Discovery Call
We learn your platform, specialty coverage, payer mix, EHR integrations, and compliance obligations in a 30-minute session.
AI medical coding deserves payer grade accuracy
Testiva delivers specialist QA for AI Medical Coding, ICD-10 and CPT accuracy, payer rule currency, claim denial prevention, and compliance audit-readiness.
500+
Coding Scenarios Evaluated
3x
Faster Release Cycles
40%
Lower Rework Costs
Why it matters
What happens when AI Medical Coding & Billing
What happens when AI Medical Coding & Billing
platforms aren’t tested properly
Incorrect codes submitted to payers cause denials and audits
An upcoded or undercoded claim generates a denial, compliance flag, or RAC audit that standard QA never surfaces.
AI coding tools trained on outdated payer rules produce errors at scale
ICD-10 and CPT rules change annually models trained on last year’s data code this year’s claims incorrectly.
Coding bias across specialties and demographics creates compliance risk
AI tools coding differently across demographics or specialty types create patterns that attract OIG scrutiny.
No validation evidence for compliance audits or payer credentialing
Payers and compliance teams ask for coding accuracy evidence most AI coding teams don’t have ready to present.
How Testiva protects your AI Medical Coding & Billing platform
- Medical coding specialists who understand payer rules — We evaluate code selections against current ICD-10, CPT, and payer-specific guidelines not just output volume.
- Payer rule currency and specialty-specific accuracy testing — We test against current payer rules and annual code updates so your AI codes this year’s claims correctly.
- Claim denial pattern analysis, not just per-code accuracy — We identify code combinations and modifier patterns that generate denials at the payer level before submission.
- Coding consistency and bias testing across patient demographics — We measure whether the AI codes equivalent procedures consistently across age, gender, and patient demographics.
- Compliance and audit-readiness documentation from day one — Coding accuracy evidence produced throughout the engagement ready before a payer audit or OIG review asks for it.
What we test
Core components of an AI Medical Coding & Billing platform we cover
Every layer that affects coding accuracy, claim acceptance rates, compliance risk, and audit-readiness is validated against current payer rules and regulatory standards.
ICD-10 & CPT Code Accuracy
Code selections validated against clinical documentation.
Payer Rule & Guideline Currency
Codes tested against current payer rules and annual updates.
Modifier & Bundling Accuracy
Modifier usage and bundling tested for payer compliance.
Claim Denial Pattern Testing
Code combinations tested for known payer denial triggers.
Coding Bias & Consistency Testing
Code consistency measured across patient demographics.
Documentation Sufficiency Testing
Clinical notes audited for code-level documentation support.
PHI & Billing Pipeline Security
Coding and billing pipelines audited for HIPAA compliance.
Regulatory & Compliance Validation
OIG, CMS, and payer compliance evidence mapped throughout.
Performance & Volume Testing
Coding accuracy verified at production submission volumes.
HOW IT WORKS
Up and running in 4 simple steps
From first contact to your first test report a process designed to be fast, transparent and low-friction.
QA Audit & Plan
We audit your coding accuracy baseline, payer rule currency, and build a validation strategy with specialty-specific clinical documentation datasets.
Test Execution
ICD-10 and CPT accuracy evaluation, denial pattern testing, modifier validation, bias analysis, documentation sufficiency, and compliance mapping every finding rated by revenue and compliance severity.
Report & Iterate
Coding accuracy report with denial risk analysis, bias findings, payer rule currency gaps, compliance traceability, and a prioritised remediation roadmap.
What People Say
Testiva is a great team to work with. I’ve hired them multiple times and recommended them to others, all impressed by their thorough work. Highly recommended for QA.
Testiva team is highly skilled and extremely thorough. I trust them for accurate and timely delivery. They are a reliable resource for any project.
Testiva team delivered outstanding quality with great professionalism. Communication was excellent and delivery met expectations. Highly recommended.
