Artificial Intelligence in Health Care
4/25/2023
AI in health care
AI/ML is making an impact across the health care ecosystem. In the 4th annual Optum AI survey, 99% of senior health care leaders agreed that AI can be trusted for use in health care and that investing in AI will result in significant cost savings for organizations.
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Hello everyone. Good afternoon.
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Welcome to the TechGig webinar
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on AI in health care
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applications, opportunities and careers.
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I'm your host Sukriti.
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Thank you so much
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for joining the session today.
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Folks, today
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AI and ML is playing
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an important role
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in making health care simpler
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and more effective for everyone.
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From empowering people
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with customized information,
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they need to make personal health
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choices, to providing physicians
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with insights to assist in their decision
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making and streamlining
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the administrative processes.
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AI/ML is making an impact
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across the health care system.
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As for the fourth annual Optum AI survey,
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99% of the senior
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health care
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leaders agree
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that AI can be trusted
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to use in health care
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and that investing in
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AI will result in significant cost
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savings for their organizations.
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Today's webinar is all
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about the fundamentals of AI,
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its application
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in health care
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and exploring career opportunities
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that can help you impact
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millions of lives
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while doing your life’s best work
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and to felicitate the session
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I have with me
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Jyothsna Kuchimanchi,
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who is the senior
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director
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software engineering from Optum
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Global Solutions.
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Jyothsna is a highly skilled leader
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with extensive experience
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in designing and guiding organizations
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on their digital transformation journey.
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Strong decision thinking methodology
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coupled with exceptional delivery
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excellence is her core competency.
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A storyteller at heart
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Jyothsna can
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provide a compelling digital vision
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for customers business process
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and product innovation.
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She is driven by new challenges
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to create something new
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with the focus on value
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generation for customers.
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She heads broker
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experience and employee and individual
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at Optum.
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Audience, please note
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that the session scheduled for an hour
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would have 40
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to 45 minutes with the presentation
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and the remaining 15-20 minutes
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for the Q&A.
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If you have any questions,
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please send them to us
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using the questions tab
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on the go to webinar control panel.
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Now, without much ado,
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let's take the session forward.
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Over to you Jyothsna.
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Thank you Sukriti.
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Hello, everyone.
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This is Jyothsna.
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I think
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Sukriti has already introduced me enough.
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So welcome to the webinar.
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At the very onset,
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I would like to thank TechGig
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for this particular opportunity
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that they have given me
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in order to take this particular
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one forward.
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So as Sukriti has said, I'm part of Optum
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and I take care of the broker experience.
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We sell a lot of health
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plans to all of our customers
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through our brokers.
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So when we say broker experience,
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I kind of take care of the entire digital
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part of the broker experience
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where we ensure that the brokers
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are able to access our portal
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and our mobile applications
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they are able to look at our plans,
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they are able to compare all of
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those plans
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and then enroll for the plans
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and the renewal part of it.
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So the entire complexity of the health
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insurance is what we kind of
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take care of.
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And a quick glance
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at what we are going to cover today.
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So the first and foremost
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that we wanted
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to look
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at is artificial intelligence in itself
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is a very, very complex
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topic, as all of you know.
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And I don't think it is very easy
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for us to define
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what artificial intelligence is.
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But what we wanted to do was
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we wanted to look
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at artificial intelligence
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in the context of health care today.
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And we are in a world today
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where the focus is moving
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towards value
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delivery and outcome,
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and especially the COVID pandemic
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has had a very profound effect
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on all of us,
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and especially on the health care
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industry, from shifting the profit pools
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to a spike in innovation
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to creating new business models
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that are evolving in this industry.
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So what we are going to do
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is we are going to look at some of these
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shifting priorities
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or the shifting expectations.
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The next one that we are going to look at
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is the experience led approach.
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Now, why is experience
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led approach
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and experience first force
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that you're going to hear
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are the latest buzzwords,
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because this is where we are going
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to keep
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the customers at the heart
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and ensure
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that we are giving precedence
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to all of our customers
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and their experiences
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and making it personalized and intuitive
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to ensure
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that we are prioritizing
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that even before
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our organizational goals itself.
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So we are going to talk about
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some of the frictionless experiences
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that we can deliver to our customers.
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In this particular section,
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the next one that we are going to look at
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is some of the applications of AI
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in health care.
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Health care is so vast that
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we probably would not be able to cover
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all the applications in the end.
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But what we are essentially talking about
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in this particular
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session is some of the use cases
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where Optum has successfully used AI
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and how are we leveraging
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artificial intelligence and machine
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learning to solve
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some of the complex health
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care problems that we have got.
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The next one
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that we are going to talk about
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is scaling AI in health care.
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So what exactly do we mean?
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As I said,
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the health care ecosystem is so vast,
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it has got so many players right
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from your providers, payers and so on.
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So it becomes even more complicated.
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So in order for us
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to actually make AI meaningful
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and to have organizations adopt
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AI in a meaningful way,
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we are going to start scaling this
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AI in health care through partnerships
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and so on.
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So we are going to talk a bit
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about the partnerships part of it
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and every coin
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has got two different sides
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and each one has got every high
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as its own challenges.
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So we will
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briefly touch base upon the challenges
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on that particular part of it.
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The last but not least,
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the main conversation for today
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is the opportunities and career.
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We are going to double down
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our efforts on
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what it means to make an AI
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career in the health care space.
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With that, let's start deep diving.
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So I wanted to start off
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introducing UnitedHealth Group itself.
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UHG as UnitedHealth Group
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is famously referred
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to as a Fortune 5 company today
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and as a health care
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and well-being organization
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with a mission to help people
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live healthier
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lives and help
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make the health systems work
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better for everyone.
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We are about 340,000
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colleagues that we are talking
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about in two distinct
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and complementary businesses
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where we are working together
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to build a modern, high
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performing health care system
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through improved access,
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affordability outcomes and experiences.
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Again, at UnitedHealth Group
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our mission calls us,
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our values guide us,
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and that diverse culture
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connects us as we seek to improve
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areas for the consumers
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we are privileged to serve
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and their communities itself.
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With that understanding of Optum,
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let's probably start talking about
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how is Optum
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powering
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some of these intelligences, right?
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So if you look at that,
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we are talking about
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we cater to
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or we have data
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close to about 240 million
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lives of clinical
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and claim data,
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which we leverage for our innovation
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and research today. We also
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have got $3.5 billion
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investment in innovation and technology
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every year.
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We are also talking about catering to
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the top 20 of the 25 US
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health plan, leveraging the Optum
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Analytics today.
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We have about 25 plus research partners
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for collaboratively researching
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and innovating at our Optum Labs.
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We also have
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something called as Optum AI Labs,
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which is kind of dedicated
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to ensure that we are solving
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some of the complex health
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care problems in this particular area.
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Now, as you are aware,
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the health care data actually
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exists in multiple systems, right?
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So one of the challenges that we face
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is also the data being siloed.
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So that's where the first and foremost
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that we have is curated data.
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What do we mean by that?
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We have built a common language
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where we have standardized
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and made it easy to link and integrate
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the data from multiple disparate systems.
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With all this information
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the next thing that we kind of do
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is apply our expertise over here.
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We guide for our action for success,
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we collaborate for the right
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action, applying
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predictive analytics,
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insights and expertise.
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Then we move
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into innovating with a purpose.
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So what do we do over here?
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We transform the data
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into insights
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with industry
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leading dynamic models
297
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and innovation in AI itself.
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Moving on so we are also working
299
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as an organization today
300
00:08:22,120 --> 00:08:23,120
with governments,
301
00:08:23,120 --> 00:08:25,800
employers, partners, providers
302
00:08:26,160 --> 00:08:26,760
to provide
303
00:08:26,760 --> 00:08:29,760
care for more than 140 million people
304
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and we share our vision
305
00:08:31,240 --> 00:08:33,360
for value based system of care
306
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that provides
307
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compassionate and also equitable care.
308
00:08:37,640 --> 00:08:39,200
So let's break down
309
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the four aims that we got right.
