Evidence map›Paper›PMID 33717311›Full record

ArticleAsian bioethics review2019

Hypocrisy Around Medical Patient Data: Issues of Access for Biomedical Research, Data Quality, Usefulness for the Purpose and Omics Data as Game Changer.

Erwin Tantoso, Wing-Cheong Wong, Wei Hong Tay, Joanne Lee, Swati Sinha, Birgit Eisenhaber, Frank Eisenhaber

Abstract read
In one paragraph

Article in Asian bioethics review, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

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

  1. Digital Health Data Quality Issues: Systematic Review.Journal of medical Internet research · 2023
    Pooled it
  2. Degree Background and NIH K-to-R01 Transition Among Investigators Using Experimental Neuroscience to Advance Psychiatry.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026
    Article
  3. Article
  4. Article
  5. Ethics in the Era of Big Data.Asian bioethics review · 2019
    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

7 authors.

Erwin TantosoBioinformatics Institute (BII), Agency for Science and Technology (ASTAR), 30 Biopolis Street, #07-01, Matrix, Singapore, 138671 Singapore.
Wing-Cheong WongBioinformatics Institute (BII), Agency for Science and Technology (ASTAR), 30 Biopolis Street, #07-01, Matrix, Singapore, 138671 Singapore.
Wei Hong TayBioinformatics Institute (BII), Agency for Science and Technology (ASTAR), 30 Biopolis Street, #07-01, Matrix, Singapore, 138671 Singapore.
Joanne LeeBioinformatics Institute (BII), Agency for Science and Technology (ASTAR), 30 Biopolis Street, #07-01, Matrix, Singapore, 138671 Singapore.
Swati SinhaBioinformatics Institute (BII), Agency for Science and Technology (ASTAR), 30 Biopolis Street, #07-01, Matrix, Singapore, 138671 Singapore.
Birgit EisenhaberBioinformatics Institute (BII), Agency for Science and Technology (ASTAR), 30 Biopolis Street, #07-01, Matrix, Singapore, 138671 Singapore.
Frank EisenhaberBioinformatics Institute (BII), Agency for Science and Technology (ASTAR), 30 Biopolis Street, #07-01, Matrix, Singapore, 138671 Singapore.ORCID 0000-0002-9599-5420

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Whether due to simplicity or hypocrisy, the question of access to patient data for biomedical research is widely seen in the public discourse only from the angle of patient privacy. At the same time, the desire to live and to live without disability is of much higher value to the patients. This goal can only be achieved by extracting research insight from patient data in addition to working on model organisms, something that is well understood by many patients. Yet, most biomedical researchers working outside of clinics and hospitals are denied access to patient records when, at the same time, clinicians who guard the patient data are not optimally prepared for the data's analysis. Medical data collection is a time- and cost-intensive process that is most of all tedious, with few elements of intellectual and emotional satisfaction on its own. In this process, clinicians and bioinformaticians, each group with their own interests, have to join forces with the goal to generate medical data sets both from clinical trials and from routinely collected electronic health records that are, as much as possible, free from errors and obvious inconsistencies. The data cleansing effort as we have learned during curation of Singaporean clinical trial data is not a trivial task. The introduction of omics and sophisticated imaging modalities into clinical practice that are only partially interpreted in terms of diagnosis and therapy with today's level of knowledge warrant the creation of clinical databases with full patient history. This opens up opportunities for re-analyses and cross-trial studies at future time points with more sophisticated analyses of the same data, the collection of which is very expensive.

Indexed as

Clinical patient data qualityElectronic health recordGenome sequencingOmics dataPatient data privacy

Identifiers

PMID33717311
PMCPMC7747340

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.