Excellent team worked well with minimal supervision and did a great job. Their work helped us improve the robustness of the platform.
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AI Medical Coding & Billing Testing Packages
| Feature | Coding Check | Accuracy Guard | Coding Shield | Apex Coding Suite |
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| CORE CODING ACCURACY TESTING | ||||
| ICD-10 & CPT code accuracy testing |  |
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| Modifier usage & bundling accuracy testing | |
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| Documentation sufficiency testing | |
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| Claim volume testing |  |
10K claims | 50K claims | Unlimited |
| Multi-specialty coding testing | |
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Setup only | Full build |
| PAYER RULES, DENIAL & BIAS | ||||
| Payer rule currency & annual update testing | |
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| Claim denial pattern testing | |
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| Coding bias & consistency testing | |
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| LCD & NCD policy compliance testing | |
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| Coding regression on model & rule updates | |
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| COMPLIANCE, SECURITY & MONITORING | ||||
| HIPAA & PHI billing pipeline audit | |
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| OIG & CMS compliance evidence mapping | |
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| Payer credentialing & audit-readiness documentation | |
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| Post-deploy coding drift monitoring | |
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| SUPPORT & REPORTING | ||||
| Dedicated medical coding QA lead | |
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| Coding accuracy scorecard & weekly report | |
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| 24/7 critical compliance defect SLA | |
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Common questions
We build synthetic clinical documentation datasets that replicate real documentation patterns across specialty types, encounter categories, and complexity levels. These are structured to surface coding failures across ICD-10 principal and secondary diagnosis selection, CPT procedure coding, modifier usage, and documentation sufficiency without real patient records entering our test environment. If you have de-identified clinical notes you wish to use under a formal data handling agreement, we can incorporate them under your de-identification protocols.
Per-code accuracy testing misses the most common source of claim denials code combination and modifier interaction failures that only surface at the payer adjudication level. We test code pairs, modifier combinations, bundling scenarios, and LCD and NCD policy compliance for your specific payer mix. We specifically design scenarios that replicate known denial triggers from Medicare, Medicaid, and commercial payer policies the failures your coders escalate but your QA team never sees in unit testing.
ICD-10 and CPT code sets are updated annually with additions, deletions, and revised guidelines effective each October and January respectively. Payer-specific LCD and NCD policies change throughout the year. A model trained or last evaluated against prior-year guidelines will generate coding errors on any claim type affected by those changes, silently and at scale. We run payer rule currency audits against your current model outputs and flag every coding scenario where rule changes since your last training or evaluation have introduced systematic errors.
We test whether the AI assigns codes consistently when the same clinical documentation appears across different patient demographic profiles varying age, gender, ethnicity, and socioeconomic indicators within the documentation context. Systematic differences in code specificity, principal diagnosis selection, or procedure code assignment across demographic groups create patterns that attract OIG scrutiny and constitute a compliance risk independent of individual code accuracy. We quantify the disparity and help you trace it to training data composition or feature handling to support remediation.
Model updates and annual code set refreshes are among the most common causes of silent coding regression. A retrained model may improve accuracy on some encounter types while degrading on others and a code set refresh without corresponding model retraining leaves the model selecting deprecated codes. We run automated regression testing against a validated coding accuracy baseline after every model update, retraining event, or code set refresh measuring accuracy changes by specialty, encounter type, and code category before the updated model reaches production claim submission.
Yes. Our CodingShield and ApexCoding Suite tiers produce coding accuracy baselines, denial pattern analysis, bias audit findings, payer rule currency assessments, and OIG and CMS compliance evidence mapping structured for payer credentialing reviews, health system procurement requirements, and OIG audit preparedness. This documentation is produced as a standard output of the testing engagement, not commissioned separately after a payer or compliance team has already requested it.
Get in touch
Start with a free AI Medical Coding QA audit.
Tell us about your AI coding platform and we’ll map out exactly what testing you need no obligation, no sales pitch.
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