Answers are grounded in this paper and cite their sources.
As healthcare systems generate growing volumes of routinely collected clinical data, there is increasing demand for high-quality real-world evidence (RWE) to inform regulatory, clinical, and policy decisions. However, fragmentation of data sources, heterogeneous structures, and inconsistent coding systems present significant barriers to conducting reliable large-scale analyses. Federated data networks using a common data model—such as the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM)—address these challenges by enabling standardised, privacy-preserving analytics across diverse data sources. We have established a federated data network of seven UK-based partners, each maintaining routinely collected health care data mapped to the OMOP CDM. A central coordinating centre develops and distributes analytic code, which each partner runs locally on their secure OMOP instance. Only aggregated, non-identifiable results are shared back, preserving data privacy while enabling reproducible, scalable analyses. The onboarding process involved three steps: database description, benchmarking, and comprehensive data characterisation to support study feasibility and data understanding. The network includes five English NHS trusts, one primary care data provider, and one Scottish NHS trust, representing approximately 57.2 million individuals (without accounting for any overlap across sources), 1.73 billion drug exposures, and 854 million condition records.
No takes yet. Share an insight, caveat, or question.
Campanile et al. (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: