
The Contraindications of High Tech Medicine - steven
https://medium.com/backchannel/how-technology-led-a-hospital-to-give-a-patient-38-times-his-dosage-ded7b3688558
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FD3SA
A very longwinded story which could have been condensed to the following: A
computer aided prescription system is not a super-intelligent AI doctor. Like
any other software, it will have bugs and errors. I would expect a physician
who uses technology extensively to understand the basics of software, along
with its shortfalls.

There were a dozen medical professionals who failed to do their jobs in
screening the patient's medication before administering it.

I'm always amazed when people rely on technology as a full crutch, and deem
the task the technology now performs to be "useless". So for example, if I get
lost on the road, knowing the sun rises in the east is useless because I have
the entire world mapped on my phone. But wait...what if my phone has no
reception, or if the battery dies? Whoops, turns out basic geography isn't so
useless after all.

Technology is not an excuse to become clueless and incompetent.

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Strilanc
Relevant paper: _Algorithm Aversion: People Erroneously Avoid Algorithms After
Seeing Them Err_ [1]

> _We show that people are especially averse to algorithmic forecasters after
> seeing them perform, even when they see them outperform a human forecaster._

> [...]

> _The aversion to algorithms is costly, not only for the participants in our
> studies who lost money when they chose not to tie their bonuses to the
> algorithm, but for society at large. Many decisions require a forecast, and
> algorithms are almost always better forecasters than humans (Dawes, 1979;
> Grove et al., 2000; Meehl, 1954). The ubiquity of computers and the growth
> of the “Big Data” movement (Davenport & Harris, 2007) have encouraged the
> growth of algorithms but many remain resistant to using them. Our studies
> show that this resistance at least partially arises from greater intolerance
> for error from algorithms than from humans. People are more likely to
> abandon an algorithm than a human judge for making the same mistake. This is
> enormously problematic, as it is a barrier to adopting superior approaches
> to a wide range of important tasks. It means, for example, that people will
> more likely forgive an admissions committee than an admissions algorithm for
> making an error, even when, on average, the algorithm makes fewer such
> errors. In short, whenever prediction errors are likely—as they are in
> virtually all forecasting tasks—people will be biased against algorithms._

1:
[http://opim.wharton.upenn.edu/risk/library/WPAF201410-Algort...](http://opim.wharton.upenn.edu/risk/library/WPAF201410-AlgorthimAversion-
Dietvorst-Simmons-Massey.pdf)

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mst
I would have preferred this to only be submitted after the other half was
published, given it's only tomorrow.

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jdminhbg
Without some kind of evidence around the rate of mistakes made by humans vs
computer systems, a story about one mistake isn't very compelling.

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st00pid
IT has only enabled copy-paste medicine. Iterating faster is not iterating
better.

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awalton
Wow a cliffhanger article on the web? Bold move.

Tragically stupid, but bold.

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vanattab
...To be continued? Are you fucking kidding me.

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fleitz
No fucking kidding, I just flagged this garbage article, post it tomorrow with
the rest of the article.

I don't want to read half a fucking article.

