Your KOL List was Built for a World Before Digital Influence

Written by -Chinmayee Goyal,Suraj Gupta

Most pharmaceutical companies still build their KOL list the way they did a decade ago. A KOL, short for key opinion leader, is a healthcare professional whose opinion shapes how other physicians think about a disease or a treatment. Companies have long found these experts by counting things that are easy to count: how many papers someone has published, how many conference talks they have given, how many years they have spent in practice.

That approach made sense when visibility and influence were close to the same thing. A physician who was publishing and speaking at major meetings was usually also the physician other doctors listened to.

That world has changed. Influence today moves through channels a publication count was never built to see.

The List was Built to Measure Visibility, Not Influence

Every signal a traditional KOL list relies on, publication volume, conference presence, academic rank, years in practice, was chosen because it was easy to measure, not because it directly measures influence. They are proxies that worked reasonably well in a world where almost all clinical learning happened in journals and in person at medical meetings.

That is no longer the only world where healthcare professionals, or HCPs, learn from each other. A growing share of clinical conversation now happens somewhere the old scorecard never looks: physician-only online communities, medical social platforms, virtual tumor boards, podcasts, and posts shared peer to peer. A physician can be genuinely influential in these spaces while barely registering on a publication count.

The reverse holds true as well. A physician with a long publication history may have stopped actively shaping how peers prescribe years ago, yet the list keeps ranking that name near the top simply because the old signals have not caught up. Neither case is rare. It is what happens when a measurement system built for one era keeps running unchanged into the next one.

Influence Moved. The List didn't.

The real risk in KOL identification is structural. A company can rank every known expert accurately and still miss an entire layer of today's influential voices, because those voices never made it onto the list to begin with.

Influence is no longer a single signal. It comes from scientific contribution, from professional networks, from clinical collaboration patterns, and increasingly from digital channels that many companies still treat as a side project rather than a core input. A list built on only one of these dimensions misses the others by design, not by accident.

Put simply, the deeper problem sits below the names on the list. The list itself was never built to see where influence actually sits today.

Influence Now Travels Through Channels the Old List Never Measured

Clinical decision-making has shifted onto digital channels, and it has stayed there. A 2024 survey of 317 US physicians across ten therapeutic areas by Sermo and LiveWorld found that 60 percent had changed their perception of a medication because of content from an online influencer, and half had changed an actual prescribing decision because of it [1]. Sixty-nine percent said they engage with this kind of content daily.

These are not isolated numbers: a similar survey fielded in late 2022 found that 57 percent of US physicians had changed their view of a medication because of social media content, and 41 percent had changed a prescribing choice because of it [2]. The pattern holds across two separate surveys taken roughly two years apart, and the more recent numbers are higher, not lower.

A KOL list with no view into this layer of activity is missing the exact channel through which half of surveyed physicians say their prescribing has already changed.

The Most Consequential Voices are not always the Most Visible Ones

Some of the most influential experts in each field today do not have the longest publication list. They include emerging specialists publishing breakthrough work who have not yet accumulated years of citations, physicians who connect two separate referral networks without being prominent in either one on their own, and regional specialists who quietly drive local adoption of a new approach long before it reaches a national conference stage.

IQVIA describes a related category it calls digital opinion leaders, or DOLs: healthcare professionals who build authority through social media, websites, forums, and podcasts, reaching other clinicians, patients, and advocacy groups that traditional scientific credentials alone do not reach [3]. IQVIA's own tracking system processes roughly 24 million digital content items a day, a volume that shows how much of this activity a manual, publication-first process simply cannot see [3].

These voices are visible to anyone looking in the right place, but most KOL lists are built to look somewhere else.

A List Built Once a Year can't Track Influence That Moves Every Week

Most companies build a KOL list once, at launch or at the start of a planning cycle, and leave it largely unchanged for a year or more. Digital-era influence does not move on that schedule. Who is publishing, who is gaining traction in physician forums, and who is building a following online all shift continuously, and a 2025 analysis of AI's role in KOL and DOL identification notes that an assessment done even three months earlier may no longer reflect a professional's current standing [4].

Because influence compounds, a stale list falls further behind every month it goes unrefreshed, while a competitor running a continuously updated process is already building the relationship your field team has not yet identified as worth pursuing, especially with early KOLs, where earlier identification makes it easier to build a long-term relationship.

What a Blind Spot in the List Actually Costs

A KOL list with gaps in it tends to fail quietly, in ways that are easy to attribute to other causes. Trial enrollment slows because the physician best positioned to refer patients into a study was never flagged as influential. Early adoption of a new therapy lags because the digital-first specialists shaping peer opinion in online forums were never part of an engagement plan. Field teams spend time reacting to relationships competitors have already built instead of starting new ones, because the companies that recognize influence earliest get to it first.

Closing this gap does not require replacing the KOL list. It requires changing what goes into building it.

