SynthesisBMC medical informatics and decision making2025
Data quality assessment in healthcare, dimensions, methods and tools: a systematic review.
Synthesis in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Article
- Opportunities and Challenges in Using National EHR Networks for AI in Learning Health Systems.Learning health systems · 2026Article
- Data pipeline quality: development and validation of a quality assessment tool for data-driven algorithms and artificial intelligence in healthcare.BMJ health & care informatics · 2026Article
- Development and application of the vaccine reporting quality score to evaluate the vaccine adverse event reporting system.Frontiers in immunology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundData quality is a complex and multifaceted concept with varying definitions depending on context. In healthcare, high-quality data is essential for clinical decision-making, patient outcomes, and research. Despite its importance, no universally accepted definition of data quality exists, and its assessment remains challenging due to the diversity of dimensions and methodologies involved. This systematic review aims to identify key dimensions of data quality in healthcare, examine methodologies used for assessment, and explore tools and software applications developed to evaluate data quality.
methodsWe searched three information databases namely PubMed, Web of Science, and Scopus for articles published up to November 11, 2024, that discussed dimensions, methods and developed tools for data quality assessment (DQA). We aimed to focus on the data quality dimensions (DQDs)evaluated in the included studies, the assessment methods applied, and the tools developed for evaluating healthcare data, and to systematically categorize these aspects.
resultsA total of 44 studies were included, revealing significant variation in the number and definitions of DQDs assessed, with completeness, plausibility, and conformance being the most frequently evaluated. Diverse methodologies were employed to assess these dimensions, including rule-based systems, statistical methods, enhanced definitions, and comparisons with external gold standards. The studies also highlighted a wide range of tools and software applications used to support DQA in healthcare.
conclusionUnderstanding and applying appropriate DQDs and assessment methods are critical for ensuring that healthcare data supports valid clinical and research outcomes. This review provides a foundation for selecting suitable evaluation frameworks and tools, thereby enhancing data quality management and utilization in healthcare settings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.