In KOL mapping, where you start is of utmost importance

Choosing the right Key Opinion Leaders (KOLs) has never been more important. As pharma teams strive to improve scientific engagement, market access, and clinical development outcomes, the people you engage with can make all the difference.

But how we identify those people is just as important as who ends up on the list.

Over the years, teams have relied on various data sources to build their stakeholder maps. Speaker program records, claims databases, publication counts, social media metrics. All of these have become standard tools. Each has value, but each also comes with trade-offs.

This article takes a closer look at those approaches and explores why starting with the science may offer a more strategic foundation.

Claims data: Real-world activity without scientific context

Claims data has long been a staple in KOL identification. It provides insight into who is treating patients, how frequently, and in which settings. This is especially helpful for Market Access teams looking to understand prescribing behavior and regional trends.

The limitation is that claims data rarely tells you anything about a person’s role in scientific discourse. It can show you who is treating the most patients, but not who is influencing clinical guidelines, driving research, or shaping the next generation of evidence.

It also tends to favor generalists over subspecialists, and well-resourced institutions over smaller centers of innovation. For scientific engagement, that can skew priorities.

Speaker lists and CRM data: Familiar but not always forward-looking

Internal stakeholder lists, often built through speaker programs and CRM tools, offer familiarity. They reflect relationships that already exist and help ensure continuity in engagement.

These records are useful, particularly when those KOLs are still active and relevant. But they don’t always reflect the current state of science. Emerging experts may be missed. Shifts in focus or influence may go unnoticed. And legacy lists can easily become echo chambers that prevent teams from engaging fresh perspectives.

They provide useful historical context, but they don’t always provide strategic clarity on where things are heading.

Starting with the science: A publication-first approach

Mapping scientific influence through publications offers a more precise view of who is shaping thinking in a given area.

By focusing on publication activity, you can see which individuals are driving evidence generation, participating in landmark studies, and engaging in the kinds of conversations that shape policy and practice. It provides a real-time lens into thought leadership, particularly in therapeutic areas where the landscape is evolving quickly.

Of course, publication data isn’t perfect either. Authors are often hard to disambiguate, especially when names or affiliations are inconsistent. Not all experts are prolific writers, and not every high-volume publisher is a good communicator or stakeholder.

That’s why the way you structure and interpret this data matters.

A science-first foundation, built with context

At VISFO, we’ve spent years refining how we work with scientific publication data. Our disambiguation algorithms help us clean, structure, and confidently assign authorship. So you get an accurate picture of who’s doing what.

But we don’t stop there.

We assign dynamic impact scores based on the specific topic or subtopic you care about. This means that if you’re looking for experts in immunotherapy-related fatigue, we’ll show you the right people for that conversation. Not just the most published person in oncology overall.

From there, we can layer in other data types like trial participation, claims activity, affiliations, and network connections. The result is a rich, contextual view of influence that reflects both reach and relevance.

This science-first foundation powers a wide range of strategic use cases. Whether you’re identifying Digital Opinion Leaders, tracking Rising Stars, analyzing Research Centers and their collaboration networks, or zeroing in on Payer-Influencing KOLs, the same principles apply: relevance over volume, and evidence over assumption. We also support deep profiling of Key Clinical Investigators and Trial Sites, helping you make smarter decisions from early development through to access and beyond. You can explore each of these areas across our platform to see how this intelligence takes shape.

HelixAI: Turning insight into action

All of this lives inside HelixAI, our precision intelligence platform.

Within HelixAI, clients can search for stakeholders by topic, geography, or institution. They can explore author networks, see AI-summarized profiles of publication activity, and get notified when relevant new work is published.

They can also tag stakeholders to projects, plan engagements, and export reports for use across teams. It transforms stakeholder mapping from a one-off task into an integrated, strategic workflow.

And because the insights are dynamic, teams can adapt as priorities change - without starting from scratch.

No one-size-fits-all: But a better fit for strategy

There’s no single correct way to build a KOL list. Different teams need different types of influence, and hybrid approaches will often be the most effective.

But in a world where scientific complexity is increasing and therapeutic areas are becoming more specialized, the need for evidence-aligned engagement is only going to grow.

Starting with the science helps ensure that your engagement is grounded in relevance, not just reach. And when layered with broader context and delivered through tools built for action, it becomes something far more powerful than just a list.

It becomes a strategy.