SBIR Phase II: Improved Maternal Health with Predictive Patient Monitoring

Award ID: 2233743

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project is to develop further a predictive model for health providers to address the life-threatening condition of preeclampsia in pregnant and postpartum patients. Preeclampsia affects 1 in 20 births, or 150,000 women in the U.S. each year. Not only does Preeclampsia cost 70,000 lives globally each year, it is also an expensive burden for healthcare systems. It costs nearly three times more to treat a patient with preeclampsia than one without this complication. When deployed at scale, the technology proposed in this SBIR grant could be a key factor to reducing maternal mortality in the United States, improving obstetric patient outcomes, reducing the cost of obstetric care, and helping to reduce health disparities. The proposed project will research and develop a method to potentially detect preeclampsia. Preeclampsia is a hypertensive disorder of pregnancy. This dangerous condition also costs the U.S. healthcare system $2.18 billion per year or one-third of the total amount spent on maternal healthcare in our country. Research objectives include optimizing a machine learning model, integrating patient-facing software into Electronic Health Records systems, implementing the predictive model for preeclampsia, integrating with peripheral wellness devices, developing reminder notification mechanisms, determining appropriate interface enhancements, and defining commercialization and regulatory strategies. Improving maternal health outcomes advances the general health and welfare of American families and can improve our countrys economic competitiveness on the world stage. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Alastair Monk