Four changes make the most difference. Add digital and network signals alongside the traditional ones, rather than treating them as optional extras. Map relationships between experts rather than ranking individual names in isolation, since some of the most useful voices connect two networks without leading either one. Pair an annual list refresh with continuous HCP activity monitoring between cycles, so shifts in influence surface before the next refresh. And give commercial, medical affairs, and brand teams one shared view of who is genuinely influential, instead of three separate lists built from three different definitions of the same HCP.

How Auxo helps Close the Gap

ProcDNA's Auxo platform builds HCP profiles from both traditional signals and digital ones, drawing on publications, clinical trial involvement, and conference activity alongside social media, online communities, and peer discussion, with HCP activity monitored weekly between annual list refreshes. It also maps professional networks directly, which is how it surfaces bridge connectors and regional influencers who do not rank highly in a publication-based list. And it groups experts by the kind of influence they hold, rather than treating every KOL as the same type of voice, so commercial, medical affairs, and brand teams can work from one shared picture of who matters and why.

The Path Forward

A KOL list should do more than capture who has influenced a therapeutic area in the past. It should provide a structured view of established leaders while also identifying how influence is evolving over time.

An effective KOL strategy combines a defined annual refresh process-with clear criteria for retaining, removing, and adding KOLs-with ongoing monitoring of HCP activity and emerging trajectories. This allows teams to maintain continuity with priority KOLs while identifying new voices and shifts in influence that may warrant attention.

The goal is not simply to have the right names on this year’s list, but to build a KOL universe that can evolve as scientific activity, clinical engagement, networks, and influence change.

Curious how your current KOL identification approach balances established influence with emerging voices? Connect with the ProcDNA team to explore how a more dynamic, data-driven approach can support your Medical Affairs strategy.

References

Sermo and LiveWorld Survey: KOLs & DOLs Genuinely Influence HCPs on Social Channels [Internet]. Sermo. 2024 [cited 2026 Oct 6]. Available from: https://www.sermo.com/press-releases/sermo-and-liveworld-survey-kols-dols-genuinely-influence-hcps-on-social-channels/
Survey Finds 57% of U.S. Physicians Have Changed Their Perception of a Medication as a Result of Info on Social Media [Internet]. www.businesswire.com. 2023 [cited 2026 Oct 6]. Available from: https://www.businesswire.com/news/home/20230215005061/en/Survey-Finds-57-of-U.S.-Physicians-Have-Changed-Their-Perception-of-a-Medication-as-a-Result-of-Info-on-Social-Media
Rise of Digital Opinion [Internet]. 2023 [cited 2026 Oct 6]. Available from: https://test-www.iqvia.com/-/media/iqvia/pdfs/library/white-papers/transforming-the-influence-landscape-in-life-sciences-and-medtech.pdf
Van Hulten E. How AI is Shaping KOL and DOL Identification [Internet]. Pharmexec.com. PharmExec; 2025 [cited 2026 Oct 6]. Available from: https://www.pharmexec.com/view/how-ai-is-shaping-kol-and-dol-identification

Frequently Asked Questions

FAQs

What is a Digital Opinion Leader (DOL) in healthcare?

A Digital Opinion Leader, or DOL, is a healthcare professional who builds credibility and reach mainly through online channels, such as social media, forums, blogs, and podcasts, rather than through publications and conference appearances alone. DOLs often reach a wider mix of audiences, including other clinicians, patients, and advocacy groups, and their influence can grow faster than a publication record because digital reach is not limited by the slower pace of academic publishing.

Why do traditional KOL lists miss digital influence?

Traditional KOL lists are built from signals that are easy to count in structured sources, such as publication counts, conference speaking slots, trial involvement, and years in practice. Digital influence shows up in less structured places, like physician communities, social platforms, and peer-to-peer sharing, which most manual identification processes were never designed to track, so an expert can be highly influential there while remaining invisible to the traditional scorecard.

How often should a KOL list be refreshed?

An annual refresh can be an effective cadence when supported by a structured process for maintaining the KOL universe. This typically involves three components: maintaining the core/static KOL list, removing KOLs based on factors such as deactivation or field team input, and adding new or emerging KOLs based on changes in influence, activity, and trajectory. In parallel, ongoing/weekly monitoring of HCP activity helps identify meaningful changes between annual refresh cycles and informs the next list update.

What does network analysis add to KOL identification that publication counts miss?

Network analysis looks at how experts connect to each other rather than only at how much each one has individually published or presented. This can surface bridge KOLs who link two referral networks together and regional influencers who drive local adoption, voices that often do not rank highly on publication-based lists alone despite their real influence on prescribing and referral patterns.

What role does AI and machine learning play in modern KOL identification?

AI enables the processing of large volumes of structured and unstructured data, combining sources such as clinical trials and publications with social content, patient advocacy, and online discussions. Machine learning models can then identify patterns across these signals, score and rank HCPs, and detect emerging trajectories that may not be visible through traditional approaches. This enables teams to monitor changes in influence and activity on an ongoing basis, rather than relying solely on periodic manual assessments.

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