‘Please, please proofread your AI notes’: A patient’s plea highlights a growing documentation risk
Industry Buzz
This exact situation is why the ‘AI chart summary’ tools give me pause. In my experience I have yet to see a single patient’s chart that doesn’t contain clinically relevant inaccuracies that it takes a human to sniff out.
—@Eastern-Ad-3586 via Reddit
For many physicians, ambient AI scribes have quickly gone from novelty to necessity. The promise is compelling: less time typing, more time with patients, and fewer hours spent finishing charts after work.
But a recent Reddit post from a physician assistant (PA)—writing not as a clinician but as a patient—offers a reminder that even small documentation errors can have consequences that extend far beyond the medical record.
“Please, PLEASE proofread your AI notes,” wrote Reddit user and PA @garden-armadillo. Currently on medical leave due to a serious illness, they described discovering multiple inaccuracies in notes generated by ambient listening software during visits with specialists and primary care clinicians.
The mistakes were the kinds of subtle errors that can easily slip through a busy clinician’s review:
Incorrect symptom onset dates
Acute problems documented as chronic, preexisting conditions
Key clinical details inaccurately characterized
Under ordinary circumstances, these discrepancies might be frustrating but manageable. However, for a patient navigating short-term disability and medical leave, the consequences were much larger. “Now my record is being scrutinized, and these obvious errors are creating so many hurdles,” they wrote.
The situation became even more complicated when other clinicians copied forward the inaccurate information into subsequent notes, effectively transforming a single documentation mistake into a longitudinal record problem.
Related: AI scribes hit the ‘modest savings’ chapter—humans still write better notesThe copy-forward effect
Physicians have long understood the dangers of copy-and-paste documentation. AI-generated notes may introduce a new version of the same challenge.
Once an incorrect detail enters the chart, it gains an aura of legitimacy. Future clinicians reviewing the record may assume the information has already been verified. The error becomes self-reinforcing, appearing repeatedly across encounters.
A symptom that began 2 months ago can suddenly appear to have existed for years. An acute event can become a chronic condition. A misunderstanding can evolve into an established “fact” in the chart.
The Reddit post serves as a reminder that AI may accelerate documentation, but it can also accelerate the spread of inaccuracies.
“This exact situation is why the ‘AI chart summary’ tools give me pause. In my experience I have yet to see a single patient’s chart that doesn’t contain clinically relevant inaccuracies that it takes a human to sniff out,” wrote Reddit user and HCP @Eastern-Ad-3586.
“This is why I don’t use an AI scribe. It creates more work for me to now have to scan, review it for mistakes, when I can just do it ‘right’ the first time. Keeping it short and concise avoids the need for me. If I need it more verbose and detailed for a ‘pleasant’ patient, I’ll typically addend it out of the office or in between patients,” wrote Reddit user and neurologist @BurstSuppression.
Why small errors matter
Many clinicians evaluate AI documentation primarily through a clinical lens: Would this mistake affect diagnosis or treatment?
But patients increasingly use their medical records for purposes that extend beyond direct clinical care:
Disability claims
Workers’ compensation cases
Prior authorization requests
Life and disability insurance reviews
Employment accommodations
Legal proceedings
In these contexts, seemingly minor wording differences can become highly consequential. A discrepancy in symptom onset, for example, may raise questions about eligibility for benefits.
Characterizing a condition as chronic rather than acute could alter how a reviewer interprets causation, severity, or timing. What looks like a harmless documentation shortcut inside the EHR may become a major obstacle for a patient months later.
Related: AI scribes promised to reduce EHR burden—are they delivering?The need for human review
One of the most common misconceptions surrounding ambient AI is that the technology’s primary value is note generation. In reality, the most important step remains clinician verification.
Most ambient systems perform well most of the time. But medicine is full of nuance, ambiguity, interrupted conversations, and contextual details that even advanced AI systems can misinterpret.
For instance, a patient may say their symptoms started “around Christmas,” and the AI converts that into a specific date. Or a patient distinguishes between a new flare and a long-standing condition, and the distinction gets blurred.
A useful perspective shift
The most striking aspect of the Reddit post is that it came from a healthcare professional who suddenly found themselves on the other side of the exam room.
As clinicians, many of us think about documentation primarily as a tool for communication among healthcare professionals. Patients, however, often experience the medical record differently. For them, the chart can determine access to benefits, financial support, workplace accommodations, and sometimes even credibility.
Ambient AI is proving to be a powerful documentation tool. But this patient’s experience is a reminder that efficiency gains are only valuable if accuracy keeps pace. The technology may write the first draft, but the physician’s review is what makes it trustworthy.
Related: AI tools that died once they met the reality of clinical workflowWhat physicians can learn
As healthcare organizations continue deploying ambient documentation tools, physicians may want to keep several principles in mind:
Review chronology carefully. Dates, symptom duration, and timelines are among the most important details to verify.
Check problem characterization. Ensure acute issues are not inadvertently documented as chronic conditions, and vice versa.
Be cautious with copy-forward practices. AI-generated content should not automatically become the foundation for future notes without independent verification.
Remember the nonclinical audience. Insurance reviewers, disability evaluators, attorneys, employers, and patients themselves may all read the note.
View AI output as a draft, not a final product. The clinician remains responsible for the accuracy of the record.