Category: Artificial Intelligence in clinical research
Artificial Intelligence in clinical research. Weekly tips about how Artificial Intelligence support evidence generation in healthcare. The generation of RWE at multisite level using Natural Language Processing on Electronic Medical Records. Our team is now combining data scientists and clinicians with experience in clinical and translational research (lead by oncologists).
To really add impact, this is the way of Artificial Intelligence in clinical research:
- Sometimes the simplest way can also be the best one.
- And the best way is not a cut of a database, nor a group of preselected variables, nor a certain number of patients. It’s not that. And neither a very costly registry.
- The best way we can imagine is to retrieve the actual complete information about what is happening at the points of care.
- It’s having access to the complete medical records information.
- In order to get this, you basically need:
- A combined team of data scientists and clinicians with experience in research (in our case, lead by oncologists).
- A system able to retrieve information from any healthcare provider (as long as they have electronic medical records -EMR-, paper doesn’t work).
- Natural Language Processing, because 80% of the variables and outcomes are going to be in the clinical narratives’ free text.
2.5 million patients analyzed with AI.
When you watch an interview with a well-known actor, you realize that one thing they have in common is that they tend to be quite shy.
This may come as a shock at first, but what happens is that good actors are precisely shy people who became actors to hide their shyness behind a character.
It is as if, while they are acting, they rest from their social anxiety because the mask protects them.
These paradoxes or psychological compensations are interesting and they are everywhere…
and I think that’s what happened to me somehow, when I got into Artificial Intelligence.
I’m so impatient that it kept me from doing research.
And it bothered me because I think research is the most noble and interesting thing a person can do.
So I invented a world in which I could do research by being impatient.
Lots of data, lots of patients, but with a fraction of the effort.
Speed and immediacy, without spending a lot of time entering data.
Associations and correlations without having to spend 20 years in a practice to intuit them.
That’s why I often describe machine learning as “sensing machines”.
If we had not done all this with Savana, it would be impossible to have published the article we have just published called “LiverTAI, a retrospective analysis of EHRs through Natural Language Processing”.
And which has the peculiarity of having studied the patient journey of almost 2.5 million patients in terms of HCV testing.
2.5 million is a lot of patients.
Article about AI and Value Based Contracting.
That was in February.
And since they were still doing it in September, Ukraine used hackers to impersonate these women and thus located Russian army positions.
This reflection on limitless human stupidity reminds us once again that digital data is and will be around.
And that its use can be beneficial or dangerous, depending.
And that to walk on the Internet mindless is like letting people drive without a license.
Or like crossing the street without looking at the traffic lights.
You can’t do that.
AI is not good or bad, it depends.
What does it depend on?
On how you look at it.
Well, sometimes you’ll be using it to see things you can’t see with normal human eyes.
Or, to do things that you can do, but it takes too much time and effort, and it is possible to automate.
We have written an article.
We wrote it together with José Luis Poveda, Head of Pharmacy at La Fe, in Valencia.
We talk about the use of AI to measure the results of drugs in all patients and thus be able to make risk-sharing agreements.
In other words, you only pay for drugs that work.
But watch out, you charge well for those.
No self-respecting modern company at this point is interested in having a drug that doesn’t work.
Some call it providing value.
We call it not paying for the car if the car doesn’t work.
But it used to be very difficult, because you had to pick them up by hand.
Now a machine does it.
We’re going to try to make a first case for a value based contract using AI.
How to write a headline involving AI.
Look,making an AI algorithm has its own set-up.
Evaluating a drug in a ridiculous amount of time.
To write a book, to take care of an orchid garden or to decorate a living room, amateurs are often better than professionals.
What happens is that amateurs usually have a passion that you don’t find in those who do things as a way of life.
Because what you do for money and routine becomes mechanical, lacking creativity, without soul.
That’s why for many things I don’t believe in professionals, because it seems to me that sometimes professionals are like prostitutes to love; I hope f I’m making myself clear.
I see this with medical publications.
The “professionals” of the publications, that is, those who only dedicate themselves to doing studies, do not usually publish things that change clinical practice.
I’m not saying that what they research isn’t interesting, but often, it has little impact.
While clinicians, in addition to seeing patients, publish about what they see and, of course, they do have an impact.
At Savana we had a department dedicated to talking to clinicians.
And we closed it.
We removed it because departments are not necessary when something comes out on its own.
We are good at talking to doctors because we speak their real language.
That of having little time and a lot of desire to do things.
That is why our obsession, our obstinate obsession, is on how to shorten the time of the studies more and more.
How to have the data clean, refined,… as ready as possible so that when someone thinks of launching a question, we only have to execute it.
This is what happened with the article “Use of N-Acetylcysteine at high doses as an oral treatment for patients hospitalized with COVID-19”, done by pulmonologists in ridiculously short times, thanks to the fact that they had the data in Savana ready for the time of their question.