ArticleJournal of medical Internet research2022
Next-Generation Capabilities in Trusted Research Environments: Interview Study.
Article in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Attitudes towards data access and sharing health data for research: a case study of Australian data custodian perspectives.Health information management : journal of the Health Information Management Association of Australia · 2026Article
- WiNGS-API: a federated genome/phenome data sharing platform enabling gene discovery and variant classification for rare diseases.Genome medicine · 2026Article
- Towards universal early screening for cerebral palsy: a roadmap for automated General Movements Assessment.EClinicalMedicine · 2025Review
- Article
- Twenty-Five Years of Evolution and Hurdles in Electronic Health Records and Interoperability in Medical Research: Comprehensive Review.Journal of medical Internet research · 2025Review
- Technical and legal aspects of federated learning in bioinformatics: applications, challenges and opportunities.Frontiers in digital health · 2025Review
- Advancing precision oncology with AI-powered genomic analysis.Frontiers in pharmacology · 2025Review
- Article
- Recent methodological advances in federated learning for healthcare.Patterns (New York, N.Y.) · 2024Review
- A Clinician's Guide to Sharing Data for AI in Ophthalmology.Investigative ophthalmology & visual science · 2024Review
- OpenSAFELY: A platform for analysing electronic health records designed for reproducible research.Pharmacoepidemiology and drug safety · 2024Article
- Article
- An interconnected data infrastructure to support large-scale rare disease research.GigaScience · 2024Article
- Efficacy of federated learning on genomic data: a study on the UK Biobank and the 1000 Genomes Project.Frontiers in big data · 2024Article
- Population health management genomic new-born screens and multi-omics intercepts.Frontiers in artificial intelligence · 2024Review
- Disclosure control of machine learning models from trusted research environments (TRE): New challenges and opportunities.Heliyon · 2023Review
- Machine learning models in trusted research environments - understanding operational risks.International journal of population data science · 2023Article
- A National Network of Safe Havens: Scottish Perspective.Journal of medical Internet research · 2022Article
- Identifying patient-level data risks in trusted research environments: Worked examples with synthetic data.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundA Trusted Research Environment (TRE; also known as a Safe Haven) is an environment supported by trained staff and agreed processes (principles and standards), providing access to data for research while protecting patient confidentiality. Accessing sensitive data without compromising the privacy and security of the data is a complex process.
objectiveThis paper presents the security measures, administrative procedures, and technical approaches adopted by TREs.
methodsWe contacted 73 TRE operators, 22 (30%) of whom, in the United Kingdom and internationally, agreed to be interviewed remotely under a nondisclosure agreement and to complete a questionnaire about their TRE.
resultsWe observed many similar processes and standards that TREs follow to adhere to the Seven Safes principles. The security processes and TRE capabilities for supporting observational studies using classical statistical methods were mature, and the requirements were well understood. However, we identified limitations in the security measures and capabilities of TREs to support "next-generation" requirements such as wide ranges of data types, ability to develop artificial intelligence algorithms and software within the environment, handling of big data, and timely import and export of data.
conclusionsWe found a lack of software or other automation tools to support the community and limited knowledge of how to meet the next-generation requirements from the research community. Disclosure control for exporting artificial intelligence algorithms and software was found to be particularly challenging, and there is a clear need for additional controls to support this capability within TREs.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.