Research Interests
Research Interests
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Broadly, I am interested in developing and applying operations research methodologies to guide patient-centered decision making in healthcare and public health, with the ultimate goal of improving healthcare outcomes.
Methodologies: Mixed Integer Linear Programming, Simulation, Stochastic Programming, Dynamic Programming
Applications: Scheduling, Resource Allocation, Healthcare, Public Health, Medical Decision-Making
Current Research
Current Research
Scheduling Prenatal Care Visits for Patients with Varying Medical and Social Risk Factors
Scheduling Prenatal Care Visits for Patients with Varying Medical and Social Risk Factors
Prenatal care is one of the most widely used health services in the United States, and despite high levels of spending on prenatal care, the U.S. has the highest annual maternal mortality rate amongst peer high-income nations. In moving away from the typical “one size fits all” prenatal care paradigm, a new conceptual model of care is proposed that will allow for anticipatory healthcare based on patients’ varying medical and social needs. We explore mixed integer linear programming and simulation approaches to assess the operational impacts and quality of this new model. By better matching patient needs to services, this work aims to improve utilization of prenatal care, while addressing disparities amongst different patient groups.
Publication(s):
Multi-Stage Patient Acceptance Decision Making for Prenatal Care
Multi-Stage Patient Acceptance Decision Making for Prenatal Care
In current practice, depending on physician and clinic-related capacity constraints, prenatal care clinics define the number of patients they can accept by the number of patients that each physician can care for at any point in time. While this method is simple and provides clear guidelines, it does not account for the stochastic nature of patients' health states -- patients may require additional appointments beyond their defined pathways due to complications during pregnancy. This can result in unexpected overutilization, overbooking, and poor scheduling practices. We propose a multi-stage stochastic programming approach to aid clinics in deciding how many patients to accept on a weekly basis, given patient-related randomness inherent in pregnancy. We derive upper and lower bounds for this model and draw insights about patient acceptance policies.
Optimizing Imaging Utilization for Breast Cancer Patients in a Jordanian Cancer Center
Optimizing Imaging Utilization for Breast Cancer Patients in a Jordanian Cancer Center
While imaging is an integral part of breast cancer diagnosis and treatment, it can be over-utilized with no significant improvement in patient outcomes. This leads to a burden on imaging resources and staff, as well as stress for patients and potentially a delay in treatment. The primary goals of this research are two-fold: 1) to analyze utilization of imaging resources at a major cancer center in Amman, Jordan and identify key factors that lead to over-utilization and 2) to propose data-driven models to aid providers in decision-making regarding imaging.
In collaboration with the King Hussein Cancer Center.
Publications
Publications
Journal Articles
Ghrayeb, L. & Damodaran, P. (2021). Minimizing Makespan of a Batch Processing Machine with Unequal Job Ready Times Using Simulated Annealing. International Journal of Industrial and Systems Engineering.
Ghrayeb, L., Muthuswamy, S., & Damodaran, P. (2020). Minimizing Makespan of Batch Processing Machine with Unequal Ready Times. International Journal of Industrial and Systems Engineering. doi:10.1504/ijise.2020.10032044
Working Papers
Ghrayeb, L., Cohn, A., Jiang, R., Peahl, A. (2024). An Optimization-Embedded Simulation Approach for Quantifying the Operational Impacts of Right-Sizing Prenatal Care. Submitted to Health Care Management Science.
Ghrayeb, L., Cohn, A., Jiang, R., Peahl, A. (2024). A Multistage Stochastic Programming Approach for Prenatal Care Patient Acceptance Decision Making. Working Paper.
Conference Presentations with Proceedings
Ghrayeb, L., Bryan, TJ., Kandiraju, M., Maire, T., Zhang, Y., Cohn, A., Peahl, A. (2023) . Measuring the Operational Impacts of Right-Sizing Prenatal Care Using Simulation. In 2023 Winter Simulation Conference. Proceedings (pp. 1065 - 1076). INFORMS.
Mathews, N.B., Ghrayeb, L., Chintala, V.S., Muthuswamy, S., McKinney, C., Lindley, B., Iyer, R. (2021). Improving Patient Discharge Process. In IIE Annual Conference Proceedings (pp. 211 - 216). Institute of Industrial and Systems Engineers (IISE).
