AI Is Writing Clinical Notes.
Who Is Checking the Accuracy?
As healthcare organizations expand AI-powered clinical documentation, a critical question emerges: Who is verifying that the information AI generates accurately reflects the patient encounter?.
University of Texas Expands Its Use of Clinical AI
On October 8, 2026, Abridge announced an enterprise-wide agreement with the University of Texas System to expand access to its AI-powered clinical documentation platform.
The agreement establishes a framework allowing eight UT health institutions to access Abridge technology while retaining local control over implementation decisions.
The expansion builds on existing AI documentation initiatives and reflects a growing trend across healthcare: using artificial intelligence to reduce administrative work and allow clinicians to spend more time with patients.
But as AI-generated documentation becomes more common, healthcare organizations face another important responsibility: verifying the accuracy and reliability of the information entering the medical record.
When AI Documentation Looks Accurate but Isn't
AI-powered documentation tools, often called ambient AI scribes, use conversations between clinicians and patients to generate draft clinical notes.
These tools can help reduce documentation time and improve workflow efficiency. However, a well-written note is not necessarily an accurate representation of the encounter.
Potential documentation errors include:
Omissions: Important symptoms, findings, or clinical details are missing.
Unsupported information: The AI introduces clinical statements that were never established during the encounter.
Incorrect interpretation: Patient statements or clinical findings are misunderstood or misrepresented.
Clinical inconsistencies: Information within the note conflicts with other documented findings.
These concerns are supported by published research.
A 2026 study examining 356 evaluated AI-generated clinical notes found that omissions were the most frequent error, appearing in 18% of reviewed notes. Hallucinations appeared in 11.5%, and 5.3% of reviewed notes contained errors rated as posing serious or imminent risk if left uncorrected.
Although most evaluated notes were free of significant errors, the findings reinforce the need for careful clinical verification.
Importantly, these results come from a separate clinical study and are not findings about Abridge's performance within the University of Texas System.
Reviewing a Clinical Note Is Not the Same as Auditing It
This distinction deserves greater attention as healthcare AI becomes integrated into clinical workflows.
A documentation review may focus on grammar, formatting, completeness of sections, and readability.
Aclinical AI audit goes further.
It evaluates whether the AI-generated information is supported by the original clinical evidence.
Consider a hypothetical encounter in which a patient reports intermittent chest discomfort, shortness of breath, and a history of cardiovascular disease.
The AI-generated note accurately describes the chest discomfort but omits the shortness of breath.
The note might appear organized, professional, and clinically reasonable.
However, the omitted symptom could be significant to subsequent clinical decisions.
A clinical AI audit would compare the generated documentation against the available source information, identify the omission, assess its potential clinical significance, and ensure the discrepancy is addressed through the appropriate review process.
A polished clinical note is not proof of an accurate clinical note.
Who Is Responsible for Checking the AI?
The American Medical Association recommends that physicians review and approve AI-generated notes before they are finalized. Its guidance also encourages healthcare organizations to establish reporting mechanisms for AI-related problems and conduct periodic audits comparing generated notes against encounter recordings and summaries.
These recommendations highlight two distinct responsibilities.
The clinician's responsibility is to verify that the documentation accurately represents the patient encounter before approving it.
The healthcare organization's responsibility is to establish processes for evaluating AI performance, identifying recurring errors, monitoring risks, and addressing problems that may affect patient safety.
Routine clinician verification and organizational AI quality auditing serve different purposes. Both contribute to responsible implementation.
An individual clinician may correct an error in one note. A structured audit can help determine whether similar errors are occurring repeatedly across encounters, specialties, or patient populations.
Clinical AI Auditing Is Becoming a Practical Healthcare Skill
For nurses and other healthcare professionals, the expansion of AI documentation creates an important area for professional skill development.
Healthcare AI auditing draws on capabilities that experienced clinicians already use: reviewing clinical records, identifying inconsistencies, assessing documentation against established criteria, and recognizing information that may affect patient care.
Applying these capabilities to AI-generated output requires additional discipline.
The reviewer must understand the intended function of the AI system, evaluate the quality of the available source information, compare the output against that evidence, identify unsupported conclusions, and document discrepancies according to defined audit criteria.
These skills may support work in clinical AI quality review, AI model evaluation, healthcare AI auditing, and organizational oversight.
The objective is not to replace the clinician's judgment. It is to establish reliable methods for evaluating whether AI technology is performing as intended.
The Next Question for Healthcare AI
The University of Texas System's agreement with Abridge represents another step toward broader adoption of AI-assisted clinical workflows.
The potential benefits of reducing documentation burden are meaningful. So is the responsibility to ensure that efficiency does not come at the expense of documentation accuracy.
As more healthcare organizations adopt AI-generated clinical notes, measuring success should involve more than how quickly documentation is produced or how much time clinicians save.
It should also include whether the information is accurate, clinically relevant, appropriately supported, and subject to effective human oversight.
AI can generate the clinical note. Healthcare professionals must still establish whether the note accurately represents the care delivered.
That is the difference between using healthcare AI and responsibly overseeing its performance.
Sources and Further Reading
Abridge (October 8, 2026).Abridge Announces Enterprise-Wide Agreement to Expand AI-Enabled Care Across the University of Texas System.
Fierce Healthcare (October 9, 2026).Abridge, University of Texas System Enter Enterprise-Wide Agreement.
Clinical Research (2026).Quality of Clinical Notes Created by Ambient Listening Generative AI: Pragmatic Prospective Pilot Study.
American Medical Association.Governance for Augmented Intelligence: Establish a Governance Framework to Implement, Manage, and Scale AI Solutions.
AMA Journal of Ethics (2025).How Should We Think About Ambient Listening and Transcription Technologies' Influences on EHR Documentation and Patient-Clinician Conversations?.
Published by Nurse Ed. | Healthcare AI Auditing Education
