AI tool cuts unexpected deaths in hospital by 26%, Canadian study finds

I would not. High false alarm rates are a problem in all sorts of industry when it comes to warnings and alerts. Too many alerts, or too many false positive alerts cause operators (or nurses in this example) to start ignoring such warnings.

No, many people working in clinical units wouldn't. Because of what might happen on false alarms. What GP said: more meds, more interventions. It's not clear at all whether such systems would help with current workflows and current technology. One of the most famous books about medicine says that good medicine is doing nothing as much as possible. It's still very true in 2024, and probably for a long time still.

I like this analysis, although I come to a different conclusion: if AI can allow nurses to manage 10x as many beds (30 vs 3), a hospital can now let go 90% of its nursing staff. Wouldn’t you?

False positives are not harmless. Textbook example is breast cancer screening https://theconversation.com/breast-cancer-screening-in-austr...

It's not just a false positive rate, but also the rate you train nurses to ignore alerts.

Would you suffer serious nonlethal complications from false alarm to (maybe) save your room neighbour that you've never met before? This wouldn't be captured.

You can't automate it. You have to look at the data and charts to figure out the specifics you want and then you plug and chug. I haven't looked deeply at this though but whenever researchers use relative risk and it shows a profound effect, I always calculate the absolute risk to make sure that the intervention is effective.

Many researchers go to relative risk because it shows better results!

I know everyone hates "I asked ChatGPT" comments but...I feel it's relevant here.

It came to roughly the same conclusion as the gp comment when provided with the study PDF.

https://chatgpt.com/share/66eb09e3-7a74-8008-afa8-3b60161d24...

(Though obviously this approach still requires you to go and look at the PDF yourself to make sure it isn't making anything up)

>How can we automate this kind of analysis?

Happy to talk about it.

Are you in the Healthcare industry?

Interesting, and I think it makes sense.

In an ideal world, the nurse to patient ratio would be high enough that patients could be seen on regular rotation frequently. I've never been in a hospital where this was the case. So a system that can correctly prioritize resources for critical cases even if it's pulling resources away from non-critical cases will probably result in a net improved outcome.

I'm also shocked at how readable this wikipedia article is relative to most articles about statistical methods.

Fun bit of trivia (though depressing) from the wiki

  The term "MARS" is trademarked and licensed to Salford Systems. In order to avoid trademark infringements, many open-source implementations of MARS are called "Earth".

Multivariate adaptive regression spline saw the hype and pivoted to AI

Canadian hospitals have largely the same cost cutting and "efficiency" measures as their US equivalents. Departments have budgets that they have to fight for, feifdoms compete for scraps, and there is an enormous and perpetually growing admin/executive side that is taking more and more of the budget. Couple this with governments such as Ontario that "starve the beast", so to speak, forcing hospitals to squeeze further.

I don't think we should ever take any sort of superior position on this. The same motivations and outcomes occur.

Having said that, efficiency is good, especially with an aging population that will require more and more care. Resources are limited, so applying them in the most effective, efficient way possible is always a win.

I totally agree. The tie to whole patient outcome is stronger in that system. Still not perfect, but a lot more direct for sure. It may be an odd thing to say, but because of that there is an argument that the Canadian system is closer to a true free market healthcare system with the patient as the consumer than the US system.

No, my argument isn't that this wouldn't be used, it is that by using it there will be overage in quality of care above 'good enough' for the same or similar cost. That will result in the most expensive resources being reduced until quality of care is back to 'good enough' at less cost. It isn't a stretch to imagine that a tool like this would lead to a reduction in nursing staff since they can make rounds more effective and now don't need as many people to get the same level of quality job done.

it's outcome quality per amount paid

Outcome is not one thing. The patient wants better health. The provider has an interest in profits. The government has an interest in optics…well anyone using “AI” does.

You realistically can’t starve the beast that is healthcare. The costs will go up disproportionately, and they do, in basically every advanced economy: https://en.m.wikipedia.org/wiki/Baumol_effect

> the female contraceptive pill is free to whoever wants it. Because that is a whole lot cheaper than extra (unwanted) pregnancies.

Abundant contraception encourages and promotes promiscuity

> the NHS has spent money on reducing smoking because that's cheaper than dealing with the health effects.

Reducing tobacco usage makes more room for nicotine OTC and vaping to replace it. Among other stimulants.

The early death of smokers tends to save a long, expensive period of end-of-life care. I believe smoking deaths reduce health care costs, ironically enough.

> Because that is a whole lot cheaper than extra (unwanted) pregnancies.

From a nation which should know better after being so very thoroughly roasted by Mr. Swift some few years ago: https://www.gutenberg.org/files/1080/1080-h/1080-h.htm

Its not a personal judgement to say private equity buying up hospitals has shifted the priorities of the hospitals from care to profit.

"Depending on the individual" here means, depending if you're a share holder, or the patient dying on the cot.

That was a rational capitalist argument. If a company has an opportunity to make money, they will. Any better than 'good enough' isn't rational and the people running that company should be fired. In the long term the entire industry will slowly adopt this and the standard of care may rise slightly as these gains are used for competitive advantage instead of pure profit but that will take a while at best and relies on a true free market, which healthcare definitely isn't.

IME anything that looks vaguely like a cost center often has something vaguely resembling an acceptable service/quality level and people typically aim to achieve that with the lowest cost. It’s not at all uncommon to cut budgets/headcount when that goal is exceeded noticeably.

Isn't regression a basic AI? Most traditional ML algorithms are advanced regression models (how we draw the line/planes)

I agree. That's a much more compelling use of statistics than the shitty neural nets and whatnot we are usually served in healthcare.

