My Client Collapsed from Not Eating. The AI Cleared Her Risk Assessment

I recently tested an AI clinical documentation tool using a real session with a real client. I chose this client because I thought she was a safe test case. No acute crisis. A session I could anticipate. I wanted to see how the tool structured a note, what themes it captured, and how it compared to my own documentation.

She is not actively in an eating disorder, and has never had an eating disorder diagnosis, but she has a long-standing pattern that I monitor closely. She forgets to eat, and her anxiety makes it hard to get to the kitchen when other people are around. The pattern has been consistent enough to stay on my clinical radar.

Then she told me something she had never mentioned before.

She had been with her family all day, making food together. She had forgotten to eat and ended up collapsed and passed out on the floor.

Both of her parents, who are divorced, had separately pulled her aside and said the same thing: ‘This is serious, we need to address this.’

She came to the session talking about her parents' plan to find her a nutritionist. She knows I am a specialist in eating disorder treatment, so she did push back a little bit. We talked about her presentation. About the fact that her symptoms really don't look like a textbook eating disorder, but the behaviors are real, and the risk is real. I gave her a referral. I gave her psychoeducation about what extended restriction does to blood sugar regulation, about her family history of diabetes, and about what happens to the body when this pattern continues unchecked.

Here is what the AI-generated session note said about all of that.

Nothing.

Not softened. Not minimized. The entire clinical thread was absent. The note covered interpersonal conflict with a friend, driving anxiety, job applications, and positive family relationships. It covered those things well, actually. The structure was clean, the affect tracking was specific, and the MSE was thorough.

But the most clinically significant thing that happened in that session did not exist anywhere in the document.

If that note had gone to the dietitian I was referring her to, she would have received what appeared to be a routine referral. No collapse. No family concern. No long-standing restriction pattern. No clinical context. Just a name on a referral with nothing to work with.

That is the first problem.

The session also generated an auto-populated treatment plan. Here is where it gets more specific.

The treatment plan attributed modalities that were not used. The session note documented supportive counseling, Socratic questioning, and normalization. The treatment plan listed DBT, Interpersonal Therapy, and CBT as the clinical approaches. Those are not the same things. Attributing modalities to a record that do not reflect what actually happened is a documentation accuracy problem.

The Risk Factors and Safety Planning section read: "No current risk factors identified."

For a client who is underweight, has a documented history of restriction, and just disclosed that she passed out from not eating all day.

And then there is this.

The Client Participation and Consent field stated: "Client reviewed and agreed to the plan on [date]."

This client did not review this plan. This client did not agree to this plan. This plan was generated by AI after the session ended without any client involvement. That field is a fabricated consent record. In a licensing board review, an insurance audit, or a legal proceeding, that is not a documentation quirk.

That is a serious problem.

I want to be clear about what I observed overall. This tool has genuinely impressive features. The session note structure was detailed and easy to read. The product has a clear purpose. I think the founder is building something out of a genuine desire to support clinicians, and that matters.

But behavioral health is not a standard documentation domain.

As therapists, we work with many populations where minimization is a symptom, where what doesn't get said is as clinically significant as what does. Our documentation goes to other providers who need accurate context to do their jobs. When a tool misses a disclosed medical event, attributes modalities that weren't used, clears a risk assessment for a client who collapsed, and generates a consent record for a plan the client never saw, the problem is not a software bug.

The problem is that you cannot build accurately for a specialty you don't understand.

Founders in this space are solving a real problem. The documentation burden on clinicians is significant, and these tools are genuinely needed. But clinical specialization has to be embedded in how the product thinks, what it flags, and what it will and will not generate for a clinician.

You don't know what you don't know. In behavioral health, that gap has real consequences.

Jayme Scarfo is a Licensed Professional Counselor, Certified Eating Disorder Specialist, and CAMS-Trained clinician specializing in trauma, eating disorders, and burnout in high-achieving women and mothers. She writes about the clinical realities that mainstream mental health content tends to miss.