ArticleBMC medical ethics2017
A critique of the regulation of data science in healthcare research in the European Union.
Article in BMC medical ethics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Specific measures for data-intensive health research without consent: a systematic review of soft law instruments and academic literature.European journal of human genetics : EJHG · 2024Pooled it
- Enabling Demonstrated Consent for Biobanking with Blockchain and Generative AI.The American journal of bioethics : AJOB · 2025Article
- High-reward, high-risk technologies? An ethical and legal account of AI development in healthcare.BMC medical ethics · 2025Review
- Global reform population health management as stewarded by Higher Expert Medical Science Safety (HEMSS).Frontiers in artificial intelligence · 2025Article
- Gaps in the Global Regulatory Frameworks for the Use of Artificial Intelligence (AI) in the Healthcare Services Sector and Key Recommendations.Healthcare (Basel, Switzerland) · 2024Review
- Advances in the Application of AI Robots in Critical Care: Scoping Review.Journal of medical Internet research · 2024Article
- Research collaboration data platform ensuring general data protection.Scientific reports · 2024Article
- What prevents us from reusing medical real-world data in research.Scientific data · 2023Article
- The future regulation of artificial intelligence systems in healthcare services and medical research in the European Union.Frontiers in genetics · 2022Article
- The social licence for data-intensive health research: towards co-creation, public value and trust.BMC medical ethics · 2021Review
- Understanding the Uptake of Big Data in Health Care: Protocol for a Multinational Mixed-Methods Study.JMIR research protocols · 2020Article
- Informed consent within a learning health system: A scoping review.Learning health systems · 2020Article
- Leveraging real-world data to investigate multiple sclerosis disease behavior, prognosis, and treatment.Multiple sclerosis (Houndmills, Basingstoke, England) · 2020Review
- A framework for analysing learning health systems: Are we removing the most impactful barriers?Learning health systems · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The EU offers a suitable milieu for the comparison and harmonisation of healthcare across different languages, cultures, and jurisdictions (albeit with a supranational legal framework), which could provide improvements in healthcare standards across the bloc. There are specific ethico-legal issues with the use of data in healthcare research that mandate a different approach from other forms of research. The use of healthcare data over a long period of time is similar to the use of tissue in biobanks. There is a low risk to subjects but it is impossible to gain specific informed consent given the future possibilities for research. Large amounts of data on a subject present a finite risk of re-identification. Consequently, there is a balancing act between this risk and retaining sufficient utility of the data. Anonymising methods need to take into account the circumstances of data sharing to enable an appropriate balance in all cases. There are ethical and policy advantages to exceeding the legal requirements and thereby securing the social licence for research. This process would require the examination and comparison of data protection laws across the trading bloc to produce an ethico-legal framework compatible with the requirements of all member states. Seven EU jurisdictions are given consideration in this critique.
Indexed as
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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.