The information needed to assign the right ICD-10 and CPT codes is almost always in the clinical documentation — the diagnosis, the procedure, the specificity that separates a code that pays from a code that gets audited. The problem isn't that the information doesn't exist. It's that extracting it accurately, consistently, and at the volume you produce takes more human bandwidth than most coding departments have. We build AI coding software that reads clinical documentation the way an experienced coder does — and surfaces the right codes, the right specificity, and the documentation gaps that need addressing before the claim goes out.
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The information needed to assign the right ICD-10 and CPT codes is almost always already in the clinical documentation. The diagnosis is there. The procedure is there. The specificity that separates a code that pays from a code that gets audited is there. The problem isn't that the information doesn't exist — it's that extracting it accurately, consistently, and at the volume your organization produces encounters requires more human bandwidth than most coding departments have.
Missing legitimate codes, or failing to capture specificity the documentation supports, leaves revenue on the table that the clinical team earned and the billing team should have collected. Defaulting to unspecified codes is the quiet version of this — technically valid, but reimbursed below what the chart supports.
Assigning codes that aren't supported by the documentation creates audit exposure, compliance risk, and potential recoupment liability that can dwarf the revenue it generated. The failure mode runs in both directions, and the cost of the second one is harder to undo.
Encounter volume grows. Experienced coders are expensive, hard to hire, and harder to retain. Backlogs delay claim submission and extend AR days. Specialty coding — chemotherapy administration, time-based E&M, surgical modifiers by payer — is complex enough that accuracy varies even in well-managed departments.
AI coding that reads documentation the way an experienced coder does — surfacing the right codes, the right specificity, and the gaps to close before the claim goes out. The engine handles the foundation; the specialty modules handle what general AI coding gets wrong.
AI coding accuracy claims are one of the most misleadingly presented metrics in health IT marketing. Here is the honest version of what the number means, so you can implement with the expectation that gets results.
When we say 92% auto-coding accuracy, we mean this: on encounters where the AI assigns a code above a defined confidence threshold, the suggested code matches what an experienced coder would assign 92% of the time. The AI is calibrated to be accurate when it's confident — not to guess across the board.
It does not mean 92% of all encounters are coded accurately by AI without human review. Encounters below the confidence threshold route to coder review rather than being auto-coded. The AI doesn't code what it can't read accurately — a deferred code reviewed by a human beats a wrong code submitted confidently.
The share of encounters that meet the high-confidence threshold varies by specialty and documentation quality — typically 40% to 70% of volume in organizations we've implemented for. That band is what gives your coding team their time back. The rest still gets human review, by the coders whose expertise the complex cases actually need.
The confidence scoring is calibrated to documentation quality. Encounters that are ambiguous, incomplete, or inconsistent with expected patterns score lower and route to coder review. The threshold is tuned to your accuracy requirements and coder capacity — not a fixed number applied to every organization.
Organizations that implement AI coding expecting accurate automation of the high-confidence portion get the results. The ones that implement expecting full automation discover the gap between the marketing and the clinical reality. We build for the first expectation.
Each result is tied to a specific coding failure — a backlog too deep to clear, E&M levels coded conservatively out of habit, specialty coding too complex for a general engine. Click through to see what each system fixed.
Talk to Us About Your Coding BuildThe NLP model is trained on your documentation, not a generic corpus — and everything around it is designed to keep it accurate after launch. Hover or tap a stage to see what it involves.
Clinical documentation style varies significantly between organizations, specialties, and individual providers. A model trained on a general medical corpus reads your documentation with less accuracy than one trained on how your providers actually write. We extract a representative sample of your historical documentation during the build phase and fine-tune the NLP model before it ever sees a live encounter.
The threshold at which the AI auto-codes versus routes to human review is not a fixed number — it's calibrated to your accuracy requirements and coder capacity. Low error tolerance routes more encounters to review; capacity constraints and a higher tolerance set a higher auto-coding threshold. We calibrate this during implementation and recalibrate as performance data accumulates.
Every coder correction — every time a coder changes an AI suggestion — is captured, reviewed, and used to improve the model. Organizations that implement AI coding without a structured feedback loop get a model that performs the same in month twelve as month one. We build the feedback infrastructure as a core component, not an optional enhancement.
AI coding that requires coders to work in a separate system from their existing workflow doesn't get adopted. We integrate the AI coding interface into the workflow your team already uses — or build the workflow around the AI system if the existing one is being replaced — so suggestions are visible in the context where coding decisions are made.
Every standard below is scoped during discovery and built into the coding platform as it's developed — across the data privacy, coding, payer, legal, and security frameworks that govern automated medical coding software.
The foundational healthcare data privacy and security frameworks — applied to every data store, transmission, and access control in the coding platform and its NLP pipeline.
The coding and classification standards that determine how diagnoses, procedures, and supplies are represented in claims — and the official guidelines that govern how they're assigned.
The fee schedule and prospective payment systems that determine what coded encounters are reimbursed at across professional, outpatient, and inpatient settings.
The compliance program guidance that shapes how auto-coding confidence thresholds, audit sampling, and documentation queries are designed to withstand review.
The security certifications required for a coding platform that processes protected health information through an NLP pipeline.
Every encounter coded at lower specificity than the documentation supports is revenue your clinical team earned and your coding operation didn't capture. Every modifier error that triggers a denial is a claim that has to be worked twice. Every documentation gap that becomes a coding query after submission extends your AR days and consumes coder time that should be going somewhere else. AI coding software doesn't fix all of this — but it fixes the high-volume portion: the routine encounters where accuracy should be consistent and isn't, the specificity gaps nobody caught, the E&M levels where conservative coding became habit rather than judgment. Thirty minutes. No pitch. Just an honest discussion about where your coding operation is losing revenue and what it would take to close the gap.
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Not in any implementation we've seen work well. What it replaces is the portion of coder time spent on high-volume, routine encounters where the documentation clearly supports specific codes and experienced judgment adds limited value — typically 40-70% of encounter volume. The remaining encounters (complex cases, specialty coding, documentation queries, audit review) still require experienced coders, and those coders are more productive because they're spending their time on work that actually requires their expertise.
It routes to human review rather than guessing. The confidence scoring that drives routing is calibrated to documentation quality — encounters that are ambiguous, incomplete, or inconsistent with expected patterns score lower confidence and go to coder review. The AI doesn't code what it can't read accurately, which is the right behavior: a wrong code submitted confidently is worse than a deferred code reviewed by a human.
The model that launches has already been trained on your historical documentation and validated against coder-assigned codes from that data — it isn't starting from zero on day one. Typical first-month auto-coding accuracy on high-confidence encounters runs 85-88%. By month three, as the feedback loop captures coder corrections and the model fine-tunes, it's typically at 90-93%. The accuracy curve after that is gradual improvement.
Depends on the specialty. For the specialties we've built dedicated modules for — oncology, surgery, behavioral health, emergency medicine, radiology — yes. For less common specialties, we assess the documentation patterns and coding complexity during discovery and tell you honestly whether the general model handles it adequately or whether specialty-specific training is required.
We've integrated AI coding systems with Epic, Cerner, Athenahealth, Meditech, and several specialty EHRs, as well as standalone coding platforms. The integration approach — whether we pull documentation directly from the EHR, from an intermediary coding system, or from a document management platform — is determined during discovery based on your system landscape.
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.
