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Assessing and improving data quality in health registries: a systematic review of dimensions, frameworks, and
Seyyedeh Fatemeh Mousavi Baigi1,2, Seyyed Mohammad Tabatabaei3,4, Abbas Sheikhtaheri5,6
1Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences, Mashhad University of Medical Sciences, Mashhad, 9177948964, Iran.
Objectives:
To systematically synthesize how data quality (DQ) in disease and health outcomes registries has been conceptualized, assessed, and improved, emphasizing artificial intelligence (AI) and machine learning methods within registry-based DQ workflows.
Materials And Methods:
Following PRISMA 2020 guidelines, a comprehensive search was conducted across PubMed, Embase, Scopus, Web of Science, IEEE Xplore, Google Scholar, and Google through October 26, 2025. Eligible studies evaluated DQ dimensions, frameworks, methods, tools, or algorithms in structured health registries using conventional or AI-based approaches. Methodological quality was appraised using the Mixed Methods Appraisal Tool. Findings were synthesized using qualitative thematic analysis.
Results:
A total of 119 studies were included. Completeness (111 studies, 93.3%) and accuracy (93 studies, 78.2%) were the most frequently assessed DQ dimensions, whereas representativeness and coverage were less frequently evaluated. Conventional rule-based, statistical, and audit approaches remained dominant. Artificial intelligence/machine learning methods were applied selectively for anomaly detection, missing-data tasks, record linkage, and automated data extraction. However, implementation remained constrained by heterogeneous data structures, limited interoperability, and insufficient reference standards. Reported limitations most commonly involved methodological and analytical constraints, incomplete data capture, and restricted source verification. Frameworks were predominantly assessment-oriented, with limited integration of lifecycle-based quality management strategies.
Discussion:
Despite increasing interest in AI-enabled DQ improvement, registry quality management remains retrospective, fragmented, and insufficiently integrated with lifecycle-oriented infrastructures.
Conclusion:
Advancing registry DQ requires a transition from retrospective validation toward proactive, lifecycle-integrated, and AI-enabled quality ecosystems. Registry-specific frameworks, interoperable infrastructures, and benchmarking strategies are essential to support scalable and trustworthy registry data systems.
Prospero Registration Id:
CRD420251176960 (https://www.crd.york.ac.uk/PROSPERO/view/CRD420251176960).
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