Evidence map›Paper›PMID 30617331›Full record

ReviewNature medicine2019

Privacy in the age of medical big data.

W Nicholson Price, I Glenn Cohen

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Nature medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 490 papers, 8 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
490citing papers in PubMed, 8 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

490 citing papers in PubMed, 8 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Pooled it
  5. Pooled it
  6. Pooled it
  7. Pooled it
  8. Pooled it
  9. Article
  10. Article
  11. Article
  12. Review
  13. Review
  14. Review
  15. Article
  16. Review
  17. Article
  18. Review
  19. Article
  20. Article

430 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

2 authors.

W Nicholson PriceUniversity of Michigan Law School, Ann Arbor, MI, USA.
I Glenn CohenProject on Personalized Medicine, Artificial Intelligence, & Law, Petrie-Flom Center for Health Law Policy, Biotechnology, and Bioethics, Cambridge, MA, USA. igcohen@law.harvard.edu.

Funding

The Lifecycle of Health Data: Polices and PracticesR01CA214829 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI KARDIA, SHARON L, PLATT, JODYN ELIZABETH · 2018 to 2021
$2.4M
NCI NIH HHS R01 CA214829
6 · The paper itself

Abstract

Big data has become the ubiquitous watch word of medical innovation. The rapid development of machine-learning techniques and artificial intelligence in particular has promised to revolutionize medical practice from the allocation of resources to the diagnosis of complex diseases. But with big data comes big risks and challenges, among them significant questions about patient privacy. Here, we outline the legal and ethical challenges big data brings to patient privacy. We discuss, among other topics, how best to conceive of health privacy; the importance of equity, consent, and patient governance in data collection; discrimination in data uses; and how to handle data breaches. We close by sketching possible ways forward for the regulatory system.

Indexed as

Big DataDelivery of Health CarePrivacyHealth Insurance Portability and Accountability ActHumansUnited States

Identifiers

PMID30617331
PMCPMC6376961

What OpenQuestion holds

Textmetadata
LicenceTDM
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.