Sramana Mitra: We’re seeing an increasing amount of AI applications in the healthcare IT domain. I don’t know if you’re familiar with this announcement that we recently made of a European partnership with the European Institute of Innovation and Technology and their digital health arm. 115 companies from Europe are going to be accelerated within the One Million by One Million program. They all have different flavors of all this stuff going on.
Matthew Sappern: Whatever we can do to put care closer to the patient is pretty remarkable. These digital platforms have the triple ability to generate data, interpret that data, and deliver information directly to the patient in milliseconds. You’re seeing more and more people who are much more comfortable with these technologies and using these technologies in everyday life.
My father could never wrap his head around an ATM. Now I can’t even remember life without an ATM and I’m in my mid-50’s. God knows what my son and daughter are looking at that is going to be prehistoric relatively shortly. The current generation kids are much more comfortable with interacting with technology in many ways and more comfortable with understanding the data that comes from them.
Another space that I meant to mention before is, there’s such a significant growth in ambulatory surgery. It used to be that all surgeries were in the hospital. Now they are putting in hip, shoulders, and knees, and doing some significant internal organ surgery in an outpatient setting. That’s great and certainly cost-efficient. It helps the clinicians be more of the masters of their domain, but it’s still a picture of a single nurse managing over two or three patients who are coming out of anesthesia.
Sramana Mitra: I think that problem is rampant – not using data and making off-the-cuff decisions. Look at doctors. Primary care physicians spend 15 minutes with the patient. How can they even make sense of all the data that’s out there in 15 minutes?
Matthew Sappern: Yes, it’s crazy. Let’s talk about the challenges for a minute though. It’s a great cocktail chatter, as you can imagine. You have these conversations far more than I. The fact of the matter is machine learning and artificial intelligence is in a very nascent form right now. You will be stunned at the amount of people I talk to who take a terabyte of information and drop TensorFlow on top of it, stir it a little bit, and then come back in 24 hours. You’ve got some great healthcare algorithms. It’s really absurd.
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.