BackgroundThe underreporting of adverse drug reactions (ADRs) through traditional reporting channels is a limitation in the efficiency of the current pharmacovigilance system. Patients’ experiences with drugs that they report on social media represent a new source of data that may have some value in postmarketing safety surveillance.ObjectiveA scoping review was undertaken to explore the breadth of evidence about the use of social media as a new source of knowledge for pharmacovigilance.MethodsDaubt et al’s recommendations for scoping reviews were followed. The research questions were as follows: How can social media be used as a data source for postmarketing drug surveillance? What are the available methods for extracting data? What are the different ways to use these data? We queried PubMed, Embase, and Google Scholar to extract relevant articles that were published before June 2014 and with no lower date limit. Two pairs of reviewers independently screened the selected studies and proposed two themes of review: manual ADR identification (theme 1) and automated ADR extraction from social media (theme 2). Descriptive characteristics were collected from the publications to create a database for themes 1 and 2.ResultsOf the 1032 citations from PubMed and Embase, 11 were relevant to the research question. An additional 13 citations were added after further research on the Internet and in reference lists. Themes 1 and 2 explored 11 and 13 articles, respectively. Ways of approaching the use of social media as a pharmacovigilance data source were identified.ConclusionsThis scoping review noted multiple methods for identifying target data, extracting them, and evaluating the quality of medical information from social media. It also showed some remaining gaps in the field. Studies related to the identification theme usually failed to accurately assess the completeness, quality, and reliability of the data that were analyzed from social media. Regarding extraction, no study proposed a generic approach to easily adding a new site or data source. Additional studies are required to precisely determine the role of social media in the pharmacovigilance system.

Computerised queries in spontaneous reporting systems for pharmacovigilance require reliable and reproducible coding of adverse drug reactions (ADRs). The aim of the Medical Dictionary for Regulatory Activities (MedDRA) terminology is to provide an internationally approved classification for efficient communication of ADR data between countries. Several studies have evaluated the domain completeness of MedDRA and whether encoded terms are coherent with physicians' original verbatim descriptions of the ADR. MedDRA terms are organised into five levels: system organ class (SOC), high level group terms (HLGTs), high level terms (HLTs), preferred terms (PTs) and low level terms (LLTs). Although terms may belong to different SOCs, no PT is related to more than one HLT within the same SOC. This hierarchical property ensures that terms cannot be counted twice in statistical studies, though it does not allow appropriate semantic grouping of PTs. For this purpose, special search categories (SSCs) [collections of PTs assembled from various SOCs] have been introduced in MedDRA to group terms with similar meanings. However, only a small number of categories are currently available and the criteria used to construct these categories have not been clarified. The objective of this work is to determine whether MedDRA contains the structural and terminological properties to group semantically linked adverse events in order to improve the performance of spontaneous reporting systems. Rossi Mori classifies terminological systems in three categories: first-generation systems, which represent terms as strings; second-generation systems, which dissect terminological phrases into a set of simpler terms; and third-generation systems, which provide advanced features to automatically retrieve the position of new terms in the classification and group sets of meaning-related terms. We applied Cimino's desiderata to show that MedDRA is not compatible with the properties of third-generation systems. Consequently, no tool can help for the automated positioning of new terms inside the hierarchy and SSCs have to be entered manually rather than automatically using the MedDRA files. One solution could be to link MedDRA to a third-generation system. This would allow the current MedDRA structure to be kept to ensure that end users have a common view on the same data and the addition of new computational properties to MedDRA.

Single-source data entry in the clinical care context for use in the clinical research context - a process enabled through the use of the EHR as single point of data entry, can - if demonstrated to be a viable strategy - not only significantly reduce data collection efforts while simultaneously increasing data collection accuracy secondary to elimination of transcription or double-entry errors between the two contexts but also ensure that all the clinical data for a given patient, irrespective of the data collection context, are available in the EHR for decision support and treatment planning. The RE-USE approach used mapping algorithms to identify semantic coherence between clinical care and clinical research data elements and pre-populate eCRFs. The RE-USE project utilized SNOMED International v.3.5 as its "pivot reference terminology" to support EHR-to-eCRF mapping, a decision that likely enhanced the "recall" of the mapping algorithms. The RE-USE results demonstrate the difficult challenges involved in semantic integration between the clinical care and clinical research contexts.

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Although MedDRA has obvious advantages over previous terminologies for coding adverse drug reactions and discovering potential signals using data mining techniques, its terminological organization constrains users to search terms according to predefined categories. Adding formal definitions to MedDRA would allow retrieval of terms according to a case definition that may correspond to novel categories that are not currently available in the terminology. To achieve semantic reasoning with MedDRA, we have associated formal definitions to MedDRA terms in an OWL file named OntoADR that is the result of our first step for providing an "ontologized" version of MedDRA. MedDRA five-levels original hierarchy was converted into a subsumption tree and formal definitions of MedDRA terms were designed using several methods: mappings to SNOMED-CT, semi-automatic definition algorithms or a fully manual way. This article presents the main steps of OntoADR conception process, its structure and content, and discusses problems and limits raised by this attempt to "ontologize" MedDRA.