Tag: Real-World Evidence generation

Generation of RWE with the most realistic clinical data through the application of AI capabilities matched with scientific capabilities.

Tip to find patients with a certain pathology.

A fellow doctor recently told me how he once was on a flight in business class with 30 oncologists and that, when asked “is there a doctor on the plane?, they all bowed their heads and none stood up.
I find this surprising because I know more doctors who find it natural to help than the opposite. 
And furthermore, it can only be explained by laziness, not by legal risk, because when I was at the hospital and we were the reference site for the airport (many inflight strokes were brought to us), reviewing the subject for a clinical session, there was no precedent of complaints against doctors for assisting on an flight.
I once took care of a first seizure right next to me. 
I did nothing more than reassuring, but isn’t it a big coincidence that you have a first seizure on a flight and the person next to you is a neurologist?
And again, and perhaps one of the most beautiful stories of my life, I decided in 60 seconds like in a movie (because that was the time they gave us at the New York airport before losing the slot to leave) that a flight attendant with a decreased level of consciousness had nothing serious and that we could take off. 
The funny thing is that the flight attendant had a history of alcoholism and apparently she was gambling with the custody of her daughter, and if we had not taken off, with the millions of dollars in losses that this would have entailed, it is likely that her life would have been derailed.
I´m not making up the story.
This was told to me months later by the pilot, whom I befriended and remain friends with to this day. 
Things connect, my friend, and it’s not like Steve Jobs on Linkedin talk; they really connect. All the time.
That’s why it’s better not to keep your head down too much.
Or at least to be as little of an asshole as possible.
And I’m an asshole everyday and sometimes I don’t recycle. 
But the chained and collaborative stories lead me to the Pharmacy Service of a big hospital (Sant Pau in Barcelona), where they have always welcomed our AI technology with interest and have persisted in its use until, finally, we have been able to demonstrate that with Savana we have found 257 patients of a certain pathology that they were looking for (I will not say which one in case of privacy), when with their gold standard information system they found 206 patients.
And this is less glamorous than attending stewardesses at JFK with 300 people looking at you and then getting out of the plane like a bullfighter.
But I think the thing is much more serious and more important than the ego of a simple doctor. 
It’s about something as basic as using technological tools that already exist in other fields to count patients.
Counting patients. That’s what we do at Savana sometimes.
How small. 
And how big. 

2.5 million patients analyzed with AI.


Article about AI and Value Based Contracting.

We don’t know if you know that Russian soldiers were caught flirting with Ukrainian women on Tinder.

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.

At what?

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.

When you have your AI model done, validated and published, it’s time to communicate it to the world.
And the important thing to realize is that you can go from less sensationalist to more sensationalist as you see fit. 
Level 1: scientific purist.
The biomarker NG17 may have implications for the development of hypophosphatemia.
Only later in the body of the text will you mention that Random Forest (a form of machine learning) was used for its development.
Pros: disease experts will respect you.
Cons: you reach 0.001% of the audience.
Level 2: you still retain dignity, but you are already starting to have fun.
Researchers develop a predictive model for the development of hypophosphatemia.
Again, it is in the body of the message when you mention that machine learning has been used. Moreover, you still stress that it is researchers, not machines, who do things.
Pros: no one spits in your face at the congress.
Cons: you are still only reaching people with an interest in this pathology.
Level 3: it’s all about impact, not accuracy.
A machine learning model is able to predict the development of hypophosphatemia.
Bye bye humans. Here we have already crossed the abyss, by putting the machine learning concept in the headline.
Pros: journalistic arrival level is high and there will be many likes on Linkedin.
Cons: your fellow residents hate you .
Level 4: what the hell, we are here to play.
An Artificial Intelligence predicts who will develop metabolic diseases.
There is no shame. AI is presented as an individual and autonomous entity that can destroy humanity at will. Moreover, you generalize, as if it can maneuver in any disease. 
Pros: you might get it published in general media.
Cons: you can never set foot in a hospital or apply for a serious grant again in your life.

Look,making an AI algorithm has its own set-up. 

Then you have to publish it, communicate it, use it with the agency or administration you are interested in… and even implement it in clinical practice.

Evaluating a drug in a ridiculous amount of time.

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