Related Experiment Video
Updated: Oct 23, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
Linking a Consortium-Wide Data Quality Assessment Tool with the MIRACUM Metadata Repository
Lorenz A Kapsner1,2, Jonathan M Mang1, Sebastian Mate1
1Medical Center for Information and Communication Technology, Universitätsklinikum Erlangen, Erlangen, Germany.
Background:
Many research initiatives aim at using data from electronic health records (EHRs) in observational studies. Participating sites of the German Medical Informatics Initiative (MII) established data integration centers to integrate EHR data within research data repositories to support local and federated analyses. To address concerns regarding possible data quality (DQ) issues of hospital routine data compared with data specifically collected for scientific purposes, we have previously presented a data quality assessment (DQA) tool providing a standardized approach to assess DQ of the research data repositories at the MIRACUM consortium's partner sites.
Objectives:
Major limitations of the former approach included manual interpretation of the results and hard coding of analyses, making their expansion to new data elements and databases time-consuming and error prone. We here present an enhanced version of the DQA tool by linking it to common data element definitions stored in a metadata repository (MDR), adopting the harmonized DQA framework from Kahn et al and its application within the MIRACUM consortium.
Methods:
Data quality checks were consequently aligned to a harmonized DQA terminology. Database-specific information were systematically identified and represented in an MDR. Furthermore, a structured representation of logical relations between data elements was developed to model plausibility-statements in the MDR.
Results:
The MIRACUM DQA tool was linked to data element definitions stored in a consortium-wide MDR. Additional databases used within MIRACUM were linked to the DQ checks by extending the respective data elements in the MDR with the required information. The evaluation of DQ checks was automated. An adaptable software implementation is provided with the R package DQAstats.
Conclusion:
The enhancements of the DQA tool facilitate the future integration of new data elements and make the tool scalable to other databases and data models. It has been provided to all ten MIRACUM partners and was successfully deployed and integrated into their respective data integration center infrastructure.
More Related Videos
09:19Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
Published on: April 18, 2025
11:09An Analytical Tool-box for Comprehensive Biochemical, Structural and Transcriptome Evaluation of Oral Biofilms Mediated by Mutans Streptococci
Published on: January 25, 2011
Related Concept Videos
Quality Assurance
Quality Control
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...