A multi-technique approach to bridge electronic case report form design and data standard adoption

Ching Heng Lin, Nai Yuan Wu, Der Ming Liou

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

We also analyze the reasons of the missed and failed results. Background and objective: The importance of data standards when integrating clinical research data has been recognized. The common data element (CDE) is a consensus-based data element for data harmonization and sharing between clinical researchers, it can support data standards adoption and mapping. However, the lack of a suitable methodology has become a barrier to data standard adoption. Our aim was to demonstrate an approach that allowed clinical researchers to design electronic case report forms (eCRFs) that complied with the data standard. Methods: We used a multi-technique approach, including information retrieval, natural language processing and an ontology-based knowledgebase to facilitate data standard adoption using the eCRF design. The approach took research questions as query texts with the aim of retrieving and associating relevant CDEs with the research questions. Results: The approach was implemented using a CDE-based eCRF builder, which was evaluated using CDE- related questions from CRFs used in the Parkinson Disease Biomarker Program, as well as CDE-unrelated questions from a technique support website. Our approach had a precision of 0.84, a recall of 0.80, a F-measure of 0.82 and an error of 0.31. Using the 303 testing CDE-related questions, our approach responded and provided suggested CDEs for 88.8% (269/303) of the study questions with a 90.3% accuracy (243/269). The reason for any missed and failed responses was also analyzed. Conclusion: This study demonstrates an approach that helps to cross the barrier that inhibits data standard adoption in eCRF building and our evaluation reveals the approach has satisfactory performance. Our CDE-based form builder provides an alternative perspective regarding data standard compliant eCRF design.

Original languageEnglish
Pages (from-to)49-57
Number of pages9
JournalJournal of Biomedical Informatics
Volume53
DOIs
Publication statusPublished - Feb 1 2015
Externally publishedYes

Fingerprint

Research
Research Personnel
Natural Language Processing
Knowledge Bases
Information Dissemination
Information Storage and Retrieval
Biomarkers
Information retrieval
Parkinson Disease
Ontology
Common Data Elements
Websites
Testing
Processing

Keywords

  • Case report form
  • Common data elements
  • Data standard
  • Natural language processing
  • Ontology-based knowledgebase

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

Cite this

A multi-technique approach to bridge electronic case report form design and data standard adoption. / Lin, Ching Heng; Wu, Nai Yuan; Liou, Der Ming.

In: Journal of Biomedical Informatics, Vol. 53, 01.02.2015, p. 49-57.

Research output: Contribution to journalArticle

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abstract = "We also analyze the reasons of the missed and failed results. Background and objective: The importance of data standards when integrating clinical research data has been recognized. The common data element (CDE) is a consensus-based data element for data harmonization and sharing between clinical researchers, it can support data standards adoption and mapping. However, the lack of a suitable methodology has become a barrier to data standard adoption. Our aim was to demonstrate an approach that allowed clinical researchers to design electronic case report forms (eCRFs) that complied with the data standard. Methods: We used a multi-technique approach, including information retrieval, natural language processing and an ontology-based knowledgebase to facilitate data standard adoption using the eCRF design. The approach took research questions as query texts with the aim of retrieving and associating relevant CDEs with the research questions. Results: The approach was implemented using a CDE-based eCRF builder, which was evaluated using CDE- related questions from CRFs used in the Parkinson Disease Biomarker Program, as well as CDE-unrelated questions from a technique support website. Our approach had a precision of 0.84, a recall of 0.80, a F-measure of 0.82 and an error of 0.31. Using the 303 testing CDE-related questions, our approach responded and provided suggested CDEs for 88.8{\%} (269/303) of the study questions with a 90.3{\%} accuracy (243/269). The reason for any missed and failed responses was also analyzed. Conclusion: This study demonstrates an approach that helps to cross the barrier that inhibits data standard adoption in eCRF building and our evaluation reveals the approach has satisfactory performance. Our CDE-based form builder provides an alternative perspective regarding data standard compliant eCRF design.",
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