Revenue Cycle & Denial Prevention
Net revenue leaks through denials, undercoding, and a rising cost-to-collect, and most vendor fixes are a black box you cannot put in front of an auditor. We build deterministic, auditable coding and denial-prevention models that recover revenue and lower cost-to-collect, engineered so a CFO can underwrite the savings and an auditor can trace every decision back to a rule.
Book a revenue-cycle reviewBuilt for the decision an auditor will re-open
Coding and denials are where "approximately right" fails. The code has to survive payer review and audit, which means the logic behind it has to be explainable and reproducible, not the opaque output of a single model call.
Deterministic, not probabilistic
The LLM reads the chart and extracts the facts. The coding and denial decisions run as versioned, unit-tested rules (AMA Table-1 MDM, data counting, risk triggers), so every code traces back to a specific rule rather than a model's guess.
Measured against ground truth
We do not claim a lift we cannot show. Every prompt or model change is scored against a clinician-validated ground-truth set before it ships, so accuracy gains are defensible, not asserted.
Denials mined to root cause
We turn free-text payer notes and claims into a structured root-cause taxonomy, surfacing the procedures and payers driving denials so your team fixes the costliest patterns first.
From the chart to a model you can defend
- A hybrid coding architecture that decouples LLM fact-extraction from a deterministic AMA Table-1 MDM rule engine, implementing the 2-of-3 rule, Category 1/2/3 data counting, and risk triggers as explicit, unit-tested rules.
- A clinician ground-truth set and scoring harness that gates every change on measured accuracy, so improvement is proven before it ships.
- A denial-note-mining pipeline that classifies payer notes into a dual-axis root-cause taxonomy and ranks the procedures and payers driving write-offs.
- Terminology guardrails (an ICD-10-to-SNOMED crosswalk) that constrain the model to clinically valid codes, cutting invalid selections.
- A defensible ROI model that ties accuracy and denial-prevention gains to a per-encounter dollar figure your CFO can underwrite.
Delivered into your environment and documented so your team owns it, not a black box you rent.
Metrics it moves
- Cost-to-collect
- Denial rate and preventable write-offs
- Net collection rate
- A/R days
- Coding accuracy and audit exposure
Typical path: an Advisory sprint to size the opportunity, then Delivery to run sustained denial-prevention and coding-intelligence operations.
Proof, measured and caveated
From a deterministic E&M coding engagement for a healthcare technology company, scored against a clinician-built ground-truth set. Figures reflect the tested configuration and a modeled ROI, not a guarantee for any single practice.
The accuracy figure is a weighted result on a 713-case clinician-validated set, up from a measured 69.6% baseline, achieved by moving the coding decision out of the model and into the deterministic rule engine. The ROI is a modeled estimate built from a 6,509-encounter multi-practice study, roughly $1.4M to $1.7M annualized for a large practice, and it scales with volume and payer mix. It is not realized revenue.
Put a number on your denials and your coding gap
Tell us where revenue is leaking. We will scope a revenue-cycle review and show you what a deterministic, auditable model would change, and what it is worth.