In Kenya pregnant women in the most deprived communities face a lot of challenges attending ANC because of distant to health facility, cultural & traditional beliefs, illiteracy, poverty and nomadism. Only 29% attend ANC during their first trimester leading to delays in detection of life-threatening morbidity like preeclampsia. Meaning 71% of expectant women are at risk of never being able to detect early these life threatening morbidity. This has contributed significantly to maternal mortality in Kenya which has increased by 71.4% since 2012, currently Kenya's Maternal mortality ratio is 594 as compared to 174 in 2012 against a target of 70 by 2030 (SDG Goal No. 3). This is not a unique problem to Kenya. 69% of the global burden on maternal mortality is in Africa.

Solution
We have built a community driven process backed by AI Technology, tracked by health practitioners and financed by Kenyans and well wishers through a program called Adopt a Mum. Our machine learning driven technology predicts early in advance the risk level of pregnant mothers triggering early interventions to support pregnant mothers identified as high risk of maternal morbidity across the country including the most deprived communities.

This process takes maternal care closer to the patient, continuously builds the capacity of traditional birth assistants/Experienced Midwives, enhances collaboration between the birth attendant and specialized doctors, and finally provides a channel through which any well wisher hundreds of kilometers away can financially support a mother through her pregnancy journey as they attend ANC.

Impact
1. Early detection of preeclampsia and maternal morbidity by up to 35% allowing effective interventions
2. Empowering expectant women to be able to promptly make the decision of seeking care
3. Increased attendance of ANC despite distance from the facility 
4. Empowers health facilities to plan resource allocation to effectively receive and manage expectant mothers when they arrive at the facility

The impact effectively and efficiently addresses all the three delays associated with maternal care