Using Deep Data and AI to Reimagine KOL Engagement: Q&A with Eric Toron

Eric Toron
Executive director, operations lead,
global medical affairs and outcomes research
Merck

Pharmaceutical Executive: How has Merck been able to leverage data to improve KOL engagements?
Eric Toron: As Merck’s product portfolio evolved and we ventured into new therapeutic areas, re-evaluating our KOL engagement strategy to meet these changes became a top priority. By leveraging deep data on KOLs and AI, we’ve streamlined KOL identification—particularly in our new therapeutic areas—increasing MSL productivity and improving engagement planning.

PE: How are you streamlining KOL identification in new therapeutic areas?
Toron: When we acquired a company, we didn’t have a complete view of the external scientific leaders in this therapeutic area. With access to deep KOL data, our team triangulated the scientific leaders we knew and validated their information with experts from the acquired company. This was all completed in real time, allowing us to quickly identify top-tier scientific leaders and immediately incorporate them into our plans. Previously, this would have taken weeks, sometimes even months, to get this information compiled and reviewed.

PE: How have MSLs become more productive?
Toron: One of the cornerstones of a great MSL is the ability to customize engagements based on the KOL’s needs. With real-time notifications, MSLs have the most current view of what’s happening with the scientific leaders they are engaging with, including their involvement in publications, congresses, and clinical trials. This valuable information helps these teams be more efficient in their meeting preparation and have more effective scientific engagement.

PE: Everyone is talking about AI and Gen AI. How can this power KOL engagement?
Toron: We knew there were exciting opportunities to incorporate AI into our engagement strategy. The software we use for our deep KOL data has a direct API so we could easily incorporate that data into our data lake. This has opened up many possibilities to do more, like the ability to get to simple and more complex quantitative analytics quickly. We can pair that quantitative data with our qualitative measures to tell a better value story about medical affairs.

We are also using our internal AI tools to find connections and examine this data. Deep KOL data that is clean and high quality is essential for getting valuable output, especially when using generative AI. Not only is this saving time to get the information needed, but ultimately results in a better and more personalized engagement.

PE: How can teams align engagement planning across the organization?
Toron: With a company of our size and scale, bridging functions can become a challenge. Using a workflow capability around our deep KOL data supports strategic engagement planning and utilization management across all functions. This is a critical capability for us, especially for pre-launch. Whether it's medical, commercial, or clinical, we now have purpose-driven planning around specific scientific leaders. Previously, we relied on spreadsheets and other cumbersome manual efforts. Streamlining the planning process also enables us to go back, evaluate, and track what happened during engagements.

This type of planning is especially helpful when it comes to areas like congresses. Take ASCO for example. It's almost impossible to go into a congress of that size and complexity without a detailed plan for engaging with KOLs and scientific leaders. Doing this on the front end for each scientific leader yields a better customer experience for them and a more productive use of our time. This capability is also very effective during product launches, and it has many other broader use cases as well.

PE: With all these innovations at Merck, what does success look like?
Toron: When implementing new tools and technology, defining how success will be measured is key. At Merck, this means aligning these implementations with medical impact, ensuring they contribute meaningfully to our medical affairs goals, company goals and, ultimately, better patient outcomes.

But beyond strategic alignment, we also need to look at practical outcomes. Are we saving our MSL teams from administrative burdens? With a large amount of product launches over the next few years, finding efficiencies starting at the field team level is critical. It allows us to scale our work and support a greater portfolio without necessarily expanding our current headcount. We're in the early stages of integrating AI and deep KOL data, but we are confident it’s going to reduce the administrative burden on our field teams. That way, they can focus on key activities like scientific engagement.

PE: And finally, how can this deep data help scale for the future?
Toron: The drive towards innovation looks different for every company. One of our focus areas on my team is productivity, tangible savings and efficiencies. Solutions we implement need to be scalable and impactful for both patients and our business.

Experimentation plays a key role. We recognize that not every initiative will succeed, but testing and learning creates progress. When looking for potential areas of focus, tap into the field medical people who are in this work every day. Their feedback, especially those who are interested in innovation, can help uncover opportunities and gauge whether they are impactful or not.

Adoption and buy-in is critical for success. That’s why change management is essential. Keep in mind that there might be variability across therapeutic areas and even within geographies in terms of adoption. Identify and empower champions who are passionate about leveraging technology to bolster scientific exchange. Their voices bring credibility and accelerate broader buy-in. Overall, I’m just really excited about the potential opportunities this will bring to both Merck and the industry.