Chargemaster Maintenance & Pricing Transparency

What You Do Today

You own the chargemaster. Every line carries a description, a code, a revenue code, a price and a department that considers it theirs, and the whole file has to move when the code sets move — the procedure code set turns over at the start of the calendar year, the level II supply and drug codes run on a quarterly cycle, the diagnosis and inpatient procedure sets change in the autumn, and the outpatient payment rules shift in between. A code gets deleted and the line keeps billing until somebody notices. A department stands up a new service and builds the line the way it thinks the line should look. Prices have to hold a defensible relationship to each other and to what the service costs. Then you publish: the machine-readable file of standard charges in the format the rule specifies, and the consumer-facing display of shoppable services, refreshed on schedule and reachable by someone who does not know your website. Sooner or later a reporter, a competitor or an employer runs your published file against somebody else's and asks you about a line nobody has looked at in years.

AI Technologies

How It Works

Update ingestion reads the published code files as they are released and maps each change to the chargemaster lines it touches — deleted, replaced, newly packaged into another payment, newly separately payable — so the work arrives as a list of affected lines rather than as a set of files to read against a spreadsheet. Anomaly detection reads the file itself and flags what does not hold together: a code and revenue code pairing that is not valid, a description that no longer matches the code beside it, duplicate lines built for the same service by two departments, a unit definition that will generate the wrong quantity, a price sitting outside the pattern of its own family or unchanged since long before the code was last revised. Validation runs the machine-readable file against the schema, the required data elements and the naming convention CMS specifies, and reports the failures before publication rather than after. And because every hospital's file is public, comparing your published rates against other published files is a query — one that anyone can run, including you.