310
00:08:41,080 --> 00:08:42,440
As you can see on the screen,
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00:08:42,440 --> 00:08:43,760
you've got an enhanced
312
00:08:43,760 --> 00:08:45,160
patient experience,
313
00:08:45,160 --> 00:08:46,960
you've got a lower cost of care,
314
00:08:46,960 --> 00:08:49,080
you've got a better health outcomes.
315
00:08:49,440 --> 00:08:51,560
And we are also talking about improved
316
00:08:51,800 --> 00:08:53,800
clinician well-being itself.
317
00:08:54,320 --> 00:08:55,640
So when we say enhanced
318
00:08:55,640 --> 00:08:57,280
patient experiences,
319
00:08:57,280 --> 00:08:59,840
what are we essentially referring to?
320
00:08:59,840 --> 00:09:01,800
We want to expand our access
321
00:09:01,800 --> 00:09:03,640
to all of our services
322
00:09:03,640 --> 00:09:06,440
and whether members are able
323
00:09:06,440 --> 00:09:07,480
to receive care.
324
00:09:07,480 --> 00:09:08,600
Some of the examples
325
00:09:08,600 --> 00:09:09,720
that we are talking about
326
00:09:09,720 --> 00:09:12,960
are virtual care, home health and so on.
327
00:09:13,360 --> 00:09:14,520
Now when we talk about
328
00:09:14,520 --> 00:09:16,080
frictionless experiences,
329
00:09:16,080 --> 00:09:16,760
which is what
330
00:09:16,760 --> 00:09:18,280
I have been talking about,
331
00:09:18,280 --> 00:09:18,960
we are committed
332
00:09:18,960 --> 00:09:20,640
to understanding our customers
333
00:09:20,640 --> 00:09:22,080
and delivering solutions
334
00:09:22,080 --> 00:09:23,680
that would gather to their needs
335
00:09:23,680 --> 00:09:25,280
with intuitiveness
336
00:09:25,280 --> 00:09:27,480
and also minimal friction.
337
00:09:27,480 --> 00:09:29,000
Now what do I mean by that?
338
00:09:29,000 --> 00:09:29,560
So where you're
339
00:09:29,560 --> 00:09:31,160
kind of improving the first call
340
00:09:31,160 --> 00:09:33,160
resolutions, you're
341
00:09:33,160 --> 00:09:35,240
taking care of their digital adoption,
342
00:09:35,600 --> 00:09:36,840
you are also trying
343
00:09:36,840 --> 00:09:38,240
to proactively provision
344
00:09:38,240 --> 00:09:40,560
the information for the required members
345
00:09:40,560 --> 00:09:40,960
and so on.
346
00:09:40,960 --> 00:09:42,680
We will come back to this one
347
00:09:42,680 --> 00:09:43,360
in a bit
348
00:09:43,360 --> 00:09:44,320
where we talk about
349
00:09:44,320 --> 00:09:46,760
some of these various examples itself.
350
00:09:46,760 --> 00:09:47,760
Now the next thing
351
00:09:47,760 --> 00:09:48,760
that we are essentially
352
00:09:48,760 --> 00:09:49,720
talking about
353
00:09:49,720 --> 00:09:52,080
is the better health outcomes also.
354
00:09:52,680 --> 00:09:54,560
Now here, what are we talking about?
355
00:09:54,560 --> 00:09:55,360
We are reducing
356
00:09:55,360 --> 00:09:57,560
administrative burden rate
357
00:09:57,560 --> 00:09:57,960
where we are
358
00:09:57,960 --> 00:10:00,160
kind of focusing on the care itself.
359
00:10:00,160 --> 00:10:02,240
You must have heard a lot today
360
00:10:02,240 --> 00:10:03,960
about physicians’ burnout
361
00:10:03,960 --> 00:10:05,400
or nurses’ burnout,
362
00:10:05,400 --> 00:10:06,600
where the health workers
363
00:10:06,600 --> 00:10:07,840
are actually complaining
364
00:10:07,840 --> 00:10:09,400
about burnout itself,
365
00:10:09,400 --> 00:10:11,160
especially the pandemic part of it.
366
00:10:11,160 --> 00:10:11,400
Right.
367
00:10:11,400 --> 00:10:12,760
They have been on their toes.
368
00:10:12,760 --> 00:10:14,520
They have been providing all the care
369
00:10:14,520 --> 00:10:15,960
that is required.
370
00:10:15,960 --> 00:10:17,720
Now, add to this entire care
371
00:10:17,720 --> 00:10:18,760
that they're providing,
372
00:10:18,760 --> 00:10:19,440
you can also
373
00:10:19,440 --> 00:10:22,000
imagine the administrative complexities
374
00:10:22,000 --> 00:10:23,280
that are sitting in.
375
00:10:23,280 --> 00:10:23,920
For example,
376
00:10:23,920 --> 00:10:24,960
you just know this kind of
377
00:10:24,960 --> 00:10:26,880
a documentation process that you'll get
378
00:10:26,880 --> 00:10:29,320
when you actually admit a patient in
379
00:10:29,320 --> 00:10:30,440
a hospital itself.
380
00:10:30,440 --> 00:10:32,000
That itself is very daunting
381
00:10:32,000 --> 00:10:33,720
in a lot of instances.
382
00:10:33,720 --> 00:10:35,720
Then you are also talking about
383
00:10:35,720 --> 00:10:36,440
the process
384
00:10:36,440 --> 00:10:37,920
related, like scheduling of
385
00:10:37,920 --> 00:10:38,760
the appointments,
386
00:10:38,760 --> 00:10:40,680
you are talking about patient
387
00:10:40,680 --> 00:10:41,720
communication as to
388
00:10:41,720 --> 00:10:44,160
how do you ensure that be it for vaccines
389
00:10:44,160 --> 00:10:44,560
or be it
390
00:10:44,560 --> 00:10:45,840
for any kind of
391
00:10:45,840 --> 00:10:47,080
medications and everything
392
00:10:47,080 --> 00:10:47,520
where you're
393
00:10:47,640 --> 00:10:48,480
asking for the people
394
00:10:48,480 --> 00:10:49,040
to come in,
395
00:10:49,040 --> 00:10:51,520
the entire communication part of it.
396
00:10:51,520 --> 00:10:52,920
Billing the patient, you know,
397
00:10:52,920 --> 00:10:54,160
sometimes it actually gets into
398
00:10:54,160 --> 00:10:55,560
a very emotional conversation
399
00:10:55,560 --> 00:10:57,480
the patient billing
400
00:10:57,480 --> 00:10:58,080
and the payments
401
00:10:58,080 --> 00:10:59,640
and everything get into a
402
00:10:59,640 --> 00:11:01,920
lot of instances, emotional conversation.
403
00:11:02,280 --> 00:11:03,120
How do you ensure
404
00:11:03,120 --> 00:11:04,000
all of these
405
00:11:04,000 --> 00:11:05,400
administrative tasks
406
00:11:05,400 --> 00:11:08,040
are actually automated to a large extent
407
00:11:08,040 --> 00:11:09,600
so that there's a seamless experience
408
00:11:09,600 --> 00:11:11,440
for all of our customers?
409
00:11:11,440 --> 00:11:14,040
The next one that we are talking about
410
00:11:14,040 --> 00:11:15,120
is also,
411
00:11:15,120 --> 00:11:15,360
you know,
412
00:11:15,360 --> 00:11:16,320
how do you reduce
413
00:11:16,320 --> 00:11:17,400
hospital admissions
414
00:11:17,400 --> 00:11:18,840
through proactive management
415
00:11:18,840 --> 00:11:20,000
of lifestyle conditions itself.
416
00:11:20,000 --> 00:11:20,560
Today
417
00:11:20,560 --> 00:11:23,840
we have a flagship product within UHG,
418
00:11:23,880 --> 00:11:26,000
which is called, as UHC Rewards.
419
00:11:26,000 --> 00:11:28,080
Now what do we do with UHC Rewards?
420
00:11:28,080 --> 00:11:31,560
We encourage all of our members to have
421
00:11:31,760 --> 00:11:32,320
or lead
422
00:11:33,560 --> 00:11:34,320
a healthy life.
423
00:11:34,320 --> 00:11:35,840
How do we go about doing it?
