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AI in health care initiatives 

See how to make artificial intelligence work for providers.

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What is the first piece of advice that you would

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give for leaders and provider organizations

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looking to bring AI technologies into their into

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their stack? Absolutely so, if you think about AI

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this is fundamentally a business problem you're

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looking to solve. So if you're, a health system or

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a provider, this is about how do you use your

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business talent to solve an analytics problem?

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This isn't necessarily a technology problem that

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you start working.

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So my advice and my suggestion is focus on what

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you want to do in terms of the use case. What's

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the use case you wanna solve whether it's

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operational or clinical

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and determine the talent that you need to

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actually go solve it that technology will be the

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underlying enabler of that.

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In fact, we launched our inaugural Optum IQ

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survey in on artificial intelligence in health

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care in 2018 and the results were

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fascinating. 91 percent of those that were

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interviewed

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identified AI is the right path to improvement in

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healthcare.

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75 percent actually are embarking on a strategy

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to embed AI into their organization.

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And if you look at it

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by organization over the next five years,

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what we heard was that there's gonna be over

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30 million dollars per organization that's got

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to be invested in the AI space.

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Interesting. Can you tell us a little bit more

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about Optum's view of practical application of AI

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versus kind of general strategy for innovation

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and approaching other projects like what makes an

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AI initiative unique? What are the unique

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factors there? Yeah so if you look at the

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key components of AI that you need to have in

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place so there's three dimension of that one is

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your data right so Machine learning and

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artificial intelligence requires large, large

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data sets. So it's access to internal and external

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data sets that you are able to stitch together,

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clean, enrich, and be able to do things on. Their

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artificial intelligence models are only going to

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be as powerful as the data that you have actually

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building them around. That's number one. Number

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two is around the methodologies and the algorithms

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that you actually build in your organization that

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you're able to actually apply on that data so

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whether these are basic regression models or deep

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learning models.

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Having the talent to be able to better understand

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what you can go do with that data is one of the

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first and most important pieces that we adopt in

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what say you start with. And then third, which

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would be quite obvious, is around healthcare

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experience.

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Oftentimes you get a data scientist that is very

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deep in the data but may not have the health

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care background. Keeping very focused in terms of

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understanding what the data scientists should be

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focused on and linking it to a business or

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clinical problem. This is important.

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Describe a little bit how you would work with

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senior leadership to build a team to

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implement an AI project? What kind of talent are

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you looking for? Are you looking for talent

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inside the organization? Are you going need to recruit as

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a combination of both? Yeah talent is a top of

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mind question for most organizations and this

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isn't just about how do you hire data scientists

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this is about the full breadth of analytics

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talent you need to embark on a broad AI

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transformation. So this starts at the top getting

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executive alignment

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in terms of what you wanna do with AI, hiring

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ahead of analytics, that oversees your AI program

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and then there's multiple dimensions. There's the

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data scientist who's in who's deep in the data

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there's a translator that actually is able to

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understand the business problem and translate it

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for the data scientist, their solution architects,

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there's data architects so there's a plethora

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of talent that you need in your organization to do this

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successfully.

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This is something that you don't solve overnight.

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Laying out a very

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talent plan in terms of what you want to be in

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the organization, where you focus and whether you

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hire or acquire this talent via partnership is

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actually very important to get started with.

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What kind of timeline should leaders be thinking

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about when they're planning this kind of you

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know, work force development as it were well with

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artificial intelligence? So what I typically

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advise is this isn't something that you should

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look to solve overnight. Have a journey, have a

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plan of multi multi year road map of what you

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wanna go do. Having said that

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the best way to succeed with us is through

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experimentation trial and pilots, so I'm fully

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supportive of hiring targeted or partnering with

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companies to target focus use cases in the near

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term, build on the success of that, show the or why of that

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and then pursue your broader talent plan

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over a multi multi month journey. Great let's

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pivot a little bit to use cases right we're

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hearing a lot about artificial intelligence and

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data here at HIMSS'19. What are some practical use

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cases for artificial intelligence that are

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beginning to emerge right? Um what are some

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things maybe that their viewers here on
HIMSS TV

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haven't thought of yet? Yeah so it's a great

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question and one of the one of the insights we

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learned from our Optum IQ survey last year was

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over 40 percent of those surveyed believe

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starting with administrative or operational use

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cases is the best way to get started and so that

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includes how do you automate processes and tackle

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the low hanging fruit in your organization of

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where you have an efficiency.

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Why is that important? If you think about

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technology around machine learning and deep

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learning, that's very much linked to how you can

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automate processes and how you use that

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technology to drive value. It's not the only

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use case so if you think about clinical outcomes and

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better being able to predict disease, that's

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another big use case for artificial intelligence

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and then there's a whole extra exposure around

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customer experience which is also critically

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important.

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If you're a health plan, one of the areas that

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health plans can focus on using artificial

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intelligence is how do you better detect claims

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that will be denied and automate that process? If

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you're a health system, how do you automate the physician

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work flows

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and potentially lower the number of prior

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authorization is required and if you're a

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consumer, how do you actually better at here to

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medication and how do you automate the process of

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notifying doctors that you need to be treated?

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Got it I mean these are emerging use cases right

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so why is adopting AI now versus 24 months from

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now or even 18 months now? Why is adopting

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now or beginning now so important for so many

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different types of provider organizations?Yeah

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It's a great question and we're

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at a turning point in the industry in terms of

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how we use data and analytics. Other industries

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such as financial services, retail and tech have

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already matured in terms of how they use

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artificial intelligence.

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It's no longer a nice to have for a provider to

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be able to have artificial intelligence as a core

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capability. This is a core competency they're

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gonna need as they move forward and actually

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build their analytics strategy and drive value

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and take better care of patients.

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Why providers need AI as a core capability

Tushar Mehrotra is senior vice president of analytics at Optum. He sat down with HIMSSTV to discuss artificial intelligence initiatives in health care and AI talent in care provider organizations. The bottom line? Focus on what you want to do in terms of a use case. Watch to learn more.

 

Learn more about our expert, Tushar Mehrotra.

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