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A Generic Method and Implementation to Evaluate and Improve Data Quality in Distributed Research Networks.

D Juárez1,2, E E Schmidt1,2, S Stahl-Toyota3

  • 1Federated Information Systems, German Cancer Research Center (DKFZ), Heidelberg, Germany.

Methods of Information in Medicine
|September 13, 2019
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Summary

A new process enables consistent data quality assessment across distributed research networks. By combining a central metadata repository with local assessment tools, researchers can ensure data accuracy for multisite clinical studies.

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Area of Science:

  • Biomedical Informatics
  • Translational Research
  • Data Science

Background:

  • Multisite research collaborations are crucial for personalized medicine, requiring high-quality data from local databases.
  • Secondary use of clinical data for research reveals significant variability in data formats and quality across institutions.
  • Ensuring data integrity is essential for the success of distributed research networks.

Purpose of the Study:

  • To develop a process for assessing data quality (completeness, syntactic accuracy) across independent data warehouses.
  • To establish a central metadata repository (MDR) for common data definitions within a research network.
  • To enable standardized data quality evaluation for multisite research.

Main Methods:

  • Implemented a framework using federated data warehouses ('bridgeheads') for network participation.
  • Utilized a central MDR to store agreed-upon data element definitions and permissible values.
  • Developed a quality report generator for local data validation against the central MDR.

Main Results:

  • A standardized quality report can be generated at each network site for cross-site comparison.
  • The system provides feedback channels to local data source systems and documentation personnel.
  • Successfully implemented and utilized across 10 sites in the German Cancer Consortium.

Conclusions:

  • Comparable data quality assessment across distributed research network partners is feasible.
  • Combining a central MDR with local assessment processes ensures data integrity.
  • The developed quality report and generation process were successfully implemented in a German research network.