Matthew Sappern: There are a lot of people who feel that artificial intelligence and machine learning is much further along than it really is. There is so much data out there right now. I think that’s an important first step. There’s data and there’s actionable data or what some of my colleagues call the ground truth – information that’s been curated in a way that you’re confident that it’s representative of what it needs to be.
If you’re not using that curated data to teach these machines, then you’re really not generating anything of real value. There is a lot of hard work in coming up with even a nominally accurate algorithm using artificial intelligence. It has taken us years and years to finally get to a point where we’ve got something that we’re confident about. It is not for the faint-hearted.
Sramana Mitra: Also I think there are a lot of issues around how you get the data out of existing systems and then build AI algorithms out of that. There’re a lot of adoption challenges. With all that being said, it’s not rocket science either. It may be not that far along today in 2018, but give it five years, it’s going to be moving very fast.
Matthew Sappern: The speed of advancement is important. I think it’s important to underscore that you will always need people who really understand this data at the front end of these processes. I’ll share some other huge challenges we have in healthcare especially. You just touched on it – the adoption of these technologies.
Sramana Mitra: Adoption is a big challenge, yes.
Matthew Sappern: I walk into rooms with nurses and doctors and I say, “How many of you used Amazon this week or interact with Alexa?” Everyone raises their hand. Everyone is interacting in some type of algorithmic approach. But they are so resistant, at first blush, to deploying it in their work. Legacy tools will always lead to the same set of outcomes.
The bottom line is, you’re not going to get much better unless we start doing things differently. It’s so logical, yet it’s so hard to get people to accept that. Our second biggest challenge is getting people to embrace it and getting them to understand that it’s compelling for them to use and can make their lives easier. It’s not a threat because ultimately there are things that these tools don’t do. They are not terribly empathetic. They don’t deal well with exceptions. These are things that are so important.
Every first meeting I have in every health system, I need to disavow them of the fact that I’m a vendor of robots coming to take their jobs away. In reality, I’m a vendor of an application that’s going to help make their jobs better and help make them more effective at their jobs.
Sramana Mitra: Very good. Thank you for your time.