DQAgui: a graphical user interface for the MIRACUM data quality assessment tool

Abstract

Background With the growing impact of observational research studies, there is also a growing focus on data quality (DQ). As opposed to experimental study designs, observational research studies are performed using data mostly collected in a non-research context (secondary use). Depending on the number of data elements to be analyzed, DQ reports of data stored within research networks can grow very large. They might be cumbersome to read and important information could be overseen quickly. To address this issue, a DQ assessment (DQA) tool with a graphical user interface (GUI) was developed and provided as a web application. Methods The aim was to provide an easy-to-use interface for users without prior programming knowledge to carry out DQ checks and to present the results in a clearly structured way. This interface serves as a starting point for a more detailed investigation of possible DQ irregularities. A user-centered development process ensured the practical feasibility of the interactive GUI. The interface was implemented in the R programming language and aligned to Kahn et al.’s DQ categories conformance, completeness and plausibility. Results With DQAgui, an R package with a web-app frontend for DQ assessment was developed. The GUI allows users to perform DQ analyses of tabular data sets and to systematically evaluate the results. During the development of the GUI, additional features were implemented, such as analyzing a subset of the data by defining time periods and restricting the analyses to certain data elements. Conclusions As part of the MIRACUM project, DQAgui is now being used at ten German university hospitals for DQ assessment and to provide a central overview of the availability of important data elements in a datamap over 2 years. Future development efforts should focus on design optimization and include a usability evaluation.

Challenges of Data Quality in Clinical Data Life Cycle: A Systematic Review (Preprint)

“…Considerable effort has been made to improve the quality of EHRs. These efforts include the development of automated data quality assessment systems [9,46,103], organization of quality indicator events, and development of metrics. Data must be sufficiently flexible to be used for multiple purposes.…”

“…Data quality management organizations must continuously implement improvements to provide highquality data by adhering to the requirements of both data providers and consumers. Moreover, data must be continuously and accurately managed to provide high-quality medical services [9]. Accordingly, a support system must be institutionalized to continuously communicate with researchers on the use of medical data, and a foundation such as medical data standards must be established to ensure the uninterrupted provision of high-quality data.…”

“…Concordance can be considered as an indicator that determines whether the characteristics of different data are best expressed and stored based on standards. Concordance was mentioned in the studies that developed, experimented on, and evaluated quality management tools [9,40,55,[60][61][62][63]. The related terms mentioned in the articles paper included structure and standardization.…”

“…Therefore, integrated data must be used seamlessly with different types of data. Currently, various methods for integrated data management are being developed [3][4][5][6][7][8][9], but quality control standards are also set differently for each data, and discussions in this regard are challenging because of the nature of EHR data [10][11][12][13].…”

Electronic Health Record Data Quality and Performance Assessments: A Scoping Review (Preprint)

“…QualityCompleteness (or The absence of data Automated data 2-6,8,9,24-26,[28][29][30][32][33][34][35][36][37][38][39] …”