Evidence map›Paper›PMID 36812509›Full record

ReviewPLOS digital health2022

Best practices in the real-world data life cycle.

Joe Zhang, Joshua Symons, Paul Agapow, James T Teo, Claire A Paxton, Jordan Abdi, Heather Mattie, Charlie Davie, Aracelis Z Torres, Amos Folarin and 5 more

Abstract readReview
In one paragraph

Review in PLOS digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 3 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

34 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Review
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  6. Article
  7. Review
  8. Observational
  9. Article
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  12. Article
  13. Review
  14. Article
  15. Review
  16. Article
  17. Article
  18. Ten simple rules for humane data science.PLoS computational biology · 2023
    Article
  19. Review
  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

15 authors.

Joe ZhangInstititute of Global Health Innovation, Imperial College London, London, United Kingdom.ORCID https://orcid.org/0000-0001-6040-2122
Joshua SymonsGenomics England, London, United Kingdom.ORCID https://orcid.org/0000-0002-7016-1104
Paul AgapowAstraZeneca, Cambridge, United Kingdom.
James T TeoDepartment of Neurology, King's College Hospital, London, United Kingdom.ORCID https://orcid.org/0000-0002-6899-8319
Claire A PaxtonVerana Health, San Francisco, United States of America.
Jordan AbdiHolmusk, London, United Kingdom.ORCID https://orcid.org/0000-0002-2096-9658
Heather MattieHarvard T H Chan School of Public Health, Harvard University, Cambridge, United States of America.ORCID https://orcid.org/0000-0002-1263-2537
Charlie DavieDepartment of Neurology, Royal Free Hospital, London, United Kingdom.
Aracelis Z TorresVerana Health, San Francisco, United States of America.ORCID https://orcid.org/0000-0001-8576-9887
Amos FolarinMaudsley Biomedical Research Centre, King's College London, London, United Kingdom.ORCID https://orcid.org/0000-0002-0333-1927
Harpreet SoodHealth Education England, London, United Kingdom.
Leo A CeliHarvard T H Chan School of Public Health, Harvard University, Cambridge, United States of America.ORCID https://orcid.org/0000-0001-6712-6626
John HalamkaMayo Clinic, Rochester, United States of America.
Sara EapenValo Health, Boston, United States of America.ORCID https://orcid.org/0000-0001-6549-8290
Sanjay BudhdeoDepartment of Neurology, National Hospital for Neurology and Neurosurgery, London, United Kingdom.ORCID https://orcid.org/0000-0001-5377-6142

Funding

Medical Research Council MC_PC_19006
6 · The paper itself

Abstract

With increasing digitization of healthcare, real-world data (RWD) are available in greater quantity and scope than ever before. Since the 2016 United States 21st Century Cures Act, innovations in the RWD life cycle have taken tremendous strides forward, largely driven by demand for regulatory-grade real-world evidence from the biopharmaceutical sector. However, use cases for RWD continue to grow in number, moving beyond drug development, to population health and direct clinical applications pertinent to payors, providers, and health systems. Effective RWD utilization requires disparate data sources to be turned into high-quality datasets. To harness the potential of RWD for emerging use cases, providers and organizations must accelerate life cycle improvements that support this process. We build on examples obtained from the academic literature and author experience of data curation practices across a diverse range of sectors to describe a standardized RWD life cycle containing key steps in production of useful data for analysis and insights. We delineate best practices that will add value to current data pipelines. Seven themes are highlighted that ensure sustainability and scalability for RWD life cycles: data standards adherence, tailored quality assurance, data entry incentivization, deploying natural language processing, data platform solutions, RWD governance, and ensuring equity and representation in data.

Identifiers

PMID36812509
PMCPMC9931348

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.