The Contraindications of High Tech Medicine

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.

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...

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

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.

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

Wow a cliffhanger article on the web? Bold move.

Tragically stupid, but bold.

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