Data on near and long-term outcomes are critical for the care of all maternal-fetal patients presenting to a fetal center. This is important since physiologic and neurodevelopmental attributes do not manifest until childhood when multi-level factors influence health outcomes. Electronic health records (EHR) documentation structures are not designed for acquisition of key attributes, and changes over time and between-clinician differences can affect resultant output. Therefore, EHR derived datasets have limited ability to accurately characterize the clinical presentation and care trajectory of patients with congenital anomalies. Moreover, the fetus lacks a digital identity and requires relinking attributes documented in the maternal chart to those in the pediatric EHR. This conundrum amplifies in the setting of multiple gestation, returning maternal patients, and pregnancies with fetal demise. Current data systems result in incomplete abstraction of variables that may confound, mediate, or moderate critical associations. Our objective was to develop and implement a prospective data capture platform to transform EHR data into an analytic-grade database for multi-purpose use. A unified platform for longitudinal follow-up of maternal-child dyads named the Clinical Outcomes Data Archive (CODA) was constructed. CODA was designed using a data dictionary based on multidisciplinary input, a relational identity for each patient, fetus, and pregnancy, and a process by which EHR-sourced and chart-abstracted data are validated. Descriptive analyses were performed for data acquired between July 2022 - July 2023, and a comparison of studies before and after implementation of CODA is presented. 5,394,106 data points were validated for 7,662 patients across 12 conditions. 2% of data points were found to be unreliable or undocumented. 91% of data points were sourced from the EHR. 85% of condition-specific variables required manual chart abstraction. The study conducted with CODA contributed to 18 studies. CODA successfully merges EHR-sourced and manually abstracted documentation for longitudinal study of the maternal-child dyad.