Ghrayeb, L., & Damodaran, P. (2020). Simulated Annealing Approach for Minimizing Makespan of a Batch Processing Machine with Unequal Job Ready Times. In IIE Annual Conference. Proceedings (pp. 49A-54A). Institute of Industrial and Systems Engineers (IISE).
Ghrayeb, L., & Damodaran, P. (2019). Minimizing Makespan of Batch Processing Machine with Unequal Ready Times. In IIE Annual Conference. Proceedings (pp. 148- 153). Institute of Industrial and Systems Engineers (IISE).
Conference Presentations
Ghrayeb, L., Cohn, A., Jiang, R., Peahl, A. (2022). An Optimization-embedded Simulation Approach for Scheduling Prenatal Care Visits. INFORMS Annual Meeting, Indianapolis, IN.
Ghrayeb, L., Cohn, A., Jiang, R., & Peahl, A. (2021). Prenatal Care Scheduling of Patients with Varying Medical and Social Risk Factors. INFORMS Annual Meeting, Anaheim, CA.
Posters
Peahl, A., Eddy, N., Ghrayeb, L., Getzen, A., Monickaraj, CJ., Campbell, A., Stout, M., Cohn, A. (2024) Quantifying the operational impacts of varying patient-provider continuity policies in prenatal care. In 2024 ACOG Annual Clinical and Scientific Meeting. American College of Obstetricians and Gynecologists.
Campbell, A., Ghrayeb, L., Monickaraj, CJ., Getzen, A., Eddy, N., Stout, M., Cohn, A., Peahl, A. (2024) The effects of dynamic patient trajectories on prenatal care utilization and clinical operations. In 2024 ACOG Annual Clinical and Scientific Meeting. American College of Obstetricians and Gynecologists.
Peahl, A., Ghrayeb, L., Kingston, C., Kandiraju, M., Stout, M., Cohn, A. (2023) Effect of Virtual Care on the Receipt of Evidence Based Services and Diagnosis of Complications in Pregnancy in Patients with and without Social Needs. In AcademyHealth 2023 Annual Research Meeting, Seattle, WA. AcademyHealth.
Ghrayeb, L., Zhang, Y., Bryan, T., Cohn, A., Peahl, A.. (2023) Operational effects of implementing hybrid prenatal care models: a retrospective analysis. In 2023 ACOG Annual Clinical and Scientific Meeting, Baltimore, MD. American College of Obstetricians and Gynecologists.
Kandiraju, M., Kingston, C., Bryan, T., Ghrayeb, L., Cohn, A., Peahl, A. (2023) A simulation model of the effects of tailored prenatal care delivery on care access. In 2023 ACOG Annual Clinical and Scientific Meeting, Baltimore, MD. American College of Obstetricians and Gynecologists.
Tang, M., Ghrayeb, L., Cohn, A., Fendrick, M., Peahl, A. Evaluation of Patient Cost Savings in Tailored Prenatal Pathways. (2023) In 2023 ACOG Annual Clinical and Scientific Meeting, Baltimore, MD. American College of Obstetricians and Gynecologists.
Peahl, A. Kingston, C., Ghrayeb, L., Bryan, TJ., Zhang, Y., & Cohn, A. (2023). The effect of tailored prenatal care policies with telemedicine on outpatient clinic operations. In 43rd Annual Pregnancy Meeting. Society for Maternal Fetal Medicine.
Peahl, A., Zacharek, N., Hocher, S., Kandiraju, M., Debnath, D., Ghrayeb, L., & Cohn, A. (2022, June). Tailored Prenatal Care Delivery Improves Care Access When Compared to Traditional Prenatal Care. In 2022 Annual Research Meeting. AcademyHealth.
Pennathur, H., Ghrayeb, L., Debnath, D., Ganzi, S., Cohn, A., & Peahl, A. F. (2022). Cumulative Effect of Medical and Social Risk Factors on Routine Prenatal Care Screening [A231]. Obstetrics & Gynecology, 139, 67S.
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