Expert advice on practical innovation
Optum Forum 2019: Emerging tech session soundbites
Kerrie Holley: How do we take ideas and do practical innovation? Well, it starts by having a landscape that we look at in terms of emerging technologies. Let's take a look at blockchain for example. So to understand blockchain, most people understand blockchain from the reality of cryptocurrency. So we've all heard about cryptocurrency. The technology underneath it is blockchain. Now, blockchain is actually very easy to understand. It's really just a collection of blocks, immutable blocks, meaning I can't change them.
Each block represents a ledger. And anytime I want to add to the ledger, I chain a new block. I have private or public blockchains. How does that apply to healthcare? One of the biggest problems we have in the healthcare system is maintaining provider directories. So maybe Optum has the correct address, correct phone numbers, maybe Humana has a different, and maybe Humana is more accurate. If we work together, we can actually create a directory that public, that's accurate in the consortium.
And that's what we've done. We've actually created with Quest with Humana and others and Optum, we've actually used blockchain to solve this problem, which creates friction, creates pain for providers, for members. So that's an example of using blockchain. Really just a public database that we can use globally.
Another technology that we're using is augmented and virtual reality. Think about behavioral health. When you think about behavioral health, there's a lot we can do in terms of, for example, phobias. Instead of taking someone to the Grand Canyon, we can actually in the therapist's office, use augmented reality to recreate that fear, and the therapist doing their magic can actually create a better outcome.
So another example, ambient computing. If you were on stage or listened on stage job this morning, you heard about the chronic conditions aging at home, in home care. One of the opportunities we have with a lot of these technologies as we use them holistically is to be able to actually make a difference in home care. We can use ambient computing, the ability for devices and things that are intelligent to sense and have contextual awareness.
For example, I know that someone has fallen or I know someone has not gone to the kitchen in five days. So I can begin to use technology to see behavior and to perhaps take corrective and preventive action. Voice analytics, think about nurses and we have something called a nurse line. People call in, they leave voicemail messages, and think about yourselves when you read a voicemail and it takes time. What if you had a hundred voicemails to go through? Wouldn't it be easier if we could take all those voicemails, give you a written summary, but still not requiring you to read them all? Instead we pinpointed what the caller was actually asking about, and then surgically help you understand what the answer is.
How do we do that? We do that by analyzing voice. We do that by natural language processing. So again, another example. So we're going to cover more of these smart speakers, things that we're doing with things like Alexa to be able to help members do adherence in terms of their medication. So a lot of technologies in a play here that we are spending time in terms of learning. The picture that you see in front of you now is a historical picture.
It shows the arrival of the personal computer and it shows the on the far end, the arrival of artificial intelligence. Even though we know artificial intelligence has been here for a number of years. We'll come back to that story in a moment. There's a couple of messages here. We talk about these kinds of technologies as general purpose technologies. When you think about electricity, electricity is a general purpose technology. It fundamentally has changed the way you and I work, live and play.
That's what a general purpose technology does. It has a fundamental impact on economies, on nations and on business and on business models. A general purpose technology is enormous and the impact that has on societies. We've seen that with the computer itself. We've seen that with the internet.
The other thing that is also showing in this picture is the modality. You and I at one time spent a lot of our modality in terms of keyboards and we're seeing the use of the web, but today we're seeing more and more use of voice as a modality. But the key message I want to point out to you as a conversation I had with some city planners in Chicago some weeks back. And one of the points I made to them is would you ever think of designing the city without electricity? And the answer of course is no.
Well, why would you think about designing a city without artificial intelligence? Artificial intelligence is the most over-hyped and under-utilized technology of our generation. It is also a general purpose technology. I realized the statement I just said is a bit of a paradox, but I point that out because we use the word generically when we talk about artificial intelligence, but artificial intelligence is much like a Russian nesting doll.
