Medical Affairs
Find the Experts Who Move the Field
See who’s actually shaping care in your therapeutic area, and share those insights with your field team to act on before the competition does.
What the Evidence Rests On
Every scientific claim traces back to its source and a method your team can rerun.
Medical Affairs Analytics
Medical teams use Komodo to set scientific priorities, find the experts treating patients, see where the evidence base is thin, and show what medical strategy changed.
Medical Strategy
See who is diagnosed, who gets treated, and who is missed entirely, then decide which gap to work on first.
Expert Engagement
Find the clinicians treating your patients, ranked by real treatment volume, referral patterns, and spheres of influence.
Evidence Gap Analysis
See where real-world treatment patterns diverge from published guidelines, and exactly how big that gap is.
Unmet Need Analysis
Size the untreated and undertreated population by therapy area and geography.
Field Medical Insights
Give field teams the local picture before the visit: who treats the condition nearby, how, and what happens to those patients.
Medical Impact
Measure whether medical activity moved real clinical behavior: diagnosis rates, guideline-concordant treatment, and time to therapy.
Built for a Room Full of Experts
Medical strategy rests on data your team can stand behind, a method that holds up to outside scrutiny, and impact you can prove without a revenue number. Here’s how Komodo backs all three.
Scientific Credibility
Come to the Table With the Data
Scientific strategy built on one data source is guessing with good intentions. The Healthcare Map combines open and closed claims into one resolved view of how a condition is actually diagnosed, treated, and abandoned, so your team’s priorities start from what’s happening in practice, not a partial picture.
Every Method on the Record
Show How You Got There
A peer-reviewed journal or a scientific committee doesn’t just want the finding, it wants the method behind it. Marmot shows the SQL behind any output, so your team can walk a reviewer through exactly how a cohort was defined and a number was calculated.
Medical Impact, Measured
Demonstrate Your Value
Medical teams are asked to prove their worth without a revenue number. Marmot tracks diagnosis rates, guideline-concordant treatment, and time to therapy, then lets you report on the same cohort, the same way again next quarter to show the change over time.
Medical Affairs FAQs
What role does AI play in medical affairs analytics?
AI in medical affairs means asking a clinical question in plain language and getting an answer directly, instead of waiting on an analyst or a static report. The strongest implementations show their method alongside the analysis, so an answer can be checked, not just trusted.
How do I know I can trust an AI-generated medical claim?
Trust an AI-generated claim the way you’d trust any scientific one: check that it’s grounded in real data, that you can see how it got there, and that it holds up if you run it again. Marmot is architected around exactly that standard, grounded, explainable, reproducible, verified, and governed, so a claim is something your team checks, not just accepts.
Why not build this AI capability in-house?
Building it yourself means owning two ongoing burdens forever: continuously cleaning and maintaining your own data pipeline, and rearchitecting for trust every time a new AI model ships. Komodo already carries both, so your team spends its time on the scientific question in front of it, not on infrastructure that needs constant upkeep.
How can I prove my medical affairs teams’ impact?
Medical impact is measured in clinical behavior rather than revenue: diagnosis rates in the indication, guideline-concordant treatment, time from symptom to therapy, and referral patterns. Reading the same cohort definition quarter over quarter is what makes the change attributable rather than anecdotal.
Can this turn a one-off analysis into a repeatable workflow?
Yes. Medical Affairs teams have used Komodo to turn a one-off analysis into a repeatable workflow: patient identification rules and clinical codesets are set once, then reused and monitored continuously across every center a program covers, instead of being requested and rebuilt from scratch each time.
See What the Evidence Says in Your Therapeutic Area
Ask a question and watch Marmot answer using clinical logic rather than a generalist model guessing at it, and show the reasoning and the source alongside the result.