Detailed clinical modelling approach to data extraction from heterogeneous data sources for clinical research

The reuse of routinely collected clinical data for clinical research is being explored as part of the drive to reduce duplicate data entry and to start making full use of the big data potential in the healthcare domain. Clinical researchers often need to extract data from patient registries and othe...

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Bibliographic Details
Published in:AMIA Summits on Translational Science proceedings 2014, Vol.2014, p.55-59
Main Authors: Lim Choi Keung, Sarah N, Zhao, Lei, Rossiter, James, McGilchrist, Mark, Culross, Frank, Ethier, Jean-François, Burgun, Anita, Verheij, Robert A, Khan, Nasra, Taweel, Adel, Curcin, Vasa, Delaney, Brendan C, Arvanitis, Theodoros N
Format: Article
Language:English
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Summary:The reuse of routinely collected clinical data for clinical research is being explored as part of the drive to reduce duplicate data entry and to start making full use of the big data potential in the healthcare domain. Clinical researchers often need to extract data from patient registries and other patient record datasets for data analysis as part of clinical studies. In the TRANSFoRm project, researchers define their study requirements via a Query Formulation Workbench. We use a standardised approach to data extraction to retrieve relevant information from heterogeneous data sources, using semantic interoperability enabled via detailed clinical modelling. This approach is used for data extraction from data sources for analysis and for pre-population of electronic Case Report Forms from electronic health records in primary care clinical systems.
ISSN:2153-4063
2153-4063