How can artificial intelligence make health care better?
We are experiencing a deluge of data that can be tapped to inform improvements in health care. Experts predict that artificial intelligence (AI) can support these improvements. What can AI help with today, and where can we expect a big impact in the future?
AI in Focus: In this mini-podcast series, we share two perspectives on where AI can add value in health care — in medicine and health care operations. Two experts deliver a point of view on the challenges, benefits and future of AI in these areas.
AI in medicine
- [Interviewer] Hi everyone, welcome to AI in Focus. A two part Podcast series where industry experts talk about artificial intelligence, it's challenges, benefits and future. I'm here with Atul Butte, Director of the Institute for Computational Health Sciences at UC San Francisco to talk about the challenges and benefits of using AI in a clinical setting. Thanks for being here, Atul.
- [Atul] It's great to be here.
- [Interviewer] So Atul, what do you think are the primary challenges in the practical use of artificial intelligence in the clinical setting?
- [Atul] Everyone's talking about the use of artificial intelligence in medicine right now. Indeed, the Food and Drug administration, the FDA, has already approved six devices and software tools in just the past 18 months. So, I think we're getting to more practical use. But they have to be very specific uses. So, for example, the approved uses include things like stroke triage in emergency rooms, diagnosing diabetic retinopathy from retinal pictures, so very targeted uses. I think we're gonna see more of those in the next couple years where physicians, start up companies, large companies, are gonna go after these very targeted, really high risk, really hard to diagnose, different aspects of medicine, and solve them with computers. I think we're getting there.
- [Interviewer] That's great to hear! Where do you see the next big inroads for artificial intelligence over the next 12 to 24 months?
- [Atul] I see a lot of inroads being made from the field of artificial intelligence machine learning in just the next year or two. Certainly, I think we have much more data and getting more data and access to data, I think it's gonna happen. We have health systems now, that are really moving onto standard electronic health record systems, and they wanna do something with that data. As I often say, I think electronic health record data is the most expensive data in America. We're paying physicians to type all this in, we have to do something with that. It's irresponsible if we don't use that data to improve the practice of medicine. So I see more companies trying to partner with some of these health systems, all trying to get into this health care machine learning space. Trying to help physicians and health care practitioners with some aspects of their job.
- [Interviewer] You know, I've always wondered, hanging out in Silicon Valley, what are some of the trends happening there that we may not be aware of?
- [Atul] Yeah, so I'm really lucky I get to hang out in, I think one of the most amazing places in the United States, in Silicon Valley. We certainly have a lot of small and large companies working in the health care space now, and more coming in every week, every month. One aspect of data science and machine learning, I don't think people have paid enough attention to is, the empowerment of patients with all this data. I think that's once major aspect that we are all missing as payers, as providers, as pharma device manufacturers. I don't think we're really that used to patients being empowered with their data for that to actually be happening. And I think those patients are going to be empowered. Not with just the direct access to their data, but with interpretations and advice given through AI on that data. So that's something I'm going to be paying more attention to in the future.
- [Interviewer] Thanks for being here, Atul. We really appreciated having you.
- [Atul] Thank you.
- [Interviewer] We invite you to stay in touch on AI topics by visiting our website optum.com/IQ
Atul Butte, MD, PhD, distinguished professor and chief data scientist, University of California, shares his perspective on the applied use of AI in medicine, the responsibility to leverage the data, and the growing voice of consumers in health care.
AI in 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 the impact of AI in health care operations, with specific examples of how deep learning works and can be used to improve administrative processes.
Video: UC Health case study
See how UC Health is working on precisely practicing medicine from 700 trillion points of University of California health data.
Will AI replace doctors?
Listen to Atul Butte, MD, PhD, distinguished professor and chief data scientist, University of California; and Isaac Kohane, MD, PhD, professor of biomedical informatics, Harvard Medical School, discuss AI in health care – moderated by Paul Bleicher, MD, PhD, CEO of OptumLabs.