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Recent studies suggest that outdated prenatal care guidelines may be a cause for this. In response to the growing need for modernized prenatal care standards, national prenatal care stakeholders are moving away from the traditional \u201cone-size-fits-all\u201d prenatal care pathways and have proposed new \u201ctailored\u201d appointment pathways. To study the operational impacts of adopting this new paradigm, we propose a discrete-event simulation model, which captures patient-related heterogeneity, with a mixed-integer linear programming model embedded within it, to schedule patients on a weekly basis. The objectives are to minimize patient delays, patient rescheduling, and overbooking. We use this model to quantify the operational impacts of adopting the new tailored care paradigm and to draw insights about varying scheduling policies. We apply our model to a case study of a single prenatal care clinic within a large academic health center. Our results suggest that tailoring care significantly reduces delays, rescheduling, and overbooking. This additional flexibility may allow a clinic to accommodate more patients, or to better adapt to social risk factors and\/or unexpected complications that require additional care. We also find that scheduling appointments one at a time, rather than by trimester or the entirety of the pathway, yields schedules with minimal delays and overbooking.<\/jats:p>","DOI":"10.1007\/s10729-026-09782-6","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T07:53:53Z","timestamp":1785138833000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An optimization-embedded simulation approach for quantifying the operational impacts of right-sizing prenatal care"],"prefix":"10.1007","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8859-1743","authenticated-orcid":false,"given":"Leena","family":"Ghrayeb","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy","family":"Cohn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruiwei","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Peahl","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,27]]},"reference":[{"issue":"1","key":"9782_CR1","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s10729-016-9380-4","volume":"21","author":"M Alvarado","year":"2018","unstructured":"Alvarado M, Ntaimo L (2018) Chemotherapy appointment scheduling under uncertainty using mean-risk stochastic integer programming. 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