When I start peeling back the layers, I began to see the various technologies, whether we're talking about machine learning, whether we're talking about deep learning, whether we're talking about natural language processing, natural language understanding, and the list goes on. There's a ton of technologies that comprise what we describe as artificial intelligence, but we're going to come back and go through that in a little bit more detail for you a little bit later in our conversation.
So for now, the message I want you to walk away with is that artificial intelligence is a game changer. And I know there's a lot of doubt in individual's minds, but think about if the year was 1950 when the computer itself was just becoming popular. There weren't a lot of applications being applied, but we're about in that same window of time. If we look at the opportunity for artificial intelligence to make a difference in healthcare, we have a huge highway ahead of us in terms of opportunity.
Whether you're a health plan, whether you're a provider, whether you're a payer, it doesn't matter. The opportunities to actually make people healthier, to actually make the system work better, to actually reduce administrative costs, to actually reduce and make operations more efficient, to create awesome customer experience. This is the opportunity we have with this general purpose technology that I'm describing as artificial intelligence.
How to solve today's business challenges with emerging technology
Look at the landscape of emerging technologies with Kerrie Holley to learn how general-purpose technologies will change health care.
Jess Lewis: I get really energized and excited whenever I hear Kerry or others speak about some of these emerging technologies and capabilities. It's just fascinating to think about where we're going to be in five to 10 years within healthcare using some of these. But I want to talk a little bit about how we can apply those even today. You're going to hear some examples about how we're taking more of a practical approach to starting to dip our toe in some of these artificial intelligence capabilities and emerging technologies.
So we've taken a very disciplined and structured approach to applying these. And as you see on the screen, it's really a three stage process. Kerry and his team and others are really looking at how to stay abreast of the emerging technologies and capabilities and looking at what's happening in other industries. It always seems to me that healthcare is a little bit lagging in terms of applying those capabilities. But we're really trying to stay abreast of how those are being used and where the maturity level is with some of those capabilities that he spoke of earlier.
Working with you and with our operations folks and our product folks, we then can really think about how to transform and change some of the processes that exist within either healthcare administration or the care management, the medical management, and even the delivery of care. So that's kind of the next step in this process. I'm going to go into a little bit more detail in the next few slides. But as you can see, this is really a cross discipline approach.
We run what we call immersion sessions from time to time where we're bringing operations, product, customers and technologists together around what could change. Given this capability, could we operate our provider network differently? Could we change the way we're interacting with patients with the way we're interacting with customer service? So a lot of that is resulting in ideas that we go through a proof of concept and a minimal viable product effort.
And then the last stage is really important, and that's really about how you scale this. We all know that there are legacy systems. There're legacy processes. There's regulations. There's a lot of scaled production criteria that we've got to take into account as we move some of these capabilities into a full scale development. And you're going to hear today about some of the efforts that have been going on related to prior auth in that realm.
So let me talk a little bit more about some of the work we're doing in the first step of this process. Technology is changing rapidly. It's tough to keep up. I live in the technology world and I can't keep up. I think Kerry does a much better job than I. So he and his team are constantly monitoring and checking to see what's coming.
We really think about this in a horizons framework. Some of these capabilities are here and now, zero to two years. Let's apply them in the business that we're running today. Some of them are a little bit further out, maybe two to five years. And even some of them are five plus years. He talked about ambient computing. He talked about so many other technologies that maybe aren't ready for prime time. But let's get our hands dirty and let's figure out what capabilities those can bring to the table so that when we are at scale or when we do have those capabilities ready to apply, we've got a little bit more experience and start to apply them into our current business.
Many of you seen the hype cycle on the slide here. One thing that we're trying to do is test and iterate early with these early stage capabilities so that we're not getting such a peak of high expectations. We really have a little bit better idea of this is what it can and can't do at this time. We're definitely trying to avoid that trough of disillusionment where we've set the expectations so high and we think that it's going to solve this process in just a completely different way. So we're trying to iterate and test and touched base with current operations early rather than waiting and hitting that trough of disillusionment.
