The definition of “data quality” varies, but the most widely accepted definition is “fitness for use” [2], which refers to data that are current, accurate, interconnected, and provide tangible value to users. Low-quality data can lead to operational losses, delays, and increased costs associated with data cleansing, potentially resulting in higher prices for products or services in data-related industries [3]. Additionally, the quality of data can significantly impact the performance of artificial intelligence (AI) models [4]. Particularly in healthcare, the use of low-quality data in areas such as treatment, surgery, research, and policy decisions can result in significant losses. In the United States, the use of big data in healthcare may save approximately $300 billion in economic costs annually [5]. In this context, low-quality healthcare data pose a threat to public life and health and can lead to inefficiencies in the healthcare system. Therefore, high-quality healthcare data are essential for ongoing healthcare data research.

Various data management quality methods are being researched and reported. In 2003, the WHO published guidelines to enhance the quality of healthcare data [6]. These guidelines provide directives for healthcare professionals and information managers, enhancing understanding of all aspects of data collection and creation. In Korea, organizations such as the National Information Society Agency (NIA) [7] have established guidelines and directives for data quality management, and the Korea Data Industry Promotion Agency (KData) (http://dataqual.co.kr/bbs/content.php?co_id=quality_concept) has been operating a data quality certification system since 2006. This highlights the national and social recognition of the need for systematic management to consistently maintain and improve data quality from the user’s perspective.

The NIA is building and releasing AI learning data. After quality assessment by the Telecommunications Technology Association (TTA)—an external organization that does not build the data—according to the guidelines and directives for data quality management, the data are released via AI-Hub (https://aihub.or.kr). In big data research using laboratory data, external quality assessment results are used to evaluate data quality or accuracy [8, 9]. However, current national and international research on healthcare data quality lacks clearly defined standards or criteria. Specifically, standards and quality management criteria reflecting the unique characteristics of healthcare data, such as imaging information and biometric signals, remain insufficient. This gap highlights the need to develop quality management measures and ensure the production and utilization of high-quality healthcare data.

Research on quality indicators and systems that align with the specific nature of healthcare data are limited [10, 11]. Furthermore, healthcare data are not only voluminous but also varied in type, form, and attributes [12]. Therefore, a quality management system suited to the characteristics of healthcare data is necessary for effective management and utilization. We propose three stages of quality management direction (Fig. 1), as follows.

First, customized quality assessment indicators for healthcare data must be established. Numerous studies on quality indicators for measuring data quality have been conducted [11]. However, the terminology and definitions of quality assessment indicators lack consistency, leading to confusion and trial-and-error because of the adoption of different quality elements and measurement items. Hwang, et al. [12] searched the literature from 1990 to 2023 using the search terms “data quality,” “data quality assessment,” and “data quality dimensions” in Google Scholar to examine the diversity of quality indicators. Their search yielded 23 publications with 80 different data quality assessment indicators with various definitions. This implies that the data quality assessment indicators used in research use the same term with different definitions or different terms with the same definition (Table 1). Therefore, a consensus on the definition of data quality assessment indicators is needed to quantify data quality. In particular, quality assessment indicator research that reflects the characteristics of healthcare data is needed. In other words, to ensure the validity and reliability of healthcare data quality assessment, it is first necessary to clearly conceptualize the definitions of terms used in measuring quality. Therefore, further studies are required to redefine customized quality assessment criteria for healthcare data.

Second, a method for quantifying customized quality indicators for healthcare data and benchmarks for assigning quality evaluation scores must be established. For example, Lee and Shin [13] reported that labeling accuracy, one of the quality indicators, can be set at different levels depending on the similarity among the characteristic variables of the data and the level of class imbalance. This implies that the level of quality indicators for healthcare data quality assessment should be determined based on characteristics such as data similarity, sample size, and imbalance. Data quality indicators are evaluated both quantitatively and qualitatively. Quantitative evaluation is based on factors such as data completeness, validity, consistency, and accuracy, and quality levels are defined in four classes (ace, high, middle, low) using the Six Sigma concept [7]. In qualitative evaluation, evaluators subjectively judge criteria through a checklist for each indicator by answering yes-or-no questions [7].

Additionally, the measurement of quality indicators varies depending on the data type (structured/unstructured). As healthcare data are multimodal, it is necessary to review the appropriateness of the criteria for quantifying quality indicators considering the data characteristics. Therefore, further studies are required to establish customized quality assessment criteria for healthcare data.

Third, a unified quality score presentation should be established to facilitate an intuitive understanding of data quality by the end user. Moges, et al. [14] analyzed the relative importance of quality indicators by surveying private companies in various countries, which revealed that the importance of quality items varies by industry. The relative importance of quality indicators for healthcare data can be analyzed to establish a unified quality score. Hence, studies on calculating a unified score through the quantification of quality assessments are needed.

Data quality assessment requires a multidisciplinary approach to reflect the demand for sustainable, high-quality data across various domains. Therefore, research on healthcare data quality should not be limited to specific domain knowledge areas but can be conducted as a joint study by experts with knowledge in various fields such as medicine, data science, statistics, and information technology. A creative approach to data quality necessitates effective interdisciplinary collaboration among experts from various fields [15].

In conclusion, we emphasize the necessity of high-quality data and present a direction for establishing quality indicators and systems suitable for the characteristics of healthcare data. Developing a clear and consistent definition of data quality and systematic methods and approaches for data quality assessment require more extensive research.