The use of Electronic Health Records (EHR) in pregnancy care and obstetrics-gynecology (OB/GYN) research has increased in recent years. In pregnancy, timing is important because clinical characteristics, risks, and patient management are different in each stage of pregnancy. However, the difficulty of accurately differentiating pregnancy episodes and temporal information of clinical events presents unique challenges for EHR phenotyping. In this work, we introduced the concept of time relativity and proposed a comprehensive framework of computational phenotyping for prenatal and postpartum episodes based on the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). We implemented it on the All of Us national EHR database and identified 6,280 pregnancies with accurate start and end dates among 5,399 female patients. With the ability to identify different episodes in pregnancy care, this framework provides new opportunities for phenotyping complex clinical events and gestational morbidities for pregnant women, thus improving maternal and infant health.
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