Evidence map›Paper›PMID 38446534›Full record

ReviewJMIR medical informatics2024

Frameworks, Dimensions, Definitions of Aspects, and Assessment Methods for the Appraisal of Quality of Health Data for Secondary Use: Comprehensive Overview of Reviews.

Jens Declerck, Dipak Kalra, Robert Vander Stichele, Pascal Coorevits

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in JMIR medical informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing 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

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

  1. Pooled it
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  16. Clinical Research Informatics: a Decade-in-Review.Yearbook of medical informatics · 2024
    Review
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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

4 authors.

Jens DeclerckDepartment of Public Health and Primary Care, Unit of Medical Informatics and Statistics, Ghent University, Ghent, Belgium.ORCID https://orcid.org/0000-0002-9743-9188
Dipak KalraDepartment of Public Health and Primary Care, Unit of Medical Informatics and Statistics, Ghent University, Ghent, Belgium.ORCID https://orcid.org/0000-0002-2998-9882
Robert Vander SticheleFaculty of Medicine and Health Sciences, Heymans Institute of Pharmacology, Ghent, Belgium.ORCID https://orcid.org/0000-0001-9118-9651
Pascal CoorevitsDepartment of Public Health and Primary Care, Unit of Medical Informatics and Statistics, Ghent University, Ghent, Belgium.ORCID https://orcid.org/0000-0002-6515-7514

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealth care has not reached the full potential of the secondary use of health data because of-among other issues-concerns about the quality of the data being used. The shift toward digital health has led to an increase in the volume of health data. However, this increase in quantity has not been matched by a proportional improvement in the quality of health data.

objectiveThis review aims to offer a comprehensive overview of the existing frameworks for data quality dimensions and assessment methods for the secondary use of health data. In addition, it aims to consolidate the results into a unified framework.

methodsA review of reviews was conducted including reviews describing frameworks of data quality dimensions and their assessment methods, specifically from a secondary use perspective. Reviews were excluded if they were not related to the health care ecosystem, lacked relevant information related to our research objective, and were published in languages other than English.

resultsA total of 22 reviews were included, comprising 22 frameworks, with 23 different terms for dimensions, and 62 definitions of dimensions. All dimensions were mapped toward the data quality framework of the European Institute for Innovation through Health Data. In total, 8 reviews mentioned 38 different assessment methods, pertaining to 31 definitions of the dimensions.

conclusionsThe findings in this review revealed a lack of consensus in the literature regarding the terminology, definitions, and assessment methods for data quality dimensions. This creates ambiguity and difficulties in developing specific assessment methods. This study goes a step further by assigning all observed definitions to a consolidated framework of 9 data quality dimensions.

Indexed as

data qualitydata quality assessmentdata quality dimensionsdata quality frameworkfit for purposesecondary use

Identifiers

PMID38446534
PMCPMC10955383

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.