Evidence map›Paper›PMID 31641688›Full record

ArticleLearning health systems2019

Commercial use of health data-A public "trial" by citizens' jury.

Mary P Tully, Lamiece Hassan, Malcolm Oswald, John Ainsworth

Abstract read
In one paragraph

Article in Learning health systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing 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

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

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Trading off accuracy and explainability in AI decision-making: findings from 2 citizens' juries.Journal of the American Medical Informatics Association : JAMIA · 2021
    Article
  12. Article
  13. Sharing administrative health data with private industry: A report on two citizens' juries.Health expectations : an international journal of public participation in health care and health policy · 2021
    Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. 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

4 authors.

Mary P TullyHealth E-Research Centre, Division of Imaging, Informatics and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health University of Manchester, Manchester Academic Health Science Centre Manchester UK.ORCID https://orcid.org/0000-0003-2100-3983
Lamiece HassanHealth E-Research Centre, Division of Imaging, Informatics and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health University of Manchester, Manchester Academic Health Science Centre Manchester UK.ORCID https://orcid.org/0000-0002-5888-422X
Malcolm OswaldSchool of Law, Faculty of Humanities University of Manchester Manchester UK.ORCID https://orcid.org/0000-0001-9197-7973
John AinsworthHealth E-Research Centre, Division of Imaging, Informatics and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health University of Manchester, Manchester Academic Health Science Centre Manchester UK.ORCID https://orcid.org/0000-0002-2187-9195

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionSurveys suggest a dichotomy in how citizens view research for public benefit and research for commercial gain. Therefore, a research initiative, such as a learning health system, which works for both public and commercial benefit, may be controversial and lower public trust.

methodsThis study aimed to investigate what informed citizens considered to be appropriate uses of health data in a learning health system and why they made those decisions. Two-paired 4-day juries were run, with different jurors but the same purpose, expert witnesses, and facilitators. Overall, 694 people applied; 36 jurors were selected to match criteria based on demographics and privacy views. Jurors considered whether and why eight exemplars of anonymised patient data were acceptable. The exemplars were either planned initiatives to improve care pathways (Planned Examples) or possible commercial data uses (Potential Examples).

resultsThese citizens' juries found that all Planned and two of the Potential Examples were considered appropriate by most, but not all, jurors because they could deliver public benefit. In general, positive health outcomes for patients were more acceptable than improved efficiency of services for the NHS, although they recognised that the latter also improved health. Jurors had concerns about whether improving efficiency would lead to inequitable distribution or closure of services, based on their existing understanding from media reports. Commercial gain that accrued secondary to this benefit was acceptable, with some jurors becoming more accepting of commercial uses as they understood them better. Prioritising profit, however, was unacceptable, regardless of any governance arrangements.

conclusionsJurors tended to be more accepting of data sharing to both private and public sectors after the jury process. Many jurors accept commercial gain if public benefit is achieved. Some were suspicious of data sharing for efficiency gains. Juries elicited more informed and nuanced judgement from citizens than surveys.

Indexed as

commercial usehealth datapublic opinion

Identifiers

PMID31641688
PMCPMC6802529

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.