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Beyond Case Counting: How AI Is Transforming Aggregate Safety Reporting.

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  Every six months, or annually depending on the product’s EU reference date (EURD) schedule, pharmacovigilance teams begin assembling one of the most resource-intensive documents in drug safety: the Periodic Benefit-Risk Evaluation Report (PBRER), or its regulatory counterpart, the Periodic Safety Update Report (PSUR). The process typically starts at least two to four months before the submission deadline and involves medical writers, pharmacovigilance scientists, signal detection specialists, regulatory affairs teams, and clinical reviewers working simultaneously across disconnected systems. The challenge is not the report itself. The challenge is everything that has to happen before the first section can be written. Signal data has to be extracted, verified, and reconciled. ICSR line listings have to be built. Cumulative case counts have to be validated against database outputs. And all of this has to be ready before the data lock point, with no room for error, because the submi...
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AI and automation in pharmacovigilance

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  A pharmacovigilance team managing multiple products globally runs hundreds of literature searches each week. They log adverse event reports from call centers, emails, patient portals, and clinical trial feeds. Every day, cases arrive that require review, coding, triage, narrative writing, duplicate checking, and regulatory submission — all under strict timelines that most health authorities will not extend. For years, the industry’s answer to this workload was more personnel, more spreadsheets, and more manual reviews. That answer no longer holds. The efficiency case for  AI-assisted literature screening  in pharmacovigilance is well supported by research. A synthesis published in Frontiers in Pharmacology in January 2025, drawing on multiple structured literature review automation studies, found that AI-assisted screening tools can reduce the volume of articles requiring human review to as low as 23% of the total retrieved, with time savings per review cycle ranging fr...
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Cutting Through the Literature Queue: What AI Screening Enables for Pharmacovigilance Teams

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  The first task of a Monday morning for many pharmacovigilance literature teams looks the same week after week: opening a review queue that has accumulated new articles across the weekend. Before any medical review begins, a reviewer must determine which articles contain safety-relevant information, which are duplicates of records already processed, which are pharmacokinetic studies or in-vitro research that mention a drug only in passing, and which contain a genuine adverse event report that may need to become an Individual Case Safety Report (ICSR). That determination,  performed manually , is where a significant portion of the pharmacovigilance team’s effort disappears each week. It is also the portion where AI can deliver the greatest operational value in  literature review automation . The efficiency case for  AI-assisted literature screening  in pharmacovigilance is well supported by research. A synthesis published in Frontiers in Pharmacology in January ...
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The Real Cost of Manual Case Intake Operations – and What GenAI Changes

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  Pharmacovigilance exists to protect patients. Case intake exists to make that possible. Yet in most organizations, the intake process routes the majority of its time and resources through work that has nothing to do with safety assessment – transcribing calls, extracting data from PDFs, populating case fields line by line, and chasing missing information. The people best positioned to evaluate drug safety are spending their day doing data entry. That is not a staffing problem. It is a process design problem. The core reason behind this challenge is that  manual case intake   is treated as a fixed operational reality by most PV teams, not as a cost with a measurable price tag. This persistent but hidden cost shows up in processing timelines, data quality failures, submission delays, follow-up gaps, and the disproportionate share of skilled reviewer time that goes toward data entry rather than medical judgment. GenAI  is changing the economics of that upstream work, ...
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What Machine Learning Really Delivers in Literature Surveillance

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  The volume of published medical literature does not pause for pharmacovigilance teams. PubMed adds thousands of records daily, EMBASE adds more, and the regulatory mandate to screen this voluminous literature does not budge. A pharmacovigilance team that manually screens literature is not reviewing safety data. It is reviewing noise, looking for the fraction of it that is safety data. This is precisely what ML in literature surveillance is designed to address. The question is not whether ML helps, but whether the way most teams implement it actually solves the right parts of the problem. The argument for ML in pharmacovigilance literature surveillance is often framed around speed. That framing is not wrong, but it is incomplete. Speed matters less if it comes without accuracy. And accuracy without systematic noise reduction does not reduce reviewer burden. What PV teams actually need is a connected set of ML capabilities that address the real failure points in the pipeline: volum...
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From First Contact to Case Record: AI-Powered Pharmacovigilance Intake with AWS Connect

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  Drug safety hotlines have operated the same way for decades. An agent picks up a call, listens, and types what they hear into a case management system. When the call ends, they review their notes, fill in gaps, and route the case for medical review. It is a process built on human attention and manual transcription — and it has not fundamentally changed since PV call centres first came into existence. The volume of adverse event reports, however, has increased. Global drug portfolios now generate case volumes growing to 20% annually. A serious adverse event reported at 11 PM in one time zone triggers regulatory obligations that don’t wait for business hours in another. And a missed or inaccurately transcribed seriousness criterion can have consequences that reach from the case record all the way to a regulatory inspection. This is the operational context in which AWS Connect telephony for pharmacovigilance is gaining serious attention from drug safety teams. Not as a replacement f...
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Pharmacovigilance Safety Database: The Intelligent Backbone of Modern Drug Safety

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  There is a quiet but significant gap widening in drug safety operations today. On one side, some organizations have modernized their pharmacovigilance infrastructure, built around   intelligent automation , real-time analytics, and seamless regulatory  connectivity. On the flipside, many teams still wrestle with legacy systems, manual case processing workflows, and the constant anxiety of submission deadlines that leave little room for error.   The  pharmacovigilance database  sits at the center of this divide. It is not simply a repository for adverse event records but the operational backbone of an entire drug safety program. And yet, for many life sciences organizations, it remains one of the most underinvested, most outdated components of the broader technology stack. As case volumes expand across clinical development and post-marketing surveillance, life sciences companies face mounting pressure to improve case processing efficiency, ensure E2B valid...
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