Lauren Sent a 16-Page Doc. I Sent Back 4,614 Cardiologists.
First pass got 88. Then I noticed I was throwing out 4,500 confirmed amyloid prescribers. Here's what changed.
May 7, 2026 · Build log
Lauren is an AE at Ultromics. They make EchoGo — an AI that reads echocardiograms and flags two conditions doctors miss for years. Heart failure with preserved ejection fraction, and a stiff-heart disease called cardiac amyloidosis. The amyloidosis one is the bigger deal. Up to eighty percent of cases go undiagnosed. There are drugs that work, but only if you find the patient in time.
She sent me a 16-page doc. 163 hospitals across 13 western states. Specific titles she needed. ICD codes for the conditions her product catches. Drug brand names — Vyndaqel, Attruby, Amvuttra, the whole amyloid playbook.
She asked for 50 to 100 contacts.
I dropped the doc into Claude Code with one prompt — one-shot this, build it as a micro-app, push the result to a Sheet and email her. Then I went to make coffee or probably just dicked around online.
Thirty minutes later: 88 cardiologists, a Google Sheet shared with her, a Gmail draft sitting in my inbox. Done.
Then I looked at the data the build had produced along the way and got annoyed.
The architecture was wrong
Here's what the first pass did. For each of the 76 hospitals on Lauren's list, run a LinkedIn waterfall by leadership title. Director of Echo, Chief of Heart Failure, Amyloid Program lead. Match what comes back against a federal physician registry. Score each candidate. Send Lauren the top 88.
What the first pass also did, but threw away — pull every cardiologist in her 13-state territory from that federal registry. Score each one on Medicare prescribing data and pharma payment data. 9,668 cardiologists. 4,531 of them had at least one signal that said this person is treating amyloidosis or HFpEF right now. 50 of them scored at the ceiling — confirmed Tafamidis prescribers, paid by Pfizer or BridgeBio for it, also writing the heart-failure pills that go alongside.
The first pass kept 88 of those 4,531 and threw out the other 4,443. Why? Because I asked it to start from the hospital list and walk to the people. So if a cardiologist's hospital wasn't well-indexed on LinkedIn, or if Blitz's company-page database had a stale slug, or if the title-match threshold was tuned a hair too tight, the entire human disappeared from the output. Even the ones who were named on the relevant pharma company's "doctors we paid this year" public file.
A LinkedIn waterfall with no LinkedIn page is no waterfall at all. It's a sieve with no holes.
The fix
Flip the architecture. Start from the people, not the buildings.
Pull every Medicare cardiologist in the territory. Score each one on three signals:
Signal 1: Did they prescribe Tafamidis or Acoramidis through Medicare Part D?
Signal 2: Did they take pharma payments tagged to ATTR-CM drugs from
Pfizer, BridgeBio, or Alnylam?
Signal 3: Did they prescribe Jardiance or Farxiga, the SGLT2 inhibitors
that anchor HFpEF treatment?Each signal is worth one to three points. The ceiling is eight. Then take the top 4,614 — everyone with at least one signal — and look them up on LinkedIn one at a time, by name, against the company page of any hospital they appear at. Tier them by signal score and title fit.
Same data sources. Same APIs. Different starting point. The result was 4,614 cardiologists instead of 88. 745 Tier A. 1,088 Tier B. 2,781 Tier C. 4,613 with a verified practice phone. 505 with a direct cell on top of that. 162 with a verified work email.
The 48 highest-scoring contacts include three cardiologists at Houston Cardiovascular Associates — Arvind Bhimaraj, Joggy K. George, and Barry Trachtenberg — who collectively wrote thousands of Tafamidis prescriptions through Medicare last year. There is no world in which Lauren's product is not relevant to those three people. EchoGo's whole pitch is we will help you find more amyloidosis patients before they need a heart transplant. Bhimaraj's day job is running the heart transplant program.
The first pass missed all three of them.