424
00:11:35,840 --> 00:11:37,920
We reward them by a dollar
425
00:11:37,920 --> 00:11:39,640
for every 10,000 steps
426
00:11:39,640 --> 00:11:41,200
that they take on a daily basis,
427
00:11:41,200 --> 00:11:42,360
for every one hour
428
00:11:42,360 --> 00:11:44,640
of constant activity that you've bought.
429
00:11:44,640 --> 00:11:46,000
So what we've identified
430
00:11:46,000 --> 00:11:48,080
as the more active you stay in your life,
431
00:11:48,080 --> 00:11:49,920
the more healthier you are.
432
00:11:49,920 --> 00:11:51,000
So we are identifying
433
00:11:51,000 --> 00:11:53,120
all of these touchpoints to ensure
434
00:11:53,120 --> 00:11:55,080
that we are kind of ensuring
435
00:11:55,080 --> 00:11:56,480
that you have a better health
436
00:11:56,480 --> 00:11:58,560
outcome for all of our customers.
437
00:11:58,560 --> 00:12:00,280
And then we are also talking about
438
00:12:00,280 --> 00:12:01,880
how do you provide the technology
439
00:12:01,880 --> 00:12:03,720
to select the right treatment
440
00:12:03,720 --> 00:12:05,280
through the right provider?
441
00:12:05,280 --> 00:12:07,000
We are also investing in areas
442
00:12:07,000 --> 00:12:09,160
that influence the members health itself.
443
00:12:09,760 --> 00:12:11,880
Now let's talk about include clinical
444
00:12:12,200 --> 00:12:13,680
well-being itself, right?
445
00:12:13,680 --> 00:12:16,080
So we are essentially talking about
446
00:12:16,080 --> 00:12:17,280
providing the tools
447
00:12:17,280 --> 00:12:18,160
and the information
448
00:12:18,160 --> 00:12:19,440
to prevent the burnout,
449
00:12:19,440 --> 00:12:21,720
which I was referring to a bit earlier.
450
00:12:21,720 --> 00:12:22,760
And we're also talking
451
00:12:22,760 --> 00:12:24,000
about diagnose decision
452
00:12:24,000 --> 00:12:25,200
augmentation
453
00:12:25,200 --> 00:12:26,400
through visual information
454
00:12:26,400 --> 00:12:27,640
standardization.
455
00:12:27,640 --> 00:12:29,280
So as you can see
456
00:12:29,280 --> 00:12:31,080
AI is part of our culture,
457
00:12:31,080 --> 00:12:33,240
and that's what we will continue to do it
458
00:12:33,640 --> 00:12:36,280
and we see that we are able to solve
459
00:12:36,280 --> 00:12:38,160
a lot of our health care problems
460
00:12:38,160 --> 00:12:38,880
and that.
461
00:12:39,400 --> 00:12:41,520
Now let's look at the next part of it.
462
00:12:41,520 --> 00:12:41,760
Right?
463
00:12:41,760 --> 00:12:43,440
So where do we get all of this
464
00:12:43,440 --> 00:12:45,280
data that we are talking about?
465
00:12:45,280 --> 00:12:47,240
If you look at some of the industry
466
00:12:47,240 --> 00:12:48,160
standard data,
467
00:12:48,160 --> 00:12:50,760
you are able to see that by 2025,
468
00:12:50,760 --> 00:12:52,600
the health care data is expected to grow
469
00:12:52,600 --> 00:12:53,880
by about 36% per year.
470
00:12:53,880 --> 00:12:56,520
Where is all this information coming in?
471
00:12:56,520 --> 00:12:58,400
I've mentioned some of the names here,
472
00:12:58,400 --> 00:12:59,640
medical imaging, patient
473
00:12:59,640 --> 00:13:01,000
monitoring, home monitoring,
474
00:13:01,000 --> 00:13:02,920
pathology, genomics,
475
00:13:02,920 --> 00:13:03,840
not just that
476
00:13:03,840 --> 00:13:04,880
you have a lot more data
477
00:13:04,880 --> 00:13:05,840
that you are talking about, right?
478
00:13:05,840 --> 00:13:07,040
The data is the new fuel
479
00:13:07,040 --> 00:13:08,480
that we are talking about.
480
00:13:08,480 --> 00:13:09,800
You get your provider data,
481
00:13:09,800 --> 00:13:12,000
which is the demographic information
482
00:13:12,000 --> 00:13:14,160
or the provider attestation and so on.
483
00:13:14,640 --> 00:13:16,400
Then you also have a lot of call center
484
00:13:16,400 --> 00:13:17,360
data that you get where,
485
00:13:17,360 --> 00:13:18,760
you know, people are calling,
486
00:13:18,760 --> 00:13:20,360
we have a call center workforce,
487
00:13:20,360 --> 00:13:22,680
we do a lot of service today.
488
00:13:22,680 --> 00:13:24,160
Then you have the claims data,
489
00:13:24,160 --> 00:13:24,960
you know,
490
00:13:25,040 --> 00:13:26,080
the entire claims
491
00:13:26,080 --> 00:13:27,040
operations itself
492
00:13:27,040 --> 00:13:28,440
for what has been a claim raised
493
00:13:28,440 --> 00:13:29,960
what was the rejected information
494
00:13:29,960 --> 00:13:31,480
and a lot of other things.
495
00:13:31,480 --> 00:13:32,680
Then you have the clinical,
496
00:13:32,680 --> 00:13:34,440
which is the prior
497
00:13:34,440 --> 00:13:35,240
authorization
498
00:13:35,240 --> 00:13:36,480
that we are talking about, your
499
00:13:36,480 --> 00:13:38,760
ER visits, your lab visit,
500
00:13:38,760 --> 00:13:39,320
the quality
501
00:13:39,320 --> 00:13:41,160
reporting, the whole entire
502
00:13:41,160 --> 00:13:42,600
gamut of things.
503
00:13:42,600 --> 00:13:44,840
Then you also have your agents,
504
00:13:44,840 --> 00:13:46,680
brokers, supervisors,
505
00:13:46,680 --> 00:13:48,480
managers, the entire workforce
506
00:13:48,480 --> 00:13:49,560
that works on this particular
507
00:13:49,560 --> 00:13:51,480
part of it is also something
508
00:13:51,480 --> 00:13:52,680
that we are talking about.
509
00:13:52,680 --> 00:13:54,640
Then we have the pharmacy data.
510
00:13:54,640 --> 00:13:57,000
How much of bills have you ordered
511
00:13:57,000 --> 00:13:59,000
and how frequently are you offering
512
00:13:59,000 --> 00:14:00,240
this particular information?
513
00:14:00,240 --> 00:14:02,320
So you get all of that information.
514
00:14:02,320 --> 00:14:04,520
Now you're talking about member data.
515
00:14:05,240 --> 00:14:06,200
It's not limited
516
00:14:06,200 --> 00:14:08,480
to just the member information,
517
00:14:08,480 --> 00:14:09,040
but also the,
518
00:14:09,040 --> 00:14:10,320
you know, the kind of issues,
519
00:14:10,320 --> 00:14:12,120
technical issues that you've got.
520
00:14:12,120 --> 00:14:13,120
What are the benefits,
521
00:14:13,120 --> 00:14:14,720
what are the plans,
522
00:14:14,720 --> 00:14:16,120
the policies and so on.
523
00:14:16,120 --> 00:14:17,640
Then you have appeals data,
524
00:14:17,640 --> 00:14:20,320
reconsiderations, appeals decisions
525
00:14:20,640 --> 00:14:21,960
and more importantly,
526
00:14:21,960 --> 00:14:23,520
what are the overpayments that
527
00:14:23,520 --> 00:14:24,320
we have got, what are the
528
00:14:25,800 --> 00:14:26,920
declines that we've got and
529
00:14:26,920 --> 00:14:28,680
what are the payments that we've done?
530
00:14:28,680 --> 00:14:30,120
There's a whole lot of data
531
00:14:30,120 --> 00:14:31,840
that you're actually talking about here.
532
00:14:31,840 --> 00:14:33,960
Now, this is where the complexity
533
00:14:33,960 --> 00:14:34,600
of the health care
534
00:14:34,600 --> 00:14:37,320
kind of comes in, it's so much of data.
