What is the name of your organization?
Menava Health
What is the name of your solution?
Menava PPH Detection Model
Provide a one-line summary or tagline for your solution.
An ML-driven platform for obstetricians to predict pregnancy complication risks using point-of-care data.
In what city, town, or region is your solution team headquartered?
Abuja, Federal Capital Territory, Nigeria
In what country is your solution team headquartered?
NGA
What type of organization is your solution team?
Hybrid of for-profit and nonprofit
Film your elevator pitch.
What specific problem are you solving?
Every two minutes, a woman dies from complications related to pregnancy or childbirth—and nowhere is this crisis more pronounced than in Africa. Postpartum hemorrhage (PPH), or excessive bleeding after birth, is the leading cause of maternal mortality across the continent, accounting for an estimated 34% of maternal deaths in sub-Saharan Africa.
In many African countries, up to 60% of maternal deaths are linked to PPH, largely due to inadequate healthcare infrastructure, a shortage of skilled birth attendants, and poor access to emergency obstetric care—especially in rural and underserved communities. Women often give birth without access to essential interventions like uterotonics or blood transfusions.
A major barrier is the inability to accurately predict who is at risk. Current clinical tools frequently miss over 40% of PPH cases, leading to preventable deaths and poor resource allocation. As a result, the women who need urgent, specialized care the most often receive it the least.
The aftermath of PPH is devastating: survivors face long-term effects like anemia and trauma, while families shoulder the economic burden. In Africa, where maternal health systems are fragile, early risk detection and targeted care are not just innovations—they’re lifesaving necessities.
What is your solution?
At the core of our solution is a machine learning model trained on retrospective clinical datasets to predict the likelihood of postpartum hemorrhage (PPH) using a minimal set of inputs such as age, parity, and vitals. Built with scalable, explainable ML architectures, the model delivers instant risk scores and is optimized for offline use in low-resource settings.
Embedded in a mobile-first application, the tool works without internet and requires under 60 seconds to complete an assessment. Its intuitive interface is designed for frontline health workers, requiring minimal training. The backend supports cloud-based updates and local adaptation to align with country-specific protocols, while maintaining full offline functionality.
The model is explainable by design, using interpretable outputs to help health workers understand and trust the recommendations. End-to-end encryption and compliance with global health data standards ensure patient privacy and system integrity.
This solution turns complex clinical data into actionable insights at the point of care, enabling earlier intervention and better resource allocation. By combining predictive analytics with offline-first technology, our system is uniquely positioned to scale across Africa and reduce preventable maternal deaths, starting with postpartum hemorrhage—the leading cause of maternal mortality.
Who does your solution serve, and in what ways will the solution impact their lives?
We are building a mobile-based risk prediction tool designed for use by frontline health workers, midwives, and clinicians. It instantly generates a personalized risk score for each pregnant woman based on basic clinical and demographic data. The tool functions offline and delivers results in under 60 seconds, making it ideal for low-resource, high-volume settings.
Our primary users are over 70,000 community health workers and 2,500 health officials who will use the platform to guide real-time decisions and ensure timely interventions for mothers most in need. The system is built to integrate seamlessly into existing workflows, requiring minimal training and zero dependency on high-end infrastructure.