The OB COAP data base is comprised of high quality, clinical data collected by our member sites. It currently contains information on over 200,000 births abstracted from the electronic health record and serves as the basis for quality improvement work as well as groundbreaking research and important collaborative projects. This data drives change, answers critical questions, and informs best practices – within the OB COAP collaborative and around the world.
Several signficant collaborations are being conducted with some of the foremost thought leaders in OB. Visit our Collaborations Page for more information!
Papers are being published, abstracts presented, and ongoing research is being conducted. Opportunities exist to be involved in research initiated by the OB COAP research committee or to submit applications for use of the data. Visit the Research Page to find out more.
Machine Learning Project to Improve Prediction and Understanding of Pregnancy Complications
Under the leadership of Vivienne Souter, MD and Rich Caruana, PhD, OB COAP data has been utilized to develop externally validated interpretable machine learning models to predict preterm preeclampsia, severe maternal morbidity, shoulder dystocia, and postpartum hemorrhage associated with significant maternal morbidity. This work has produced new insights into the relationship between patient characteristics and risk, and uncovered new risk factors for pregnancy complications. Contact us for more information and see the OB COAP bibliography.
THANK YOU to Vivienne Souter, MD; Rich Caruana, PhD; Ian Painter, PhD; Ben Lengerich, PhD and Tomas Bosschieter
This work was supported by Azure sponsorship credits granted by Microsoft’s AI for Good Research Lab.