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BioMed Research International
Volume 2016, Article ID 6741418, 18 pages
Research Article

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

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

Received 12 June 2015; Accepted 4 October 2015

Academic Editor: Vassilis Koutkias

Copyright © 2016 Mustafa Yuksel et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.