Comment by infamouscow | Hacker News Reader

Comment by infamouscow | Hacker News Reader

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As someone that built an EMR that sold to Epic, I think I can say with some authority these studies don't suggest this is ready for the real world.

While tech workers are unregulated, clinicians are highly regulated. Ultimately the clinician takes on the responsibility and risk relying on these computer systems to treat a patient, tech workers and their employers aren't. Clinicians do not take risks with patients because they have to contend with malpractice lawsuits and licensing boards.

In my experience, anything that is slightly inaccurate permanently reduces a clinician's trust in the system. This matters when it comes time to renew your contracts in one, three, or five years.

You can train the clinicians on your software and modify your UI to make it clear that a heuristic should be only taken as a suggestion, but that will also result in a support request every time. Those support requests have be resolved pretty quickly because they're part of the SLA.

I just can't imagine any hospital renewing a contract when their support requests is some form of "LLMs hallucinate sometimes." I used to hire engineers from failed companies that built non-deterministic healthcare software.

(Co-founder of PicnicHealth here; we trained LLMD)

Accuracy and deploying in appropriate use cases is key for real world use. Building guardrails, validation, continuous auditing, etc is a larger amount of work than model training.

We don't deploy in EHRs or sell to physicians or health systems. That is a very challenging environment, and I agree that it would be very difficult to appropriately deploy LLMs that way today. I know Epic is working on it, and they say it's live in some places, but I don't know if that's true.

Our main production use case for LLMD at PicnicHealth is to improve and replace human clinical abstraction internally. We've done extensive testing (only alluded to in the paper) comparing and calibrating LLMD performance vs trained human annotator performance, and for many structuring tasks LLMD outperforms human annotators. For our production abstraction tasks where LLMD does not outperform humans (or where regulations require human review), we use LLMD to improve the workflow of our human annotators. It is much easier to make sure that clinical abstractors, who are our employees doing well-defined tasks, understand the limitations in LLM performance than it would be to ensure that users in a hospital setting would.

Haven't read the whole paper yet, but what are the possibilities for academic and evaluation use of this model?

The answer is a little nuanced.

We train on real records, and even though they are de-identified in training we still have to keep the model closed and under careful management to protect against the possibility of information leaking.

We are, though, definitely invested in this corner of research, and want to be able to work with others to push medical AI forward.

Given that, the best model for us is to collaborate on an engagement-by-engagement basis. For now we'd look to find ways to do the work directly involving LLMD within our systems.

If you research in the field and have some ideas, I'd love to chat!

> Clinicians do not take risks with patients

Some nuance here — they absolutely take risks, but with informed consent.

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