A Pharmacovigilance Bundle: Intertwining Drug Safety with Technology Platforms


How molding these elements into one can add efficiencies to work processes.

In my ongoing quest to provide Pharma Commerce readers with a plethora of content, I recently had the opportunity to chat with Updesh Dosanjh, practice leader for the pharmacovigilance (PV) technology solutions business unit at IQVIA, to discuss the all-encompassing topic of PV as it pertains to its relationship with manual reporting and using technology—including artificial intelligence (AI), machine learning (ML), and natural language processing (NLP)—to simplify this process.

Gathering this information came via two mediums: A PC Podcast recording and a contributed piece from Dosanjh himself. When it comes to our podcast conversation, listeners can expect to gain insight on the following topics and questions:

  • The rising cost of pharmacovigilance (PV) tasks has been an ongoing challenge for the pharma sector, seen by companies electing to outsource these tasks to overseas vendors. How can PV platforms decrease the time, expense, and risk involved in manual reporting?
  • How do these the platforms provide insights to regulators, industry bodies, and internal experts required to make judgments about the safety of marketed products based on solid data?
  • How are technologies—such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP)—impacting PV workloads?
  • Of the efficiencies that result from this tech, which stand out to you the most?
  • Keeping all of this in mind, how do you envision these drug safety applications/platforms evolving in the coming years?

Dosanjh elaborates on several of the above topics in his article, especially when it comes to the efficiencies of technology, in saying that “NLP helps computers identify adverse events more precisely and efficiently than human analysts, which is why many might argue that it is particularly useful in pharmacovigilance. To identify adverse occurrences more rapidly and accurately, this system can evaluate hundreds of case files and spot trends that human analysis would miss. … Together with NLP, other technologies like ML, data analytics, and AI are also enhancing pharmacovigilance effectiveness.”

There is no doubt that tech is impacting the PV landscape in a manner in which patient safety is enhanced. That said, if readers are intrigued by what can be expected in the future (without giving too much away of course), they should be on the lookout for the evolution of digital health tech, along with an increase emphasis on personalized medicine.

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