AI Medical Scribe: How ICD-10 and CPT Code Capture Can Reduce Undercoding and Claim Denials

Clinical documentation and medical coding are closely connected to the financial health of a medical practice. When an encounter note does not fully capture the diagnoses, services, clinical decisions, or procedures performed, coding teams may have less information to support accurate code selection. That can contribute to undercoding, claim rework, payment delays, and documentation-related denials.

This is where an AI Medical Scribe can become more than a documentation tool. By capturing conversations and organizing clinical information into structured notes, AI can help create a stronger foundation for ICD-10 coding, CPT code suggestions, E/M documentation, and revenue cycle workflows.

The objective is not to let AI automatically maximize reimbursement. Instead, AI can help healthcare organizations identify clinically supported information that might otherwise be overlooked while providers and qualified coding professionals retain responsibility for reviewing and validating the final record.

CMS reported a 10.3% improper payment rate for Medicare E/M codes for the 2024 reporting period. Incorrect coding accounted for 49.1% of E/M improper payments, while insufficient documentation accounted for 34.1%.

That makes the connection between complete documentation and accurate coding increasingly important.

What Is AI ICD-10 Coding?

AI ICD-10 coding uses artificial intelligence to analyze clinical documentation and identify diagnosis concepts that may correspond to ICD-10-CM codes.

An AI Medical Scribe can capture information such as:

• Diagnoses and symptoms
• Chronic conditions
• Clinical findings
• Assessment and treatment plans
• Relevant medical history
• Procedures and services discussed or performed
• Follow-up instructions

The AI can then organize these details into structured AI Medical Documentation and, when supported by the workflow, surface potential diagnosis codes for review.

However, AI-generated code suggestions should not automatically become billing codes. The final selection must be supported by the documentation and applicable coding, payer, and medical-necessity requirements.

How CPT Code Suggestions From an AI Scribe Work

While ICD-10-CM codes primarily describe diagnoses and conditions, CPT codes describe medical services and procedures.

An AI scribe with coding support may analyze the clinical note and identify information relevant to CPT selection, including documented services and E/M elements.



For example, the workflow can help bring attention to:

• Services performed during the encounter
• Documented evaluation and management details
• Procedures
• Clinical decision-making information
• Diagnoses supporting the service
• Relevant documentation that may require clarification

The important distinction is suggestion versus final coding.

AI can function as a documentation and coding assistant, while qualified professionals review the suggested codes and determine whether they are appropriate.

How AI Medical Scribes Can Help Reduce Undercoding

Undercoding occurs when the codes submitted for an encounter do not fully represent the services or diagnoses supported by the medical record.

An AI Medical Scribe can help address one contributing factor: incomplete or inconsistent documentation.

When clinical conversations are captured automatically, providers may have less need to reconstruct the encounter from memory later. The resulting AI SOAP Notes, progress notes, or clinical summaries can organize information that is relevant to coding review.

A coding workflow can then ask:

Does the documentation support the code being considered?

If the answer is yes, the information is easier to validate. If important information is missing, the provider or coding team can identify the documentation gap before the claim is submitted.

This supports compliant revenue capture, rather than simply pursuing higher reimbursement.

From Patient Encounter to Coding Review

The value of AI becomes clearer when documentation and coding are connected within one workflow.

Patient encounter → AI Medical Scribe → Structured clinical note → Code suggestions → Human review → Claim submission

Each stage has a different purpose:

• Patient encounter: The physician focuses on the patient and clinical decision-making.
• AI documentation: The AI Medical Scribe captures and organizes the encounter.
• Clinical review: The provider verifies that the note accurately reflects what occurred.
• Coding support: Potential ICD-10 and CPT codes can be surfaced for review.
• Human validation: A qualified coder or authorized professional validates code selection.
• Claim submission: The finalized documentation supports the billing workflow.

This human-in-the-loop approach is especially important because AI can misunderstand context, miss clinical nuances, or produce plausible but unsupported code suggestions.

Can Better AI Documentation Help Reduce Claim Denials?

AI cannot eliminate claim denials. Denials can result from eligibility, authorization, payer policies, coding errors, medical necessity, missing information, and many other factors.

However, stronger documentation can help address documentation- and coding-related problems.

CMS states that improper payments can occur when services have no documentation or insufficient documentation, and that proper payment requires sufficient documentation to support payment requirements.

