Evidence map›Paper›PMID 31152528›Full record

ArticleJournal of medical Internet research2019

Use and Understanding of Anonymization and De-Identification in the Biomedical Literature: Scoping Review.

Raphaël Chevrier, Vasiliki Foufi, Christophe Gaudet-Blavignac, Arnaud Robert, Christian Lovis

Abstract readScoping Review
In one paragraph

Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

62 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Ten principles for data sharing and commercialization.Journal of the American Medical Informatics Association : JAMIA · 2021
    Guideline
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. Review
  9. Article
  10. Article
  11. Article
  12. Making the case for an International Childhood Cancer Data Partnership.Journal of the National Cancer Institute · 2025
    Article
  13. [Applications, challenges and a trustworthy use of artificial intelligence in public health].Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz · 2025
    Review
  14. Review
  15. Review
  16. Article
  17. Article
  18. Review
  19. Review
  20. Review

2 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Raphaël ChevrierDivision of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland.ORCID 0000-0002-0334-4883
Vasiliki FoufiDivision of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland.ORCID 0000-0002-8625-0734
Christophe Gaudet-BlavignacDivision of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland.ORCID 0000-0001-6527-5898
Arnaud RobertDivision of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland.ORCID 0000-0001-8692-7402
Christian LovisDivision of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland.ORCID 0000-0002-2681-8076

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe secondary use of health data is central to biomedical research in the era of data science and precision medicine. National and international initiatives, such as the Global Open Findable, Accessible, Interoperable, and Reusable (GO FAIR) initiative, are supporting this approach in different ways (eg, making the sharing of research data mandatory or improving the legal and ethical frameworks). Preserving patients' privacy is crucial in this context. De-identification and anonymization are the two most common terms used to refer to the technical approaches that protect privacy and facilitate the secondary use of health data. However, it is difficult to find a consensus on the definitions of the concepts or on the reliability of the techniques used to apply them. A comprehensive review is needed to better understand the domain, its capabilities, its challenges, and the ratio of risk between the data subjects' privacy on one side, and the benefit of scientific advances on the other.

objectiveThis work aims at better understanding how the research community comprehends and defines the concepts of de-identification and anonymization. A rich overview should also provide insights into the use and reliability of the methods. Six aspects will be studied: (1) terminology and definitions, (2) backgrounds and places of work of the researchers, (3) reasons for anonymizing or de-identifying health data, (4) limitations of the techniques, (5) legal and ethical aspects, and (6) recommendations of the researchers.

methodsBased on a scoping review protocol designed a priori, MEDLINE was searched for publications discussing de-identification or anonymization and published between 2007 and 2017. The search was restricted to MEDLINE to focus on the life sciences community. The screening process was performed by two reviewers independently.

resultsAfter searching 7972 records that matched at least one search term, 135 publications were screened and 60 full-text articles were included. (1) Terminology: Definitions of the terms de-identification and anonymization were provided in less than half of the articles (29/60, 48%). When both terms were used (41/60, 68%), their meanings divided the authors into two equal groups (19/60, 32%, each) with opposed views. The remaining articles (3/60, 5%) were equivocal. (2) Backgrounds and locations: Research groups were based predominantly in North America (31/60, 52%) and in the European Union (22/60, 37%). The authors came from 19 different domains; computer science (91/248, 36.7%), biomedical informatics (47/248, 19.0%), and medicine (38/248, 15.3%) were the most prevalent ones. (3) Purpose: The main reason declared for applying these techniques is to facilitate biomedical research. (4) Limitations: Progress is made on specific techniques but, overall, limitations remain numerous. (5) Legal and ethical aspects: Differences exist between nations in the definitions, approaches, and legal practices. (6) Recommendations: The combination of organizational, legal, ethical, and technical approaches is necessary to protect health data.

conclusionsInterest is growing for privacy-enhancing techniques in the life sciences community. This interest crosses scientific boundaries, involving primarily computer science, biomedical informatics, and medicine. The variability observed in the use of the terms de-identification and anonymization emphasizes the need for clearer definitions as well as for better education and dissemination of information on the subject. The same observation applies to the methods. Several legislations, such as the American Health Insurance Portability and Accountability Act (HIPAA) and the European General Data Protection Regulation (GDPR), regulate the domain. Using the definitions they provide could help address the variable use of these two concepts in the research community.

Indexed as

Biomedical ResearchData AnonymizationHumansReproducibility of Resultsanonymisationanonymizationconfidentialitydata protectionde-identificationdeidentificationprivacypseudonymizationscoping reviewsecondary use

Identifiers

PMID31152528
PMCPMC6658290

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.