535
00:14:37,680 --> 00:14:39,000
How are you actually going?
536
00:14:39,000 --> 00:14:39,720
And also
537
00:14:39,720 --> 00:14:41,440
all of this is what quality issues
538
00:14:41,440 --> 00:14:42,880
if you actually look at it.
539
00:14:42,880 --> 00:14:44,280
So our major challenge
540
00:14:44,280 --> 00:14:46,360
when we are talking about AI and ML is
541
00:14:46,680 --> 00:14:48,560
how are you going to actually
542
00:14:48,560 --> 00:14:51,080
derive insights or meaningful insights
543
00:14:51,400 --> 00:14:52,840
that would assist the health care
544
00:14:52,840 --> 00:14:54,120
professionals and patients
545
00:14:54,120 --> 00:14:56,200
in making sense of all of this data,
546
00:14:56,200 --> 00:14:57,480
given the inherent data
547
00:14:57,480 --> 00:14:58,440
quality issues
548
00:14:58,440 --> 00:15:00,120
and also the data complexities
549
00:15:00,120 --> 00:15:01,600
that we have got.
550
00:15:01,920 --> 00:15:04,280
So we need to have solutions
551
00:15:04,280 --> 00:15:05,200
that can assist
552
00:15:05,200 --> 00:15:07,240
all of our health care professionals
553
00:15:07,240 --> 00:15:08,240
in order to make sense
554
00:15:08,240 --> 00:15:09,960
of this particular data.
555
00:15:09,960 --> 00:15:11,160
Now let's look at some of the
556
00:15:11,160 --> 00:15:13,400
shifting expectations, right?
557
00:15:13,400 --> 00:15:14,920
We talked about pandemic.
558
00:15:14,920 --> 00:15:15,720
We talked about,
559
00:15:15,720 --> 00:15:17,040
you know, given the pressure
560
00:15:17,040 --> 00:15:17,640
that was there
561
00:15:17,640 --> 00:15:18,240
on the health care
562
00:15:18,240 --> 00:15:20,840
professionals, there was a sudden need
563
00:15:21,040 --> 00:15:22,000
for all of us
564
00:15:22,000 --> 00:15:22,680
to ensure
565
00:15:22,680 --> 00:15:24,160
that the expectations
566
00:15:24,160 --> 00:15:25,960
have kind of shifted, right?
567
00:15:25,960 --> 00:15:28,400
There's a value addition
568
00:15:28,400 --> 00:15:30,400
that people were kind of looking at.
569
00:15:30,400 --> 00:15:32,440
The marketplace also started
570
00:15:32,440 --> 00:15:34,200
addressing the full patient's
571
00:15:34,200 --> 00:15:35,560
full journey itself,
572
00:15:35,560 --> 00:15:37,920
leading to more improved affordability,
573
00:15:38,400 --> 00:15:41,080
quality, access and experience.
574
00:15:41,600 --> 00:15:42,320
So, you know,
575
00:15:42,320 --> 00:15:43,800
what are some of the questions
576
00:15:43,800 --> 00:15:45,000
that we are talking about?
577
00:15:45,000 --> 00:15:46,280
If you look at the health care
578
00:15:46,280 --> 00:15:47,640
industry itself,
579
00:15:47,640 --> 00:15:48,600
all the doctors
580
00:15:48,600 --> 00:15:50,120
or the health care workers
581
00:15:50,120 --> 00:15:51,680
want to ask these kind of
582
00:15:51,680 --> 00:15:52,920
questions, right?
583
00:15:52,920 --> 00:15:55,000
How can we find signs of diseases
584
00:15:55,000 --> 00:15:57,880
like cancer and heart diseases sooner.
585
00:15:57,880 --> 00:16:00,960
By detecting all of these issues,
586
00:16:01,080 --> 00:16:02,880
you have a probability
587
00:16:02,880 --> 00:16:04,720
where you can probably go
588
00:16:04,720 --> 00:16:05,560
and pre-empt
589
00:16:05,560 --> 00:16:07,200
some of these diseases itself.
590
00:16:07,200 --> 00:16:09,080
It could be through lifestyle changes,
591
00:16:09,080 --> 00:16:11,200
it could be through the wellness programs
592
00:16:11,200 --> 00:16:12,800
or a lot of things
593
00:16:12,800 --> 00:16:14,720
that we can actually do with that.
594
00:16:14,720 --> 00:16:16,360
Then we are also talking about
595
00:16:16,360 --> 00:16:17,400
how do you ensure
596
00:16:17,400 --> 00:16:19,720
that you have your diagnosis made right
597
00:16:19,720 --> 00:16:21,400
the first time itself?
598
00:16:21,400 --> 00:16:23,080
You must have heard a lot of times
599
00:16:23,080 --> 00:16:24,560
that you actually get a fever,
600
00:16:24,560 --> 00:16:25,360
you go to the doctor
601
00:16:25,360 --> 00:16:26,880
and the doctor only treats you
602
00:16:26,880 --> 00:16:28,360
for that particular fever.
603
00:16:28,360 --> 00:16:29,320
Then you go back home
604
00:16:29,320 --> 00:16:30,200
and then you get cold
605
00:16:30,200 --> 00:16:30,960
and then you come back
606
00:16:30,960 --> 00:16:32,400
to the hospital again
607
00:16:32,400 --> 00:16:33,720
because you have a cold now,
608
00:16:33,720 --> 00:16:35,320
and then he treats for the cold.
609
00:16:35,320 --> 00:16:36,880
Now what happens is
610
00:16:36,880 --> 00:16:39,720
you're being very tactical in providing
611
00:16:39,720 --> 00:16:41,840
all of these health solutions itself.
612
00:16:42,120 --> 00:16:43,360
You're treating for a fever,
613
00:16:43,360 --> 00:16:44,760
you're treating for a cold.
614
00:16:44,760 --> 00:16:45,680
You're not addressing
615
00:16:45,680 --> 00:16:47,720
the actual fundamental issue
616
00:16:47,720 --> 00:16:49,720
that is probably there in that one.
617
00:16:50,120 --> 00:16:51,400
What is causing this one?
618
00:16:51,400 --> 00:16:53,480
Is it a lung issue that is causing him
619
00:16:53,520 --> 00:16:54,840
or is it something else
620
00:16:54,840 --> 00:16:56,000
that is wrong within the body
621
00:16:56,000 --> 00:16:57,880
that is causing all of these issues?
622
00:16:57,880 --> 00:16:59,040
I think
623
00:16:59,080 --> 00:17:00,120
where we can use
624
00:17:00,120 --> 00:17:01,960
AI/ML very, very well or,
625
00:17:01,960 --> 00:17:02,240
you know,
626
00:17:02,240 --> 00:17:03,480
where the health workers are
627
00:17:03,480 --> 00:17:05,440
kind of trying to understand is
628
00:17:05,440 --> 00:17:06,560
how can I actually get
629
00:17:06,560 --> 00:17:09,120
the acute diagnosis right the first time?
630
00:17:09,480 --> 00:17:11,280
How can I treat the root cause problem,
631
00:17:11,280 --> 00:17:12,520
not the peripheral issues
632
00:17:12,520 --> 00:17:14,000
of a cold or a cough,
633
00:17:14,000 --> 00:17:15,680
but how I'm actually able to solve
634
00:17:15,680 --> 00:17:17,400
that particular problem is
635
00:17:17,400 --> 00:17:18,960
I think, one of the questions
636
00:17:18,960 --> 00:17:20,280
that all the health workers
637
00:17:20,280 --> 00:17:21,680
are looking at.
638
00:17:21,680 --> 00:17:22,800
Now, we are talking about, you know,
639
00:17:22,800 --> 00:17:24,280
the hospital operations are very,
640
00:17:24,280 --> 00:17:25,200
very costly affair.
