AI is rapidly becoming part of the physician documentation process, but not all tools simply transcribe what physicians say. Some systems go further, interpreting clinical reasoning and inserting their own language into the medical decision-making (MDM) section of the record.

Because specific wording can influence E/M code selection, AI-generated changes to the MDM can create new compliance and reimbursement risks by altering how the physician's clinical reasoning is represented in the record.

Why the Words Matter

For emergency department E/M services, time is not used to select the level of service. For codes 99282–99285, code selection is based on MDM, which is evaluated across three elements:

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• The number and complexity of problems addressed
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• The amount and complexity of data reviewed and analyzed
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• The risk of complications, morbidity, or mortality associated with patient management

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The framework includes four levels of MDM complexity: straightforward, low, moderate, and high. The overall MDM level is determined by meeting the requirements for two of the three elements.

The final diagnosis alone does not determine the complexity of an encounter. Instead, the full clinical picture matters — including the problems addressed, data evaluated, and management decisions made. How those elements are documented can directly affect the reported level of service.

Consider a common emergency medicine example: chest pain.

A 55-year-old patient presents to the emergency department with symptoms concerning for cardiac ischemia. Based on their history, risk factors, and clinical presentation, the physician evaluates the patient for a potentially serious cardiac condition with an ECG, serial troponins, chest X-ray, and other appropriate testing.

Ultimately, the workup is reassuring, and the patient is discharged with a diagnosis that is less serious than initially considered.

That outcome alone does not define the complexity of the encounter. The full clinical presentation, evaluation, and management still matter. The potential problem arises when technology attempts to interpret that clinical reasoning and translate it into standardized language.

When Technology Reinterprets the Physician

A physician may document the encounter this way: "The patient has multiple risk factors for coronary artery disease, and the presentation is concerning for cardiac ischemia. An ECG, serial troponins, and chest X-ray were obtained to evaluate for an acute cardiac ischemic process. Ultimately, the ECG and chest X-ray were normal, troponins were negative, and the patient was discharged with appropriate follow-up."

That language communicates more than the tests performed. It also documents what the physician was evaluating and why.

Now imagine an AI-assisted documentation tool interprets that reasoning and inserts a standardized phrase such as: "undiagnosed new problem with uncertain prognosis,” terminology associated with Moderate Complexity of Problems Addressed under the MDM framework.

The concern is not that the phrase itself is necessarily incorrect. The question is whether it accurately reflects the physician's clinical reasoning and the circumstances of the encounter.

If it does not, a more nuanced clinical assessment may be reduced to a standardized classification that tells a different story than the physician intended.

The clinical situation has not changed. The physician's reasoning has not changed. But the way that reasoning is represented in the medical record has.

The Same Problem Can Affect Risk

The same concern applies to the risk element of MDM.

For example, imagine the physician documents a decision regarding hospitalization, including the risks and benefits considered, but an AI-generated summary classifies the encounter as “Moderate— prescription drug management.”

Prescription drug management is associated with Moderate Risk, while a decision regarding hospitalization may support High Risk when the clinical circumstances and documentation support it. The issue, again, is whether the AI-generated classification accurately reflects the physician's decision-making.

Importantly, coders should evaluate the complete authenticated record rather than rely solely on an AI-generated MDM classification. If the documentation is conflicting or unclear, clarification may be appropriate.

AI can help organize the physician's reasoning, but it should not become the final authority on what that reasoning means.

This Is Bigger Than an Individual Claim

For an emergency medicine group, the impact of a single inaccurate phrase may be relatively small. Across thousands or hundreds of thousands of encounters, however, the effect can become much more significant.

If AI-assisted documentation routinely changes or oversimplifies how physicians describe problem complexity or management risk, the impact could eventually appear in a group's overall acuity distribution. Over time, that could affect reimbursement, physician performance data, payer analytics, and comparisons against historical benchmarks.

The concern also runs in the opposite direction. If technology introduces language that overstates problem complexity or management risk, it could contribute to overcoding and create compliance exposure.

This is ultimately a documentation integrity issue.

The medical record should reflect the physician's actual clinical thinking. Technology can help capture and organize that thinking, but it should not redefine it.

The Next AI Conversation for EM Leaders

Emergency medicine groups should not abandon AI-assisted documentation. These technologies offer significant potential to reduce physician burden and improve efficiency. But adoption should come with oversight.

Leaders should understand whether a tool simply captures documentation or actively interprets, rewrites, classifies, or adds clinical language. Physicians should review and correct AI-generated content before signing the chart, and practices should periodically compare the resulting documentation with the clinical reasoning it is intended to represent.

Key questions include:

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• Does the final MDM accurately reflect the physician's clinical reasoning and the rest of the medical record?
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• Is standardized terminology appearing that physicians would not ordinarily use, and does it carry a specific meaning under CPT?
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• Are physicians reviewing and correcting AI-generated language before signing their charts?
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• Are coders evaluating the complete record rather than relying on AI-generated classifications?
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• Has the group's E/M distribution changed since the technology was introduced?

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These questions should become part of the governance process for any technology capable of adding to or changing the medical record.

As AI becomes increasingly embedded in clinical workflows, physicians and practice leaders need to understand not only what these technologies are doing for them, but also what they may be saying on their behalf.

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