01
Read
A large language model ingests the full chart, extracts every codable clinical fact, and captures the reasoning chain.
Live · ICD-10-CM · ICD-10-PCS Live · ICD-10
Medical coding, automated.
AI medical coding that reads clinical documentation and returns accurate, audit-ready ICD-10 (CM + PCS), CPT and HCPCS codes — in seconds, not half-hours.
Free to try — create an account in under a minute.
- HIPAA-aware
- Audit-ready
- No hallucinations
$10T
Annual global healthcare spend, all routed via coded claims.
500K+
Medical coders worldwide — and every hospital is short-staffed.
15-30 min
A coder spends on a single inpatient case, today.
Our Approach
LLMs for reasoning. Retrieval for grounding. Humans for trust.
02
Ground
Candidate codes are retrieved from an indexed ICD / CPT / HCPCS corpus — never hallucinated.
03
Verify
Coders review, accept, or correct. Every correction becomes training data — the loop tightens.
Inside the pipeline
Three layers. One coded chart. Bounded. Grounded. Reviewable.
Every clinical note runs through the same three-layer pipeline — keyword extraction, group selection, then code selection — across both ICD-10-CM and ICD-10-PCS. The architecture is bounded and deterministic, with large language models doing the reasoning and an indexed code corpus doing the grounding. No runaway agent loops, no surprise bills, no stochastic latency tails.
LAYER 01
Keyword extraction
A single pass over the chart by a large language model emits every codable clinical concept — diagnoses, procedures, devices, laterality, episode of care. Shared input for both ICD-10-CM and ICD-10-PCS.
LAYER 02
Group selection
Each keyword is mapped against an indexed dictionary of ICD-10-CM and ICD-10-PCS code groups. The model is shown only those candidate groups, with their official descriptions, and selects the ones that actually fit the case.
LAYER 03
Code selection
Child codes for the chosen groups are pulled from the live coding corpus. The model picks the final ICD-10-CM and ICD-10-PCS codes, sequences Principal vs Secondary, and returns each one tied to the chart span that justified it.
Clinical note
Step 1 Keyword extraction shared · CM + PCS
One pass over the chart. Emits every codable clinical concept for both code systems.
Step 2 · CM CM groups select candidates
Step 2 · PCS PCS groups select candidates
Step 3 · CM CM codes pick + sequence
Step 3 · PCS PCS codes pick + sequence
ICD-10-CM ≤ 10 codes
ICD-10-PCS ≤ 10 codes
Humans, kept in the loop
The pipeline runs. Coders steer.
The AI proposes — humans dispose. Reasoning is captured at every layer of the pipeline, coders can intervene at any step, and every correction sharpens the next run.
Saved at every layer
Every keyword, every group selection, every final code carries the model's reasoning chain alongside it. Coders see why a code was picked — not just what was picked — and auditors get the same trail end-to-end.
Edit at any layer
Drop a keyword the model over-extracted, swap a group, replace or reorder a code. The pipeline re-runs from your edit downward — coders stay in the seat where their judgment matters, without starting over from the chart.
Each correction trains the next run
Accepted, rejected, and revised codes accumulate as training signal. Over time the system fits your hospital's documentation patterns, your case mix, and your coders' preferences — the loop tightens with every claim.
Code systems supported
Four code systems. One review surface.
U.S. medical coding spans four overlapping code books, each with its own grammar, sequencing rules, and update cycle. MedicalCode AI indexes all of them — so a single chart pass returns diagnoses, inpatient procedures, outpatient procedures, and supplies in one consistent, sequenced output.
ICD-10-CM
AvailableClinical Modification
~74,000
codes indexed
Diagnosis codes used on every U.S. claim — what is wrong with the patient, with full specificity for laterality, episode, and severity. Maintained by CDC/NCHS.
ICD-10-PCS
AvailableProcedure Coding System
~78,000
codes indexed
Inpatient procedure codes built from a 7-character grammar (section, body system, root operation, body part, approach, device, qualifier). Maintained by CMS.
CPT
Coming soonCurrent Procedural Terminology
~10,000
codes indexed
Outpatient and physician procedure codes for evaluation & management, surgery, radiology, pathology and medicine. Maintained by the AMA.
HCPCS
Coming soonLevel II Healthcare Codes
~7,500
codes indexed
Codes for durable medical equipment, prosthetics, supplies, drugs and ambulance services not covered by CPT. Maintained by CMS.
Why MedicalCode AI
Built for the coders who keep claims moving.
Seconds, not half-hours
Cases that take a coder 15-30 minutes are turned around in seconds, with a full reasoning trace.
Audit-ready by default
Every code links back to the exact span of the chart that justified it — defensible from day one.
Four code systems, one workflow
ICD-10-CM, ICD-10-PCS, CPT and HCPCS — all in a single review surface, with sequencing built in.
Privacy first, by design
Your patient data, kept where it belongs.
MedicalCode AI is built privacy-first. Sensitive identifiers are masked before anything reaches our AI system, prompts and outputs are never logged, and the platform is designed to respect the privacy regimes that govern healthcare data in every region we serve.
