The overlap matters more than the distinction

Here is the part strategy teams most often get wrong: KOL and DOL are not mutually exclusive categories. They are different modes of influence, and the map of who holds each is constantly shifting. Some of the most influential voices in a therapeutic area are hybrids: guideline authors with large digital followings. Others are pure DOLs with modest publication records but enormous reach among community physicians. And some deeply influential KOLs have no digital footprint at all.

Treating these as one list produces blind spots in both directions: engage only traditional KOLs and you miss day-of-data sentiment; chase follower counts and you miss the experts who will actually write the guidelines.

Medical affairs leaders increasingly describe the answer as an integrated influence map, a single view that combines digital influence signals with traditional KOL identification so that field teams can prioritize experts who carry weight in both worlds [6, 7].

How life sciences teams identify DOLs

Follower counts and raw volume are among the least informative signals available. A network analysis of the ESMO 2018 congress conversation illustrates why: commercial accounts made up under 10% of tweeters yet produced roughly 16% of tweets and drew nearly a quarter of all retweets. Even so, they were far less likely than non-commercial voices to be cited or mentioned by their peers [2]. Volume and even retweets can be inflated by sources that carry little genuine influence among clinicians. Rigorous DOL identification weighs signals like these instead:

1. Relevance. Does this voice consistently discuss the disease area, mechanism, or treatment landscape in question, or is healthcare a minor part of a general feed?

2. Engagement quality. Are clinicians and researchers responding, sharing, and debating this person's content, or is engagement primarily from the public?

3. Network position. Is this voice connected to, cited by, or amplified by other recognized experts?

4. Velocity and timing. Is this person shaping the conversation at key moments such as data readouts, congress sessions, and label changes, or commenting only after consensus forms?

5. Trajectory. Rising voices matter as much as established ones. DOL influence changes far faster than KOL standing, and today's active fellow with a sharp tweetorial habit may be tomorrow's hybrid expert.

Because these signals live across platforms and change daily, DOL identification at scale is an analytics problem, not a manual research task.

Why the distinction changes engagement strategy

KOLs and DOLs warrant different engagement models. Traditional KOL engagement runs through advisory boards, investigator relationships, congress meetings, and publication planning. DOL engagement centers on scientific exchange and education, ensuring that accurate, balanced information reaches the digital conversations where sentiment is actually forming. It also carries compliance considerations specific to public digital channels, including privacy and data usage, fair market value, pharmacovigilance obligations when listening to public conversation, and the distinction between passive listening and active engagement. Medical affairs bodies have treated this as a priority as DOL collaboration has matured [5, 6].

The practical implication for medical affairs teams is a single stakeholder view that tracks both modes of influence: who publishes, who presents, who posts, who gets amplified, and how each expert's beliefs about science are evolving across all of those channels at once.

How SteepRock helps

SteepRock approaches this as one problem, not two.

OLA®(Opinion Leader Analytics) is a proprietary advanced search engine within SteepRock's Embedded Intelligence service, spanning scientific, commercial, regulatory, and digital data domains across life sciences. OLA identifies and profiles both KOLs and DOLs in any disease area, analyzing publications, clinical trials, congress activity, claims and referral data, and social and digital media in real time. It surfaces the digital voices traditional KOL lists miss alongside the institutional experts they overlap with.

SteepRock Embedded Intelligence is the unified, agentic intelligence layer that operates natively across those data domains as a single environment. It brings generative AI directly into the workflow, summarizing expert profiles, synthesizing engagement history, drafting insights, and answering natural-language questions across your stakeholder data. For KOL and DOL strategy, that means a team can pose a question no single data source can answer, such as which rising DOLs matter in a therapeutic area and how expert sentiment has shifted since a congress readout, and receive a synthesized, evidence-linked answer drawn from publications, congress activity, and digital conversation at once.

OLMS™ (Opinion Leader Management System) then gives medical affairs and commercial teams one compliant system of record for engaging both KOLs and DOLs, connecting external influence signals with internal engagement history so that the influence map and the engagement plan live in the same place.

See how OLA and Embedded Intelligence map KOL and DOL influence in your disease area