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A clinical data outcomes archive for patients presenting to a fetal center, a focus on efficiency..

Tom Reynolds MFA MBA Matt Goldshore MD PhD MPH Sabrina Flohr MPH Sierra Land MA Anna Bostwick MPH Leny Mathew PhD MS Taylor Wild MD Juliana Gebb MD Holly Hedrick MD (all Children's Hospital of Philadelphia affiliated)


Poster # 89


Introduction: Robust clinical datasets beginning with fetal diagnosis through long-term follow-up are an important adjunct to biologic/omics discovery. This is especially important for patients of a fetal center since many physiologic and neurodevelopmental attributes do not manifest until later childhood when multi-level factors have a direct influence on health outcomes. Electronic medical records (EMR) create opportunity for efficient data collection. However, documentation structures are not designed for acquisition of key attributes, and changes over time and between-clinician differences may affect resultant output. In most EMRs, the fetus lacks a digital identity, requiring relinking attributes documented in the maternal chart to the pediatric EMR. Therefore, EMR derived datasets have limited ability to accurately characterize the phenotypic presentation and care trajectory of complex patients. Moreover, current data capture 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 EMR data into an analytic-grade database for multi-purpose use. Methods: A unified platform for longitudinal follow-up of maternal-child dyads cared for at our fetal center was constructed with a data dictionary based on multidisciplinary/interprofessional expert input; a multi-dimensional identity for each patient, fetus, and pregnancy, and a process by which EMR derived and chart-abstracted data is validated by a well-trained team. Descriptive analyses were performed for data acquired between July 2022 - June 2023. Results: 6,182,978 datapoints were validated for 7,662 patients across 12 conditions. 2% of data points were found to be unreliable/undocumented. 84% of data points were derived from the EMR. 85% of condition specific variables required manual chart abstraction. Conclusions: Our unified clinical data outcomes archive successfully merges EMR-derived and manually abstracted documentation for perinatal prognostication, longitudinally characterizing medical, cognitive, and psychosocial follow-up of maternal-child dyads important to augmenting biologic and genomic discovery.

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