.

Keywords: Big Data, reproductive health, processes, midwives, decision support, DRC, artificial intelligence, modelling, public health.

Received Date: February 22, 2026

Accepted Date: March 14, 2026

Published Date: April 02, 2026

Available Online at: https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/618