The Independent Verification Layer for Clinical AI

AIwritesthenote.Weproveit’sright.

Every claim in every AI-generated clinical note, independently verified against the patient's medical record. Before it becomes permanent.

Zero.

That's how many false positives we've produced.
Across 300 clinical notes. 1,210 seeded errors. 25 specialties.

Compatible With
Epic
Cerner
AWS
FHIR R4
HL7
SMART on FHIR
Synthea
SNOMED CT
Epic
Cerner
AWS
FHIR R4
HL7
SMART on FHIR
Synthea
SNOMED CT
LOINC
RxNorm
ICD-10
UMLS
US Core
OpenFDA
MedCPT
Neo4j
LOINC
RxNorm
ICD-10
UMLS
US Core
OpenFDA
MedCPT
Neo4j
The Problem

AI scribes are writing millions of clinical notes. Nobody's checking them.

Ambient AI scribes are transforming clinical documentation. But they hallucinate medications. They confuse dosages. They miss allergies. And every error that reaches a patient chart is a liability, a safety risk, and a breach of trust.

Health systems need an independent layer between the AI and the permanent record. Not more AI promises — proof.

$9.8B
Annual cost of medical documentation errors in the US
46%
Of malpractice claims cite documentation failures
3.2M
AI-generated clinical notes per month and growing
The Proof

Numbers that speak for themselves.

0%
Precision

Every flag is a real error. Zero false positives. When Apodixis alerts, your clinicians can trust it completely — no alert fatigue, no wasted time.

0%
F1 Score

Across 300 notes, 25 specialties, and ages 3 to 80. Not a cherry-picked demo — a rigorous, reproducible benchmark against 1,210 seeded errors.

$0.000
per note

Less than a penny. The average documentation error costs $12,000 to resolve.

<0s
verification time

Real-time verification that fits into existing clinical workflows without friction.

0
specialties tested

Cardiology to pediatrics. ED to surgery. Validated across the full spectrum of clinical documentation.

Detection

What we catch.

Six specialized verifiers cross-reference every clinical claim against EHR source data via FHIR. Each number below is recall rate — how many real errors we find.

100% Precision
Every flag is a confirmed error
Wrong AllergiesMisrepresented allergy records
0%
Hallucinated MedicationsDrugs not in the patient's record
0%
Dose MismatchesIncorrect dosage, frequency, or route
0%
Age ErrorsIncorrect patient demographics
0%
ContraindicationsDangerous drug interactions
0%
Lab Value ErrorsIncorrect lab results
0%
How It Works

Three stages. Full auditability.
Under two seconds.

Extract

The AI-generated note is ingested and every clinical claim is extracted — medications, allergies, diagnoses, vitals, labs, procedures. Each claim is normalized against RxNorm, SNOMED, LOINC, and ICD-10.

1

Verify

Six specialized verifiers cross-check each claim against the patient's EHR data via FHIR. Drug interactions are checked against a knowledge graph. Omissions are detected. Nothing slips through.

2

Report

A severity-weighted verification report with a full evidence chain surfaces every error — ranked by clinical impact. The clinician reviews, the note is corrected, the record stays clean.

3
In Practice

See it in action.

Apodixis Verification Report
Score:72.5 / 100

14 claims extracted|10 confirmed|2 contradicted|2 unverifiable
312ms
Built for Trust

Designed for the most regulated
industry on earth.

Every architectural decision is made with SOC 2, HITRUST, and HIPAA auditors in mind. Compliance isn't a feature we added — it's the foundation we built on.

HIPAA Compliant

Protected health information is never stored. SHA-256 hashes only. Ephemeral processing with auto-expiring cache.

SOC 2 Type II

Immutable, hash-chained audit trail for every verification. Automated evidence collection via Vanta.

HITRUST Ready

Architecture designed for e1 and r2 certification from day one. Not retrofitted — built in.

FHIR R4 Native

Integrates with Epic, Cerner, and any SMART on FHIR-enabled EHR. US Core profiles. CDS Hooks support.

FDA CDS Exempt

Verification only. Clinician-in-the-loop. We flag and alert — we never recommend, diagnose, or prescribe.

Immutable Audit Trail

Append-only, hash-chained entries in FHIR AuditEvent format. 6-year retention. Tamper-evident by design.

The cost of not verifying.

Without Apodixis
Errors reach the permanent record undetected
Clinicians manually review every AI-generated note
No audit trail for AI verification decisions
Liability exposure with every hallucinated claim
~$12,000
average cost per documentation error
With Apodixis
Every claim verified before it's signed
Clinicians review only flagged issues, not every note
Complete audit trail for every verification
Zero false positives — no alert fatigue
$0.007
per note verified

The verification layer
clinical AI needs.

Join health systems building trust in AI-generated clinical documentation. See Apodixis verify your notes in a secure, private demo.