Why Mining FAERS Alone Is a Signal Detection Blind Spot

 For pharmacovigilance teams in the United States, the FDA Adverse Event Reporting System (FAERS) is the most familiar starting point for post-marketing safety surveillance. It is large, regulator-maintained, freely accessible through a public dashboard, and structured around the same ICH E2B(R3) framework that safety teams already use daily. None of that is in dispute.



What is increasingly in dispute is the quiet assumption that runs underneath many signal detection programs: that mining FAERS – on its own – is enough.

It is not. And the gap between what FAERS reliably surfaces and what is actually happening to patients in the real world has been widening for years. Underreporting, structural reporting biases, missing denominators, latency in spontaneous data, and entire categories of safety information that simply never arrive in FAERS combine to create what is best described as a structural blind spot. Drug safety teams that anchor their entire signal detection methodology to FAERS feeds are not detecting fewer signals because their analysts are less skilled. They are detecting fewer signals because they are looking at one data layer of a multi-layered problem.

This blog examines why FAERS data alone is not enough for reliable signal detection, what recurring failure patterns look like in practice, and how modern pharmacovigilance signal detection programs are widening their evidence base by building automated, multi-source workflows that include literature, case intake, and international ICSR repositories alongside FAERS.

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