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AccuCode reads the complete patient record and assigns ICD-10, CPT, and HCPCS codes, returning each one linked to its supporting passage in the documentation. The pipeline is fully automated. Human review is reserved for initial validation, ongoing compliance sampling and auditing.
This interface is used during initial deployment, for ongoing compliance sampling, and for charts the system routes to a human via facility, provider, or insurance-specific rules. You choose how much you audit. Routine production coding is fully automated.
Patient is a 67-year-old male with a known history of hypertension, type 2 diabetes mellitus, and hyperlipidemia presenting with 3 days of substernal chest pain radiating to the left arm. Admitted from the emergency department to CCU for further evaluation and management.
Cardiac catheterization performed on hospital day two revealed atherosclerotic heart disease of the native coronary artery, 70% stenosis in the proximal LAD, without associated angina pectoris or myocardial infarction→ I25.10. Given the clinical stability and absence of acute coronary syndrome, the patient was managed medically with initiation of atorvastatin 40 mg daily, aspirin 81 mg, and continuation of his home metoprolol succinate.
The patient required initial hospital inpatient care with high-complexity medical decision-making→ 99223, including review of multiple data sources (catheterization report, serial troponins, echocardiography), management of multiple chronic conditions, and coordination with cardiology and endocrinology consults.
Type 2 diabetes mellitus, stable on metformin 1000 mg BID, without acute complications during this admission→ E11.9. Hemoglobin A1c from admission labs was 7.2%, consistent with adequate chronic control. No evidence of diabetic ketoacidosis, hyperglycemic hyperosmolar state, nephropathy progression, or retinopathy findings documented during this stay.
Hypertension and hyperlipidemia managed with existing regimen; no medication adjustments required during admission.
Discharged home on hospital day three in stable condition. Follow-up with primary cardiology in 2 weeks. Cardiac rehabilitation referral initiated. Patient and family education provided regarding medication compliance and lifestyle modification.
In most health systems, a chart arrives on a coder's desk, the coder reads the documentation, pulls up the encoder, searches for applicable codes across a taxonomy of thousands, cross-references guidelines, validates bundling and sequencing rules, and manually enters the final codes into the billing system.
This is the largest single source of Days Not Final Billed (DNFB) aging in most US hospitals. It is also the leading cause of coder burnout, which in turn is the leading driver of coder attrition, which in turn drives up salary premiums for the ones who remain. A hospital's revenue cycle lives or dies on this bottleneck.
AccuCode replaces the workflow entirely. Charts are processed automatically from ingestion through code assignment to billing-system handoff. The coder's role is no longer per-chart coding. It is initial validation during deployment, statistical sampling for ongoing compliance, or codes automatically flagged for review via facility, provider, or insurance-specific rules.
DNFB ages drop to below twenty-four hours because there is no longer a human bottleneck for ordinary charts. The work that remains for the coding team is the high-judgment work: compliance sampling, denial defense, documentation improvement, exception handling, and audit response. This is the work the credentialed CCS in your organization should be doing, and it is the work that justifies their salary.
Most healthcare-AI vendors publish single accuracy numbers without explaining what they measured against. This is the version that survives scrutiny.
In medical coding, two credentialed CCS coders independently coding the same chart agree about 82% of the time. That figure, called inter-coder reliability, is the consistent finding across peer-reviewed studies of credentialed ICD-10 coders.
That 82% figure is also the structural ceiling on any AI system trained against human-coded data: a model trained on past human work cannot reliably exceed the noisy ground truth it was trained against. A vendor claiming "96% accuracy against human coders" is implicitly claiming to be substantially worse than the average human coder against panel consensus.
AccuCode's models were never trained on patient encounters, on customer data, or on proprietary content from the AMA, the AHA, or the AAPC. No licensed code set text, no published coding guidance, and no certification material was used in training. The architecture does not pattern-match against past human-coded examples. The system was built the way you would train a person to perform the same task: it reads clinical documentation and reasons from what is documented, then produces output that conforms to the current ICD-10-CM, CPT, and HCPCS code sets, current NCCI edits, and current payer-specific rules. The result is a system whose performance is not capped by the accuracy of a training corpus, because there is no such corpus. The accuracy ceiling is the accuracy of the reasoning applied to the documentation in front of it.
