Every risk-adjusted claim you submit is a liability call. OIG has already published the patterns it keeps finding, and audits extrapolate. So: which claims get a coder's eyes before they go out the door? That's HARROW.

It runs inside your environment. It reads what you already have: claims, encounters, your model output. Nothing leaves. Everything is hashed the moment it comes in, so every finding traces back to exact bytes.

The logic isn't ours, and it isn't a model's. It's built from the audit logic OIG published and CMS's mappings, pinned and hashed, with every current-year adaptation documented for independent coder review. When an auditor asks "says who?", the answer is a published document, not a vendor.

Three stages, fully deterministic. If a batch can't be screened cleanly, the engine refuses and tells you why. A refusal, never a guess.

The whole thing in one picture. The machine finds a stroke code with no facility claim backing it up. It does not decide the claim is wrong. It hands the flag to a certified coder, with a receipt: the finding, the citation, the version, the hash. The machine detects structure. The human decides medicine.

Because it is deterministic, the rest comes free: a pattern review you hand compliance, byte-for-byte replay, an audit trail on every run. Same rows in, same answer out. And the patterns are data, so it grows without a rebuild.

Nothing to rip out. It reads extracts from what you already run: QNXT, Facets, your clearinghouse feeds. Works with what you have.

That's the argument. The next step is watching it run live: real engine, synthetic batch, right in the browser. Then, if it earns it, a screen run against your own book. Request the walkthrough →