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Biomed Res Int. 2016;2016:6741418. doi: 10.1155/2016/6741418. Epub 2016 Mar 31.

An Interoperability Platform Enabling Reuse of Electronic Health Records for Signal Verification Studies.

Author information

1
SRDC Software Research & Development and Consultancy Ltd., 06800 Ankara, Turkey.
2
SRDC Software Research & Development and Consultancy Ltd., 06800 Ankara, Turkey; Department of Computer Engineering, Middle East Technical University, 06800 Ankara, Turkey.
3
Lombardia Informatica S.p.A., Via Torquato Taramelli, 26 20124 Milano, Italy.
4
WHO Collaborating Centre for International Drug Monitoring, Uppsala Monitoring Centre (UMC), 753 20 Uppsala, Sweden.
5
Advanced Clinical Applications Research Group, Agfa HealthCare, 9000 Gent, Belgium.

Abstract

Depending mostly on voluntarily sent spontaneous reports, pharmacovigilance studies are hampered by low quantity and quality of patient data. Our objective is to improve postmarket safety studies by enabling safety analysts to seamlessly access a wide range of EHR sources for collecting deidentified medical data sets of selected patient populations and tracing the reported incidents back to original EHRs. We have developed an ontological framework where EHR sources and target clinical research systems can continue using their own local data models, interfaces, and terminology systems, while structural interoperability and Semantic Interoperability are handled through rule-based reasoning on formal representations of different models and terminology systems maintained in the SALUS Semantic Resource Set. SALUS Common Information Model at the core of this set acts as the common mediator. We demonstrate the capabilities of our framework through one of the SALUS safety analysis tools, namely, the Case Series Characterization Tool, which have been deployed on top of regional EHR Data Warehouse of the Lombardy Region containing about 1 billion records from 16 million patients and validated by several pharmacovigilance researchers with real-life cases. The results confirm significant improvements in signal detection and evaluation compared to traditional methods with the missing background information.

PMID:
27123451
PMCID:
PMC4830705
DOI:
10.1155/2016/6741418
[Indexed for MEDLINE]
Free PMC Article

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