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

Author:

Liaw Siaw-Teng1ORCID,Guo Jason Guan Nan1,Ansari Sameera1,Jonnagaddala Jitendra1ORCID,Godinho Myron Anthony1ORCID,Borelli Alder Jose1,de Lusignan Simon2ORCID,Capurro Daniel3,Liyanage Harshana2,Bhattal Navreet4,Bennett Vicki4,Chan Jaclyn4,Kahn Michael G5ORCID

Affiliation:

1. WHO Collaborating Centre on eHealth, School of Population Health, Faculty of Medicine, UNSW Sydney, Sydney, New South Wales, Australia

2. Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom

3. Faculty of Engineering and Information Technology, University of Melbourne, Melbourne, Victoria, Australia

4. Australian Institute of Health and Welfare, Canberra, Australian Capital Territory, Australia

5. Department of Pediatrics (Section of Informatics and Data Sciences), University of Colorado Anschutz Medical Campus, Denver, Colorado, USA

Abstract

Abstract Objective Data 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 Methods The 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. Results The 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. Conclusions A 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.

Funder

Australian Institute of Health and Welfare

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

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