Phase :  Exploration  Grant : 30 000CHF Duration : 4 months

Project partners

• EPFL Lab : Machine Learning and Optimization Laboratory
• Global partners : Swiss TPH, LiGHT Yale
• Local partner : D-tree 

❌ Problem

According to the World Health Organization, around 300,000 women die every year worldwide from complications related to pregnancy and childbirth, mainly in low-resource settings. Nearly 50% of midwives in these areas report a lack of up-to-date information to deal with unforeseen complications, putting the health of mothers and newborns at risk. Midwives have the potential to prevent and avert maternal and newborn deaths.

East Africa is one of the regions with highest maternal and newborn mortality. In Zanzibar, nurses – providing midwifery care – lack resources for evidence-based decision making, particularly in difficult clinical situations.

💡 Challenge

How can we create a simple, accessible and effective digital tool that enables midwives to make informed clinical decisions in low-resource environments?

✅ Solution

Development of a digital clinical decision support tool, co-designed with end-users (midwives), using artificial intelligence and human-centered design principles. This tool will provide rapid access to evidence-based information and resources to support decision-making in critical situations and improve maternal and neonatal care.

🌍 Impact

The tool has the potential to provide rapid access to evidence-based information and resources to support decision-making in critical situations, leading to better quality of care and improved maternal and neonatal outcomes and contributing to the Sustainable Development Goals.

📷 Exploration Phase images

Team members