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Abstract Background Hysterectomy is a common surgery among reproductive-aged U.S. patients, with rates highest among Black patients in the South. There is limited insight on causes of these racial differences. In the U.S., electronic medical records (EMR) data can offer richer detail on factors driving surgical decision-making among reproductive-aged populations than insurance claims-based data. Our objective in this cohort profile paper is to describe the Carolina Hysterectomy Cohort (CHC), a large EMR-based case-series of premenopausal hysterectomy patients in the U.S. South, supplemented with census and surgeon licensing data. To demonstrate one strength of the data, we evaluate whether patient and surgeon characteristics differ by insurance payor type. Methods We used structured and abstracted EMR data to identify and characterize patients aged 18–44 years who received hysterectomies for non-cancerous conditions between 10/02/2014–12/31/2017 in a large health care system comprised of 10 hospitals in North Carolina. We used Chi-squared and Kruskal Wallis tests to compare whether patients’ socio-demographic and relevant clinical characteristics, and surgeon characteristics differed by patient insurance payor (public, private, uninsured). Results Of 1857 patients (including 55% non-Hispanic White, 30% non-Hispanic Black, 9% Hispanic), 75% were privately-insured, 17% were publicly-insured, and 7% were uninsured. Menorrhagia was more prevalent among the publicly-insured (74% vs 68% overall). Fibroids were more prevalent among the privately-insured (62%) and the uninsured (68%). Most privately insured patients were treated at non-academic hospitals (65%) whereas most publicly insured and uninsured patients were treated at academic centers (66 and 86%, respectively). Publicly insured and uninsured patients had higher median bleeding (public: 7.0, uninsured: 9.0, private: 5.0) and pain (public: 6.0, uninsured: 6.0, private: 3.0) symptom scores than the privately insured. There were no statistical differences in surgeon characteristics by payor groups. Conclusion This novel study design, a large EMR-based case series of hysterectomies linked to physician licensing data and manually abstracted data from unstructured clinical notes, enabled identification and characterization of a diverse reproductive-aged patient population more comprehensively than claims data would allow. In subsequent phases of this research, the CHC will leverage these rich clinical data to investigate multilevel drivers of hysterectomy in an ethnoracially, economically, and clinically diverse series of hysterectomy patients.