CTO of healthcare org here.
I just put a hold on a new RPA project to keep an eye on this and see how it develops.
According to their docs, Anthropic will sign a BAA.
Sometimes, the AI is more accurate or safer than humans, but it still reads better to say "we always have humans in the loop". In those cases, we reap the benefits of both: Use the AI for safety, but still have a human fallback.
We haven't deployed a model like this, it's new.
I've done a ton of various RPAs over the years, using all the normal techniques, and they're always brittle and sensitive to minor updates.
For this, I'm taking a "wait and see" approach. I want to see and test how well it performs in the real world before I deploy it, and wait for it to come out of beta so Anthropic will sign a BAA.
The demo is impressive enough that I want to give the tech a chance to mature before my team and I invest a ton of time into a more traditional RPA.
At a minimum, if we do end up using it, we'll have solid guard rails in place - it'll run on an isolated VM, all of its user access will be restricted to "read only" for external systems, and any content that comes from it will go through review by our nurses.
In my case I have a bunch of nurses that waste a huge amount of time dealing with clerical work and tech hoops, rather than operating at the top of their license.
Traditional RPAs are tough when you're dealing with VPNs, 2fa, remote desktop (in multiple ways), a variety of EHRs and scraping clinical documentation from poorly structured clinical notes or PDFs.
This technology looks like it could be a game changer for our organization.
I'd bet that until we get the risks whittled down enough for larger organizations to adopt this on a wide scale, the biggest user group for AI automation tools will be at the level of individual workers who are eager to streamline their own tasks and aren't paid enough to care about those same risks.
I don't think this is going to work in that industry until local models get good enough to do it, and small enoguh to be affordable to hospitals.
So the solution to that is to add another layer of complex AI tech on top of it?
I’ve implemented quite a few RPA apps and the struggle is the request/response turn around time for realtime transactions. For batch data extract or input, RPA is great since there’s no expectation of process duration. However, when a client requests data in realtime that can only be retrieved from an app using RPA, the response time is abysmal. Just picture it - Start the app, log into the app if it requires authentication (hope that the authentication's MFA is email based rather than token based, and then access the mailbox using an in-place configuration with MS Graph/Google Workspace/etc), navigate to the app’s view that has the data or worse, bring up a search interface since the exact data isn’t known and try and find the requested data. So brittle...
Similarly I expect that once processing/searching laws/legal records becomes easy through LLMs, we'll compensate by having orders of magnitude more laws, perhaps themselves generated in part by LLMs.
