In 2019, the Innovative Medicines Initiative (IMI) funded the ConcePTION project-Building an ecosystem for better monitoring and communicating safety of medicines use in pregnancy and breastfeeding: validated and regulatory endorsed workflows for fast, optimised evidence generation-with the vision that there is a societal obligation to rapidly reduce uncertainty about the safety of medication use in pregnancy and breastfeeding. The present paper introduces the set of concepts used to describe the European data sources involved in the ConcePTION project and illustrates the ConcePTION Common Data Model (CDM), which serves as the keystone of the federated ConcePTION network. Based on data availability and content analysis of 21 European data sources, the ConcePTION CDM has been structured with six tables designed to capture data from routine healthcare, three tables for data from public health surveillance activities, three curated tables for derived data on population (e.g., observation time and motherchild linkage), plus four metadata tables. By its first anniversary, the ConcePTION CDM has enabled 13 data sources to run common scripts to contribute to major European projects, demonstrating its capacity to facilitate effective and transparent deployment of distributed analytics, and its potential to address questions about utilization, effectiveness, and safety of medicines in special populations, including during pregnancy and breastfeeding, and, more broadly, in the general population.

Objectives Creation of new algorithms to identify pregnancies in automated health care claims databases is of public health importance, as it allows us to learn more about medication use and safety in a vulnerable population. Previous algorithms were largely created using international classification of disease codes, but despite the U.S. code transition in 2015, few algorithms are available with the latest ICD‐10‐CM codes. Methods Using U.S. IBM MarketScan® Commercial Claims and Encounters and Multi‐State Medicaid databases for women aged 10–64 years during 2014 and 2016, two pregnancy algorithms (ICD‐9‐CM and ICD‐10‐CM) were created using expert clinical review. The algorithms were evaluated by assessing the distribution of pregnancy outcomes (live birth and pregnancy losses) within each time‐based cohort and the ability of the algorithms to identify select medication use during pregnancy. Medication exposure, demographics, comorbidities, and pregnancy outcomes were compared to published literature estimates for the two time periods. Results For the IBM MarketScan® Commercial database, the algorithms identified 687,228 pregnancies in 2014 and 444,293 in 2016. In the IBM MarketScan® Medicaid database, 389,132 pregnancies in 2014 and 406,418 in 2016 were identified. Percentages of most pregnancy outcomes identified using the algorithms were similar to national data sources; however, percentages of preterm births and pregnancy losses were not comparable. Most medication use estimates from the algorithms were similar to or higher than published estimates. Conclusions By incorporating the latest ICD‐10‐CM codes, the new algorithms provide more complete estimates of medication use during pregnancy than algorithms using the outdated codes.