AccuCode is not AAPC-certified, and does not claim to be. The AAPC certifies individual coders, not software, and AccuCode has no affiliation with or endorsement from the AAPC. As an internal benchmark, in Spring 2025 the system was administered a retired CPC examination form together with publicly available CPC practice examinations, with no preprocessing and no contextual hints, and scored 100%. The CPC is the credentialing exam human medical coders sit to become professionally certified; its published passing threshold is 70%. This was a benchmark exercise run for our own validation purposes, not a certification process, and the result is reported here as a capability measure rather than a credential.
Over eighteen months, AccuCode has been audited against credentialed human coders on tens of thousands of encounters by two independent organizations, on the highest-volume and the highest-complexity codes in the field.
Two things to notice. First, the initial agreement rate on the hardest specialty in our audit, 95.6%, is already nearly fourteen points above the peer-reviewed inter-coder reliability benchmark for credentialed human coders. Even before adjudication, the system is more reliable than the human ground truth used to measure it. Second, the post-adjudication rate of 99.1% on that same dataset is the system's actual error rate once human errors are removed from the comparison: less than one percent on the hardest codes in the audit, less on everything else. This is why AccuCode operates as a fully automated pipeline. Routing every chart through a human reviewer who is themselves 82% reliable does not increase accuracy. It decreases it.
The audit was concentrated on two specialty categories chosen deliberately: E/M codes, the highest-volume codes in healthcare and the most commonly disputed in inter-coder review; and cardiovascular surgery codes, among the most complex in any specialty, with intricate bundling and sequencing rules notorious for inter-coder disagreement. The audit was designed to test the easy cases at scale and the hardest cases at depth.
The cardiovascular surgery audit was conducted by MedAxiom, the American College of Cardiology's cardiovascular collaborative, before MedAxiom signed a partnership agreement with AccuCode. The audit was the gating test for that partnership. It was led personally by Nicole F. Knight, LPN, CPC, CCS-P, MedAxiom's Executive Vice President for Revenue Cycle Solutions, together with her team of credentialed coders. The partnership that followed became the foundation of AccuCode CV, the joint cardiovascular coding product at accucodecv.com.
The E/M audit was conducted jointly by MedAxiom and Professional Consulting Services (PCS), Arkansas's largest third-party medical billing firm. The broader specialty audits were conducted by PCS. PCS is led by Scott Roper (AccuCode's COO) and was founded by Jeff Roper (AccuCode's revenue cycle advisor) thirty years ago. They personally led the audit, deploying PCS's most senior CCS-credentialed coders against the system. Their professional reputation, and their company's continued financial responsibility for hundreds of provider organizations, depended on getting this right.
Every disagreement between AccuCode and the human coder was jointly adjudicated against the source documentation, current coding guidance, and current payer rules. The initial agreement rate measures the system against the human coder before that adjudication. The post-adjudication rate measures the system against the consensus answer after each disputed code was investigated to ground truth. Cardiovascular surgery is the floor of our specialty audit. Every other specialty AccuCode covers produces higher scores than these on both measures.
A single large language model call is the wrong architecture for clinical coding. LLMs pattern-match, and pattern matching hallucinates. AccuCode runs every chart through seven specialized stages, each acting as a gate with its own evaluation, fallback logic, and evidence requirements.
The complete patient record is pulled from your EMR through HL7/FHIR, including notes, imaging, medications, flowsheets, and labs in parallel.
documents: 42 note_types: 12 // H&P, Progress, Consults, // Nursing, OR, Discharge... imaging_reports: 2 lab_panels: 14 med_reconciliation: yes
Encounter context is established before any code is considered: inpatient vs. outpatient, setting, attending, specialty, primary reason, chronic conditions, and acute presentations.
encounter: inpatient setting: acute_care specialty: cardiology presenting: chest_pain chronic: [HTN, T2DM, HLD] acuity: high_complexity
Specific passages from the documentation are identified that support each potential diagnosis, procedure, and modifier, with source locations retained for citation binding.
atherosclerosis: // Hospital Course ¶2 "70% stenosis proximal LAD, without associated angina" dm_status: // Chronic Conditions ¶1 "stable on metformin, A1c 7.2%, no complications"
An ensemble of specialized models proposes ranked code sets with probability distributions: where LLM-based systems stop, AccuCode is at the midpoint, treating candidates as hypotheses, not answers.
I25.10 → 0.982 I25.11X → 0.043 // w/ angina I25.9 → 0.012 // unspec 99223 → 0.924 99222 → 0.071 // mod MDM E11.9 → 0.941 E11.22 → 0.038 // w/ nephro
Candidates are cross-checked against the current ICD-10-CM, CPT, and HCPCS code sets, NCCI edits, and payer-specific rules, with anything that fails dropped even at high confidence.
