$14.1B
$14.1 Billion in Improper Medicare Payments Last Year. The Root Cause: Coding and Documentation Errors That AI Catches Before Submission.
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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.
Get Your Medical Coding Assessment
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
A 5% MCC capture-rate gain across 15,000 inpatient discharges recovers $6.4M a year — specificity the clinical record already supports.
Compliance Confidence Before the Claim Leaves the Building
The AI agent validates every claim against current OIG Work Plan priorities and MAC LCDs before submission — not after the audit.
Coders as Clinical Coding Experts — Not Data Entry Operators
With AHIMA projecting a 20–30% coder shortfall by 2027, the agent handles extraction and validation so coders focus on clinical judgment.
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
Epic EHR FHIR R4 AI medical coding integration for inpatient and outpatient charge capture
Oracle Health
Oracle Health Cerner EHR AI medical coding agent integration for ICD-10 and DRG assignment
Availity
Availity clearinghouse AI medical coding integration for pre-submission claim validation
athenahealth
athenahealth EHR AI medical coding agent integration for ambulatory CPT and ICD-10 coding
eClaimLink
eClaimLink UAE AI medical coding integration for DHA and HAAD payer claim submission
Daman
Daman health insurance UAE AI medical coding agent integration for clinical coding compliance
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.
Get in touch
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 EHR clinical documentation, extracts diagnoses and procedures using clinical NLP, assigns codes from the applicable sets, validates the combination against compliance rules, and presents the result for coder review with a full audit trail.
[ 2 ]
How does the NLP model handle ambiguous or poorly structured clinical documentation?
The NLP is trained on real clinical documentation across specialties and EHR formats, not a general-purpose model applied to medical text. Entity extraction uses contextual language models that understand negation, uncertainty, and temporal context — "rule out sepsis" generates no sepsis code.
[ 3 ]
What is the first-pass claim acceptance rate and how is it measured?
First-pass claim acceptance rate (FPAR) is the share of claims paid on first submission without denial or rework. The manual baseline is 71% FPAR (MGMA/Advisory Board); Bonami deployments achieve 94%+, measured by clearinghouse clean claim rate and first-pass adjudication from payer EOBs.
[ 4 ]
How does the agent handle DRG optimisation without creating upcoding risk?
DRG optimisation is not upcoding. Upcoding assigns codes documentation does not support; optimisation captures every CC and MCC genuinely documented by a treating clinician so DRG assignment reflects actual complexity.
[ 5 ]
How does the physician query process work, and does it comply with AHIMA guidelines?
Yes. Queries comply with the AHIMA and ACDIS 2019 joint guidelines: non-leading, citing specific clinical evidence, offering multiple response choices including "clinically undetermined", and generated only on genuine clinical basis.
[ 6 ]
Which EHR systems and coding platforms does the agent integrate with?
EHR integration via FHIR R4 APIs and HL7 v2 for Epic (incl. SMART on FHIR and Charge Router), Oracle Health/Cerner, athenahealth, NextGen, and eClinicalWorks — any documented FHIR R4 endpoint is connectable, and it enhances existing CAC environments rather than replacing them.
[ 7 ]
How does the agent handle specialty-specific coding requirements?
Coding requirements differ materially by specialty in code sets, documentation patterns, and payer policy. The agent deploys specialty-specific NLP models calibrated per specialty: inpatient medicine and surgery for discharge summaries and
[ 8 ]
How long does implementation take and what ROI timeline should we expect?
A focused deployment (one EHR, top five payers, top 10 DRGs) runs 10–14 weeks: FHIR connector setup and specialty NLP calibration on 6 months of coded encounters, then shadow mode with daily coder review, a controlled live pilot, and phased expansion to full volume.
[ 9 ]
How is computer assisted coding software different from this medical coding software?
Traditional computer assisted coding software suggests codes and leaves full assignment and validation to the coder. Bonami's medical coding software goes further as AI medical coding software that codes end to end: it reads the documentation, assigns the complete ICD-10 and CPT code set, validates every combination against NCCI edits, and surfaces source documentation for exception-based coder review.
[ 10 ]
Does the automated medical coding handle both ICD-10 and CPT coding software requirements?
Yes. This medical coding software is purpose-built ICD-10 and CPT coding software: it assigns ICD-10-CM diagnosis codes for all care settings, ICD-10-PCS inpatient procedure codes, and CPT and HCPCS codes for outpatient and professional encounters.
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