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. 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 — one view that combines digital influence signals with traditional KOL identification, so field teams can prioritize experts who carry weight in both worlds [6, 7].

How life sciences teams identify DOLs

Follower counts are the least informative signal available, not least because congress-period conversation attracts noise: one analysis of ESMO congress activity found heterogeneous sources competing for attention in the same hashtags, including substantial commercial and low-quality content [2]. 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 — data readouts, congress sessions, label changes — or commenting 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 — making sure accurate, balanced information reaches the digital conversations where sentiment is actually forming — and it carries compliance considerations specific to public digital channels, a topic medical affairs bodies have flagged as a priority as DOL collaboration matures [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 the 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 — surfacing 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/DOL strategy, that means a team can ask questions no single data source can answer — “Who are the rising DOLs in our therapeutic area, and how has expert sentiment shifted since the congress readout?” — and get 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 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: