Verify Med Codes is a deterministic clinical state compiler. It converts finished clinical notes into structured, machine-consumable clinical intelligence — with relationships, evidence linking, and specificity resolution — before your coding, claims, or AI systems execute.
Normal note in. Deterministic clinical state out.
We don't replace your workflow. We compile clinical truth from the note so your AI, coders, and claims systems receive structured state instead of raw narrative.
Note in. Deterministic clinical state out. Relationships, evidence, gaps. PHI-free.
Not just codes — a causal graph. "NSTEMI explains dyspnea." "Troponin supports NSTEMI." Your AI consumes structured relationships, not text.
46 family specs. 417 code mappings. Three-layer assertion evidence. Tested on 30 blind notes across 10 specialties with adversarial evaluation.
Runs alongside existing workflows. Paste a note, get clinical state. No EHR integration required. No workflow disruption.
We can.
Same note in, same codes out. Every time.
No sampling variance. No temperature. No drift.
Tested and verified.
Every code traces back to a sentence in the note.
No black box. Full decision trace.
AI proposes. The engine decides. The human confirms.
0% AI-decided.
“Run the same note 100 times. Get the same codes 100 times. No other AI coder can do that.”
This is the simplest way to understand the product: a normal finished note goes in, our extraction layer organizes it, and what comes out is evidence-linked clinical state that the rest of the workflow can safely use.
RCM systems assume the clinical input is already correct. It isn't.
We stabilize the clinical input before it reaches your claim engine.
Before
What downstream systems receive today
What breaks today
Problems
Middle layer
What happens to the note before the claim
PHI stays local
The note is structurally transformed before anything leaves your environment.
We reconstruct the clinical truth across the entire note
Diagnoses, labs, symptoms, sections, and plan actions are reconciled into one evidence-linked clinical state.
Only claim-safe, evidence-backed data reaches downstream systems
Coding, claims, and automation consume deterministic clinical state instead of fragmented narrative.
Why this matters
This is the layer that turns a note from something humans must interpret into something systems can reliably consume.
After
Claim-ready output for downstream use
Claim-ready diagnoses
K35.30 — Acute appendicitis without perforation or gangrene
I21.4 — Non-ST elevation myocardial infarction (NSTEMI)
I50.23 — Acute on chronic systolic heart failure
Safety layer
Suppressed
Review, not billed
Evidence linking
Treatment validation
Before
Unstable clinical input → guesswork → denials
After
Deterministic clinical state → validated claims → higher FPAR
This is the same note — we just made it usable.
That is the product: PHI-safe extraction that turns normal finished notes into structured clinical truth before coding, claims, or automation run.
We don't know what our system would do to your company — but you do. Play with the sliders and see.
Percentage point lift from cleaner first-pass claims
Specificity upgrades, HCC capture, OCR recovery
Fewer denials from cleaner first-pass claims
More claims per coder when search time drops 60%+
Better coded data → more downstream referrals, services, RAF
Volume pricing: higher volume → lower rate
27× return on VMC investment
30% EBITDA capture × 12× multiple
The missing layer in revenue architecture.
Automation assumes the clinical input is stable. It isn't.
We sit between finished notes and downstream claim logic.
Notes go in. Deterministic clinical state comes out. Existing RCM systems keep running — they just receive better clinical input.
Input
Finished clinical notes and note sections — narrative, symptoms, labs, assessment, and plan.
Transformation
VMC reconstructs the clinical truth across the entire note and emits deterministic clinical state.
Output
Claim-ready diagnoses, evidence links, suppressions, review-lane items, and treatment validation.
Integration
Directly before coding, claims, edits, denial management, and downstream RCM automation.
No replacement
Your existing RCM platform, submission workflow, and payer connectivity stay in place.
We do not replace your claim engine. We stabilize the clinical input before coding, edits, claims, and payer workflows execute.
Where unstable clinical states are contained.
Each artifact demonstrates a failure point that would have propagated downstream.
The note contains conflicting documentation: 'acute exacerbation' in HPI vs 'stable COPD' in assessment. The system flagged this conflict before submission, preventing a certain denial.
Unstable clinical states prevented before submission. Deterministic rule execution at production scale.
These are not optimizations. This is upstream containment — not downstream rework.
46 family specs. 417 code mappings. Three-layer assertion evidence. Tested on 30 blind notes across 10 specialties.
This engine executes before claims logic, not after.
Versioned. Auditable. Production-tested. Not assembled via prompt orchestration.
Because extraction is deterministic, PHI is contained at the architecture level — not by policy, by structure.
Because PHI is contained, AI reasoning can safely operate on structured clinical state — without leaking protected data.
Because it is versioned and auditable, AI becomes an assistant, not a liability.
Four lenses. One platform.
Integrates upstream of enterprise RCM platforms. No workflow disruption. No process change.
“Denials are not a payer problem. They are an extraction problem.”
Every entity has assertion evidence — speech acts, treatment linkage, negation scope analysis. Conflicts surface before submission, not after denial.
“PHI cannot leave unless explicitly cleared. By architecture, not policy.”
Browser-side PHI filter → tag stripping → zero PHI on the wire. Three-layer fail-closed egress. Your security team audits every rule before a single note is processed.
“Your AI is only as reliable as its clinical input layer.”
Output schema (clinical-state/v1): clinical state, entity graph, causal relationships, specificity gaps, severity profiles. Coding candidates are optional — clinical truth is the product.
“Missed HCCs are not documentation gaps. They are linkage failures.”
46 family specs with axis-driven specificity resolution. Causal priority framework ensures acute conditions explain symptoms before chronic ones. K35.80 → K35.30 automatically.
Extraction gives you the data. This gives you control.
Your rules. Your denial learnings. Encoded as deterministic governance — not code changes.
Local rule overlays — NCCI edits, modifier requirements, linkage checks, payer-specific denial patterns — run before submission.