Hosseinzadeh Elham, Afkanpour Marziyeh, Momeni Mehri, Tabesh Hamed
Department of Medical Informatics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Student Research Committee, Mashhad University of Medical Sciences, Mashhad, Iran.
BMC Med Inform Decis Mak. 2025 Aug 9;25(1):296. doi: 10.1186/s12911-025-03136-y.
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.
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.
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.
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.
The online version contains supplementary material available at 10.1186/s12911-025-03136-y.
数据质量是一个复杂且多维度的概念，其定义因上下文而异。在医疗保健领域，高质量数据对于临床决策、患者治疗结果和研究至关重要。尽管其重要性不言而喻，但目前尚无普遍接受的数据质量定义，并且由于涉及的维度和方法的多样性，其评估仍然具有挑战性。本系统综述旨在确定医疗保健领域数据质量的关键维度，研究用于评估的方法，并探索为评估数据质量而开发的工具和软件应用程序。
我们在三个信息数据库，即PubMed、科学网和Scopus中搜索截至2024年11月11日发表的文章，这些文章讨论了数据质量评估（DQA）的维度、方法和开发的工具。我们旨在关注纳入研究中评估的数据质量维度（DQD）、应用的评估方法以及为评估医疗保健数据而开发的工具，并对这些方面进行系统分类。
共纳入44项研究，结果显示所评估的DQD数量和定义存在显著差异，完整性、合理性和一致性是最常评估的维度。采用了多种方法来评估这些维度，包括基于规则的系统、统计方法、强化定义以及与外部金标准的比较。这些研究还强调了用于支持医疗保健领域DQA的广泛工具和软件应用程序。
理解和应用适当的DQD和评估方法对于确保医疗保健数据支持有效的临床和研究结果至关重要。本综述为选择合适的评估框架和工具提供了基础，从而加强医疗保健环境中的数据质量管理和利用。
在线版本包含可在10.1186/s12911-025-03136-y获取的补充材料。
