Deep Learning: Opportunities in health care operations
- [Andrea] Hi, everyone. Welcome to AI in Focus, a two-part podcast series where industry experts talk about artificial intelligence, its challenges, benefits, and future. I'm here with Paul Bleicher, CEO of OptumLabs, to talk about the impact of AI on healthcare operations. Thanks for being here, Paul.
- [Paul] My pleasure, Andrea.
- [Andrea] So, Paul, what are some of the lower-hanging fruit where AI or machine learning can be applied to improve operational performance?
- [Paul] I think with the tools that we currently have, we have a lot of opportunity. For many years, we have developed a system of reimbursement that involves coding and the submission of codes, the evaluation of codes, and payment from that, and then around that, a number of limits on the use of care, such as prior authorization, to make sure someone who receives a medication or who is authorized to get a treatment is medically appropriate to get a treatment. These kind of activities, all of them, coding, identification of fraud, prior authorization, are all involved with usually a physician or a nurse who spends a lot of time with a medical chart, and based upon their knowledge and experience, makes a decision. What's interesting is, those decisions have been made for a long, long time. That's a perfect example where you have electronic information with hundreds of thousands of examples that you can use to train an artificial intelligence model to give you, essentially, equivalent responses to what a trained professional would. But, while they may take an hour and a half to read a chart carefully and come up with a conclusion, the artificial intelligence model may read it in seconds. The physician or nurse doesn't have to read and focus on those charts for which an obvious decision to be made, and the tool can say, "Well, these things in the middle, "that really takes maybe some subtlety "that the artificial intelligence doesn't have. "This is a chart that you should look at "and focus on." So, it doesn't take away physician jobs. What it does is, it makes sure that the physicians are operating at the peak of their expertise, rather than with the mundane and straightforward things, and with each decision that's made, the model itself can continue to be trained and can improve over time.
- [Andrea] So, I noticed you've been talking about artificial intelligence a little more generically there. Are you thinking of any specific type of artificial intelligence that's best suited to these operational cases?
- Yes. Well, specifically deep learning, and deep learning is based on a technology that goes back to the early 1960s. The idea was to have something that looked like a neuron, a nerve cell, and if you remember your biology, nerve cells get a lot of stimulation from senses or from other nerves, and then at a certain point when they've had enough, they reach a threshold and they trigger, and they can trigger other nerves. There's a complex of nerves. So mathematically, it was modeled back in, again, I think 1963, something called a perceptron, which took in a lot of information from a variety of factors, and then used mathematical weighting of each of those factors, added them up, and decided whether it was triggered or not. And the exciting thing is, with more and more computing power brought about from video games, believe it or not, we now have the ability to do the kind of mathematics that are necessary to be able to not just use one of these or five of them, which, you know, go back decades, but to use dozens and dozens of them and to use hundreds of layers where each layers gets information, and then passes out information to the next layer, and passes out information to the next layer, and in the end, what you do is, if the model gets it wrong, you send back the signal across the model, it's a mathematical signal that corrects all those weights. If the model gets it right, it says, "Great job, let's keep going," and over time, the model learns, the model adjusts those weights so that it does a better and better job at making those discriminations. The exciting thing about using text is that in order to understand text, very often, it's not just to be able to pick out words, but to be able to remember that something happened first, then something happened second, then something happened third, and there are new methods in deep learning that allow you to assemble things in the order in which it happened, or even from first note to second note to third note. And so, you begin to get a lot more subtlety. So, I think for administrative purposes, the use of deep learning for decision making for administrative processes is potentially very exciting.
- [Andrea] That's great, I really appreciate that. We invite you to stay in touch on AI topics by visiting our website at Optum.com/IQ.
Paul Bleicher, MD, PhD, CEO of OptumLabs, discusses how deep learning works and how it can be used to improve health care administrative processes.
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