3 ms·

Not sure how this proves me wrong. What it does prove is that analysis of data should not be left (entirely) to humans, who are inherently flaky. Dark irony of

Not sure how this proves me wrong. What it does prove is that analysis of data should not be left (entirely) to humans, who are inherently flaky. Dark irony of this situation is that _more_ data from additional test had a chance of saving the patient. The way it should have worked: based on previous labeled examples, computer system should have noticed the uptrend in potassium and flag this client for blood gas, and then make classification based on available and _additional_ data.

arkades 9y ago
It’s not the blood gas that would have saved him; just seeing the potassium trending up would have done it. Thing is, there was a context where that mattered. In others, it wouldn’t have. That’s why you need someone capable of interpretation to -look- at it. EMRs have done more to harm patients than help them, now that everyone constantly copy-pastes old patient hx before adding their notes (so nothing can be said to have been missed), people are constantly shifting through piles of be and missing the pertinent information. To rephrase: algos intelligent enough to be useful in the manner you imagine are a long way off from existing, but data deluge that harms patient care is -already here-. Don’t worsen the deluge until you improve the ability to digest and present it. That EMR “decision aids” aren’t anywhere near sophisticated enough to tell the difference in the myriad of situations we have is why docs fight so hard against those automated “flags.” We get piles of them, all the time. QI people call it alarm fatigue, they know it’s a thing, and yet... they do it anyway, because it sounds (like in your post) like the obvious thing to flag. It’s not that AI is inherently incapable of getting there. It’s that when it does is a long way off. Medicine looks a lot simpler from the outside.
- canes123456 9y ago
  I agree with you and are suspicious of the power of increasing raw data in the near term. I think there are a few dumb things we can do that have a chance at being useful. The usefulness of something like this would have to be extremely narrowly focused. For example, it can look at only potassium trends. We would first need to just monitor people for long periods of time and see what happens. We might see certain combinations of simple markers that make additional tests worth the risk and minimize false alarms. After this research, would we turn on the alarms and start worrying people.
- pcmaffey 9y ago
  Thanks for the insight. I wonder though about the difference between scattershot data collected in a hospital setting, vs personalized data collected over the normal, healthy course of a person's life. Establishing relevant baselines would go a long way towards eliminating noise and flagging real outliers.
- Lordarminius 9y ago
  I have nothing but agreement with all your points.

arkades 9y ago

It’s not the blood gas that would have saved him; just seeing the potassium trending up would have done it. Thing is, there was a context where that mattered. In others, it wouldn’t have. That’s why you need someone capable of interpretation to -look- at it. EMRs have done more to harm patients than help them, now that everyone constantly copy-pastes old patient hx before adding their notes (so nothing can be said to have been missed), people are constantly shifting through piles of be and missing the pertinent information. To rephrase: algos intelligent enough to be useful in the manner you imagine are a long way off from existing, but data deluge that harms patient care is -already here-. Don’t worsen the deluge until you improve the ability to digest and present it. That EMR “decision aids” aren’t anywhere near sophisticated enough to tell the difference in the myriad of situations we have is why docs fight so hard against those automated “flags.” We get piles of them, all the time. QI people call it alarm fatigue, they know it’s a thing, and yet... they do it anyway, because it sounds (like in your post) like the obvious thing to flag. It’s not that AI is inherently incapable of getting there. It’s that when it does is a long way off. Medicine looks a lot simpler from the outside.

canes123456 9y ago
I agree with you and are suspicious of the power of increasing raw data in the near term. I think there are a few dumb things we can do that have a chance at being useful. The usefulness of something like this would have to be extremely narrowly focused. For example, it can look at only potassium trends. We would first need to just monitor people for long periods of time and see what happens. We might see certain combinations of simple markers that make additional tests worth the risk and minimize false alarms. After this research, would we turn on the alarms and start worrying people.
pcmaffey 9y ago
Thanks for the insight. I wonder though about the difference between scattershot data collected in a hospital setting, vs personalized data collected over the normal, healthy course of a person's life. Establishing relevant baselines would go a long way towards eliminating noise and flagging real outliers.
Lordarminius 9y ago
I have nothing but agreement with all your points.