Data, AI, and the New Era of Field Teams: Q&A with Paul Shawah, executive vice president of commercial strategy at Veeva

Setting up a field team for the future necessitates equipping it with a toolbox of cutting-edge technological advancements. These tools enable the team to swiftly acquire essential, accurate information, as collectively advocated by biopharma organizations at Veeva's May 2023 Commercial Summit. Senior editor Fran Pollaro spoke with Paul Shawah, Veeva's executive vice president of commercial strategy, about the roles of data, ChatGPT, and AI in introducing new medicines to the market.

Pollaro: Life sciences companies are looking to keep up with the latest technologies and strategies to bring new medicines to market. Where do you see the most innovation?

Shawah: Innovation is really peaking in a few key areas. One is the type of data required to identify and educate the appropriate healthcare professionals and patients for today’s precision medicines. The old ways of tracking down traditional prescriptions from pharmacies doesn’t work for complex therapies that are administered in hospital settings, or rare diseases that don’t present definitive symptoms that may be indicative of a specific condition. So, there’s been a lot of work on modernizing data with a patient-first approach.

The other area is in leveraging technology for the speed and accuracy today’s field teams need. Beyond the data, analytics and communications tools, the technology for generative AI is now catching up to act as a personal assistant to the field rep.

Veeva's Paul Shawah

There’s a lot of talk about AI disrupting the industry. Do you see that happening?

The industry can benefit from AI in many ways, but it needs to create value in specific areas across R&D and commercialization. In a highly regulated industry, safe, accurate use of AI that adheres to IP rights and ensures compliance is important. Biopharma companies are looking for the specific areas where AI can advance productivity with accuracy.

What does the future of AI look like in life sciences? How will it impact field teams?

There is a lot of excitement around generative AI, and rightfully so. It represents a fundamental technological shift that can advance enterprise software applications.

Successful AI for life sciences lies in developing focused applications where the technology and use case is the right fit. With the right ecosystem, biopharmas can leverage their own data to answer specific questions tied to their business needs. While nothing compares to human-to-human interaction, AI can serve field representatives as a powerful tool to help quickly surface the information they need for relevant and timely conversations with customers.

We refer to this as ChatGPT for biopharma. Imagine a customer relationship management (CRM) chatbot where reps can analyze volumes of data in real time. AI can equip field teams with deeper insights into their customers and key stakeholders in their wider ecosystem. For example, understanding a doctor’s patient population and patterns in treatment to be best prepared for a call.

Where should companies start with new data and AI innovations? What specific use cases are most effective?

Biopharmas bringing complex therapies to market should begin with modern data designed for their unique products, commercial strategy, and operations. By bringing patient-first data into their commercial ecosystems, brands can gain insights in new ways that go beyond the traditional markets of limited data sets.

Companies will use granular daily patient data and projected prescription data to go beyond standard volume metrics and prescriber intent to fill data gaps in complex therapeutic areas like rare disease and oncology and utilize insights to tailor interactions.

As for AI, the application for field teams to use at scale is still relatively new. The use cases and change management associated with new insights is a work in progress. There will be a learning period that will build trust and credibility.In order for AI to be valuable for biopharma field reps, it needs the best, most complete and up to date data. There’s work to do there and that’s why customers see the best use case for generative AI is using their own trusted data sets.

If implemented responsibly and effectively, patient-first data and chatbots for representatives can lead to more targeted education, improved conversations, and healthcare providers receiving more precise information tailored to the patients they treat.