Clinevo Technologies | Clinical Research & Healthcare Technology Solutions

  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, ...

  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...

  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...

  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...

  Every pharmaceutical and medical device company operating in a regulated environment runs a quality management system. The FDA finalized the Quality Management System Regulation (QMSR) in February 2024, aligning 21 CFR Part 820 with ISO 13485:2016, with a compliance deadline of February 2026. Yet, in FDA fiscal year 2025, 38 out of 44 Warning Letters issued to medical device manufacturers cited Quality System Regulation violations under 21 CFR 820. Corrective and Preventive Action (CAPA) deficiencies topped the list for the first time, appearing in 26 of those letters.  These were not companies without quality systems. They were companies whose quality systems could not keep up with the demands placed on them. This is exactly the problem that  QMS automation  in regulated environments is built to solve. Manual quality management processes work on a small scale. However, as organizations grow and product portfolios expand, spreadsheets and paper-based sign-offs star...

  Every week, a pharmacovigilance team opens their literature monitoring queue to find hundreds of articles pulled from PubMed, EMBASE, and regional databases. The majority of these articles are often duplicates, off-label mentions, or pharmacokinetic studies that lack any meaningful adverse event content, adding unnecessary noise to the process.  This is precisely the problem that  AI literature management  is designed to solve — not by adding another layer of retrieval, but by bringing intelligence into every stage of the workflow. The EMA’s own 2024 Annual Report on EudraVigilance puts this problem in clear numbers. In 2024, the agency reviewed 1,254 potential safety signals. Of those, 76% were not validated and closed. Only 3.1%, were ultimately prioritised and assessed by PRAC.  That ratio reflects a broader industry challenge: when literature monitoring generates excessive noise upstream, safety teams spend more time triaging irrelevant content than evalua...