641
00:17:25,200 --> 00:17:26,640
You have got a lot of nurses,
642
00:17:26,640 --> 00:17:27,520
you have got
643
00:17:27,520 --> 00:17:28,480
you've got people
644
00:17:28,480 --> 00:17:29,760
who actually ensure
645
00:17:29,760 --> 00:17:30,800
that there is cleanliness
646
00:17:30,800 --> 00:17:32,200
in your particular hospital,
647
00:17:32,200 --> 00:17:33,240
you have got the doctors,
648
00:17:33,240 --> 00:17:35,080
you have multiple shifts
649
00:17:35,080 --> 00:17:37,000
because hospital is not something
650
00:17:37,000 --> 00:17:38,440
that shuts down,
651
00:17:38,440 --> 00:17:39,320
emergency can come
652
00:17:39,320 --> 00:17:40,440
any given point of time.
653
00:17:40,440 --> 00:17:42,480
And then you have your emergency care
654
00:17:42,480 --> 00:17:43,120
and then you have
655
00:17:43,120 --> 00:17:44,400
got your ambulatory care.
656
00:17:44,400 --> 00:17:46,200
And there are so many things
657
00:17:46,200 --> 00:17:46,920
that a hospital
658
00:17:46,920 --> 00:17:48,240
actually has to take care of.
659
00:17:48,240 --> 00:17:50,560
How do you actually ensure
660
00:17:50,560 --> 00:17:52,680
that you are providing a better care
661
00:17:52,680 --> 00:17:53,760
at a cheaper price?
662
00:17:53,760 --> 00:17:55,320
That is one of the questions
663
00:17:55,320 --> 00:17:57,400
that people are looking at.
664
00:17:57,400 --> 00:17:58,280
We've talked
665
00:17:58,280 --> 00:17:59,880
about a lot on affordability,
666
00:17:59,880 --> 00:18:00,680
What are we talking about
667
00:18:00,680 --> 00:18:00,960
on affordability?
668
00:18:01,560 --> 00:18:02,400
Affordability
669
00:18:02,400 --> 00:18:04,440
is not making something cheap,
670
00:18:04,440 --> 00:18:05,480
but ensuring that
671
00:18:05,480 --> 00:18:07,640
the quality of the care is not reducing,
672
00:18:07,680 --> 00:18:09,400
but you are just
673
00:18:09,400 --> 00:18:12,040
making it much more cost efficient for
674
00:18:12,520 --> 00:18:13,480
the health workers
675
00:18:13,480 --> 00:18:15,880
or for the hospital owners.
676
00:18:15,880 --> 00:18:18,560
Then we talk a lot about the staff
677
00:18:18,560 --> 00:18:19,920
burnout itself.
678
00:18:19,920 --> 00:18:21,000
Now how can we prevent
679
00:18:21,000 --> 00:18:22,400
some of the staff burnout
680
00:18:22,400 --> 00:18:23,760
and how can we make sure
681
00:18:23,760 --> 00:18:25,680
that they put their time to the best use,
682
00:18:25,680 --> 00:18:27,320
which is like, for example,
683
00:18:27,320 --> 00:18:28,400
a lot of instances
684
00:18:28,400 --> 00:18:29,560
you must have heard that
685
00:18:29,560 --> 00:18:31,000
the doctor says I don't want to use
686
00:18:31,000 --> 00:18:33,160
this medical transcription thing at all.
687
00:18:33,160 --> 00:18:34,840
I don't want to use this portal
688
00:18:34,840 --> 00:18:35,560
where I want to go
689
00:18:35,560 --> 00:18:37,200
and input the data part of it
690
00:18:37,200 --> 00:18:38,600
because he feels
691
00:18:38,600 --> 00:18:40,800
he should be using the entire time,
692
00:18:40,920 --> 00:18:42,600
spending time with the patient,
693
00:18:42,600 --> 00:18:44,880
understanding what his problem is
694
00:18:44,880 --> 00:18:46,280
and trying to address
695
00:18:46,280 --> 00:18:47,680
that particular problem
696
00:18:47,680 --> 00:18:49,080
rather than spending the time
697
00:18:49,080 --> 00:18:50,760
on the technology where he is
698
00:18:50,760 --> 00:18:51,480
making notes.
699
00:18:51,480 --> 00:18:52,080
And then, you know,
700
00:18:52,080 --> 00:18:53,720
he is looking at the HL7 codes
701
00:18:53,720 --> 00:18:55,800
that he has to probably use over here
702
00:18:55,800 --> 00:18:57,840
or IC codes that he has to use.
703
00:18:57,840 --> 00:18:58,760
He just doesn't want
704
00:18:58,760 --> 00:19:01,120
that does not what his primary forte is,
705
00:19:01,400 --> 00:19:02,600
his primary forte is
706
00:19:02,600 --> 00:19:03,600
how does he ensure
707
00:19:03,600 --> 00:19:05,040
he is giving a better care?
708
00:19:05,040 --> 00:19:06,880
So that is where we are talking about
709
00:19:06,880 --> 00:19:09,040
how do you prevent the staff burnout
710
00:19:09,360 --> 00:19:10,040
and ensure
711
00:19:10,040 --> 00:19:11,360
we take his focus away
712
00:19:11,360 --> 00:19:13,320
from his secondary activities
713
00:19:13,320 --> 00:19:14,800
and ensure that you're
714
00:19:14,800 --> 00:19:15,600
putting their time
715
00:19:15,600 --> 00:19:16,640
on the primary focus,
716
00:19:16,640 --> 00:19:17,440
which is delivering
717
00:19:17,440 --> 00:19:19,440
that particular care itself.
718
00:19:19,440 --> 00:19:21,280
Now, a couple of other things
719
00:19:21,280 --> 00:19:21,840
that has come
720
00:19:21,840 --> 00:19:23,280
is with all this work from home
721
00:19:23,280 --> 00:19:23,760
and everything,
722
00:19:23,760 --> 00:19:25,680
one of the shifts that we have seen
723
00:19:25,680 --> 00:19:27,360
is where the patients are also looking
724
00:19:27,360 --> 00:19:29,200
for more convenient options,
725
00:19:29,200 --> 00:19:31,080
where they want to actually
726
00:19:31,080 --> 00:19:33,240
handle their own care from home.
727
00:19:33,240 --> 00:19:34,320
They want to ensure
728
00:19:34,320 --> 00:19:35,880
one of the well-being tips
729
00:19:35,880 --> 00:19:37,120
that they have to handle.
730
00:19:37,120 --> 00:19:38,960
They want to actually know
731
00:19:38,960 --> 00:19:40,080
what are the do's and don'ts.
732
00:19:40,080 --> 00:19:41,160
So what are the kind of foods
733
00:19:41,160 --> 00:19:42,400
that have to be avoided?
734
00:19:42,400 --> 00:19:43,360
What are the superfoods
735
00:19:43,360 --> 00:19:44,840
that they have to go after?
736
00:19:44,840 --> 00:19:46,160
So there are a lot of things
737
00:19:46,160 --> 00:19:48,000
that we are actually talking about.
738
00:19:48,000 --> 00:19:49,160
Now at Optum,
739
00:19:49,160 --> 00:19:50,840
what is the context over here?
740
00:19:50,840 --> 00:19:53,280
So, we have Optum Labs,
741
00:19:53,400 --> 00:19:55,160
as I was talking about an Optum
742
00:19:55,160 --> 00:19:56,240
AI Labs
743
00:19:56,240 --> 00:19:57,520
where we are trying to answer
744
00:19:57,520 --> 00:19:59,240
some of these particular questions.
745
00:19:59,240 --> 00:20:00,600
These could be through your image
746
00:20:00,600 --> 00:20:02,160
analytics, your video
747
00:20:02,160 --> 00:20:04,200
analytics, your text analytics,
748
00:20:04,200 --> 00:20:05,360
or any other
749
00:20:05,360 --> 00:20:06,600
AI methodology
750
00:20:06,600 --> 00:20:08,520
that you can use over here.