When we get to the point where Kerry and his team and others have worked on a technology that's now ready for deployment, something like, I'll take the voice area. We've done a lot with voice transcription and natural language processing, and are now really starting to apply that. You'll see that even today in the example. How do we then take that and start to move into the second phase of this practical innovation approach.
This is, again, a cross disciplinary opportunity. It's where I really get a energized because now we're bringing the groups together and really looking at how can we change the process that we're currently employing. What are those ideas? The backlog of ideas and the backlog of concepts coming out of that is really what we use to drive the development. Some of those ideas aren't going to pan out. We're going to iterate around those. We're going to spend the first few months really looking at what is the solution really need to entail?
It might be a combination of people and process and technology. That technology might be emerging technology and existing technology. So there's a lot to consider there. Very importantly, what we try to do is set up some hypothesis around when we get this accomplished, we'll see these metrics impacted, whether it's improved engagement, whether it's lower medical expense, whether it's efficiency in the operations. Then we'll test and iterate and incubate that.
And really that incubate stage is all about reducing risk when we get to the point where we're scaling this. We've now tested this to the point where we've got a high degree of confidence that this works in a production environment. You're going to hear an example of that today. That paves the way for that integration phase. Then we get into a lot of the scaling and the work of really embedding this within the at scale business that we're all running.
Again, if you think about your business, our business together, there's healthcare administration, there's clinical management, there's the delivery of healthcare. One of the examples that we're going to talk about today is a process that we all hold near and dear to our heart. It's called prior authorization. Prior authorization really breaks down into two components. We're going to talk a little bit about the administrative aspect of that, their administrative review of prior authorization. Kerry's going to describe a little bit of what we're doing in that arena using deep learning. Then we're going to hand it over to William and Robbie to talk about the clinical review. If you think about those two processes, they warrant different approaches. I think you're going to hear some of that today.
An approach to consider for practical innovation
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Practical innovation applied to prior authorization's administrative review process
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Practical innovation applied to prior authorization's clinical review process
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Speaker 1: Just a couple thoughts.
First, I think you've seen the application of emerging technology, AI, natural language processing, deep learning, to a real world problem here. It's now. But it's the convergence of truly deep technical resources with deep clinical resources and the operations folks that brings that all together. And that's not an easy thing to do. I think that's the example, why we picked that example is because it shows that cross disciplinary work and how you get, at the end of the day, some of these practical innovations across the board.
Three things that kind of come to mind. First, William really talked about it there at the end, focus on the people, the process, and the technology. We could have, and we did, demonstrate what the AI component could do in going out and finding that evidence, and in going out and approving the administrative process. But the real work was the adoption, the clinical buy-in. A lot of this is a people, a process, and a technology component.
Second is, we continue to take a very structured approach. If you think about it, the administrative side of things, approving in network or benefit coverage is something that we can take deep learning and machine learning and really get after in a very automated fashion. But when you talk about clinical review, where you're going to have to defend some of that decision, that's a different approach. So we take that disciplined approach to which technology makes the most sense and how that process feeds into it.
And then finally, there's an integration with the emerging technology and existing technology. This is where we're working with our systems that, whether it's the claims, or the billing, or the-
Speaker 2: Built a few years ago.
Speaker 1: Exactly. Just a few years ago.
So there's a lot of effort to put this into the integrated scaled business as well. But that's part of this process.
We talked today about the administrative, prior authorization process. We talked at the beginning about some of the big changes that we can make in the clinical management. If you think about some of the technologies that we've talked about today and addressing, what if we have genetic information, all of the claims' data and the individual health record information that we can gather on an individual, and arm the physician with that at the point that they're making a decision around which medication and which care regimen makes sense. But you got to do it in their workflow. And they've got to have the ultimate say.
You hear a lot of people talk about AI replacing jobs. I think this is just going to make our jobs much more effective, much more efficient, much more consistent. So I'm excited. I hope you're excited. We're anxious to hear about what problems and opportunities you're trying to get after. And we hope you can join us on our journey.
Thanks a lot.
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