Transforming Fragmented Health Data into Federated Research Infrastructure

Research environments were constrained by structural fragmentation across systems and institutions.

This resulted in:

• Inconsistent data models and lack of standardisation
• Limited ability to combine datasets across organisations
• High overhead in data preparation and reconciliation
• Governance constraints on data access and processing
• Lack of scalable environments for analysing sensitive data

A federated data infrastructure was implemented to enable controlled integration and analysis across distributed datasets.

This included:

• Standardisation of data structures and metadata across sources
• Federated access models enabling distributed data control
• Controlled analytical environments for secure data processing
• Reproducible pipelines for data transformation and analysis

• Reduced time required for dataset preparation
• Increased consistency across integrated datasets
• Improved ability to conduct multi-source analysis
• Strengthened governance, traceability, and control
• Scalable infrastructure for ongoing research use

Fragmentation is not only a technical issue. It is a structural property of how systems are implemented across organisations.

Centralising data without resolving differences in structure, semantics, and governance does not scale. The constraint is not access, but the ability to process and align data consistently across independent systems.

Federated models only work when structure, access, and processing are aligned. Without this, increasing access increases complexity rather than capability.

Standards and governance frameworks were embedded directly into data models, processing pipelines, and access controls to ensure consistent, traceable, and controlled data use across distributed research environments.

This included:

• FAIR Principles structured, reusable, and traceable data
• OMOP Common Data Model harmonised structure for multi-source research datasets
• openEHR where applicable consistent clinical data modelling and semantics
• IHE interoperability profiles XDS XDR XCA structured exchange across institutions
• Audit and lineage frameworks full traceability from ingestion through analysis
• Data governance and security frameworks controlled access, accountability, and compliance