NHS England Launches Synthetic EHR for Safe Clinical Research
Daily Brief

NHS England Launches Synthetic EHR for Safe Clinical Research

NHS England launched a synthetic EHR program for safer clinical research data sharing. It offers researchers access to 10 million anonymized health record…

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NHS England has launched a synthetic electronic health record (EHR) program intended to let researchers work with realistic, EHR-like data while reducing exposure of real patient records. The program is positioned as a safer route to data sharing for clinical research at national scale.

NHS England opens synthetic EHR program with 10 million anonymized records

NHS England announced a synthetic EHR initiative designed to support safer clinical research data sharing. The program provides researchers access to a synthetic dataset representing 10 million anonymized health records, with example research areas including diabetes and cancer.

The stated goal is to enable analysis and model development on realistic clinical patterns without directly exposing underlying patient records, aligning the research workflow with strict data protection expectations while still giving teams enough signal to explore hypotheses and build prototypes.

  • Faster research cycles without live PHI: Data teams can start feature engineering, cohort logic, and model experimentation on EHR-shaped data earlier—before negotiating access to sensitive production datasets.
  • Lower re-identification risk by design: For privacy and compliance stakeholders, synthetic EHRs can reduce the operational need to distribute identifiable or quasi-identifiable records across institutions and projects.
  • New baseline for “safe sharing” expectations: A national health system offering synthetic EHR access may shift what funders, IRBs, and partners consider acceptable for exploratory work—raising pressure to justify why real records are required.
  • Validation still matters: Teams will need clear guidance on how results from synthetic cohorts translate to real-world performance (e.g., bias, rare event behavior), especially for studies tied to clinical decisions.