Behavioral health
Every referral source in your market, ranked — not just the ones your reps remember.
SAMHSA facilities, psychiatrists and PCPs from NPPES, hospital discharge volume, overdose mortality, and Medicaid utilization — assembled into the referral universe for a specific county.
Questions you can answer
Who can refer to our IOP in Middlesex County, and which of them sit closest to the facility?
Rank psychiatrists and PCPs by Medicare patient and service volume within 40 miles.
Where are the treatment deserts — high SUD burden, no licensed facility within the county?
Which hospitals in this market carry the most inpatient psychiatric capacity and volume?
The datasets behind it
Selected at runtime against your question, each with a stated reason. Anything a source cannot cover at your geography is reported as a gap, not filled in.
SAMHSA facility locator
Licensed substance use and mental health treatment facilities with service lines, levels of care, and payment types accepted — the competitive and referral map in one file.
NPPES provider registry
Every enrolled psychiatrist, psychologist, PCP, and clinic in the geography, by taxonomy, with practice address for distance ranking from your facility.
Physician utilization by provider and service
Named-clinician Medicare volume, specialty, service counts, and paid amounts — the volume signal that separates a high-potential referrer from a name on a list.
CMS inpatient psychiatric facilities and DRG discharges
Inpatient psychiatric facility quality and capacity plus behavioral-health-related DRG discharge counts, so you know which hospitals actually move patients.
CDC overdose and mortality, HRSA shortage areas
Market burden and workforce shortage designations at county grain — the demand-side context behind a saturation or expansion decision.
Medicaid enrollment and state drug utilization
Who pays in this market. Medicaid enrollment and quarterly product-level drug utilization to sanity-check whether your program's payer mix is viable here.
Find → Enrich → Understand → Score
Four stages between your question and a call list you can defend.
01
Find
Ask in plain language. The AI reads the intent, resolves the place you named down to county and FIPS, and pulls the datasets that actually apply from the public catalog — no query syntax, no source picking. What comes back is the whole target universe for that market, not a sample.
02
Enrich
Each target is taken past the registry row. RepVector reads the organisation's own website for services, locations, phone numbers, and named decision-makers, and attaches the page every fact came from. If a fact isn't published, it says "not published" — it is never inferred.
03
Understand
Capabilities are lined up against competing providers to expose gaps and whitespace, and each target is re-read over time so you can see what changed — a new location, a new service line, a decision-maker who left — with the evidence URL beside it.
04
Score
Everything folds into one 0–100 fit score per name: published volume, payer mix, opportunity gap, web completeness, whether a named contact was found, and how recently the target changed. The breakdown is shown on the row, not hidden behind a black box.
What you get
What lands in the dashboard
One run produces the whole picture for a market — the names, the people at them, the gaps worth calling about, and the proof behind every figure.
A ranked call list with people on it
Organisation, address, phone, and — where the site publishes one — a named decision-maker with their title and the page it was read from.
Tiers with a stated reason per name
Tier 1, 2 and 3 targets, each carrying one line explaining why it ranked there and the source row that line came from.
A 0–100 fit score you can open
Volume, payer mix, opportunity gap, web completeness, named contact and change recency, each shown as its own contribution.
Charts that drill down
Volume, payer mix, capacity and population widgets — click any figure to see the rows, geography, vintage and query behind it.
A competitive matrix and a one-pager
Your targets against competing providers by capability, with whitespace highlighted, plus a cited one-page positioning summary as PDF.
A market plan, sequenced
A week-by-week territory plan built from the ranked list — who to see first, in what order, and the evidence behind the sequence.
Change alerts on the market
Saved markets are re-checked, so you get a digest of the providers, capabilities and locations that moved since your last run.
Exports that keep the receipts
CSV, Excel and a full PDF build report, each carrying query, parameters, geography, vintage, capture time and citation URL into whatever CRM you run.
Then hand it off
Export the ranked referral universe as CSV or JSON. Every row carries the source, geography, vintage, and the one-line reason it ranked where it did, so your rep can defend the call order and your CRM can ingest it unchanged.
RepVector doesn't log calls or manage pipeline. Here's where we stop.
Built by operators in this space. The dataset selection reflects how admissions and referral relationships actually work — facility proximity, clinician volume, payer viability, and market burden together, not a provider list sorted alphabetically.
Build the list for one county and see whether the names are right.
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