751
00:20:08,520 --> 00:20:09,520
And one of the things
752
00:20:09,520 --> 00:20:10,200
which I'm very,
753
00:20:10,200 --> 00:20:11,480
very proud of
754
00:20:11,480 --> 00:20:12,280
is recently
755
00:20:12,280 --> 00:20:14,160
we have concluded the ‘Tech Tank’,
756
00:20:14,160 --> 00:20:16,920
which is modeled after the Shark Tank
757
00:20:17,200 --> 00:20:19,920
and where what we had done over there is,
758
00:20:20,160 --> 00:20:21,600
we have different business units
759
00:20:21,600 --> 00:20:23,240
and the number of CXOs
760
00:20:23,240 --> 00:20:24,400
we have brought in
761
00:20:24,400 --> 00:20:26,360
our organizations are amazing
762
00:20:26,360 --> 00:20:27,800
and all of these guys
763
00:20:27,800 --> 00:20:29,040
have actually become
764
00:20:29,040 --> 00:20:30,280
the judges for the ‘Tech
765
00:20:30,280 --> 00:20:31,640
Tank’ for the very first time.
766
00:20:31,640 --> 00:20:33,920
We sat through all of this particular
767
00:20:33,920 --> 00:20:34,440
one day
768
00:20:34,440 --> 00:20:36,320
program, really heard
769
00:20:36,320 --> 00:20:37,800
some of the solutions
770
00:20:37,800 --> 00:20:39,880
and how technology can be an enabler
771
00:20:39,880 --> 00:20:42,000
to solve some of the complicated health
772
00:20:42,160 --> 00:20:42,800
care problems.
773
00:20:42,800 --> 00:20:44,640
They discussed on all of these,
774
00:20:44,640 --> 00:20:46,840
some of the best ideas have been chosen
775
00:20:47,160 --> 00:20:49,040
and they are funded right now
776
00:20:49,040 --> 00:20:50,400
and they are being sponsored
777
00:20:50,400 --> 00:20:50,880
for us
778
00:20:50,880 --> 00:20:52,760
to actually take it to the next level.
779
00:20:52,760 --> 00:20:54,280
So again and again,
780
00:20:54,280 --> 00:20:55,440
I would like to reiterate
781
00:20:55,440 --> 00:20:57,720
for the fact that I'm so proud
782
00:20:57,720 --> 00:20:59,160
the way that we are doubling down
783
00:20:59,160 --> 00:21:00,240
all of our efforts
784
00:21:00,240 --> 00:21:02,560
on ensuring that we use technology
785
00:21:02,560 --> 00:21:04,040
as an enabler
786
00:21:04,040 --> 00:21:05,480
to solve the larger problems
787
00:21:05,480 --> 00:21:06,240
at hand that we have got.
788
00:21:07,840 --> 00:21:08,760
All right.
789
00:21:08,760 --> 00:21:10,280
So let's move on to the next one,
790
00:21:10,280 --> 00:21:12,000
which is the experience led
791
00:21:12,000 --> 00:21:13,200
that we are talking about.
792
00:21:13,200 --> 00:21:13,680
As I said,
793
00:21:13,680 --> 00:21:15,240
this is one of the biggest buzz words
794
00:21:15,240 --> 00:21:16,080
that you would see,
795
00:21:16,080 --> 00:21:17,640
this called the design led,
796
00:21:17,640 --> 00:21:20,160
or it is called as the experience led.
797
00:21:20,520 --> 00:21:23,680
There's a lot of words and a lot of
798
00:21:24,120 --> 00:21:25,560
terminology and buzzwords
799
00:21:25,560 --> 00:21:27,000
that go on around this.
800
00:21:27,000 --> 00:21:28,440
So, let's take
801
00:21:28,440 --> 00:21:29,640
some of the examples, right,
802
00:21:29,640 --> 00:21:30,080
and talk
803
00:21:30,080 --> 00:21:31,800
about what exactly do we mean
804
00:21:31,800 --> 00:21:33,240
by experience led.
805
00:21:33,240 --> 00:21:34,920
What are the business
806
00:21:35,320 --> 00:21:36,520
benefits of actually
807
00:21:36,520 --> 00:21:38,320
going through this experience led?
808
00:21:38,320 --> 00:21:40,160
So, before I go into this,
809
00:21:40,160 --> 00:21:43,200
recently our leaders came down from U.S.
810
00:21:43,200 --> 00:21:44,120
and this last week
811
00:21:44,120 --> 00:21:45,960
our leaders were over here.
812
00:21:45,960 --> 00:21:47,520
So my leader, my boss,
813
00:21:47,520 --> 00:21:49,320
his name is Chris Hanson.
814
00:21:49,320 --> 00:21:50,480
What he wanted to do was
815
00:21:50,480 --> 00:21:51,720
he wanted to demonstrate
816
00:21:51,720 --> 00:21:53,160
what was the
817
00:21:53,160 --> 00:21:55,240
example of our experience led,
818
00:21:55,320 --> 00:21:57,080
how can we increase collaboration
819
00:21:57,080 --> 00:21:58,600
and how can we bring closer
820
00:21:58,600 --> 00:22:00,320
to our own customers?
821
00:22:00,320 --> 00:22:01,320
In this activity,
822
00:22:01,320 --> 00:22:02,160
what he has done is
823
00:22:02,160 --> 00:22:03,000
he has conducted
824
00:22:03,000 --> 00:22:05,120
something called as an experience
825
00:22:05,120 --> 00:22:06,360
led workshop,
826
00:22:06,360 --> 00:22:07,960
and he's used these Lego blocks.
827
00:22:07,960 --> 00:22:09,160
I'm sure all of you are familiar
828
00:22:09,160 --> 00:22:11,080
with what Lego blocks are.
829
00:22:11,080 --> 00:22:13,280
So, he has used all of those Lego blocks
830
00:22:13,280 --> 00:22:14,640
to derive,
831
00:22:14,640 --> 00:22:15,080
you know,
832
00:22:15,080 --> 00:22:16,720
how you can actually achieve
833
00:22:16,720 --> 00:22:17,400
your results
834
00:22:17,400 --> 00:22:19,480
in a much more methodical way
835
00:22:19,480 --> 00:22:21,480
and being very customer obsessed.
836
00:22:21,480 --> 00:22:23,440
And that's something that I really came
837
00:22:23,440 --> 00:22:25,320
out of that workshop last week
838
00:22:25,320 --> 00:22:25,680
and I'm
839
00:22:25,680 --> 00:22:27,600
so sold into this entire experience
840
00:22:27,600 --> 00:22:28,920
led itself.
841
00:22:28,920 --> 00:22:30,120
But let's look at it
842
00:22:30,120 --> 00:22:31,600
from a business perspective, right?
843
00:22:31,600 --> 00:22:32,880
So, what does it do?
844
00:22:32,880 --> 00:22:35,240
It augments all of your experience
845
00:22:35,240 --> 00:22:36,800
and expertise itself.
846
00:22:36,800 --> 00:22:38,480
So let's look at some of the clinical
847
00:22:38,480 --> 00:22:39,560
use cases of
848
00:22:39,560 --> 00:22:41,040
AI which support
849
00:22:41,040 --> 00:22:41,760
image segmentation
850
00:22:41,760 --> 00:22:44,200
as I was talking about in the radiology,
851
00:22:44,600 --> 00:22:46,120
that would help identify
852
00:22:46,120 --> 00:22:47,440
early signs of patient
853
00:22:47,440 --> 00:22:48,960
deterioration in acute
854
00:22:48,960 --> 00:22:50,760
and post-acute care.
855
00:22:50,760 --> 00:22:51,880
Now what does this help?
856
00:22:51,880 --> 00:22:53,640
How does AI help over here
857
00:22:53,640 --> 00:22:54,880
or experience led help?
858
00:22:54,880 --> 00:22:55,640
You're essentially
859
00:22:55,640 --> 00:22:56,480
helping the health care
860
00:22:56,480 --> 00:22:58,200
providers perform for their best
861
00:22:58,200 --> 00:23:00,240
for the patient care, right.
862
00:23:00,240 --> 00:23:01,920
So that is the experience,
863
00:23:01,920 --> 00:23:02,920
the patient is happy
864
00:23:02,920 --> 00:23:04,760
because he's getting the right treatment
865
00:23:04,760 --> 00:23:05,800
on time
866
00:23:05,800 --> 00:23:07,600
with the required information,
867
00:23:07,600 --> 00:23:08,840
with all the side effects
868
00:23:08,840 --> 00:23:09,400
and everything
869
00:23:09,400 --> 00:23:10,520
that would actually tell him.
