From Data Silos to Standardized, Linked, and FAIR Data for Pharmacovigilance: Current Advances and Challenges with Observational Healthcare Data

• Published in: Drug safety 2019-05, Vol.42 (5), p.583-586
• Main Author: Koutkias, Vassilis
• Format: Article
• Language: English
• Subjects: Applications programs; Clinical outcomes; Clinical trials; Commentary; Data models; Dictionaries; Digital media; Drug Safety and Pharmacovigilance; Electronic health records; Electronic medical records; Gene mapping; Genotype & phenotype; Health care; Informatics; Interoperability; Laboratories; Laboratory tests; Life cycle assessment; Life cycles; Mapping; Medical research; Medicine; Medicine & Public Health; Pharmacology; Pharmacology/Toxicology; Pharmacovigilance; Product safety; Semantic web; Semantics; Standardization; Studies
• ISSN: 0114-5916; 1179-1942
• DOI: 10.1007/s40264-018-00793-z

Pharmacovigilance (PV) encompasses all data gathering and processing activities related to the detection, assessment, understanding, and prevention of adverse effects throughout the entire life cycle of drugs. The current era of data explosion or big data affects the entire spectrum of health sciences, including PV. In particular, the data employed for PV have been recently extended, considering not only traditional/dominant data sources, i.e. spontaneous reporting systems, clinical trials, and the scientific literature, but also observational healthcare databases (i.e. electronic health records [EHR] and administrative claims) with potential linkage to genetic data, as well as social media platforms and mobile health (mHealth) apps. In the scope of multi-center EHR-based PV studies, various data elements such as diagnoses, medical procedures, medications, and laboratory tests shall be expressed uniformly by selecting and combining codes from diverse vocabularies as well as proprietary coding schemes. Using reference terminologies for this mapping process facilitates standardization and semantic interoperability.