I25.10 ✓ PASS // ICD-10-CM §I25 // AHA CC Q3 2024 I25.11X ✗ FAIL // no angina documented 99223 ✓ PASS // MDM: 3+ data sources // 3+ chronic conditions
The final code set is checked for sequencing, bundling, medical necessity, and payer-specific edit rules, with anything that would trigger a denial flagged for review before the codes leave the system.
sequencing: OK // I25.10 primary, E11.9 sec bundling_ncci: OK medical_necessity: OK modifier_required: none payer_edits: Medicare OK denial_risk: low
Every final code is bound to the exact source passage that supports it, so the coder reviewing a chart sees the code and the words in the record that justify it, not the model's reasoning.
I25.10 conf: 0.98 cite: "Hospital Course ¶2, line 3–5" 99223 conf: 0.92 cite: "Admission Complexity" E11.9 conf: 0.94 cite: "Chronic Conditions ¶1"
One system, one configuration, across inpatient and outpatient. Cardiovascular is our first specialty-optimized module, developed jointly with MedAxiom, the nation's largest cardiovascular collaborative. Every other specialty, including orthopedics and oncology, is fully supported.
The cardiovascular intelligence inside AccuCode was built in direct collaboration with MedAxiom, the American College of Cardiology's cardiovascular collaborative. MedAxiom's coding leadership and clinical informatics team contributed the specialty depth that distinguishes CV surgery codes from the rest of the encoder: bundling logic, sequencing rules, payer-specific edge cases, and the nuanced documentation patterns that make this specialty notoriously difficult to code accurately at scale.
The audit that followed was the validation step in that joint development, not a separate event. MedAxiom's most senior CCS-credentialed coders, led personally by Nicole F. Knight, LPN, CPC, CCS-P, audited the engine against thousands of cardiovascular surgery encounters. The results, 95.6% initial agreement and 99.1% post-adjudication consensus on the hardest codes in the specialty, made the formal partnership inevitable.
That partnership is now AccuCode CV, the full-service tier of the AccuCode platform for cardiovascular programs. The underlying engine is the same fully automated coding pipeline used across every other specialty. The AccuCode CV difference is the service layer on top: MedAxiom's credentialed coders provide ongoing audit, cardiovascular-specific model refinement, and the human-in-the-loop review that cardiology programs prefer for their workflow. The engine handles the coding. MedAxiom handles the partnership.
By a blinded panel of external, CCS-credentialed coders who independently code a sample of charts without seeing AccuCode's output. The results are then compared. We publish the methodology, sample size, specialty mix, and panel composition for every audit cycle. We do not use self-measured accuracy.
No. The AccuCode pipeline is fully automated. Charts move from ingestion through code assignment to billing handoff without per-chart human review. Coder review is reserved for two purposes: initial validation during the deployment phase (typically sixty to ninety days) so your team can build confidence in the system on your own data, and ongoing compliance sampling thereafter (typically one to five percent of charts) to verify accuracy, catch drift, and provide audit defense. Charts that the system routes to a coder via facility, provider, or insurance-specific rules also receive human review, but these represent a small percentage of total volume. Adding human review to every chart in production has been shown to reduce accuracy, not increase it.
Architecturally. The system cannot emit a code that isn't bound to a specific passage in the source documentation. Unlike general-purpose LLMs, our seven-stage architecture treats "no supporting passage" as a reason to withhold the code, not to generate one. Hallucinated codes are prevented by the system's structure, not caught by a post-hoc filter.
No. Never, under any circumstances. AccuCode does not train models on patient data of any kind, whether identified, de-identified, aggregated, or synthetic. There is no contractual path by which your PHI becomes training data, because the system does not learn from patient encounters at all. It reasons from the documentation in front of it against the current published code sets and coding rules. Full details in our Security & Compliance documentation.
Every employee who can access customer PHI is based in the United States: Little Rock and Seattle. We maintain a firm, contractual policy against offshoring and third-party outsourcing of PHI access. No exceptions.
Epic App Available and FHIR-compliant EMR systems via API. Custom HL7 v2 interfaces are available for legacy environments as well as standard CSV import/export.
Per-encounter pricing for coding, with volume tiers. A pilot deployment typically runs on a fixed-fee basis so you can measure accuracy before committing to production pricing. Contact us for a quote based on your expected volume and specialty mix.
A signed BAA and measured results on your own data in a no cost pilot. You'll see accuracy, citation quality, and workflow integration before any commitment to purchase.