870
00:23:10,520 --> 00:23:12,000
So you're kind of augmenting
871
00:23:12,000 --> 00:23:13,320
the expertise.
872
00:23:13,320 --> 00:23:14,160
Health care
873
00:23:14,160 --> 00:23:15,920
professionals are one of the best
874
00:23:15,920 --> 00:23:16,920
in the world that we have got.
875
00:23:16,920 --> 00:23:18,800
We are not replacing them,
876
00:23:18,800 --> 00:23:19,760
but we are augmenting
877
00:23:19,760 --> 00:23:21,240
their expertise itself.
878
00:23:21,240 --> 00:23:22,760
The second one that we are talking about
879
00:23:22,760 --> 00:23:24,720
is operational efficiency.
880
00:23:24,720 --> 00:23:26,280
Now, AI can actually help
881
00:23:26,280 --> 00:23:27,680
streamline operations
882
00:23:27,680 --> 00:23:29,760
within and across the departments,
883
00:23:29,760 --> 00:23:31,320
so every department has to work
884
00:23:31,320 --> 00:23:32,160
seamlessly, right?
885
00:23:32,160 --> 00:23:34,360
So for example, I'm sure a lot of you
886
00:23:34,360 --> 00:23:36,600
probably might have visited hospital
887
00:23:36,640 --> 00:23:37,680
sometime in your life
888
00:23:37,680 --> 00:23:39,600
and imagine the checkout process.
889
00:23:39,600 --> 00:23:41,040
The doctor says that, you know,
890
00:23:41,040 --> 00:23:41,840
this particular patient
891
00:23:41,840 --> 00:23:42,880
can be discharged,
892
00:23:42,880 --> 00:23:45,240
but the sheer amount of the documentation
893
00:23:45,240 --> 00:23:45,760
that is there
894
00:23:45,760 --> 00:23:47,640
and for you to get your cashless
895
00:23:47,640 --> 00:23:49,560
and your entire insurance
896
00:23:49,560 --> 00:23:50,640
figured out before
897
00:23:50,640 --> 00:23:52,880
this particular patient is discharged,
898
00:23:52,880 --> 00:23:54,360
if you started 10 o’clock,
899
00:23:54,360 --> 00:23:55,520
it completes
900
00:23:55,520 --> 00:23:56,280
at 4 o’clock atleast in India
901
00:23:56,280 --> 00:23:57,600
that we are talking about,
902
00:23:57,600 --> 00:23:58,600
that is because you have got
903
00:23:58,600 --> 00:23:59,600
so many departments
904
00:23:59,600 --> 00:24:01,200
that are working separately.
905
00:24:01,200 --> 00:24:02,320
All these departments
906
00:24:02,320 --> 00:24:03,720
have to come together
907
00:24:03,720 --> 00:24:04,800
to be able to deliver
908
00:24:04,800 --> 00:24:06,240
that optimal patient care
909
00:24:06,240 --> 00:24:07,840
while they're also reducing
910
00:24:07,840 --> 00:24:09,640
costs and inefficiencies.
911
00:24:09,640 --> 00:24:11,880
And, they're also avoiding fraud.
912
00:24:12,360 --> 00:24:15,240
Because why is it that there is so much
913
00:24:15,240 --> 00:24:18,360
of scrutiny in terms of your data
914
00:24:18,360 --> 00:24:19,200
and documentation.
915
00:24:19,200 --> 00:24:20,600
It is primary
916
00:24:20,600 --> 00:24:21,840
because fraud is very easy
917
00:24:21,840 --> 00:24:23,880
to happen in these kind of places.
918
00:24:23,880 --> 00:24:24,960
So this is another one
919
00:24:24,960 --> 00:24:26,240
that we're talking about.
920
00:24:26,240 --> 00:24:27,120
And then we're talking
921
00:24:27,120 --> 00:24:28,360
about empowering people.
922
00:24:28,360 --> 00:24:30,520
Now empowering people
923
00:24:30,520 --> 00:24:31,280
to take care of their
924
00:24:31,280 --> 00:24:32,480
health and well-being.
925
00:24:32,480 --> 00:24:34,600
We are also using AI to help
926
00:24:34,600 --> 00:24:36,440
transform the health systems
927
00:24:36,440 --> 00:24:37,560
by preventing people
928
00:24:37,560 --> 00:24:38,880
from developing diseases
929
00:24:38,880 --> 00:24:39,880
in the first place itself.
930
00:24:39,880 --> 00:24:41,120
So we are kind of coming up
931
00:24:41,120 --> 00:24:42,600
with wellness programs
932
00:24:42,600 --> 00:24:43,440
that we are offering
933
00:24:43,440 --> 00:24:45,480
to all of our employers and employees
934
00:24:45,720 --> 00:24:47,400
to ensure that we are
935
00:24:47,400 --> 00:24:48,760
promoting healthier habits
936
00:24:48,760 --> 00:24:49,680
and we are also having
937
00:24:49,680 --> 00:24:51,840
an increased patient engagement itself.
938
00:24:51,840 --> 00:24:53,400
So that's on the experience
939
00:24:53,400 --> 00:24:54,600
led part of it.
940
00:24:54,600 --> 00:24:56,600
So we talked about so many,
941
00:24:56,600 --> 00:24:57,840
you know, theory,
942
00:24:57,840 --> 00:24:59,680
let us look at some of the use cases
943
00:24:59,680 --> 00:25:01,200
that we are referring to.
944
00:25:01,200 --> 00:25:02,880
If you look at the skin lesion
945
00:25:02,880 --> 00:25:05,160
itself, dermatologists are using
946
00:25:06,120 --> 00:25:06,880
the kind of, you know,
947
00:25:06,880 --> 00:25:09,000
they want to pick out the cancerous ones.
948
00:25:09,000 --> 00:25:10,320
They want to identify
949
00:25:10,320 --> 00:25:11,880
whether this particular imaging
950
00:25:11,880 --> 00:25:14,040
has got the cancerous ones or not.
951
00:25:14,040 --> 00:25:16,680
This is where AI can actually augment it.
952
00:25:16,920 --> 00:25:18,960
Once you train your algorithms,
953
00:25:19,120 --> 00:25:22,400
they would be able to identify the cancer
954
00:25:22,400 --> 00:25:22,920
part of it
955
00:25:22,920 --> 00:25:24,200
and thus leading to
956
00:25:24,200 --> 00:25:26,400
an early detection of skin cancer.
957
00:25:26,880 --> 00:25:30,120
So that if you look at the left down one,
958
00:25:30,120 --> 00:25:31,560
you have got lung cancer.
959
00:25:31,560 --> 00:25:33,360
That is also something very similar
960
00:25:33,360 --> 00:25:34,200
to the skin lesion
961
00:25:34,200 --> 00:25:35,560
that we are talking about,
962
00:25:35,560 --> 00:25:36,840
where your CT scans are
963
00:25:36,840 --> 00:25:38,200
used for the detection of the
964
00:25:39,960 --> 00:25:42,240
pulmonary nodules itself.
965
00:25:42,480 --> 00:25:44,240
Then you've got sinusitis.
966
00:25:44,240 --> 00:25:45,120
I know a lot of us
967
00:25:45,120 --> 00:25:46,720
probably have got the sinusitis
968
00:25:46,720 --> 00:25:48,480
and the operation is a very minimal
969
00:25:48,480 --> 00:25:49,960
one that we are talking about.
970
00:25:49,960 --> 00:25:52,200
But the precision that it requires
971
00:25:52,320 --> 00:25:53,080
to identify
972
00:25:53,080 --> 00:25:55,160
what is the amount of inflammation
973
00:25:55,320 --> 00:25:57,480
that is there in the sinus membranes
974
00:25:57,480 --> 00:25:59,480
can be visualized through the scans
975
00:25:59,480 --> 00:26:00,640
and that is an input
976
00:26:00,640 --> 00:26:01,400
that would
977
00:26:01,400 --> 00:26:02,880
help doctors
978
00:26:02,880 --> 00:26:05,120
in the precision medicine itself.
