Novo and Lilly disclosed USD 180.2 million in payments to healthcare professionals from 2021 to 2025. Inside are two opposite strategies
In short
The doctor who prescribes you Ozempic may be getting paid by the company that makes it. A lunch, a trip, a fee for giving a talk. It is legal, and it is on the record: in the United States every payment from a drugmaker to a healthcare professional is disclosed by law and ends up in a public file anyone can download.
That file is published by CMS, the federal agency that runs Medicare and Medicaid: between 11.6 and 16.1 million rows per year. From those rows I rebuilt five years of the GLP-1 drugs for diabetes and obesity (Ozempic, Wegovy, Mounjaro, Zepbound): USD 180.2 million disclosed between 2021 and 2025, Novo 111.05 million and Lilly 69.13. The file does not say whether a payment changes a prescription, and this case does not claim it either: it says who gets paid, how much, and what each company buys.
And what each one buys is opposite. Lilly buys concentration: its hundred highest-paid professionals receive 35.6% of everything it spends, against 20.4% at Novo, and almost all of that money is speaker and consulting fees. Novo buys reach: 114,861 professionals in 2025 against 79,526, and it leads on number of payments in all five years.
Both bet on the endocrinologist, who receives 41 times more than a nurse practitioner or physician assistant. But Lilly started to pull back from there and opened a front in pulmonology and sleep medicine; Novo held its bet and added another one in cardiology, nephrology and gastroenterology, two years earlier.
I close with what a commercial team would do with this: the route left open, the two experiments that validate it or kill it, and the map of which data you would have to buy to decide it properly, with the limits of each public source noted alongside.
Lilly buys voice and Novo buys reach: same category, same period, two opposite commercial strategies.
Every number in this case comes from counting rows in an official file, and before publishing I closed my totals against the aggregates CMS itself publishes through its query interface: 36 comparisons, a difference of 0.00% in all of them. What there is instead are eleven methodological decisions that change the numbers, written down before implementing them in the decision log of the repository(opens in a new tab). The two that move the result the most: which products count as GLP-1 (nine, including tirzepatide, a dual GIP/GLP-1) and how I split a payment that reports several products (in equal parts). The full table is at the end of the case.
The file holds a contradiction. In 2023 and 2024 Lilly spent more dollars than Novo; in those same years, Novo made more payments than Lilly, as in the whole series. Both things are true, and the key is what each payment buys. Speaker and consulting fees buy the professional's voice; meals, travel and educational material buy field contact. Lilly's whole lead lives in voice: in the field, Novo wins all five years, by 1.5 to 6.8 times.
The mechanism is in the count. Lilly almost doubled its voice payments, from 6,521 in 2021 to 12,604 in 2025; Novo cut its own to two thirds, from 7,361 to 5,201. Novo pays fewer times and more per time; Lilly pays more times and less per time.
The first footprint is how many people receive the money. Lilly's hundred highest-paid professionals take 35.6% of everything it spends on GLP-1; Novo's take 20.4%. That is 1.74 times more concentrated, whichever cut you look at, from the top 10 to the top 1,000.
The striking part is not the money, it is the rhythm. Getting into the top 100 costs almost the same at both companies: USD 172,608 at Lilly and 172,768 at Novo, a difference of 160 dollars over 172 thousand. But Lilly's hundred accumulate 431 payments on average against 246 at Novo. They receive similar amounts spread over almost twice the number of contacts.
The circle that gets paid for its voice is also smaller at Lilly: it spreads USD 48.42 million in fees across 657 professionals, against 61.73 million across 1,139 at Novo. Those are two different operations: 657 intense relationships, or 1,139 looser ones.
So far, how each one buys. The other question is who gets the money, and there the two agree: both pay the same specialist a lot and the mass channel very little. An endocrinologist received 12,129 dollars over the five years; a nurse or physician assistant, 293: 41 times more per head. Endocrinology is 2.1% of the professionals reached and 36% of the money; the nurse and assistant channel, with 120,145 people against 5,367, is the mirror image: many people and little money per head.
Both bet on the same profile in different doses: endocrinology is 43.6% of Lilly's spending and 31.5% of Novo's. Novo compensates on two sides, with more weight on the volume channel (21.8% to nurses and assistants against 16.0%) and with a group of specialties Lilly barely touches.
The 41 times are a commercial decision: both companies chose to buy something different from each profile. The chart says who gets paid, and nothing more.
Everything above is the photo: five years added up. The film, year by year, shows both companies moving. In proportion they converge: the gap in the weight of endocrinology went from 30 points in 2023 to 3.2 in 2025: Lilly fell from 51.6% to 27.8% and Novo rose from 21.6% to 24.5%.
In dollars they diverge, and that is where the story is. Lilly pulled USD 1.85 million out of endocrinology, its only category going down, and put it into primary care. Novo grew in all of them, and its biggest jump was to cardiology, nephrology and gastroenterology: from 0.96 to 7.39 million in two years, 40.8% of everything it added.
