Precision Health Economics in Maternal and Child Health: Long-Term Cost-Effectiveness and Equity Impact of AI-Supported Care
Background
Pregnancy and early childhood are critical stages that shape lifelong health for both mothers and children. Conditions such as gestational diabetes, hypertension in pregnancy, and perinatal depression are common and can have serious long-term consequences. In the UK alone, maternity care accounts for around 60% of NHS clinical negligence payouts, despite representing only 10% of all claims—highlighting the need for safer and more effective care models. Artificial intelligence (AI) offers an opportunity to transform maternity care by analysing large amounts of health data, predicting risk, and supporting personalised interventions. Yet, little is known about the long-term economic and equity impacts of these technologies.
Novelty and Importance
This project will use the EMBRACE (Empowering Maternal and Child Health through Evidence, AI and Collaboration) programme—the world’s largest digital pregnancy study involving over 60,000 families in six countries—as a case study. It is the first to integrate AI-driven risk predictions into economic evaluation models, estimate lifetime outcomes for both mothers and children, and apply distributional cost-effectiveness analysis (DCEA) to examine how AI-enabled maternity care affects health inequalities across countries and socio-economic groups.
Aims and Objectives
The project aims to develop and apply precision health economics methods for assessing the long-term cost-effectiveness and equity impact of AI-supported maternity care. It will:
– Integrate AI risk predictions into decision-analytic models.
– Estimate lifetime costs, outcomes, and health gains for mothers and children.
– Evaluate the equity effects of AI-enabled interventions within and between countries.
Findings will provide policy-relevant evidence for the NHS, NICE, and global health organisations, guiding responsible, equitable adoption of digital maternity care worldwide.