Been on the product side of a healthtech rollout for ambient AI documentation, the kind that listens to a patient encounter and autogenerates the clinical note. Doctors love it. Physicians on our pilot were almost evangelical about getting their evenings back, which I understand completely because charting is a soulcrushing time sink.
But here is what keeps nagging at me. The model is transcribing and interpreting conversations it was not originally trained on: slang, accents, chaotic ER noise, patients who talk around their actual symptoms instead of describing them directly. And the output gets reviewed for maybe 45 seconds before it gets signed and lives in the medical record permanently.
That review step is doing a lot of heavy lifting and I am not sure anyone is honest about how thin it actually is. Not blaming the clinicians. They are exhausted and the tool is supposed to reduce burden, so they are going to use it that way.
The cost argument makes sense on paper. Less admin time, faster throughput. But when I think about what a confidently wrong note looks like downstream, a missed allergy or a misattributed symptom sitting quietly in someone's chart, it gets uncomfortable fast.
Curious whether people here think the accuracy bar these tools are held to is anywhere close to high enough, or if we are just accepting a new category of error because the old category was also bad. Just my 2 cents.
[link] [comments]