Meghan Meredith, a Ph.D. student in the H. Milton Stewart School of Industrial and Systems Engineering, received one of this year’s fellowships from the National Science Foundation’s Graduate Research Fellowship Program. The honor is designated for her project titled Mathematical Models to Provide Personalized and Equitable Maternal and Neonatal Care.

The award provides three years of financial support, including $34,000 per 12-month fellowship year. In addition, it covers all of tuition and student fees.

“It’s a great honor,” Meredith said. “To have NSF fund a proposal that was fueled by racial disparities and soft interventions, it really validates the work. This just allows me to focus even more on my research.”

Meredith works with ISyE Assistant Professor Lauren Steimle. Together last summer, they began a project investigating maternal health to evaluate gaps in care for which they could propose solutions. This particular project looked at decision analytics that go into deliveries. These include deciding between vaginal or cesarean delivery, inducing labor, how long someone should experience the trial of labor before an intervention is introduced, and, ultimately, how to incorporate patient preference into these decisions.

“A lot of women have strong feelings, as they should, about what they prefer their delivery experience to look like,” Meredith said. “A lot of these decisions are made by doctors based on their own experiences, and we haven’t really had a lot of decision analytics that look at data and understanding the outcomes.”

This gap in care is especially evident in the context of different outcomes based on racial disparities. Black and Hispanic women often have a shorter trial of labor before interventions are introduced meaning higher rates of C-section for low-risk births.

“We want to first understand why that’s happening,” Meredith said. “What are the factors and are the factors actually relevant? Or, has this become something that is a bias in the data or the caregivers?”

Ultimately, the goal of the project is to develop theory for multiple objectives within operations research. Can you quantify, somehow, a patient’s preference within available deliver data for different experiences?

“For example,” Meredith said, “if we’re deciding between vaginal and cesarean, can we factor in a woman’s preference of whether they would mind being in the hospital longer or whether they would would have a problem with not seeing their baby for the first three days because surgery has rendered them incapable.”