Find doctors and clinics
A places census of doctors and clinics gets cross-referenced against France's practitioner registry and facility directory so nothing is missing from a city's real coverage.
- TriggerTriggeredClaudeChatGPT
Resolve the specialty to both registries
- Completed
SerperCensus the city on maps
- CompletedClaudeChatGPT
Dedupe by address and phone
- Completed
INSEE SirenePull the practitioner registry
- CompletedSanté (FR)
Pull the facility directory
- CompletedClaudeChatGPT
Flag what maps missed
Mapping every doctor or clinic in a city usually means someone running one search, scrolling the first page of map results, and calling it done — with no way to know how many practices never made it online at all, because there is nothing on that first page to say a gap exists.
A tiled census closes most of that gap on its own. The other half is two independent public registries — one for individual and group practitioners, one for clinics and larger facilities — each catching a different type of provider the other structurally can't. Cross-referencing all three turns "probably most of them" into a list with the actual gap named, instead of hidden inside a search result count that looks complete and is not.
What you get
A deduplicated, complete roster of every doctor and health facility in one city, cross-checked against two independent public registries so the count is defensible, not a guess.
The part worth getting right
Checking two registries, not one, is what keeps the count from silently dropping a whole category of provider. A solo generalist practice registers under the practitioner registry's activity code; a clinic or health center is a facility-directory entry instead, not a match in the practitioner registry at all. Query only one and every provider of the other type just disappears from the count with no error to say so.
Tiling the search area is what a single query can't do. One anchored search caps out around twenty results and skews toward its own anchor point — real coverage requires paving the city in a grid and paging every anchor, which is slower but the only way the count stops silently undercounting.
Deduping by address and phone, not name, is what keeps the count honest at the low end. A shared cabinet routinely lists three or four practitioners under one map pin; matching on name alone turns one location into three or four rows.
Treating both registries as a completeness benchmark rather than a name source is what keeps the gap list defensible. In one real run, a city's practitioner registry counted roughly ten times more active entries under one specialty's code than the census found distinct listings for — most of that gap was addresses the census had already found under a different practitioner's name at the same cabinet, but a real slice were practices with genuinely no online presence, plus a share of individual practitioners with their address suppressed from public diffusion entirely. Neither registry hands over a name for those; each hands over a reason to go check by hand instead of assuming the map was the whole market.
The prompt
The real prompt behind the steps above — detailed enough that an agent with a people-search tool, email/phone enrichment, and CRM access can run it as written.
# Census every doctor and clinic in a city
You have access to a local/maps search tool and two French public
health-data lookups: a practitioner registry and a facility directory.
Run this for **[a specialty — e.g. "generalist doctors", "dentists" —
and a target city]**.
## 0. Set up (skip if you're already connected)
If you don't already have live tool access for this, connect it first
— this prompt is only as real as the tools behind it:
1. Create a free account at app.tulina.ai.
2. Add Tulina's MCP server to your assistant: https://mcp.tulina.ai/mcp
3. In Tulina, connect your own API keys for the tools this prompt uses
— local/maps search, plus the two open-data health lookups (no key
required for either).
Once connected, your assistant has real tool access and the rest of
this prompt runs as written.
## 1. Resolve the specialty to both registries
A solo or group medical practice and a clinic or health center live in
two different repositories. Before running anything, resolve the
target specialty to:
- the practitioner registry's exact activity code (e.g. the code for
generalist doctors, not a neighboring specialty), and
- the facility directory's matching category, for the larger
structures that register there instead.
Getting either one wrong silently changes what step 4 and step 5 count
as exhaustive.
## 2. Census the city on maps
- Tile the target city into a grid of search anchors rather than
running one query from the city center — a single anchored search
caps out around twenty results and biases toward its own anchor
point, undercounting silently with no error raised.
- Page every anchor to exhaustion, not just the first page.
- Record name, address, phone, and website for every hit in one
running list.
## 3. Dedupe by address and phone, not by name
Collapse the raw list on address and phone number. A single cabinet
very often lists multiple practitioners under one map pin — several
doctors sharing a secretariat, or an associate not yet reflected in
the listing — and deduping by name alone will double- or
triple-count that location.
## 4. Pull the practitioner registry
Query the national practitioner registry for every **active**
establishment under the resolved activity code, scoped to the same
city (by commune code, not city-name string-matching). This is the
regulatory record, not a search index — no bias toward an online
presence, and no result cap.
Note going in: a real share of individual-practitioner entries will
have their address suppressed in the public registry (opted out of
public diffusion) — those count toward the total but can't be
address-matched in step 6.
## 5. Pull the facility directory
Query the national health-facility directory for every registered
clinic, health center, or larger structure in the same city. This is
a separate repository from individual practitioners — it catches
group facilities the practitioner registry structurally does not
cover, the same way step 4 catches solo practices the facility
directory does not.
## 6. Flag what maps missed
Match each addressable entry from both registries against the
deduped map list by address. Anything registered but never surfaced
by the census gets flagged as a gap — for manual verification, not
silently discarded. Some of these will be real (a practice with no
online booking or listing); some will be stale registrations for a
practice that has since closed. The flag is what makes that
distinction checkable instead of invisible.
## Output
Report: total distinct locations found by census, total active
entries across both registries (and how many practitioner addresses
were suppressed), how many registry entries matched a census hit, and
the final gap list with registered addresses for manual follow-up.
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