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Cut through the noise with NLP: Empower medical affairs to deliver richer insights
Hywel Evans, Director, IQVIA NLP Insights Hub
Apr 29, 2021
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  • Cut through the noise with NLP: Empower Medical Affairs to deliver richer insights

Dramatic changes in the healthcare environment have been re-defining expectations for Medical Affairs. The role traditionally involves engaging key stakeholders, driving the development of effective and market-responsive evidence strategies, and communicating products in areas such as speciality care. But therapies are increasingly complex, and regulations now call for a clear line between medical and commercial functions.

What does this mean on a day-to-day basis for our Medical Affairs teams? In short, they’ve got to meet an exceedingly high bar. Medical Affairs is expected to operate at strategic and technical levels from evidence generation to education. They have to capture insight and evidence from the near-infinite data that exists out there in unstructured text and communicate it meaningfully across multiple stakeholders at various levels. And they have to do it all without increasing cost to the business.

The good news for Medical Affairs is that while demands of the role have expanded, so has technology. At IQVIA, our award-winning natural language processing (NLP) capabilities are leading the market, and we’ve developed a technology that complements Medical Affairs (and many other roles) in cutting through vast and noisy textual information sources to quickly capture pertinent data points that support good advice, decisions and communications.

Using the IQVIA NLP Insights Hub platform, Medical Affairs teams can search and analyze siloed textual information from across a wide range of information sources — including internal SharePoint folders, external publication platforms, social media, news, and clinical trials databases — to get the answers they need.

NLP is a game-changer for Medical Affairs

A recent McKinsey & Company report explores this evolution of Medical Affairs, positing that the role of data interpretation has become so elevated that it now serves as a third strategic pillar of the pharmaceutical enterprise, alongside R&D and Commercial. As the report points out, 90 percent of the total data in existence today was generated in just the past two years, and it is only continuing to increase exponentially. As the function charged with interpreting and giving context to this flood of information, Medical Affairs needs tools to rapidly extract unstructured data across varied sources and produce a rich, visual interpretation. If that sounds like a complicated process, don’t worry — with the IQVIA NLP Insights Hub, it’s not.

Real-world applications of NLP Insights Hub really showcase the power and utility of these solutions, so I will share a few with you here.

  1. Conducting post-market surveillance

    In one case study, we helped a Safety team carry out post-market surveillance that would ultimately go to Medical Affairs to support better product understanding. The team needed to systematically analyze their call center feed in order to get to the root causes of reported side effects for certain products. We used the NLP Insights Hub to tag the call center feeds for metadata, exploring things like demographics, reason for calling, and more. Using NLP, we were able to quickly understand not only trends and topics for brands, but also of drug/disease relationships. In the end, we were able to categorize or tag over 70 percent of the reported side effects as being related to underlying pre-existing conditions and not true adverse drug reactions.


  2. Enriching structured data

    In addition to generating insights on trends, we can also use NLP to enrich structured data sources by mining unstructured information fields for deeper analysis. We do that by exploring the time series of events like field activity and then overlaying it with a topic analysis in order to understand if there are certain topics that are driving different outcomes, behaviors, or activities. As a result, Medical Affairs can be more responsive and quickly adapt to changing needs, redirecting resources towards areas that need them most. This application of NLP also helps Medical Affairs teams give data-driven evidence for changes in feedback, medical requests, in time spent talking to doctors, and more.


  3. Automating extraction from notes

    The final example I’ll share is an instance where we used NLP to capture data across notes submitted by global site event investigators to electronic document capture, which we then ran across multi-lingual NLP algorithms to look for patients at risk of high levels of metabolic imbalance. Through this process we automated extraction of features from more than 10 countries and effectively captured and normalized reasons for treatment decisions. The result was granular, real world insight into treatment patterns that would otherwise not have been available.

If your Medical Affairs teams are overburdened by increasing data and demands, I invite you to explore the NLP Insights Hub and our broader suite of NLP solutions to help them do their job more effectively. You no longer need to be an NLP expert to tap into large sets of textural, unstructured data. On the contrary, rapid attainment of these insights is now expected as the new normal for Medical Affairs - and the NLP Insights Hub is a valuable, trusted solution to that end. Reach out to us to request a demo.

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