Pharmacovigilance and Clinical Environment: Utilizing OMOP-CDM and OHDSI Software Stack to Integrate EHR Data Vlasios K. DIMITRIADIS a ,George I. GAVRIILIDIS a and Pantelis NATSIAVAS a,1 a Institute of Applied Biosciences, Centre for Research & Technology Hellas, Thermi, Thessaloniki, Greece Abstract. Information Technology (IT) and specialized systems could have a prominent role towards the support of drug safety processes, both in the clinical context but also beyond that. PVClinical project aims to build an IT platform, enabling the investigation of potential Adverse Drug Reactions (ADRs). In this paper, we outline the utilization of Observational Medical Outcomes Partnership - Common Data Model (OMOP-CDM) and the openly available Observational Health Data Sciences and Informatics (OHDSI) software stack as part of PVClinical platform. OMOP-CDM offers the capacity to integrate data from Electronic Health Records (EHRs) (e.g., encounters, patients, providers, diagnoses, drugs, measurements and procedures) via an accepted data model. Furthermore, the OHDSI software stack provides valuable analytics tools which could be used to address important questions regarding drug safety quickly and efficiently, enabling the investigation of potential ADRs in the clinical environment. Keywords. Pharmacovigilance, Drug Safety, OMOP-CDM, Electronic Health Records, Adverse Drug Reactions 1. Introduction Adverse Drug Reactions (ADRs) represent a peril to public health. ADRs are among the most common causes of morbidity and mortality worldwide causing dismal patient outcomes in the clinical setting, severe hurdles in the development and authorization of novel therapeutics as well as significant economic burdens to public healthcare providers [1]. To this end, Pharmacovigilance (PV) is defined as the science focusing on the detection, assessment, understanding and prevention of ADRs [2]. Traditional approaches for PV “signals” (potential new or partially documented ADRs) detection, entail the investigation of Individual Case Safety Reports (ICSRs) via Spontaneous Reporting Systems (SRSs), based on “Disproportionality Analysis” 1 Corresponding author: Pantelis Natsiavas, Institute of Applied Biosciences Centre for Research and Technology Hellas, 6th Km. Charilaou – Thermi Road, P.O. BOX 60361 GR – 57001, Thermi, Thessaloniki, Greece, +30 2311257608; Email: pnatsiavas@certh.gr Public Health and Informatics J. Mantas et al. (Eds.) © 2021 European Federation for Medical Informatics (EFMI) and IOS Press. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0). doi:10.3233/SHTI210232 555