Presentation (CTI)

OBJECTIVES: Multinational real-world evidence (RWE) is increasingly important for regulatory, health technology assessment (HTA), and reimbursement decisions. However, studies that combine patient-level data across countries can be difficult, particularly in Asia-Pacific, where privacy laws and data-transfer rules differ across China, Japan, South Korea, Singapore, and India. Federated data networks (FDNs) offer a privacy-preserving alternative by allowing the same standardized analysis to run within each participating site, while only summary results are shared. This review describes the global FDN landscape and considers their value for HTA-relevant RWE generation in multinational settings.
METHODS: A structured landscape review used peer-reviewed publications, regulatory reports, and network documentation available through 2025. Networks were assessed by geography, common data model, governance approach, scale, and Asia-Pacific presence. Briganti et al.’s 2024 OHDSI taxonomy was used as the reference framework, and key figures were checked against primary sources, including reports and publications from EHDEN, DARWIN EU, Sentinel, PCORnet, TriNetX, HONEUR, and OHDSI APAC.
RESULTS: The review identified 43 FDNs globally, with the strongest concentration in Europe and the United States. Major examples include EHDEN, DARWIN EU, FDA Sentinel, PCORnet, TriNetX, and HONEUR, demonstrating the ability of federated approaches to support evidence generation while keeping data under local control. In Asia-Pacific, existing OMOP-based and network activity suggests growing readiness for multi-country studies. These platforms may be useful in disease areas with high regional burden where data access, sovereignty, and privacy concerns can limit traditional pooled-data research. Key limitations include inconsistent coding, variable data quality, residual confounding, site-level governance differences, and effort required to harmonize analyses across countries.
CONCLUSIONS: FDNs provide a practical and scalable approach for generating privacy-preserving, multinational RWE. With appropriate data standards, governance, and quality checks, they can support more reliable evidence generation for HTA, regulatory, and healthcare decision-making across diverse regions.