Lilly opened its own front two years later and somewhere else: pulmonology, sleep medicine and critical care go from USD 13 thousand in 2023 to 1.39 million in 2025, 25.7% of its net growth, and 98.9% of that money is Zepbound. And it opened that front with new people: 81.5% of the spending went to professionals who had never received a GLP-1 payment before. Novo's front is the opposite: three quarters went to people already in the registry.
The overlap with each molecule's newly approved indications is strong and fits in time. I do not claim it: the database does not record which indication a payment is for, and without that column the cause cannot be held up.
Up to here, the file did the talking. What follows is my read.
The two known routes are taken and they have opposite cost structures. Lilly's is cheap to build and expensive to hold: 657 well chosen professionals are enough to move the category, but every exit hurts and the program depends on those people staying willing. Novo's is expensive to build and hard to copy: 114,861 professionals reached in a year is a field operation with people, territories and logistics.
Copying either one means fighting on the other's ground with a two year handicap. The route that stays open is in the same table: the nurse and physician assistant channel receives 293 dollars each against 12,129 for the endocrinologist: twenty-two times more people, and neither company is really fighting for it. The one that comes closest (Novo, with 21.8% of its spending) does it with meals, travel and educational material, the cheapest contact to match.
The recommendation has an assumption that can knock it down: I assume that a professional who receives 293 dollars a year responds the same way as one who receives 12,129, and that is not proven. It may be that the channel is cheap because it does not pay off. So before investing, two experiments with their kill criterion fixed in advance:
A case made with open data has to say where the open data ends. This is the map for whoever approves the budget: which decision depends on which source, what Open Payments answers for free, and what each purchased source drags along with it.
| Decision you have to make | What the public source answers | What is missing and where it comes from | What it drags along |
|---|---|---|---|
| Who the competition pays and how much | Everything: amount, payment type, specialty, year, product | nothing | nothing: it is public and reproducible |
| Whether a rival's speaker program rotates | Everything: identifiers are stable across years | nothing | nothing |
| Whether the spending moves prescriptions | Nothing: the database records payments, behavior stays outside | prescription data at professional level (IQVIA, Symphony and other vendors) | a contract with usage restrictions: disaggregated results stay unpublished |
| What happens to the patient after the script | Nothing | longitudinal claims databases from payers | health data: anonymization, usage agreements and legal review |
| How much direct-to-consumer advertising weighs | Nothing: it falls outside the law that creates the database | advertising spend trackers | little: it is market data |
| What coverage each product has in each plan | Partial: formularies of public plans are published | the formularies of private plans | constant updating: it ages in months |
Prices for licensed sources are not public and I do not estimate them. What can be decided without asking for a single quote is the order: the first two rows are already answered, and that work is paid for with analysis time.
Two of the six decisions are already answered with free material, and a third one can be narrowed down quite a bit before signing anything.
Each one changes the numbers, and each one is written down with the alternatives I dropped and what would invalidate it.
| Element | Status | Source or decision |
|---|---|---|
| Amounts, payment counts and who received each payment | data | CMS Open Payments, General Payments 2021-2025, downloaded 2026-08-25 |
| Declared specialty of each professional | data | field of the database itself, under the national specialty classification |
| The 2021-2025 window | my call | D-001: 2021 is the first year nurses and assistants enter the registry |
| Which legal entities are "Novo" and which are "Lilly" | my call | D-002: full corporate group, seven identifiers |
| Which products count as GLP-1 | my call | D-003: nine products, including tirzepatide, a dual GIP/GLP-1 |
| How I split a payment that reports several products | my call | D-004: prorated in equal parts |
| Grouping payments into "voice" and "field contact" | my call | D-006: by what the payment buys, beyond the CMS label |
| Measuring concentration by the top 100 | my call | D-007: the top 1% would mix concentration with network size |
| The specialty categories | my call | D-008, reopened in D-009 and D-011 |
| Current dollars, not adjusted for inflation | my call | D-010, with the break-even point published |
| Whether the money changes what gets prescribed | no data | out of scope: the database records payments, and clinical behavior stays outside |
| Why each specialty gets paid | no data | not in the database: it does not record indications |
Any argument about these figures is an argument about the rules that produce them, and that is why the rules are written down.
The CMS control covers the universe of payments and the row volume of each company. The figures by therapeutic class are my own calculation under these rules, because CMS publishes no aggregates by product or by drug category to close them against.
I ran 70 attacks against my own findings, grouped in three families: data artifacts, sensitivity to my decisions, and alternative business explanations. Surviving an attack requires a test that was run; the ones that failed are published with the same prominence as the ones that passed.
What would no longer change my mind, because it has a test: that Lilly's concentration is a mirage of its network size (match the networks at four sizes and it holds), that professional identifiers mix up people (zero collisions against the national licence number), that the new cardiologists and pulmonologists are the same people as before with a different label (relabelling is 0.7% and 4.8% of each front's spending), or that everything depends on my specialty grouping rule (it holds the same with the inverse rule).
What would:
The repository with the code, the decisions and the attacks is published in full(opens in a new tab). If you work in pharma commercial or data strategy and you see something badly framed, write to me: that is the kind of error I would rather fix with someone who knows the ground.
This is independent work, made with public sources. I have no commercial relationship, and never had one, with Novo Nordisk, Eli Lilly or any of the companies mentioned.
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