How Natural Language Processing Enhances Sales Productivity in Pharma

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Recently I found myself in a discussion with some colleagues around whether artificial intelligence (AI) could increase sales productivity; specifically, the Linguamatics flavour of AI, natural language processing (NLP). My first reaction was no – our customers tend to use NLP to pull out critical information for safety assessment from internal reports, genotype-phenotype associations from literature, inclusion/exclusion criteria from clinical trial records; and many more examples that impact drug R&D.

But as the discussion progressed, I realized that as our customers drill more and more into the power of NLP to unlock value from real world data, the answer is actually yes. NLP enables data-driven, rather than document-driven decision support, by extracting key concepts and context from unstructured documents, which can then be rapidly reviewed and analyzed. So, since much real-world data is unstructured text, NLP can bring real productivity gains.

Challenges for Pharma Medical Field Teams

Let me give you some background, and then some examples.

Over recent years, pharma sales reps and medical science liaison staff (MSLs) have faced increasing challenges around access to Key Opinion Leaders (KOLs), physicians and prescribers, due to a more restrictive regulatory environment, new healthcare business models and evolving economic conditions. The boundaries for how pharma sales reps can interact with physicians are more limited. For example, the “lunch and learn” meetings that used to be a key tool have been significantly curtailed. In parallel, the pressures on physicians to see more patients also reduces the time they have to learn about new drugs or improved therapies.

To be successful, reps and MSLs need to have the right content, impactful and concise, on hand at every opportunity. They need to know who to speak to across their region, and they need to have the current trends, concerns, pain points and perceptions of their drugs, plus information about competitor products available at their fingertips.

So how does NLP fit into this challenging picture? NLP can help provide intelligence on who to call, how to find the right key opinion leaders, trends from real world data and even what is the best collateral to use for a particular healthcare professional (HCP) or drug product.

Here are three snapshots from Linguamatics’ customers that suggest some innovative practices that can help.

Who to Call – Finding the Right Key Opinion Leaders

One key task where NLP can provide value is the identification of key opinion leaders (KOLs). Many top pharma organizations use NLP to mine scientific abstracts, conference reports, or clinical trial records to extract, structure and visualise who are the KOLs in particular therapeutic areas.

Mining social media can provide insights into newer “digital” key opinion leaders. Novo Nordisk have talked about finding digital opinion leaders by using NLP to mine Twitter. They were able to clean the notoriously noisy Twitter data by using NLP algorithms and business rules to identify physicians and medics for key influencers in the obesity area and assess their level of influence by their Twitter network.

Understanding Real World Product Issues & Trends

Knowing the current thoughts and perceptions of patients, HCPs and KOLs is important; sales reps need to enter every meeting feeling informed, aware and up-to-date. Taking a variety of real world data feeds (e.g. social media, call center transcripts, medical field notes and more) and integrating and consolidating these data feeds provide a powerful background in advance of any visit. AstraZeneca, Pfizer, Johnson & Johnson and Novo Nordisk have experienced the benefits of using NLP to mine the nuggets from these noisy and varied channels. Workflows use NLP to structure the data and advanced visualization tools to provide easy-to-digest dashboards, for their medical affairs and product commercial teams to access and assimilate the insights.

NLP Can Enable Sharing of Best Practices

Identifying the best collateral from MSLs and sales reps for use once a meeting is planned is also an important component for productive conversations. Pharma product teams can use NLP to pull out metadata and map the most commonly used product sheets, slide decks and other product collateral that sales reps use. The metadata enables visualization of what materials are well used and where MSLs are finding gaps – critical for product teams to know and thus be able to fill.

NLP Brings Insight for Pharma Product Teams

After reconsidering my first reaction, I realized that yes, AI – in the shape of NLP – has been proven to bring value to pharma field, medical affairs and sales teams, providing insights and information that can boost communication, understanding and productivity. And, as the volume and variety of real world data source grows, I am sure that the opportunity for value contributed by AI technologies will grow as well.

Related Reading:

 

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Jane Reed

Author: Jane Reed

Jane Reed joined Linguamatics in March 2014, as the head of life science strategy. She is responsible for developing the strategic vision for Linguamatics’ growing product portfolio and business development in the life science domain. Jane has extensive experience in life sciences informatics. She worked for more than 15 years in vendor companies supplying data products, data integration and analysis and consultancy to pharma and biotech – with roles at Instem, BioWisdom, Incyte, and Hexagen. Before moving into the life science industry, Jane worked in academia with post-docs in genetics and genomics

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