Undercoding Leaves Revenue Behind
Missing legitimate codes or failing to capture documentation-supported specificity leaves revenue on the table. Defaulting to unspecified codes is technically valid — but reimbursed below what the chart supports.
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Medical Coding in Theory. Medical Coding in Practice.
The diagnosis, procedure, and specificity that separates a paid claim from an audit are almost always in the clinical documentation. The problem isn't missing information — it's extracting it accurately at volume with the human bandwidth most coding departments don't have.
Overcoding Creates Audit Exposure
Assigning codes not supported by documentation creates audit exposure and recoupment liability that can dwarf the revenue it generated. The failure mode runs both directions — and the second is harder to undo.
Volume Outpaces Coder Capacity
Encounter volume grows. Experienced coders are expensive, hard to hire, and harder to retain. Backlogs delay claims and extend AR days. Specialty coding — chemotherapy, time-based E&M, surgical modifiers — varies in accuracy even in well-managed departments.
What We Build
The engine surfaces the right codes, the right specificity, and documentation gaps before the claim goes out. Specialty modules handle what general AI coding gets wrong.
What 92% Accuracy Actually Means — and What It Doesn't
AI coding accuracy is one of the most misleadingly presented metrics in health IT. Here's what the number actually means — so you implement with the right expectation.
Accuracy Is Measured on High-Confidence Encounters
92% accuracy means: when the AI assigns a code above its confidence threshold, it matches what an experienced coder would assign 92% of the time. It's calibrated to be accurate when confident — not to guess across the board.
It Does Not Mean Full Automation
It doesn't mean 92% of encounters are coded without human review. Encounters below the confidence threshold route to coder review — a deferred code reviewed by a human beats a wrong code submitted confidently.
The 40–70% That Gives Your Team Time Back
Encounters meeting the high-confidence threshold typically run 40–70% of volume, depending on specialty and documentation quality. That's what gives your coding team their time back. Complex cases still get human review — by the coders whose expertise they actually need.
Confidence-Based Routing, Not Guesswork
Confidence scoring is calibrated to documentation quality — ambiguous or incomplete encounters score lower and route to coder review. The threshold is tuned to your accuracy requirements and coder capacity, not a fixed number.
The Honest Version Gets the Results
Organizations that implement expecting accurate automation of the high-confidence portion get the results. Those expecting full automation discover the gap between the marketing and the clinical reality. We build for the first.
AI Coding Systems We've Built. What Changed.
Each result is tied to a specific coding failure — a backlog, conservative E&M habits, specialty coding too complex for a general engine. See what each system fixed.
Talk to Us About Your Coding Build15 Days
Faster claim submission — Multi-specialty Group. 8-day coding backlog eliminated. E&M revenue up 18%. Coder turnover reversed.
85%
Reduction in coding errors — Oncology Practice. Chemotherapy and drug sequencing errors corrected. Drug administration revenue fully captured. Audit findings eliminated.
96%
Clean claim rate — Behavioral Health Network. Up from 72%. Time-based E&M inconsistency resolved. Telehealth place-of-service coding compliance at 100%.
+0.4
E&M levels — Emergency Medicine Group. Conservative ED E&M determination recalibrated with documentation support. Critical care coding captured on 31% more eligible encounters.
74%
Fewer modifier denials — Ambulatory Surgery Center. Operative note coding inconsistent across surgeons. Coding consistency up 89% after standardization.
$4.2M
Annual revenue impact — Health System (Inpatient). DRG coding accuracy below benchmark. CC/MCC capture rate up 22%. Case mix index improved 0.11.
How We Build
The NLP model is trained on your documentation, not a generic corpus — and everything around it keeps it accurate after launch.
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NLP Trained on Your Documentation
NLP Trained on Your Documentation
We train on your historical documentation — not a generic corpus. Fine-tuned to your specialty before it sees a live encounter.
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Confidence Thresholds Set to Your Requirements
Confidence Thresholds Set to Your Requirements
The auto-code threshold is calibrated to your accuracy requirements and coder capacity, not a fixed number. We set it during implementation and adjust as data accumulates.
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Feedback Loop Built In From Day One
Feedback Loop Built In From Day One
Every coder correction improves the model. Without it, month twelve performs like month one.
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Workflow Integration Designed First
Workflow Integration Designed First
AI coding outside the existing workflow doesn't get adopted. We integrate into what your team already uses — or build around the AI system if the existing one is replaced.
Compliance We Treat as Engineering Inputs, Not a Checklist
Every standard is scoped during discovery and built into the platform — not checked off after.
Data Privacy
HIPAA & Data Privacy
Applied to every data store, transmission, and access control in the coding platform and NLP pipeline.
Coding Standards
Coding & Classification Standards
Standards that determine how diagnoses, procedures, and supplies are represented in claims — and the guidelines governing how they're assigned.
Reimbursement
Fee Schedules & Payment Systems
Fee schedule and prospective payment systems governing reimbursement across professional, outpatient, and inpatient settings.
Legal & Compliance
Audit & Compliance Guidance
Shapes how confidence thresholds, audit sampling, and documentation queries are designed to withstand review.
Security
Security & Certification
Required for a coding platform processing PHI through an NLP pipeline.
AI Governance
AI & Model Governance
Transparency and audit trails for every AI-assigned code, so decisions stay explainable and reviewable.
Coding Accuracy Isn't Just a Billing Problem. It's a Revenue Integrity Problem.
Missed specificity, undetected gaps, E&M levels coded conservatively by habit — that's where revenue leaks. AI coding fixes the high-volume portion. Thirty minutes. No pitch.
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Frequently Asked Questions
[ 1 ]
Will AI coding replace our coders?
Not in any implementation we've seen work well. It replaces the high-volume, routine portion — encounters where documentation clearly supports specific codes, typically 40–70% of volume. Complex cases, specialty coding, and audit review still require experienced coders. Those coders get their time back.
[ 2 ]
How does the AI handle poor or inconsistent documentation?
It routes to human review rather than guessing. Ambiguous, incomplete, or inconsistent encounters score lower confidence and go to coder review. A wrong code submitted confidently is worse than a deferred code reviewed by a human.
[ 3 ]
How long before the model is accurate enough to be useful?
The model launches already trained on your historical documentation and validated against coder-assigned codes. First-month accuracy on high-confidence encounters typically runs 85–88%. By month three, 90–93%. Improvement continues gradually from there.
[ 4 ]
Can it handle our specialty's coding requirements?
Depends on the specialty. For oncology, surgery, behavioral health, emergency medicine, and radiology — yes. For others, we assess during discovery and tell you honestly whether the general model handles it or specialty-specific training is needed.
[ 5 ]
How does it integrate with our existing systems?
We've integrated with Epic, Cerner, Athenahealth, Meditech, and several specialty EHRs, as well as standalone coding platforms. Whether we pull from the EHR, an intermediary system, or a document management platform is determined during discovery.
[ 6 ]
Who owns the model and the training data?
You do. The trained model, the training pipeline, and the fine-tuning data derived from your documentation all transfer to your ownership at project close. No per-encounter licensing fees.
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