> 60% of evaluated AI Scribe systems mixed up prescribed drugs in patient notes, auditors say
Not mentioned, as far as I can see: the comparative human mistake rate.
Having seen a lot of medical records, 60% sounds about normal lol.
Not mentioned, as far as I can see: the comparative human mistake rate.
Having seen a lot of medical records, 60% sounds about normal lol.
> Medication errors were common (nearly 1 of every 5 doses in the typical hospital and skilled nursing facility). The percentage of errors rated potentially harmful was 7%, or more than 40 per day in a typical 300-patient facility. The problem of defective medication administration systems, although varied, is widespread.
https://jamanetwork.com/journals/jamainternalmedicine/fullar...
> In all, 91 unique studies were included. The median error rate (interquartile range) was 19.6% (8.6-28.3%) of total opportunities for error including wrong-time errors and 8.0% (5.1-10.9%) without timing errors, when each dose could be considered only correct or incorrect
(And if you already see 60% error rates in standard, pre-AI note taking, how does that not translate into many deaths and injury? At least one country's health system in the world should have caught that)
Presumably most doctor's visits are a one-problem-one-solution-one-doctor type of thing. Done deal, notes are never read again. So that alone would explain why high rates of errors doesn't result in injuries or death very often.
Any injury or death caused by poor notes would have to occur when mistakes are done if you're followed for a serious chronic condition, or if you're handled by a team where effective communication is required.
Because most of it is just written down and never looked at again until there’s a lawsuit or something.
The management human who offered the bad tool to the other human is responsible.
The robot cannot be responsible in place of us.
I do wonder if people would be pushing AI so hard if their organizations were planning to hold them accountable for mistakes the AI made
I bet if that were the case we'd see a lot slower rollout of AI systems
Does it?
- Speech recognition and frontier models are continuing to get better at handling these types of conversations across accents, languages and specialties. The trend is obvious and clear here. Compare GPT 4 with Opus 4.7 and there is no contest. I'd even take GPT 5.4 nano over GPT 4 right now. So, yeah, they have been improving and, yeah, they will keep on improving.
- The pipelines these models are being built into are getting much more sophisticated than just 'transcribe with x and have GPT XX clean it up'. The people building these things aren't standing still. Even if they did keep using the same models the pipeline improvements would make things get better over time. Add that in with the model improvements and the gains are even greater.
- The companies doing this work are seeing more and more edge cases. Data matters. More and more practitioners are using these things. That means more to learn from. It also means more stories of things being wrong. If you cut your error rate in half but increase your customer base by 10x then you will be hearing about 5x the problems. We are seeing that right now.
- Providers are starting to adjust to the technology (repeat areas they know may cause trouble, adjust their audio setups, etc etc) Just like any technology both sides shift and it matters. The first users were champions. The second wave were mixed between champions, haters and people that didn't care yet. Now people are really starting to count on this technology. They know it isn't a fad and isn't going away and are actually using it day to day to get their work done. This means they are adjusting to it as needed to get to the next patient/note/etc.
This stuff is just a few years old and the gains are obvious and massive. They aren't going to suddenly stop improving. There is an argument that they will asymptotically approach some level of utility, but we are still gaining quickly right now.
What do you base this on?
As someone who can both see the amazing things genAI can do, and who sees how utterly flawed most genAI output is, it's not obvious to me.
I'm working with Claude every day, Opus 4.7, and reviewing a steady stream of PRs from coworkers who are all-in, not just using due to corporate mandates like me, and I find an unending stream of stupidity and incomprehension from these bots that just astonishes me.
Claude recently output this to me:
"I've made those changes in three files:
- File 1
- File 2"
That is a vintage hallucination that could've come right out of GPT 2.0.
That's because, despite the many claims to the contrary, the models haven't actually gotten any smarter. They are still just token prediction engines at the end of the day, without any understanding of what they are doing. That's why one should not rely on them.
I'm not sure, but it seems to me that if scale or small architectural tweaks were going to solve comprehension, they would have done so by now.