$14.1B
$14.1 Billion in Improper Medicare Payments Last Year. The Root Cause: Coding and Documentation Errors That AI Catches Before Submission.
AI Medical Coding Agent
An AI medical coding agent that reads clinical documentation and assigns ICD-10, CPT, and DRG codes with NCCI validation, CC/MCC capture, and revenue integrity auditing.
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Six Core Capabilities of the AI Medical Coding Agent
From clinical NLP analysis and ICD-10/CPT/DRG assignment to NCCI validation, CC/MCC capture, query generation, and revenue integrity auditing — six pillars that help healthcare organisations reclaim coding accuracy, clear submission backlogs, and recover defensible revenue from missed complexity.
Clinical NLP Analysis & Code Extraction
Clinical NLP reads discharge summaries, operative reports, labs, and consult notes — extracting diagnoses and procedures at 96.7%+ accuracy.
DRG Optimisation & CC/MCC Capture
MS-DRG and APR-DRG engine lands the accurate DRG by evaluating every diagnosis against Medicare Severity grouper logic.
NCCI Validation & Edit Management
Real-time NCCI PTP validation checks every CPT/HCPCS code pair against the CMS edit table — catching bundling conflicts before claim generation.
Coding Query Generation & CDI Integration
AHIMA/ACDIS-compliant: the agent issues structured, non-leading physician queries when documentation needs attestation.
Revenue Integrity & Compliance Auditing
Pre-submission audit screens claims against OIG Work Plan, RAC targets, and MAC LCDs — flagging high-risk code combinations before submission.
Coding Performance & Productivity Analytics
Real-time dashboard tracks time-to-code, first-pass acceptance, denial rates by DRG, and CC/MCC capture — by coder, service line, and payer.
85%
$14.1 Billion in Improper Medicare Payments Last Year.
AI Medical Coding Agent
The same documentation gaps that prevent accurate DRG assignment also create audit risk when RAC contractors review the claim.
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The $14 Billion Coding Error Crisis Draining Revenue Cycles Across Every Specialty
Every number comes from production revenue-cycle deployments — measured live, not projected in a pitch deck.
94%
first-pass claim acceptance rate achieved when AI medical coding replaces manual coding workflows — up from the 71% industry baseline — translating…
100+
Enterprise customers trusting Bonami X AI for mission-critical healthcare and revenue cycle operations.
24/7
Autonomous monitoring with real-time alerts — continuous automated intervention across every workflow.
Our Process
Why Health System Revenue Cycle Leaders Deploy Bonami's AI Medical Coding Agent
Drag, click, or use the dots to walk through each reason.
Coding Specificity at Scale — Not Just Coding Speed
The most common argument for AI medical coding centres on throughput, but the larger financial opportunity is in specificity. A 5% improvement in MCC capture rate across 15,000 annual inpatient discharges at a $8,500 average DRG weight…
Compliance Confidence Before the Claim Leaves the Building
The OIG Work Plan, Recovery Audit Contractor programmes, and MAC post-payment reviews scrutinise high-risk diagnosis-procedure combinations each year — precisely the code patterns most commonly assigned incorrectly under manual…
Coders as Clinical Coding Experts — Not Data Entry Operators
The AAPC's salary survey reports that the national shortage of qualified medical coders is intensifying, with AHIMA projecting a 20–30% shortfall against demand by 2027. The AI agent handles the extraction, suggestion, and validation…
Works With Your EHR, Coding Platform, and Billing System
The AI Medical Coding Agent ships with certified connectors for the leading EHR platforms, computer-assisted coding environments, and clearinghouse networks — enhancing the tools your coders already use rather than replacing them.
Epic
Oracle Health
Availity
athenahealth
eClaimLink
Daman
From the Desk of Our Esteemed Clients
Real results from enterprises that have deployed Bonami's AI solutions across industries.
Bonami's AI platform revolutionized our content creation process. Their natural language generation tools helped us scale our content production by 300% while maintaining exceptional quality and brand voice.
85%
Stop Leaving Clinical Complexity Uncoded and Unreimbursed
Every missed CC/MCC, every unspecified ICD-10 code, every NCCI edit that generates a denial — revenue that existed in the clinical record, lost to a documentation-to-code translation failure.
