Research Article An Interoperability Platform Enabling ... - CORE › publication › fulltext › An-Intero... › publication › fulltext › An-Intero...by M Yuksel · 2016 · Cited by 9 · Related articlesOct 4, 2015 — the data sets used for such studies are lim
Research Article An Interoperability Platform Enabling Reuse of Electronic Health Records for Signal Verification Studies Mustafa Yuksel,1 Suat Gonul,1,2 Gokce Banu Laleci Erturkmen,1 Ali Anil Sinaci,1 Paolo Invernizzi,3 Sara Facchinetti,3 Andrea Migliavacca,3 Tomas Bergvall,4 Kristof Depraetere,5 and Jos De Roo5 1
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 2
Correspondence should be addressed to Mustafa Yuksel;
[email protected] 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.
1. Introduction All medicinal products are subject to strict testing and assessment of their quality, efficacy, and safety before being authorized. While premarket safety analysis through clinical trials remains vital, there is considerable attention towards improving the reporting and collection of postmarket data to enhance patient safety. After authorization, all medicinal products continue to be observed through pharmacovigilance studies to monitor their safety profiles. Currently, pharmacovigilance activities are mainly based on signal detection studies run on voluntarily sent spontaneous reports. Although spontaneous reporting remains a cornerstone of
pharmacovigilance in the regulator environment and is indispensable for signal detection, due to examples of drug withdrawals [1] stemming from uncommon adverse events after millions of patients were exposed, the need for a more effective and proactive surveillance is reinforced. The current postmarket drug surveillance process has several bottlenecks, with the first one being underreporting [2, 3]; it has been estimated that only about 5% of harmful Adverse Drug Events (ADEs) (Abbreviations are provided at the end of the paper) are being reported through spontaneous reporting [4, 5]. Secondly, the quality of the data collected through spontaneous reporting is low [6], and finally spontaneous reports only report adverse incidents, while
2 the information related to other patients who used the drug but not experienced adverse events, that is, the denominator data, is not retrievable [7]. For these reasons, there is a clear need for complementary pharmacovigilance activities. Relative to Individual Case Safety Reports (ICSRs), Electronic Health Records (EHRs) cover extended parts of the underlying medical histories, include more complete information on potential risk factors, and are not restricted to patients who have experienced a suspected ADE [8]. Hence, there is great potential in accessing EHRs for tracing safety reports back to medical summaries of patients and also secondary use of EHRs for complementary pharmacoepidemiology studies for clinical signal evaluation and validation. For example, Uppsala Monitoring Centre (UMC) on behalf of the WHO International Programme for International Drug Monitoring analyses the WHO global ICSR database, VigiBase, for potential signals [9, 10]. The objective is to characterize the reported cases in comparison with a selected background population for checking whether there a
