AI scribe upcoding risk refers to the possibility that billing or reimbursement claims based on notes generated by this technology may not withstand payer audits. The concern is that a medical record produced with AI assistance may suggest a higher level of service than was actually rendered.
AI scribes can be valuable tools for reducing the documentation burden on medical staff. They listen to clinician-patient discussions and turn them into notes. But organizations that use such systems to assist with documentation and coding for reimbursement purposes should be aware of the compliance implications. To maintain seamless clinical operations, practice administrators should evaluate these systems alongside broader EHR integration and workflow optimization strategies.
This article discusses the AI scribe upcoding risk in detail, along with strategies to address this concern.
What This Article Covers
- Why billing based on AI-assisted notes requires clinician oversight
- How E/M coding guidelines apply to AI-generated documentation
- How payers may view medical records created with AI
- What documentation coding intensity means in this context
- What an AI scribe HCC coding risk entails
- How providers can reduce AI scribe upcoding risk in 2026
AI Scribe Upcoding Risk Overview
AI scribes can inadvertently cause coding issues if their outputs are not carefully reviewed. The CMS guidance and E/M coding guidelines are particularly relevant for clinicians using these technologies.
AI scribes can cause coding risk when the final note contains unsupported diagnoses, unneeded clinical information, or other details that could suggest a higher level of service than was actually rendered. Qualified clinicians should review and edit notes created with AI assistance before they become part of a patient's official medical record or are used for billing purposes.
The CMS guidance indicates that the determination of the level of an E/M service should be based on the services provided rather than the amount or complexity of documentation. So, providers should ensure that the note's content reflects an accurate representation of the services rendered. Understanding these nuances is critical for practices assessing the overall ROI of AI scribes in healthcare.
Why AI Notes Require Human Review
An ambient scribe listens to a conversation and typically creates a note based on what it hears. The note can be helpful, but providers using ambient scribes need to understand that this form of AI assistance does not eliminate the need for careful documentation review.
There are various reasons why this is the case, including the following:
- An AI scribe may include clinical information in the note that the provider did not actually evaluate or consider. For example, a patient's conversation with the provider might include a discussion of various conditions or medications from the past, but this information would not be relevant to the current visit's documentation.
- This is why it is necessary for the provider to carefully review the final note and remove any information that was not actually part of the discussion or evaluation during the encounter before billing.
- A record containing unneeded or unrelated information may lead to errors during the coding process, resulting in claims that are inappropriate or otherwise at risk of being rejected.
Integrating human verification steps directly into daily operations ensures patient safety while preserving the efficiency benefits outlined in our ambient clinical intelligence implementation guide.
AI Scribe Upcoding Risk For E/M Coding
The E/M coding guidelines are especially relevant for determining the level of service for an outpatient encounter. The guidelines emphasize that the level of an E/M service should be dictated by the extent of the provider's decision-making or, in cases when time is used as a basis for coding, the amount of time spent on the service.
An AI-generated note has the potential to raise concerns during a payer audit if it makes the documentation appear more involved than it actually was. For instance, an otherwise uncomplicated conversation might include information that an AI scribe converted into a detailed note. If a coder or coding software then uses that information to select a higher-level E/M code, it could lead to claim issues.
The key point is that before finalizing a note for billing purposes, the provider should make sure that the final documentation accurately reflects the services rendered.
AI Scribe Billing Compliance Considerations
An effective compliance program related to AI-assisted documentation and coding must contain clearly defined roles and responsibilities for all individuals involved in the process. Some of the most important policies to consider include the following:
- Clinician review of AI-assisted notes
- Correction of any inaccuracies introduced by AI
- Coding and billing policies regarding AI-assisted documentation
- Privacy and security considerations
- Information retention policies
- Employee training
- Periodic audits of the process
AI technology can be error-prone, and its use in note-taking can introduce inaccuracies to the documentation. It is important for a provider to understand that human intervention is necessary at every stage of the process. Aligning these reviews with robust HIPAA-compliant AI documentation practices protects practices from both billing and privacy liabilities.
Documentation Coding Intensity
Documentation coding intensity refers to the tendency for certain diagnoses to be reported more often than others. For instance, coding for higher levels of E/M services or a higher number of risk-adjustment diagnoses may be considered anomalous if they occur far more frequently than would be expected.
The use of ambient scribes has prompted concerns about documentation coding intensity because an AI tool may produce notes that suggest a higher level of service than was actually rendered. In some cases, an increase in coding intensity may be appropriate and reflect a true rise in the level of care.
However, it is important for organizations to understand that documentation coding intensity can also be a sign of improper upcoding practices.
Organizations can analyze coding intensity before and after implementing ambient scribes to identify any irregularities that warrant further investigation.
AI Scribe HCC Coding Risk
Risk-adjustment coding is another area of concern for organizations that use AI-assisted note-taking. An AI scribe may identify a diagnosis mentioned by a patient and include it in the note. However, simply mentioning a diagnosis does not mean that it should be reported for risk-adjustment purposes. The diagnosis needs to be valid, relevant to the services rendered, and properly supported by the documentation.
Organizations that participate in Medicare Advantage or other risk-adjustment programs should be aware of this consideration and ensure that proper review processes are in place when diagnosis-related information is generated by an AI scribe. Evaluating vendor capabilities through a structured medical AI scribe comparison can help identify solutions with built-in risk-adjustment safeguards.
Can AI Notes Cause Claims To Be Denied?
