Overview
Healthcare Pricing by Douro Data#
Douro Data harvests, parses, and quality-gates hospital-published machine-readable price files (the CMS hospital price transparency rule, 45 CFR §180.50) and publishes them as clean, query-ready pricing data on the Snowflake Marketplace.
What makes this data different#
Everything here is built from what hospitals themselves publish — not from payer-side Transparency-in-Coverage aggregates. That is a data-class difference, not a styling choice: when you quote this data, you are quoting the hospital's own asserted rates, with contract methodology and care setting first-class on every row. For a provider-side payer renegotiation, that is the right evidentiary basis.
We publish only records that clear a strict quality gate, and where a hospital's file shows a detectable constant-value pattern, we annotate the row rather than altering or deleting it. See Data Quality Methodology for how that works.
The product family#
One product family, three tiers:
| Tier | Status | What you get |
|---|---|---|
| Hospital Directory — New York | Live, free | One row per NY hospital whose price file we hold parsed data for: identity, CMS profile, freshness, compliance signal. |
| Negotiated Rates — New York | Live, paid trial | Tens of millions of hospital-published negotiated dollar rates, plus standard charges and compliance findings. |
| Multi-state expansion | Planned | Additional states follow demand during the trial. |
The catalog spans 150+ New York hospitals across both rate surfaces, and every directory row holds parsed rate data — negotiated rates, standard charges, or both — with zero registration-only rows. The data itself is the source of truth for counts: COUNT(DISTINCT CMS_CCN) on any table gives you the current number, and the Marketplace listing carries the precise, date-stamped figures for each release.
Where to start#
- Hospital Directory — New York (Free) — the peer-scoping layer
- Negotiated Rates — New York (Paid Trial) — the benchmark
- Example Queries — eight worked examples, smoke-tested live
- Data Quality Methodology — how we gate and annotate
- Known Limitations — read these here, not discover them later
- FAQ & Query Conventions — counting, joining, methodology, freshness
Sales and support: steve@dourodata.com