Linking a Consortium-Wide Data Quality Assessment Tool with the MIRACUM Metadata Repository

Kapsner, Lorenz A.; Mang, Jonathan M.; Mate, Sebastian; Seuchter, Susanne A.; Vengadeswaran, Abishaa; Bathelt, Franziska; Deppenwiese, Noemi; Kadioglu, Dennis; Kraska, Detlef; Prokosch, Hans‐Ulrich

doi:10.1055/s-0041-1733847

Cited by 28 publications

(35 citation statements)

References 27 publications

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“…For providing the GUI-version of the DQA tool to the MIRACUM partner sites and to allow for a seamless integration into their local DIC, Docker was used to simplify the deployment of the application across different environments [ 33 ]. A container image [ 34 , 35 ] was developed that integrates well within the MIRACUM DIC infrastructure, similar to the deployment of the command-line-based version of the DQA tool [ 20 ]. Since some sites already use Kubernetes for container orchestration, a Kubernetes manifest [ 35 , 36 ] was also provided.…”

Section: Resultsmentioning

confidence: 99%

“…Users without prior R programming knowledge can use it to analyze databases regarding their data quality and to compare different data sets. For customizing the DQA tool to the specific requirements within MIRACUM, such as connecting it to the central M-MDR and to provide the configurations to the MIRACUM research data repositories, the R package miRacumDQA was previously developed [ 20 ]. During the development of the GUI, this package was extended to also establish the connection to the prototype of the MIRACUM datamap.…”

Section: Resultsmentioning

confidence: 99%

“…To address the previously mentioned points, a GUI was developed to provide a frontend to the functions from the R package DQAstats [ 20 ]. The GUI itself is provided with the new R package DQAgui and directly builds upon DQAstats to serve as an interface to configure the connection to databases, carry out DQ analyses, and visualize the results via a web-based user interface (see Fig.…”

Section: Methodsmentioning

confidence: 99%

“…Since this framework is well established and used in a large number of scientific papers [16,[21][22][23][24], the development of the MIRACUM DQ software is also aligned with it. Details on the specific implementation of the various DQ categories in DQAstats are described in a previous publication of the authors [20].…”

Section: Data Quality Frameworkmentioning

confidence: 99%

“…One principle of the MIRACUM project was to provide its concepts and implementations open source. Since an open source software tool addressing the need to ensure DQ across the research repositories with heterogeneous data models was lacking at the beginning of the project, the authors developed a DQ Assessment (DQA) tool and they demonstrated its connection to the MIRACUM MDR (M-MDR), which allows centralized management of all data elements and required DQ definitions and ensures that DQ checks are performed at each site in a standardized manner [ 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

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

Mang

Seuchter

Gülden

et al. 2022

BMC Med Inform Decis Mak

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Data Quality Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Mang

Seuchter

Gülden

et al. 2022

BMC Med Inform Decis Mak

Self Cite

View full text Add to dashboard Cite

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Data quality assurance practices in research data repositories—A systematic literature review

Stvilia,

Pang,

Lee

et al. 2024

Asso for Info Science & Tech

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Data quality issues can significantly hinder research reproducibility, data sharing, and reuse. At the forefront of addressing data quality issues are research data repositories (RDRs). This study conducted a systematic analysis of data quality assurance (DQA) practices in RDRs, guided by activity theory and data quality literature, resulting in conceptualizing a data quality assurance model (DQAM) for RDRs. DQAM outlines a DQA process comprising evaluation, intervention, and communication activities and categorizes 17 quality dimensions into intrinsic and product‐level data quality. It also details specific improvement actions for data products and identifies the essential roles, skills, standards, and tools for DQA in RDRs. By comparing DQAM with existing DQA models, the study highlights its potential to improve these models by adding a specific DQA activity structure. The theoretical implication of the study is a systematic conceptualization of DQA work in RDRs that is grounded in a comprehensive analysis of the literature and offers a refined conceptualization of DQA integration into broader frameworks of RDR evaluation. In practice, DQAM can inform the design and development of DQA workflows and tools. As a future research direction, the study suggests applying and evaluating DQAM across various domains to validate and refine this model further.

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Rare Diseases in Hospital Information Systems—An Interoperable Methodology for Distributed Data Quality Assessments

et al. 2023

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Background: Multisite research networks such as the project “Collaboration on Rare Diseases” connect various hospitals to obtain sufficient data for clinical research. However, data quality (DQ) remains a challenge for the secondary use of data recorded in different health information systems. High levels of DQ as well as appropriate quality assessment methods are needed to support the reuse of such distributed data. Objectives: The aim of this work is the development of an interoperable methodology for assessing the quality of data recorded in heterogeneous sources to improve the quality of rare disease (RD) documentation and support clinical research. Methods: We first developed a conceptual framework for DQ assessment. Using this theoretical guidance, we implemented a software framework that provides appropriate tools for calculating DQ metrics and for generating local as well as cross-institutional reports. We further applied our methodology on synthetic data distributed across multiple hospitals using Personal Health Train. Finally, we used precision and recall as metrics to validate our implementation. Results: Four DQ dimensions were defined and represented as disjunct ontological categories. Based on these top dimensions, 9 DQ concepts, 10 DQ indicators, and 25 DQ parameters were developed and applied to different data sets. Randomly introduced DQ issues were all identified and reported automatically. The generated reports show the resulting DQ indicators and detected DQ issues. Conclusion: We have shown that our approach yields promising results, which can be used for local and cross-institutional DQ assessments. The developed frameworks provide useful methods for interoperable and privacy-preserving assessments of DQ that meet the specified requirements. This study has demonstrated that our methodology is capable of detecting DQ issues such as ambiguity or implausibility of coded diagnoses. It can be used for DQ benchmarking to improve the quality of RD documentation and to support clinical research on distributed data.

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Linking a Consortium-Wide Data Quality Assessment Tool with the MIRACUM Metadata Repository

Cited by 28 publications

References 27 publications

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

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

Data quality assurance practices in research data repositories—A systematic literature review

Rare Diseases in Hospital Information Systems—An Interoperable Methodology for Distributed Data Quality Assessments

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