Book a Coding Accuracy Demo
Six Core Capabilities of the AI Medical Coding Agent
From clinical NLP analysis and ICD-10/CPT/DRG assignment to NCCI validation, CC/MCC capture, query generation, and revenue integrity auditing — six pillars that help healthcare organisations reclaim coding accuracy,…
01
Clinical NLP Analysis
Clinical NLP reads discharge summaries, operative reports, labs, and consult notes — extracting diagnoses and procedures at 96.7%+ accuracy.
02
DRG Optimisation
MS-DRG and APR-DRG engine lands the accurate DRG by evaluating every diagnosis against Medicare Severity grouper logic.
03
NCCI Validation
Real-time NCCI PTP validation checks every CPT/HCPCS code pair against the CMS edit table — catching bundling conflicts before claim generation.
04
Coding Query Generation
AHIMA/ACDIS-compliant: the agent issues structured, non-leading physician queries when documentation needs attestation.
05
Revenue Integrity
Pre-submission audit screens claims against OIG Work Plan, RAC targets, and MAC LCDs — flagging high-risk code combinations before submission.
06
Coding Performance
Real-time dashboard tracks time-to-code, first-pass acceptance, denial rates by DRG, and CC/MCC capture — by coder, service line, and payer.
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Ready to Recover the Revenue Your Clinical Complexity Has Already Earned?
Talk to a healthcare AI coding specialist — get a live demo of the Medical Coding Agent running against your encounter volume and a coding accuracy assessment identifying CC/MCC capture gaps and NCCI risk exposure in your current claim data.
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Frequently Asked Questions
[ 1 ]
What is an AI Medical Coding Agent and which code sets does it handle?
An AI Medical Coding Agent reads clinical documentation from the EHR, extracts diagnoses, procedures, and clinical context using clinical NLP, assigns the appropriate codes from applicable code sets, validates the complete code combination against compliance rules, and presents the result for coder review — with a full audit trail for every code assigned.
[ 2 ]
How does the NLP model handle ambiguous or poorly structured clinical documentation?
The NLP architecture handles documentation variability through three mechanisms: (1) the model is trained on a representative corpus of real clinical documentation spanning multiple specialties and EHR-generated formats, not a
[ 3 ]
What is the first-pass claim acceptance rate and how is it measured?
First-pass claim acceptance rate (FPAR) measures the percentage of submitted claims accepted and paid on the first submission without denial, rejection, or request for additional information.
[ 4 ]
How does the agent handle DRG optimisation without creating upcoding risk?
DRG optimisation and upcoding are different activities. Upcoding assigns codes that documentation does not support — fraudulent billing. DRG optimisation ensures every CC and MCC genuinely present in the patient's clinical record and documented by a treating clinician is captured so that DRG assignment accurately reflects actual clinical complexity.
[ 5 ]
How does the physician query process work, and does it comply with AHIMA guidelines?
Physician queries generated by the agent comply with the AHIMA and ACDIS 2019 joint guidelines on compliant query practice: queries must not be leading, must cite specific clinical evidence, must offer multiple response choices including "clinically undetermined" and "other", and must be generated only when there is a genuine clinical basis in the documentation.
[ 6 ]
Which EHR systems and coding platforms does the agent integrate with?
EHR integration via FHIR R4 APIs and HL7 v2 interfaces for: Epic (FHIR R4, SmartOnFHIR app framework, and Epic Charge Router integration), Oracle Health/Cerner (FHIR R4 and Millennium HL7 interfaces), athenahealth (athena APIs and Marketplace integration), NextGen (FHIR R4), and eClinicalWorks (FHIR connector).
[ 7 ]
How does the agent handle specialty-specific coding requirements?
Medical coding requirements differ materially by specialty — not just in code sets but in documentation patterns, coding guideline nuances, and payer policy requirements.
[ 8 ]
How long does implementation take and what ROI timeline should we expect?
A focused deployment covering one EHR, the top five payer contracts, and the top 10 DRGs by encounter frequency runs 10–14 weeks: Weeks 1–3 cover FHIR connector configuration, specialty NLP model calibration using 6 months of historical coded encounters, and NCCI/compliance rule layer validation.
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