What it does
ClearPriceHealth brings hospital Machine-Readable Files (MRFs) into one place and turns them into a price comparison anyone can use. Pick a procedure, see how the hospitals compare against the state median and how the negotiated price itself differs across the insurance carriers and plans.
Today: every hospital in Colorado. Next week: Texas. By end of July: seventeen states, covering 75% of US hospitals.
Why MRFs do not work in their raw form
Federal price transparency rules require hospitals to publish their negotiated rates. The intent was simple — make healthcare pricing legible. The execution is harder than it looks.
MRFs are not a single format. Hospitals publish in three: CSV-tall, CSV-wide, and JSON. The schema changes with each regulatory revision. A file that parsed last quarter often does not parse this one without code changes. A typical hospital MRF runs two to four gigabytes. Open one in a browser and the tab crashes.
So a "price transparency mandate" produces files that are technically published, practically invisible, and operationally unstable. Patients still call hospitals one at a time for cash-price estimates. Employers still negotiate without market context. The data exists; the comparison does not.
What ClearPriceHealth actually does
Before anyone compares prices, someone has to make the data comparable. That is the implicit work nobody talks about.
Three formats become one schema. Schema drift gets tracked across regulatory revisions. Every file gets verified for fidelity — did the parser actually capture what the hospital published? Reliability gets monitored over time — when a hospital re-publishes, does the new file match the prior one in shape and substance, or has something silently changed?
This is the unglamorous infrastructure layer that makes price comparison possible in the first place. It is the precondition for every downstream decision.
On top of that foundation, the consumer product is straightforward. Search by procedure code or by plain-English name. The output is not just a single price — it is your hospital options ranked against the state median, with the price spread across every insurance carrier and plan that hospital accepts. The result is a price for a thing you might actually need, with the context to know whether that price is reasonable — not a four-gigabyte JSON file you cannot open.
How it connects to the framework
This is what the Messy-Data Advantage looks like in production. The messiness is not size — it is format heterogeneity, schema evolution, and the constant work of verifying that what you parsed today still means the same thing it meant last quarter. Anyone can demo a price-comparison tool on three clean test cases. Doing it across thousands of hospitals with shifting upstream formats is a different problem entirely.
On the IRDA framework, ClearPriceHealth sits at the Information → Recommendation seam for the US healthcare pricing ecosystem — because the Information stage is where the real work is, and where most "price transparency" solutions are skipping it. Hospitals publish (Information, but inconsistent). We normalize, verify, and standardize (Information, finally trustworthy) — and surface the state median and carrier-level spread so a price has context (Recommendation, since now the data points toward "this price is reasonable" or "this price is an outlier"). Decision and Action follow from there.
The Scorecard's Ecosystem-tier example walks through exactly this progression.
What is next
Colorado is live today. Texas launches next week. By the end of July, seventeen states are online — about 75% of US hospitals. The data shape is national; the rollout is sequenced state by state because each state's payer mix and regulatory quirks need their own pass.
If you are an employer, broker, or healthcare operator trying to make sense of hospital pricing, the site is live at clearprice-health.com.