Pharmacovigilance and Clinical Environment: Utilizing OMOP-CDM and OHDSI Software Stack to Integrate EHR Data

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.

An open-source platform integrating emerging data sources to support multi-modal active pharmacovigilance

“…However, this information was not used in our platform as our main use case was to facilitate PV signal elaboration and thus, already confirmed ADRs were considered out of scope. It should be clarified that DrugBank data integrated in our Knowledge Graph are not used as to provide information for ADRs but it is only used to facilitate in terms of synonym's finding and query space expansion to facilitate the analysis of data sources which could be used to identify novel ADR signals, i.e., the data sources elaborated in the workspaces of the PVClinical platform (Dimitriadis et al, 2021), openly available web analytics apps using openFDA data 15 etc).…”

“…-OHDSI workspace: In the OHDSI workspace, analytics tools used for observational studies were integrated to support the exploration of RWD sources as they are typically used in the context of the ATLAS tool built and supported by the OHDSI community. To this end, the following analysis tools were integrated (Dimitriadis et al, 2021) 17 : + "Incidence rate" is defined as a measure of the frequency with which some event, such as a disease diagnosis or other incidence, occurs over a specified time period. Typically, the incidence rate is calculated based on the ratio of the "target cohort" (i.e., the set of patients under investigation, e.g., all the patients of the hospital which the specific drug was administered to) and the "outcome cohort" (i.e., the set of patients which have been diagnosed with the respective adverse condition).…”

Applications of Machine Learning in Bone and Mineral Research

“…It might be due to the relatively easier access to cross-sectional than survival datasets and the use of more widely distributed models than survival tasks. However, as the large-sized standardized datasets have become more widely available [ 8 ] and the attractive methodologies are continuously evolving at this very moment, it is expected to help solve the currently unmet needs of bone and mineral research.…”