Masked before the model sees it
Sensitive identifiers — names, MRNs, dates of birth, addresses, contact details — are automatically detected and masked before any chart reaches our AI system.
Nothing stored. Nothing trained on.
Prompts and outputs are not logged, not retained, and never used to train shared models. What flows through the pipeline leaves no residue on our side.
HIPAA · GDPR · PDPA aware
Designed around the major healthcare-data regimes our customers operate under: the U.S. Health Insurance Portability and Accountability Act (HIPAA), the EU's General Data Protection Regulation (GDPR), and Singapore's Personal Data Protection Act (PDPA).
Built for
The teams who turn charts into revenue.
Coders sit at the choke point of every U.S. healthcare dollar. MedicalCode AI slots into the workflows where that bottleneck is most acute — inpatient facility coding, outpatient physician practices, third-party billing, and value-based-care risk adjustment.
Hospitals & health systems
Inpatient DRG coding on complex case-mix charts. Reduce DNFC days, shorten the discharge-to-bill cycle, and free senior coders for the cases that actually need them.
Outpatient & physician groups
High-volume CPT and E/M coding across primary care, surgery, and specialty practices. Consistent code selection, level-of-service support, and modifier reasoning on every encounter.
Medical billing & RCM
Multi-client throughput for revenue-cycle and billing services. Plug AI coding into your existing workflow, scale headcount-bound contracts, and improve clean-claim rates without growing the coder bench.
Risk adjustment & HCC
Medicare Advantage, ACA marketplace, and ACO programs. Surface every chronic condition the chart supports, with documentation links a CMS auditor can follow.
FAQ
Frequently asked questions about AI medical coding.
What is autonomous AI medical coding?
Autonomous AI medical coding uses large language models to read clinical documentation — admission notes, operative reports, discharge summaries — and produce the ICD-10-CM, ICD-10-PCS, CPT and HCPCS codes a human coder would assign. "Autonomous" means the model proposes the full coded output for an encounter, not just suggestions on individual lines. A human coder still reviews and signs off, but the AI does the reading and lookup work end-to-end.
How does MedicalCode AI handle ICD-10-CM and ICD-10-PCS together?
MedicalCode AI runs a single shared keyword extraction pass on the chart, then branches into parallel code-selection pipelines for ICD-10-CM (diagnoses) and ICD-10-PCS (inpatient procedures). Both systems share the same clinical reasoning, but each is grounded in its own indexed code corpus — and each output is sequenced and traced back to the chart spans that justified it.
Does the AI hallucinate ICD-10 or CPT codes?
No. The model never generates codes from memory. Every candidate code shown to the LLM is retrieved from an indexed copy of the official ICD-10-CM, ICD-10-PCS, CPT and HCPCS corpus. The LLM is constrained to pick from that retrieved set. If a code is in the output, it exists in the live code book — the architecture makes hallucinated codes structurally impossible.
How fast is AI medical coding compared to a human coder?
A human coder typically spends 15 to 30 minutes on a moderately complex inpatient case, longer for high-acuity charts. MedicalCode AI returns a fully proposed coded output — diagnoses, procedures, sequencing, and supporting chart spans — in seconds. The coder still reviews, but starts from a complete, defensible draft instead of a blank screen.
How does MedicalCode AI handle audits and compliance?
Every code in the output is tied back to the exact span of the chart that justified it, plus the reasoning chain the model used to pick that code over its siblings. Audit packages can be exported per encounter — the same evidence a CDI specialist or external auditor would build by hand. The coder always has final say; the AI proposes, humans dispose.
How is this different from Computer-Assisted Coding (CAC)?
Traditional CAC tools use rule-based NLP to highlight terms in a chart and suggest individual codes. They leave the coder to assemble the final code set, sequence diagnoses, and resolve conflicts. MedicalCode AI returns the complete coded output for the case — Principal and Secondary diagnoses, procedures with sequencing, and the documentation links — because large language models can finally do clinical reasoning at the level the work actually requires.
Is patient data secure, and is the platform HIPAA / GDPR / PDPA aware?
MedicalCode AI is built privacy-first and aware of the major healthcare-data regimes our customers operate under — HIPAA in the U.S., GDPR in the EU, and PDPA in Singapore. Sensitive identifiers (names, MRNs, DOBs, addresses, contact details) are automatically masked before any chart reaches our AI system, and we operate under a strict no-logging principle: prompts and outputs are not retained and are never used to train shared models. The platform also supports de-identified pilots, BAAs with covered entities, and tenant isolation for billing-company deployments. Production architecture, key management, and access controls are documented in our security review package.
Can MedicalCode AI integrate with my EHR or billing platform?
Yes. The platform exposes a coding API that takes a clinical note (or a structured chart payload) and returns the proposed code set with documentation links. We have integration paths for common EHR feeds (HL7 v2, FHIR, flat-file) and major billing/RCM platforms. Live demos and pilot integrations are available — contact the team for the current connector list.
Ready when you are
See a clinical note become coded claims, live.
Paste any de-identified note. Watch our pipeline read, ground, and propose audit-ready ICD-10 codes — with the reasoning chain visible at every step.
Free to try — create an account in under a minute.