CDEGenerator: an online platform to learn from existing data models to build model registries
Received 4 April 2018
Accepted for publication 22 May 2018
Published 10 August 2018 Volume 2018:10 Pages 961—970
Checked for plagiarism Yes
Review by Single-blind
Peer reviewers approved by Dr Andrew Yee
Peer reviewer comments 3
Editor who approved publication: Professor Vera Ehrenstein
Julian Varghese,1 Michael Fujarski,2 Stefan Hegselmann,1 Philipp Neuhaus,1 Martin Dugas1,3
1Institute of Medical Informatics, University of Münster, 2Faculty of Mathematics and Computer Sciences, University of Münster, 3Institute of Medical Informatics, European Research Center for Information Systems (ERCIS), Münster, Germany
Objective: Best-practice data models harmonize semantics and data structure of medical variables in clinical or epidemiological studies. While there exist several published data sets, it remains challenging to find and reuse published eligibility criteria or other data items that match specific needs of a newly planned study or registry. A novel Internet-based method for rapid comparison of published data models was implemented to enable reuse, customization, and harmonization of item catalogs for the early planning and development phase of research databases.
Methods: Based on prior work, a European information infrastructure with a large collection of medical data models was established. A newly developed analysis module called CDEGenerator provides systematic comparison of selected data models and user-tailored creation of minimum data sets or harmonized item catalogs. Usability was assessed by eight external medical documentation experts in a workshop by the umbrella organization for networked medical research in Germany with the System Usability Scale.
Results: The analysis and item-tailoring module provides multilingual comparisons of semantically complex eligibility criteria of clinical trials. The System Usability Scale yielded “good usability” (mean 75.0, range 65.0–92.5). User-tailored models can be exported to several data formats, such as XLS, REDCap or Operational Data Model by the Clinical Data Interchange Standards Consortium, which is supported by the US Food and Drug Administration and European Medicines Agency for metadata exchange of clinical studies.
Conclusion: The online tool provides user-friendly methods to reuse, compare, and thus learn from data items of standardized or published models to design a blueprint for a harmonized research database.
Keywords: common data elements, semantic interoperability, metadata repositories, Unified Medical Language System
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