Unknown reason why AI research fails.
We have no freaking idea, but we imagine that if we have a restaurant and cook well, we only have the beginning.
We also need to manage personnel and suppliers well.
And we suppose that many people will fail because they only think about the first part.
That’s what a child would think.
The second part is what someone adult thinks about and often suffers when it’s too late.
This is what often happens to us in calls and meetings.
When there are people who believe that the key to doing good Real World Evidence with AI is to apply very good mathematical models to quality data.
That’s true.
But it’s insufficient.
As people who have no freaking idea about anything and talk about everything, we think that it’s forgotten, really, forgotten all the time, that there’s a key piece.
It’s ugly, unpleasant. And no one wants to talk about it until you’re in the shit.
It’s precisely how to get healthcare centers to give access to their data.
And we’re not talking about ethics or law.
We assume that the data is anonymized, approved by the Ethics and Legal Committee.
We’re talking about the other stuff.
The IT guy who has no time.
The politician who dislikes the pharmaceutical industry and stops the project.
The hospital that doesn’t even know where its databases are.
The Clinical Director who says that the Committee has to sign it, the Committee that says that the IT guy should sign it first, and the IT guy who answers that, of course, he’ll sign it as soon as the Clinical Director signs it.
The labyrinth of data.
We work for a company.
It’s called Savana.
And we’re experts, very experts, in navigating that turbulent river.
The thing is, we have 26 people dedicated solely to this task and have worked with:
200 hospitals 11 countries 32 different EMR systems