"AI in Healthcare: Digging in the Wrong Spots"
[Overseas friends: how much of this is true in your country?]
At the HL7 Annual Meeting I just saw what's *easily* the most useful talk about AI in healthcare I've ever seen. It was by John Zimmerman of Carnegie Mellon and focused (FAR more than most of LinkedIn) on which uses actually GET anywhere, which uses CAN actually achieve a useful outcome. That focus is diametrically opposed to the wet-dream culture of seeking unicorns - and PATIENTS need y'all to achieve something USEFUL. Bottom line is to stop looking at the hardest, most amazing things, even though they're fascinating.
While listening intently I could only capture a small fraction of points to share here but I hope to get much more from him. A few examples, not prioritized, shown in the photo composite I cobbled together:
1. Despite our imagination that AI will figure out everything from EMR access, IT'S HUGELY DIFFICULT, because the freaking data is heavily skewed, e.g. to whatever is billable. So, although sepsis is a huge killer, IT'S OFTEN NOT NOTED in ICU charts, because it's not billable(!!).
2. He cited Cassie Kozyrkov who has a great YouTube course on "making friends with machine learning - she says to find a really practical application for AI, you need to think of it as an island full of drunk people :-) Eager and friendly and usually pretty good but REALLY likely to make mistakes. So think: what kind of tasks can you give them? (I'm again reminded of the "trust but verify" rule from our paper at Division of Clinical Informatics DCI at BIDMC)
3. A taxonomy of 40 *commercially successful* AI products, segmented by :
- How perfect does it need to be, for success? (Y axis)
- How perfect *is* AI at the task? (X axis)
LOOK: 25 of the 40 are in the left column, where the AI is moderately good at it but not brilliant!
So think: what applications can you find where it'd be valuable to be wicked fast and PRETTY smart but not perfect?
He also had a photo of a grizzly bear and cited the 2022 study that said 6% of people think they could win a fight with a grizzly ... and he said he's pretty sure all of them are AI product managers :) :)
I'm going to take this thinking into the rest of the meeting week and beyond: what CAN we do with genAI, practically, without seeking perfection?
Grace Cordovano, PhD, BCPA, Grace Vinton, Liz Salmi, Danny Sands, MD, MPH, Brian Ahier, Jan Oldenburg, Kim Whittemore, Anna McCollister, Amy Price MS, MA, MS, DPhil, James Cummings, Daniel Kraft, MD, Matthew Holt
Providing HIPAA Compliance solutions for health tech leaders. Solving what software can’t.
4wI think this is a natural human trait, to believe what anyone else says over their own feelings. Its a symptom of not trusting\believing in yourself. Social media has amplified it as well.