Alexis Gimovsky, MD is a Maternal-Fetal Medicine specialist and Assistant Professor in the Department of Obstetrics and Gynecology at Women & Infants Hospital, Brown University. Dr. Gimovsky completed her undergraduate education at the University of Pennsylvania, where she earned degrees in both Bioengineering and Biology. She then received her medical degree from Albert Einstein College of Medicine in New York. She completed her residency in ObGyn at The George Washington University and she completed her fellowship in Maternal Fetal Medicine at Thomas Jefferson University Hospital, where she was awarded the “Maternal Fetal Medicine Fellowship Research Award.”
Dr. Gimovsky has presented her research at numerous national conferences including The Society for Maternal Fetal Medicine, the American Institute of Ultrasound in Medicine and The American Congress for Obstetricians and Gynecologists. She has been involved in several global health projects including improving ultrasound education in Ethiopia and Guyana. Her research interests include using machine learning tools to reduce maternal morbidity during labor and the development of simulation techniques for medical education. Dr. Gimovsky’s clinical interests include preconception counseling, prenatal diagnosis, management of high-risk pregnancies, preterm birth prevention and ultrasound.
Cesarean delivery is the most common surgery performed in the United States with
one in three women undergoing cesarean birth.
Cesarean birth can be life-saving for the fetus, the mother, or both.
The rapid increase in cesarean birth rates without similar decreases in maternal or neonatal morbidity or mortality raises significant concern that cesarean delivery is over utilized.
Cesarean birth is associated with a multitude of short-term and long-term morbidities for both the mother and the newborn.
A reduction in the rate of primary cesarean deliveries requires different and novel approaches.
Our goal is safe reduction in maternal and neonatal morbidity and mortality, with a focus on cesarean delivery by augmenting physician data with knowledge.
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