Evidence map›Paper›PMID 32839878›Full record

ArticleLife sciences, society and policy2020

Just data? Solidarity and justice in data-driven medicine.

Patrik Hummel, Matthias Braun

Abstract read
In one paragraph

Article in Life sciences, society and policy, 2020. 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.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

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

  1. Pooled it
  2. Review
  3. Article
  4. Review
  5. Review
  6. Review
  7. Article
  8. Towards trust-based governance of health data research.Medicine, health care, and philosophy · 2023
    Article
  9. Article
  10. Review
  11. Article
  12. Data justice and data solidarity.Patterns (New York, N.Y.) · 2022
    Review
  13. Review
  14. Article
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

2 authors.

Patrik HummelDepartment of Theology, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Kochstraße 6, 91054, Erlangen, Germany. patrik.hummel@fau.de.ORCID http://orcid.org/0000-0001-9668-0810
Matthias BraunDepartment of Theology, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Kochstraße 6, 91054, Erlangen, Germany.ORCID https://orcid.org/0000-0002-6687-6027

Funding

Bundesministerium für Bildung und Forschung (DE) 01GP1903ABundesministerium für Gesundheit (DE) ZMV/1 - 2517 FSB 013
6 · The paper itself

Abstract

This paper argues that data-driven medicine gives rise to a particular normative challenge. Against the backdrop of a distinction between the good and the right, harnessing personal health data towards the development and refinement of data-driven medicine is to be welcomed from the perspective of the good. Enacting solidarity drives progress in research and clinical practice. At the same time, such acts of sharing could-especially considering current developments in big data and artificial intelligence-compromise the right by leading to injustices and affecting concrete modes of individual self-determination. In order to address this potential tension, two key elements for ethical reflection on data-driven medicine are proposed: the controllability of information flows, including technical infrastructures that are conducive towards controllability, and a paradigm shift towards output-orientation in governance and policy.

Indexed as

Big DataArtificial IntelligenceBiomedical ResearchHumansInformation DisseminationMedicineAlgorithm ethicsControllabilityData-driven medicine, Precision medicineData ethicsJusticePrivacySolidarity

Identifiers

PMID32839878
PMCPMC7445015

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.