Big Data in medical research and EU data protection law: challenges to the consent or anonymise approach
Open accessAbstract
Medical research is increasingly becoming data-intensive; sensitive data are being re-used, linked and analysed on an unprecedented scale. The current EU data protection law reform has led to an intense debate about its potential effect on this processing of data in medical research. To contribute to this evolving debate, this paper reviews how the dominant 'consent or anonymise approach' is challenged in a data-intensive medical research context, and discusses possible ways forwards within the EU legal framework on data protection. A large part of the debate in literature focuses on the acceptability of adapting consent or anonymisation mechanisms to overcome the challenges within these approaches. We however believe that the search for ways forward within the consent or anonymise paradigm will become increasingly difficult. Therefore, we underline the necessity of an appropriate research exemption from consent for the use of sensitive personal data in medical research to take account of all legitimate interests. The appropriate conditions of such a research exemption are however subject to debate, and we expect that there will be minimal harmonisation of these conditions in the forthcoming EU Data Protection Regulation. Further deliberation is required to determine when a shift away from consent as a legal basis is necessary and proportional in a data-intensive medical research context, and what safeguards should be put in place when such a research exemption from consent is provided.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Natural language processing methods have shown their usefulness for extraction of structured clinical data from unstructured data elements.
“Natural language processing (NLP) methods have shown their usefulness for extraction of structured clinical data from unstructured data elements [36], [37].”
From Secondary Use of Electronic Health Record: Opportunities and Challenges (2020) · cites this paper as [37]
Natural language processing (NLP) methods have shown their usefulness for extraction of structured clinical data from unstructured data elements.
“Natural language processing (NLP) methods have shown their usefulness for extraction of structured clinical data from unstructured data elements[36],[37].”
From Secondary Use of Electronic Health Record: Opportunities and Challenges (2020) · cites this paper as [37]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
The citation lists haven’t been fetched yet
References 65 · Cited by 143
Research works printed but not linked: 1
- Mooney SJ, Westreich DJ, El-Sayed AM: Epidemiology in the era of Big Data. Epidemiology 2015; 26: 390–394. [DOI] [PMC free article] [PubMed] [Google Scholar]
- Authors
- Mostert, M., Bredenoord, A. L., Biesaart, M. C. I. H., & van Delden, J. J. M.
- Affiliations
- University Medical Center Utrecht (NL)
- Corresponding author
- Mostert, Menno
- Venue
- European Journal of Human Genetics
- Published
- 2015-11-11
- Volume
- 24
- Issue
- 7
- Pages
- 956-960
- ISSN
- 1018-4813 (ISSN-L)
- 1476-5438
- PMC
- PMC5070910
- OpenAlex
- W2129713986
- Cited by
- 143
- References
- 65
- Citations by year
- 2026: 4 · 2025: 10 · 2024: 10 · 2023: 18 · 2022: 16 · 2021: 20 · 2020: 21 · 2019: 16 · 2018: 15 · 2017: 9 · 2016: 4
- FWCI
- 126.85
- Type
- journal article
- Language
- en
- Update notices
- erratum (2016-06-16)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- European Journal of Human Genetics (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Physical Sciences › Computer Science › Information Systems › Thoreau and American Literature
- Keywords
- EU data protection law, sensitive personal data, data anonymization, EU data protection regulation, legitimate interests, data harmonization, data-intensive research
- MeSH terms
- Data Anonymization, European Union, Humans, Databases as Topic, Biomedical Research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review