ReviewNPJ digital medicine2025
Crossing borders securely: synthetic data and federated networks for privacy-preserving access to real-world data and emerging use cases.
Review in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Real-world data sources for pediatric cancers: a targeted literature review.ESMO real world data and digital oncology · 2026Article
- R WE ready for reimbursement? A round-up of developments in real-world evidence relating to health technology assessment: part 26.Journal of comparative effectiveness research · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The demand for demographically and geographically diverse, high-quality, fit-for-purpose real-world data has been increasing to support regulatory and other healthcare decision making. Accessing and sharing healthcare data across sites, regions, and countries while ensuring data privacy has been a long-standing challenge. We discuss synthetic data and federated data networks as examples of emerging privacy-preserving technologies and provide real-life use cases from government, industry, and academia with their opportunities and challenges.
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.