Evidence map›Paper›PMID 33496785›Full record

ReviewJournal of the American Medical Informatics Association : JAMIA2021

Quality assessment of real-world data repositories across the data life cycle: A literature review.

Siaw-Teng Liaw, Jason Guan Nan Guo, Sameera Ansari, Jitendra Jonnagaddala, Myron Anthony Godinho, Alder Jose Borelli, Simon de Lusignan, Daniel Capurro, Harshana Liyanage, Navreet Bhattal and 3 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
47citing 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

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

  1. Pooled it
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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

13 authors.

Siaw-Teng LiawWHO Collaborating Centre on eHealth, School of Population Health, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia.ORCID 0000-0001-5989-3614
Jason Guan Nan GuoWHO Collaborating Centre on eHealth, School of Population Health, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia.
Sameera AnsariWHO Collaborating Centre on eHealth, School of Population Health, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia.
Jitendra JonnagaddalaWHO Collaborating Centre on eHealth, School of Population Health, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia.ORCID 0000-0002-9912-2344
Myron Anthony GodinhoWHO Collaborating Centre on eHealth, School of Population Health, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia.ORCID 0000-0002-0081-2506
Alder Jose BorelliWHO Collaborating Centre on eHealth, School of Population Health, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia.
Simon de LusignanNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.ORCID 0000-0002-8553-2641
Daniel CapurroFaculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia.
Harshana LiyanageNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom.
Navreet BhattalAustralian Institute of Health and Welfare, Canberra, Australian Capital Territory, Australia.
Vicki BennettAustralian Institute of Health and Welfare, Canberra, Australian Capital Territory, Australia.
Jaclyn ChanAustralian Institute of Health and Welfare, Canberra, Australian Capital Territory, Australia.
Michael G KahnDepartment of Pediatrics (Section of Informatics and Data Sciences), University of Colorado Anschutz Medical Campus, Denver, Colorado, USA.ORCID 0000-0003-4786-6875

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveData quality (DQ) must be consistently defined in context. The attributes, metadata, and context of longitudinal real-world data (RWD) have not been formalized for quality improvement across the data production and curation life cycle. We sought to complete a literature review on DQ assessment frameworks, indicators and tools for research, public health, service, and quality improvement across the data life cycle. MATERIALS AND

methodsThe review followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Databases from health, physical and social sciences were used: Cinahl, Embase, Scopus, ProQuest, Emcare, PsycINFO, Compendex, and Inspec. Embase was used instead of PubMed (an interface to search MEDLINE) because it includes all MeSH (Medical Subject Headings) terms used and journals in MEDLINE as well as additional unique journals and conference abstracts. A combined data life cycle and quality framework guided the search of published and gray literature for DQ frameworks, indicators, and tools. At least 2 authors independently identified articles for inclusion and extracted and categorized DQ concepts and constructs. All authors discussed findings iteratively until consensus was reached.

resultsThe 120 included articles yielded concepts related to contextual (data source, custodian, and user) and technical (interoperability) factors across the data life cycle. Contextual DQ subcategories included relevance, usability, accessibility, timeliness, and trust. Well-tested computable DQ indicators and assessment tools were also found.

conclusionsA DQ assessment framework that covers intrinsic, technical, and contextual categories across the data life cycle enables assessment and management of RWD repositories to ensure fitness for purpose. Balancing security, privacy, and FAIR principles requires trust and reciprocity, transparent governance, and organizational cultures that value good documentation.

Indexed as

Data AccuracyQuality ImprovementAnimalsLife Cycle Stagesdata custodianshipdata qualitydata stewardshipDQ assessment toolsDQ indicatorsDQ measuresliterature review

Identifiers

PMID33496785
PMCPMC8475229

What OpenQuestion holds

Textmetadata
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.