Plug and Play FAERS quarterly data files
Rapidly upload, process, clean, and filter the standard data files from the FAERS public database with a Dataiku Application.Explore !
The goal of this ready-to-use solution is to accelerate the discovery of potential Adverse Drug Reaction (ADR) signals. It enables users to upload quarterly data from spontaneous reporting systems and uses statistical metrics to generate disproportional frequencies of drugs and even pairs across various populations. More details on the specifics of the solution can be found on the knowledge base.
The efficiency of post-market drug safety surveillance functions plays a critical role in reinforcing patient safety and securing successful drug launches. Compliance in safety reporting and surveillance is a must-meet regulatory requirement (Good Pharmacovigilance Practice) and failure to appropriately report, detect, and address adverse drug reactions can lead to patient harm, drug recall, and significant costs.
The focus on pharmacovigilance efficiency is further reinforced as Adverse Drug Reactions (ADR) are increasingly found to be a leading cause of both mortality and morbidity, particularly in older populations. Research has shown ADRs are responsible for anywhere from 5% to 30% of all hospitalizations with a potential global economic burden of over $1 trillion USD. Given the rigid criteria of clinical research, pharmacovigilance practices often enable the first comprehensive assessment of the benefit-risk profile of marketed drugs in diverse, and potentially under-represented, real-world patient populations.
As the volume/velocity/variety of safety reporting data grows, it is becoming essential for global safety teams at drug manufacturers, health outcomes research institutions, and regulatory bodies alike to adopt new analytics-driven approaches, that can be automated at scale, to improve early signal detection and reliability in the pharmacovigilance process.