AI Medical Coding
Superhuman accuracy meets compliance
Our AI coding engine reads clinical documentation like an expert coder, assigning accurate codes in seconds while ensuring full compliance with payer requirements and coding guidelines.
ICD-10 Coding
CPT Assignment
HCC Capture
CDI Queries
Audit Defense
Real-time Validation
Comprehensive Code Coverage
AI-powered coding across all major code sets
Coding intelligence reimagined
AI-Powered Coding
Deep learning models trained on millions of medical records for accurate ICD-10, CPT, and HCPCS code assignment.
Real-time Validation
Instant validation against CCI edits, LCD/NCD policies, and payer-specific rules.
CDI Integration
Clinical documentation improvement queries generated automatically to capture full acuity.
Risk Adjustment
HCC capture optimization for Medicare Advantage and value-based care contracts.
Audit Defense
Complete audit trail with AI rationale for every code assignment.
Compliance Alerts
Proactive identification of coding patterns that may trigger audits.
From documentation to accurate codes
Document Ingestion
Clinical notes, lab results, and imaging reports are automatically ingested and parsed.
NLP Analysis
Advanced NLP extracts diagnoses, procedures, and clinical indicators from unstructured text.
Code Assignment
AI assigns appropriate codes with confidence scores and supporting documentation.
Validation & QA
Multi-layer validation against coding guidelines and payer rules with human review for exceptions.
What medical billing and coding automation does
Coding converts a clinical narrative into the standardised codes payers reimburse against. A physician documents an encounter in prose; a coder reads it and assigns ICD-10-CM diagnosis codes, CPT or HCPCS procedure codes, the correct evaluation and management level, and any modifiers that explain the circumstances of care.
Medical billing and coding automation with agentic AI performs that translation directly from the documentation. The model reads the note the way a coder does — problem list, history, examination, assessment and plan — and proposes codes with the specific passage of text that justifies each one, so a human reviewer can confirm or correct the reasoning rather than starting from a blank screen.
Diagnosis coding to full specificity
ICD-10-CM rewards specificity: laterality, encounter type, and severity all change the code. Documentation frequently supports a more specific code than the one selected, and automated extraction surfaces that gap by reading the entire note rather than the assessment line alone.
Procedure coding and modifiers
CPT and HCPCS selection carries the bulk of reimbursement risk. Modifiers explain circumstances a bare code cannot — a bilateral procedure, a distinct service, a repeat encounter on the same day — and omitting them is a common cause of underpayment and of denials that look like coding errors.
Evaluation and management levels
Since the 2021 revision, office visit E&M levels are selected on either medical decision making or total time, not on bullet counts of history and examination. That change made levelling a matter of interpreting complexity, which is exactly the kind of judgement that suits a language model with a coder reviewing the result.
Compliance edits before submission
NCCI procedure-to-procedure edits, medically unlikely edits, and local and national coverage determinations all constrain what may be billed together. Checking those at coding time rather than after a denial removes an entire category of rework.
Clinical documentation integrity
When documentation does not support the code the encounter warrants, the answer is a physician query, not a guess. Automation drafts compliant, non-leading queries and routes them to the clinician, which is the difference between improving documentation and inflating it.
Audit trail on every code
Each proposed code carries the text that supports it. That evidence trail is what makes the output defensible in an audit and reviewable by a coder, and it is the reason automated coding belongs in a human-in-the-loop workflow rather than running silently.
Why coding stays human-in-the-loop
Coding sits directly on the compliance boundary. Codes that overstate the service performed are upcoding, and the consequences run from recoupment to False Claims Act exposure. Codes that understate it are lost revenue that no report will ever show. Both failure modes are expensive and neither is self-correcting.
The defensible design is therefore assistive rather than autonomous: the system proposes, a certified coder disposes, and the reviewer sees the supporting documentation alongside every suggestion. Throughput rises because review is faster than origination, while accountability for the submitted claim stays with a credentialed human.
Coding is one job inside RCM Employee
We do not sell coding as an isolated product. It is one of the responsibilities carried by RCM Employee, the autonomous AI FTE for US healthcare providers that we built for XEHR LLC, alongside eligibility and benefits verification, claim scrubbing, denial appeals and payment posting.
That structure matters, because a code proposed in isolation is a guess. A code proposed by a system that has also seen the eligibility response, the payer's edit history and the denial that came back on a similar claim last month is a decision made with context. Where a practice runs XEHR, the AI-native EHR we built for XEHR LLC, that context extends to the clinical documentation itself.
Questions we get asked most
Can AI replace a certified medical coder?
No, and a system designed to do so would be difficult to defend in an audit. Automated coding is assistive: it proposes codes with the documentation that supports them, and a certified coder reviews and finalises. The gain is throughput and consistency, not the removal of the credentialed reviewer who remains accountable for the submitted claim.
How accurate is automated medical coding?
Accuracy depends far more on documentation quality than on the model. A thorough, specific note supports precise coding; a sparse one does not, regardless of who or what reads it. This is why clinical documentation integrity and coding automation are usually deployed together rather than separately.
Does automated coding increase audit risk?
It reduces it when built correctly. Every proposed code is linked to the text that justifies it, producing a more complete audit trail than manual coding typically leaves behind. Risk rises only when a system is allowed to submit codes without review, which is why the reviewer step is a design requirement rather than a preference.
What about E&M levelling under the current guidelines?
Office and outpatient E&M levels are selected on medical decision making or total time following the 2021 revision. Both routes require interpreting the encounter rather than counting documentation elements, so the system evaluates the complexity of problems addressed, data reviewed and risk assumed, and presents its reasoning for the coder to confirm.
Which specialties does this suit?
Specialties with high encounter volume and repeatable documentation patterns see returns first, including primary care, behavioural health and outpatient specialties. Highly procedural areas such as surgery and interventional cardiology benefit as well, though they carry more modifier complexity and warrant a longer supervised phase.
How does it handle payer-specific rules?
Payer rules are applied as edits at coding time rather than discovered through denials. That includes NCCI procedure-to-procedure and medically unlikely edits, local and national coverage determinations, and individual payer policies where they diverge from the national standard.
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