The classic dogs and muffins image has been beaten in my mind by this. How do you tell the diference between dalmations and ice cream? Imagine how hard this will be for AI. This level of find discrimination necessary is why AI is not easy.
There are some companies working on the control of longitudinal patient records using blockchain. Can't believe I didn't drop that word in previously.
Thanks for the thoughts. I'm generally aligned with your thoughts on use of my data.
I think many of us in the industry are still wondering about access to data and who should have it. NHS Digital do a great job of protecting access to health records and as one of the companies that has earned the rigth to access the national records, I can say it is a very rigorous process to maintain that privilege.
More broadly we are seeing companies get in trouble for using data in the wrong way from individual hospitals, non-anonymised records being shipped (by mistake) to a company and other things that citizens in some countries (I'm looking at friends in Sweden) would find unacc
Absolutely. Also there is the rush to apply things at present which perhaps erodes some of the safety processes.
Your point is why I was involved in a project to deliver synthetic data to then test software against a dataset that would highlight the efficacy or otherwise of the results.
Hi @Clive Flashman. I suspect many of us, when told not to look up an ailment online, do the exact opposite. The availability of information has changed in our lifetimes beyond all recognition. However, the quality of that information has also changed. Previously there were limited number of experts and now we have sources at our fingertips. The danger is with misinformation and an inability to know what is correct and what is not. The vaxxer/anti-vaxxer argument is perhaps a prime example or the use of bleach and other products to combat Covid-19.
However, I think patient involve
I think there is potential to develop scenarios far quicker and more tailored to particular situations. So for example, you can create AI based images in bulk to show a clinician far more cases than they would normally see and build systems to keep people up to date and up to scratch. You can build subtler cases in bulk to help discrimination between different cases of an illness or disease. You can create synthetic data sets to test medical software and build in whatever bias you need to truly test something by packing the data with suitable case profiles while actual anonymised data may h
The wonderful team here at Patient Safety Learning think we need to talk about AI and the impact it can have on healthcare.
So I'll be putting up a few topic starters in here but feel free to use this space and start your own conversations.
AI means two things at the limit. It means software can change without instruction and the answers can sometimes change between a 'yes' or a 'no' for the same question.
So how do we build safe, dependable applications that incorporate AI?
How do we test them?
How do we approve them?
In the pandemic there is a rush to deploy so
A significant backlog of elective surgical cases has built up during the COVID-19 crisis. The freeze on elective surgery has produced a waiting list that may take years to clear.
In the US, the CDC has issued guidelines that "facilities should establish a prioritization policy committee consisting of surgery, anesthesia and nursing leadership to develop a prioritization strategy appropriate to the immediate patient needs".
According to the CDC, this committee should work around 'objective priority scoring'. The MeNTS (Medically-Necessary, Time-Sensitive Procedures) instrument is a
Great article and a very important topic Lorri.
We have just been named as one of the '10 Digital Health Ideas for a UK National Covid-19 Response' by Healthcare UK (a joint initiative of NHS England, UK Departments of Health and International Trade) and it would be very good to discuss how patient safety approaches can make a big difference in the crisis.
During the pandemic, we are deploying a risk-assessment tool, sythesized from our patient safety system and reductions in AKI of over 90% (publisihed approach in BJN and winning an HSJ Patient Safety Award) and HAP by 60%.