Improperly reviewed notes generated by an AI scribe can lead to a variety of claim denial reasons. Claims can be denied if the documentation does not support the level of service rendered or the diagnoses reported. Other issues that may contribute to claim denials include the following:
- An unsupported diagnosis
- An inappropriate E/M code
- Lack of documentation of medical necessity
- Inaccurate information
- Information that does not match the services rendered
Prior to submitting a claim, the final documentation should reflect an accurate representation of the services rendered and be consistent with the diagnoses, procedures, and services reported.
Common Compliance Risks With AI Scribes
Unsupported Clinical Information
An AI scribe can introduce unsupported clinical information if it includes a diagnosis or other clinical detail that was not actually addressed by the provider.
Unneeded Information
An AI scribe can also introduce unneeded information, such as excessive detail that does not relate to the services rendered.
Incorrect Diagnoses
An AI scribe can make errors, including generating incorrect diagnoses.
Influence On Coding
Information included in a note generated by an AI scribe can lead to coding errors if staff fails to carefully review the documentation prior to billing.
Lack Of Review
Failing to carefully review documentation created by an AI scribe can lead to errors in the medical record, which can then impact reimbursement.
Privacy And Security
Organizations using AI scribes should carefully evaluate how this technology accesses, stores, and processes patient information. Ensuring your vendor adheres to strict standards for data security in clinical AI tools is essential to protecting patient records.
How To Reduce AI Scribe Upcoding Risk In 2026
A robust governance model can help an organization reap the benefits of AI while minimizing the risks. Some of the most useful approaches to reducing AI-related compliance concerns include the following:
- Keeping Control Over Note Creation: The provider needs to have final say over the creation of the note. The AI should function as an assistant rather than an autonomous note creator.
- Separating Note Creation From Coding Decisions: A coder should not select a higher-level E/M code simply because an AI-assisted note contains more information. The note's content needs to reflect an accurate representation of the services rendered.
- Monitoring Coding Trends: An organization can detect potential issues by analyzing coding-related patterns, such as changes in coding intensity, before and after adopting a new AI-based technology.
- Reviewing Payer Feedback: Reviewing denial reasons, medical-record requests, and audit results can help an organization identify problem areas in its AI-assisted documentation and coding processes.
- Training: Employees need to be aware of the potential issues associated with AI-assisted documentation and coding and understand their role in addressing them.
- Developing Policies: A written policy needs to be created to establish guidelines concerning appropriate use of AI technology, including documentation review and coding considerations. Developing clear guidelines also helps optimize physician burnout reduction strategies without sacrificing compliance.
AI Scribe Upcoding Risk: Key Compliance Comparison
This table provides a brief overview of the most important considerations for managing upcoding risk when using AI-assisted note-taking:
| Risk Area | Lower-Risk Approach | Higher-Risk Approach |
|---|---|---|
| Note review | Clinician reviews every generated note | AI output is accepted without review |
| Clinical accuracy | Unsupported or incorrect content is removed | All generated information remains in the record |
| E/M coding | Code reflects actual services provided | Higher code is selected because the note is lengthy |
| Diagnosis coding | Conditions are verified for relevance | Every diagnosis detected by AI is reported |
| Audits | Coding and note patterns are periodically reviewed | No monitoring after implementation |
| Staff responsibility | Roles for clinicians and coders are clearly defined | AI output effectively drives billing decisions |
| Privacy | Vendor data handling is reviewed | Patient information is shared without adequate oversight |
Best Practices When Using An AI Scribe
Organizations can reduce the risks associated with AI-assisted note-taking by following a few basic best practices, including the following:
- Reviewing every AI-generated note
- Eliminating any information that was not actually addressed by the provider or that is otherwise unrelated to the services rendered
- Avoiding the inclusion of information that could impact the accuracy of the coding
- Reviewing and validating all diagnosis-related information
- Monitoring trends in coding and documentation
- Conducting regular audits
- Ensuring the privacy and security of patient information
- Training employees
These practices should enable an organization to take advantage of the benefits of AI-assisted note-taking while minimizing the risks. Adopting these protocols alongside tailored specialty-specific AI scribe workflows ensures long-term operational success.
FAQs
1. Can an AI medical scribe cause upcoding?
An AI medical scribe itself does not cause upcoding, but an AI-generated note could include information that leads to improper coding.
2. Can payers audit notes created with AI?
Yes. Payers can audit medical records to verify that the services billed are appropriate and that the documentation supports the services rendered. This applies even when AI-assisted note-taking has been used.
3. Is AI-generated documentation acceptable for E/M coding?
AI-generated documentation can be used for E/M coding when the final note accurately reflects the services rendered.
4. How can organizations reduce compliance risks associated with AI scribes?
Organizations can reduce compliance risks associated with AI scribes by ensuring that these tools do not dictate the note-taking or coding process, reviewing documentation trends, performing regular audits, protecting patient privacy, and establishing appropriate policies.
5. Are AI scribes a risk for causing improper HCC coding?
AI scribes can contribute to improper HCC coding when the documentation they produce includes diagnoses that are inaccurate or unrelated to the services rendered.
Conclusion
An AI scribe can be a powerful tool for reducing the documentation burden on medical staff. However, organizations should recognize that this valuable asset needs to be carefully managed in order to avoid compliance issues. The most important consideration is whether the documentation accurately reflects the services actually rendered and appropriately supports the billing claims.
Keeping clinicians in control over the documentation process, reviewing documentation trends, and conducting regular audits can enable an organization to reap the benefits of AI-assisted note-taking while minimizing the risks.