BACKGROUND

Data 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.

METHODS

We 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.

RESULTS

A 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.

CONCLUSION

Understanding 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.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1186/s12911-025-03136-y.