Are AI Coding Tools Putting Your Practice at Risk?
Do you know the Pitfalls of Not Auditing Your Medical Coding and Documentation when using AI?
It is extremely important to audit AI-assisted coding and documentation tools within physician practice. Relying on AI outputs without consistent human oversight could be devastating to your practice.
AI tools are increasingly embedded in EHRs, dictation platforms, and coding workflows. In the physician practice where documentation is often brief, encounter volumes are high, and coding relies heavily on provider wording, AI can introduce subtle but significant errors that directly affect compliance, revenue, and provider risk.
Key Pitfalls When AI Is Not Audited
1. Misinterpretation of Provider Intent
AI may overinterpret or under interpret brief outpatient notes, leading to:
Unsupported diagnoses (e.g., chronic conditions inferred but not documented)
Missed diagnoses that affect risk adjustment
Incorrect E/M level suggestions based on incomplete documentation
2. Overcoding or Undercoding E/M Services
AI tools may:
Inflate medical decision-making by misreading templated language
Miss time-based coding opportunities
Misclassify split/shared or incident-to scenarios
These errors can create patterns that expose the practice to payer audits.
3. Incorrect Procedure Coding
AI may misassign CPT codes when documentation includes:
Abbreviations
Ambiguous procedure descriptions
Multiple services performed in one encounter
Without auditing, these errors can become systemic.
4. Modifier Misuse
AI often struggles with modifier logic in pro-fee billing, such as:
E/M Modifiers 24, 25 and 57
Misuse of Modifier 59 vs. X Modifier-X{EPSU}
Distinction between Modifier TC and 26
Global surgical package rules
Incorrect modifiers are one of the most common denial drivers in professional billing.
5. Risk Adjustment (HCC) Vulnerabilities
AI may:
Suggest HCC diagnoses without clear documentation support
Miss chronic conditions that require annual capture
Fail to recognize MEAT (Monitor, Evaluate, Assess, Treat) criteria
This can lead to compliance exposure or lost RAF value.
6. Provider Overreliance on AI
When providers assume AI-generated documentation or coding suggestions are “correct,” critical thinking decreases. This can lead to:
Copy-forward errors
Inaccurate problem lists
Documentation that does not reflect the actual encounter
Safeguard Recommendations
1. Implement Routine AI-Specific Coding Audits
Include encounters where AI influenced:
E/M level selection
Diagnosis capture
Procedure coding
Documentation prompts or summaries
Audit both high-volume and high-risk specialties.
2. Require Coder Validation
Credentialed coders/auditors should review AI-suggested codes, especially for:
E/M services
Procedures with modifier requirements
HCC/RAF encounters
High-dollar or high-risk specialties (cardiology, ortho, general surgery, ENT, etc.)
3. Strengthen Provider Education
Reinforce that AI is a support tool, not a coding authority. It is critical to educate providers on:
Documentation requirements
AI limitations
When to escalate concerns
4. Monitor Trends and Denials
Track:
Denials tied to AI-assisted encounters
Shifts in E/M distribution
Changes in RAF capture
Modifier-related denials
5. Establish Governance and Oversight
Assign responsibility for:
Reviewing AI updates
Monitoring accuracy
Approving new AI features
Communicating changes to coding staff and providers
Action Items for Your Team
Identify all AI tools currently influencing documentation or coding.
Audit a sample of AI-assisted encounters to establish a baseline accuracy rate.
Correct issues through coding education, provider feedback, or workflow adjustments.
Monitor ongoing performance with AI-specific audit metrics.
Contact us at KZAnow.com or call us at 312-642-5616 to schedule your audit today. Remember, coding, documentation, and your revenue are the lifeblood of your practice.
