Clinevo Technologies | Clinical Research & Healthcare Technology Solutions

  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...

  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...

  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 ...