Abstract: Despite high levels of spending on prenatal care, the U.S. has the worst maternal mortality rate amongst peer high income nations. Driven by the growing need to move away from the status-quo “one-size-fits-all” model for prenatal care in the U.S., clinicians at the University of Michigan, in collaboration with the American College of Obstetricians and Gynecologists, proposed a new paradigm – the Michigan Plan for Appropriate Tailored Healthcare in Pregnancy (MiPATH) – which incorporates targeted appointment pathways for patients based on their individual medical factors. As MiPATH is being adopted and implemented in prenatal care clinics at Michigan Medicine, clinicians and stakeholders are interested in quantifying its operational impacts and deriving data-driven insights about related policies. We propose discrete-event simulation modeling, mixed-integer linear programming, and stochastic programming approaches to answer questions related to the effects of switching to the new policy, appointment scheduling, and capacity planning under the new prenatal care paradigm.


About the speaker: Leena Ghrayeb is a PhD candidate in Industrial and Operations Engineering at the University of Michigan, co-advised by Dr. Amy Cohn and Dr. Ruiwei Jiang. Her research interests lie in the development and application of operations research methodologies, mainly mixed-integer linear programming, discrete-event simulation modeling, and stochastic programming, to guide patient-centered decision-making in healthcare. Her dissertation research is focused on the application of optimization techniques to address challenging operational problems related to prenatal care. She is also a recipient of the NSF Graduate Research Fellowship and a Fulbright U.S. Student Research grant, which allowed her to conduct research in Jordan during the 2023-2024 academic year.