In AI applications, especially those involving predictive modeling, MARS can be used to improve the accuracy of predictions. For example, MARS models are used in time series forecasting, financial predictions, environmental modeling, and other domains where relationships between inputs and outputs are complex and non-linear. By adding time-awareness, the model can handle time-based data more effectively.

I am tired of people redefining AI to exclude fully viable and useful technologies in favour of the latest hype. AI should be a functional concept, not defined by technological choices.

Most modern AI does even less. It simply flows values through a graph. No decision is ever made. The consumer of the network interprets the result and makes a decision.

I heard AI is just if-statements, so perhaps the reverse is true.

I think the real question is why is this being reported on. There are always medical advancements, but because this one gets chosen as a news story because "AI" in the headline gets clicks.

Because healthcare (and banking and ....) are horribly behind on tech. We have life saving devices in hospitals still running Windows 95 as an OS. Also, the main problem in healthcare is misaligned incentives. As said elsewhere in this thread, this kind of tech will get when it enables cost reductions larger than its costs.

Because tech people don't understand how healthcare systems work, and reciprocally healthcare workers have neither the education nor the time to understand new tech. The result is what you get today: people from both sides shouting at deaf ears on the internet. Also, the usual corporate culture issues.

This was traditional automation. They used a bog standard statistical techniques and called it AI for fundraising purposes.

because its not useful ?

"A difference-in-differences comparison between GIM and subspecialty units demonstrated no statistically significant difference in outcomes"

Machine learning is indeed extremely good at pattern recognition, but I wouldn't trust an LLM to reliably identify patterns, especially in a medical context. As other commenters have said, this article is evidence of classical methods continuing to be useful.

This doesn't make sense on many levels. "Hospital IT" does not code the hospital EHR systems, just like the airport doesn't code flight management systems.

These are life-long software engineers, just like others reading this comment, using the best tools at their disposal to engineer lifesaving software. They're not using "regex" to develop algorithms for monitoring patients (???), and frankly that suggestion is so wild that one has to assume you don't know anything about algorithm design at all.

An LLM literally hallucinates incorrect answers by design and struggles to get extremely basic math and spelling correct.

You're welcome to put your literal life in the hands of a hallucinating english generator, but when it comes to healthcare, I want a "0% LLM" policy. LLM's will be the cheap things that offer substandard care to poor people, while the wealthy and elite enjoy personalized and human-centered care.

This sentence contains two diametrically opposed hypothesis.

LLM's and accuracy in one sentence in the context of quantifying thresholds is stunning.

LLM's don't have a concept of numerical accuracy.

This method has nothing to do with LLMs or even "deep learning" though.

Medicine isn't science because science is not as advanced as many would think. The lack of workplace integration is also a big factor.

I don't think we discourage second opinions, except maybe in some for-profit structures. The bad idea is to have multiple people making decisions in parallel. I'm not in the US, though.

Regarding advocacy, I don't think it's so crazy. It's very good to have a valid interlocutor when the patient is diminished. Also, hospitals are big systems with limited personalization. If someone's there to call out the system when it's trying to shoehorn too hard, it's also very good.

Enjoy the privilege of seeing multiple doctors as long as you still can. With steady cost reduction (AI, automation, less effort per patient) and increase in medical authoritarianism ("expert said so") that privilege is on thin ice. In the UK it's already normal to have a single area-designated doctor you're allowed to go to, and that doctor is also a gatekeeper to refer you to specialists. Hope he likes you! Beyond that, AI diagnosis would likely require an extensive medical online profile of you. Such e-med profiles obviously already exist in various countries, as opt-out features. In the name of cost reduction through automation, I'll be so free and call it: These profiles will become mandatory over the next ten years. Either way, good luck getting a second opinion once a false diagnosis ended up in your file or once AI continuously misidentifies a pattern present there.

I was semi-retired two years ago and decided to do a LPN program to work part time, do something physical, something that felt like a moral win and good for society.

I would have had no problem intellectually getting through the program but quit after the first night in a hospital.

Anyone sitting at a desk can not understand how tough and miserable a nursing job is. Everyone is basically miserable and stressed out. The work is completely thankless, disgusting and dangerous with personal liability on the line if you make a mistake. Everything that we take for granted in an office setting just doesn't apply in a medical setting.

I eventually just went back to a bullshit project management job, for more money than a nurse of course. This is obviously part of the problem.

It is easy to complain about the system when it is someone else who has to help grandma to the bathroom. There is no easy solution for any of this given the demographics. It is basically a disaster.

Yeah, I'm not sure how this qualifies as AI outside of marketing, but wanted to get ahead of the people whose opinions would be biased by the current en vogue LLMs.

Well, personally I'd like to read that novel!

> Across the board thresholds is exactly what we usually have. I'm not so sure about the false positives being so high

The article would have been stronger with those numbers. But I wouldn’t be convinced that a high WBC count for an average ER visitor would have been sensitive enough to trigger an alarm. The prior knowledge that it’s a cat bite is important.

Well, maybe it would be even more efficient to have enough staff to keep you alive...

What is an example of a computer that doesn't employ AI, by this definition?

Muh AI shill

Either you go with America and get bankrupted by medical care if you don’t have excellent insurance, or you go to Canada or Europe where the average doctor is paid 1/3rd as much and there are significant waiting periods for non immediately necessary procedures. Heads I lose, tails you win.

People wonder why folks hate doctors or get “white coat” syndrome. Same shit from dentists wondering why everyone hates them.

Visit a hospital and see how measurement samples are taken. Your idea might be applicable with 10x the budget.

Well, calling this AI seems like a long shot. What seems causal here is 'warn early', and indeed I'm sure it would work even better if you outputted a warning displayed full screen on the nurse's phone. It's quite possible you could have the same effect with trivial thresholds instead of a stat model. Still, I'd say it's indeed a good use of computers in general to produce targeted warnings.