An AI-powered documentation workflow can help by making relevant clinical information easier to locate and review before submission.

That can potentially reduce:

• Documentation-related claim rework
• Coding clarification requests
• Missing clinical details
• Manual chart review
• Avoidable coding inconsistencies
• Delays caused by incomplete documentation

The goal is not a guarantee of fewer denials. The goal is to create a stronger documentation foundation for accurate claims.

AI Medical Scribe + Medical Coding: Connecting Two Workflows

Traditionally, clinical documentation and medical coding can operate as separate processes. AI creates an opportunity to connect them more closely.

An AI Medical Scribe can focus on capturing the clinical encounter, while medical coding workflows focus on interpreting the documentation according to coding rules.

This can complement services such as:

• Medical Coding Services
• Medical Transcription Services
• Virtual Medical Scribe services
• Virtual Medical Assistant support
• Medical Dictation Services

The technology does not need to replace these functions. Instead, it can help reduce repetitive documentation work and make relevant information easier for professionals to review.

For practices using a Virtual Medical Scribe, AI Medical Scribe, or blended documentation model, the same principle applies: accurate documentation should remain the foundation for coding.

Why AI Code Suggestions Should Not Replace Coders

Medical coding involves more than matching words to codes.

Context, specificity, documentation requirements, payer policies, modifiers, medical necessity, and current coding guidelines can all affect the final decision.

Research into generative AI for medical coding also highlights this challenge. Recent work has found that general-purpose models can struggle with accurate ICD-10 and CPT code generation without domain-specific adaptation and appropriate controls.

Therefore, a responsible AI Medical Scribe workflow should include:

• Provider verification
• Qualified coding review
• Current coding references
• Documentation audits
• AI output monitoring
• Privacy and security controls
• Clear escalation processes for ambiguous cases

AI should make professional review more efficient not remove the professional from the process.

How EzyScribe Supports AI-Powered Clinical Documentation

EzyScribe is our AI Medical Scribe software, designed to support healthcare providers with AI-powered clinical documentation.

Within a documentation and coding workflow, EzyScribe can help transform patient encounters into structured clinical notes that are easier for providers and coding teams to review.

The broader value of an AI Medical Scribe is not simply generating text. It is helping create organized, clinically useful documentation that can support downstream workflows such as coding, chart review, and EHR documentation.

• For more information, visit EzyScribe.
  

Best Practices for AI-Assisted ICD-10 and CPT Coding

Healthcare organizations considering AI-assisted coding should establish clear safeguards.

• Treat AI code suggestions as decision support, not automatic billing decisions.
• Require providers to review AI-generated documentation.
• Have qualified coding professionals validate final code selection.
• Confirm that every submitted code is supported by the medical record.
• Check current ICD-10-CM and CPT requirements.
• Consider payer-specific documentation requirements.
• Monitor AI output for omissions and inaccurate suggestions.
• Audit coding patterns regularly.
• Protect PHI and maintain appropriate HIPAA safeguards.
• Establish a process for handling uncertain or conflicting recommendations.

These controls help keep clinical documentation improvement, compliance, and revenue integrity aligned.

The Revenue Cycle Opportunity

The strongest revenue-cycle opportunity is not simply "getting paid more." It is making sure that legitimate, clinically supported services are accurately documented and appropriately coded.

When an AI Medical Scribe captures encounter details consistently and coding workflows can identify potential ICD-10 and CPT selections for review, practices may have a clearer path from patient care → documentation → coding → claim.

That can help reduce avoidable rework while supporting more complete documentation.

The result is a more connected workflow in which technology supports provider productivity without compromising coding integrity.

Conclusion

The intersection of AI Medical Scribe technology and medical coding is becoming an important part of modern healthcare documentation and revenue cycle workflows.

AI-powered documentation can capture clinical details, organize information into structured notes, and potentially surface ICD-10 and CPT code suggestions for professional review. When implemented correctly, this can help practices identify documentation gaps that may contribute to undercoding or claim rework.

The most effective model is not autonomous coding. It is intelligent assistance with human oversight.

AI captures and organizes the encounter. Providers verify the clinical record. Qualified coding professionals validate the codes. Together, these steps can support accurate documentation, cleaner claims, and more consistent legitimate revenue capture.

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