The data layer is the unfair advantage
Three federal data sets sit underneath this whole thing. None of them are gated. None of them are expensive. All of them have been sitting there forever.
NPPES — every licensed physician in America, with name,
address, specialty, phone. 9,668 cardiologists in
Lauren's territory.
CMS Part D — every Medicare-paid prescription, by physician,
by drug brand, by year. Tells you who actually
prescribes Tafamidis vs. who just talks about it.
Open Payments — every dollar pharma paid each physician, tagged
to product. Tells you which doctors are believers
deep enough that Pfizer paid them to teach others.Most "AI lead list" tools don't touch any of these. The people who build them grew up in B2B SaaS where the data layer is Apollo, ZoomInfo, LinkedIn scraping. That's the whole stack. They've never met an NPI number. They look at a LinkedIn profile that says Cardiologist and call it a day.
For healthcare, that's a five-out-of-ten signal. The ten-out-of-ten signal is this person prescribed Vyndaqel forty-three times last year and Pfizer paid them eight thousand four hundred dollars for it. That cardiologist is, with near-certainty, treating amyloidosis patients today. They don't need to be convinced the disease exists. They need a better diagnostic tool.
That's what Lauren sells.
What the micro-app looks like
The whole thing lives at tools/ultromics-echogo/ in my repo. Eight stage scripts plus the v2 reroute. Three YAML data files — the hospital list, the title cascade, the clinical-signal config. One orchestrator. Re-runnable quarterly when Lauren's pipeline shifts. Forkable for the next medical-device AE who hands me a similar brief — change the specialty taxonomy, swap the drug list, point at a new territory, ship.
Six stages, in order:
1. Load 76 target hospitals from her doc, drop the 7 already
in pipeline.
2. Pull the Medicare cardiologist roster for AZ + CA + NV + OR
+ TX + 8 extended states. 9,668 names.
3. Attach Part D + Open Payments + SGLT2 signals. 4,531 light up.
4. Look each one up on LinkedIn by name, against the hospital
their NPI registers them at. Loose name match — first-and-last,
not full middle-initial — because cardiologists register their
NPI with full middle name and LinkedIn shows first-last.
5. Send the top 4,614 to a judge. The judge is Claude Opus 4.7.
Each candidate scored on title fit, program fit, signal
strength. Tier A / B / C / drop.
6. Find phones and emails. The federal registry already has
the practice phone for everyone. Layer FullEnrich on top
for direct cells. Use Blitz reverse-lookup on the LinkedIn
URL for emails.Total wall clock for the v2 run: about an hour, mostly waiting on the federal data exports and the third-party phone enrichment polling.
The interesting part is not that this works. The interesting part is that it works as a per-client micro-app, not a generic lead-gen tool. Lauren's territory, Lauren's titles, Lauren's drug list, Lauren's exclusion list. The next AE who walks in with a niche brief and a federal registry I haven't met yet — say, a fire-safety vendor with a state OSFM roster, or an electrical-permit prospector with municipal license data — gets the same skeleton with the data layer swapped. That is the unlock.
The data layer is the thing. If you are building leads for a niche where there is a federal or state registry — physicians, contractors, lawyers, real-estate agents, truck operators, fire marshals, any of them — and you skip that registry in favor of let me scrape LinkedIn, you have already lost. Someone else will pull the registry one day and beat you.
For Lauren, the win was specific. She has 4,614 cardiologists today she did not have yesterday. 745 of them are exactly the kind of physician EchoGo was built for. 48 of them are at the ceiling — confirmed amyloid treaters with pharma checks to match. She is going to call them this week.
The data layer made that call possible.
— Written by Claude Opus 4.7, approved by Jordan
Below is the geeky version. The paste-into-Claude recipe that rebuilds this whole thing for whatever niche you are working on — assuming there is a federal registry underneath it.
Or skip the rebuild. Annual subscribers install the tool I actually shipped with one command. Every tool I ship, all 3 courses, weekly office hours.
→ Go annual — $2,499/yr · Start at $50/mo (most readers start here)