979
00:26:05,120 --> 00:26:05,800
Then we are talking
980
00:26:05,800 --> 00:26:06,960
about the spine surgery
981
00:26:06,960 --> 00:26:10,040
and you've got your MRIs, which I use
982
00:26:10,040 --> 00:26:12,160
for diagnosis of spinal stenosis
983
00:26:12,160 --> 00:26:13,320
to aid the diagnosis
984
00:26:13,320 --> 00:26:14,640
and the care coordination.
985
00:26:14,640 --> 00:26:16,880
Now look at the knee surgery
986
00:26:16,880 --> 00:26:17,960
and the chest X-ray
987
00:26:17,960 --> 00:26:19,360
that we are talking about,
988
00:26:19,360 --> 00:26:20,880
where the speed of approvals
989
00:26:20,880 --> 00:26:22,440
and facilitating the treatment
990
00:26:22,440 --> 00:26:23,280
becomes easy
991
00:26:23,280 --> 00:26:24,080
because you have got
992
00:26:24,080 --> 00:26:26,040
the required medical imaging
993
00:26:26,040 --> 00:26:27,640
that would augment your decision
994
00:26:27,640 --> 00:26:28,920
support system itself.
995
00:26:30,120 --> 00:26:30,760
Right?
996
00:26:30,960 --> 00:26:31,920
So then let's look at
997
00:26:31,920 --> 00:26:33,920
the administrative workflow.
998
00:26:34,240 --> 00:26:35,200
The only callout that
999
00:26:35,200 --> 00:26:36,120
I've got because, you know,
1000
00:26:36,120 --> 00:26:38,040
we kind of spoke a lot about this.
1001
00:26:38,040 --> 00:26:39,600
We talked about, you know,
1002
00:26:39,600 --> 00:26:42,400
how we can automate a lot of these
1003
00:26:43,880 --> 00:26:44,520
things like,
1004
00:26:44,520 --> 00:26:46,560
you know, voice-to-text transcriptions.
1005
00:26:46,560 --> 00:26:47,840
You can also assist in
1006
00:26:47,840 --> 00:26:49,200
automating the non-patient
1007
00:26:49,200 --> 00:26:50,320
care task like, you know,
1008
00:26:50,320 --> 00:26:52,520
requesting tests recommending drugs.
1009
00:26:52,520 --> 00:26:54,720
You are also creating various charts.
1010
00:26:54,800 --> 00:26:57,000
But what I wanted to call out is
1011
00:26:57,000 --> 00:26:57,800
could you imagine
1012
00:26:57,800 --> 00:26:59,160
that the administrative
1013
00:26:59,160 --> 00:27:00,840
workflow automation
1014
00:27:00,840 --> 00:27:01,800
that we are talking about
1015
00:27:01,800 --> 00:27:03,000
could potentially lead to
1016
00:27:03,000 --> 00:27:04,840
about $18 billion savings
1017
00:27:04,840 --> 00:27:06,560
that we are talking about annually?
1018
00:27:06,560 --> 00:27:07,920
And that's a lot of money
1019
00:27:07,920 --> 00:27:09,480
that we are essentially talking about,
1020
00:27:09,480 --> 00:27:10,840
which can then be channelized
1021
00:27:10,840 --> 00:27:12,920
into the research part of it,
1022
00:27:12,920 --> 00:27:15,320
especially in the health care it itself.
1023
00:27:15,320 --> 00:27:16,040
Right.
1024
00:27:16,160 --> 00:27:17,840
So I've got the next few minutes.
1025
00:27:17,840 --> 00:27:19,160
We going to spend some time
1026
00:27:19,160 --> 00:27:21,200
on the case studies. Right.
1027
00:27:21,560 --> 00:27:23,600
We talked about a lot of peripheral case
1028
00:27:23,640 --> 00:27:24,120
studies.
1029
00:27:24,120 --> 00:27:26,160
Let's exactly look at one of the case
1030
00:27:26,200 --> 00:27:27,560
studies that we are talking about.
1031
00:27:28,640 --> 00:27:29,400
The next two case
1032
00:27:29,400 --> 00:27:29,760
studies,
1033
00:27:29,760 --> 00:27:31,200
the first one that we are talking about
1034
00:27:31,200 --> 00:27:32,600
is the patient experience.
1035
00:27:32,600 --> 00:27:35,520
Now, imagine, you know, you've called
1036
00:27:35,880 --> 00:27:37,000
a particular person
1037
00:27:37,000 --> 00:27:39,280
and then you said, I'm not feeling well.
1038
00:27:39,720 --> 00:27:40,800
And
1039
00:27:42,120 --> 00:27:43,320
it looks like, you know,
1040
00:27:43,320 --> 00:27:46,720
a preliminary case of cancer and I
1041
00:27:46,720 --> 00:27:48,440
would like to actually go
1042
00:27:48,440 --> 00:27:50,640
for the preferred provider.
1043
00:27:50,640 --> 00:27:52,440
So what is my eligibility?
1044
00:27:52,440 --> 00:27:54,160
How much of claim can I make
1045
00:27:54,160 --> 00:27:55,200
or, you know,
1046
00:27:55,200 --> 00:27:57,240
how much of it would be my out-of-pocket
1047
00:27:57,600 --> 00:27:59,520
expenses that we are talking about?
1048
00:27:59,520 --> 00:28:01,120
Those are complicated questions
1049
00:28:01,120 --> 00:28:02,760
that you are talking about right.
1050
00:28:02,760 --> 00:28:04,440
But let's imagine that, you know,
1051
00:28:04,440 --> 00:28:05,560
you have your advocates,
1052
00:28:05,560 --> 00:28:06,720
advocates are typically
1053
00:28:06,720 --> 00:28:08,720
the people who actually
1054
00:28:08,720 --> 00:28:10,240
handle all of these calls
1055
00:28:10,240 --> 00:28:10,920
that we are getting
1056
00:28:10,920 --> 00:28:12,280
from all of our members.
1057
00:28:12,280 --> 00:28:15,360
Imagine this particular advocate
1058
00:28:15,360 --> 00:28:16,080
getting a call.
1059
00:28:16,080 --> 00:28:18,120
He doesn't have a clue as to why
1060
00:28:18,120 --> 00:28:19,920
this particular member is calling.
1061
00:28:19,920 --> 00:28:21,840
And he starts from grounds-up,
1062
00:28:21,840 --> 00:28:23,520
imagine yourself to be the person
1063
00:28:23,520 --> 00:28:25,640
who's calling and I'm that gate.
1064
00:28:25,720 --> 00:28:28,440
And then I keep asking, hi, how are you?
1065
00:28:28,440 --> 00:28:29,960
How can I help you today?
1066
00:28:29,960 --> 00:28:30,960
How are you feeling?
1067
00:28:30,960 --> 00:28:32,520
How does that sound?
1068
00:28:32,520 --> 00:28:33,440
Versus,
1069
00:28:33,440 --> 00:28:35,440
I already have a lot of information
1070
00:28:35,440 --> 00:28:36,240
with me, where I'm
1071
00:28:36,240 --> 00:28:38,040
getting all this information?
1072
00:28:38,040 --> 00:28:39,480
Let's take a step back. Right.
1073
00:28:39,480 --> 00:28:40,400
I get my information
1074
00:28:40,400 --> 00:28:41,520
from voice of customers.
1075
00:28:41,520 --> 00:28:44,040
So today we have a lot of places
1076
00:28:44,040 --> 00:28:45,400
where the customers can go ahead
1077
00:28:45,400 --> 00:28:46,520
and provide the feedback
1078
00:28:46,520 --> 00:28:47,840
while they're using the portal
1079
00:28:47,840 --> 00:28:49,560
or the mobile application. Right?
1080
00:28:49,560 --> 00:28:50,400
So you get all of those
1081
00:28:50,400 --> 00:28:52,440
voice of customers part of it.
1082
00:28:52,440 --> 00:28:53,960
Then you have your technology
1083
00:28:53,960 --> 00:28:54,760
that would assist you
1084
00:28:54,760 --> 00:28:56,200
in getting you this data
1085
00:28:56,200 --> 00:28:57,720
for all the transaction records
1086
00:28:57,720 --> 00:28:58,960
that you have got.
1087
00:28:58,960 --> 00:29:00,720
Then, given that this
1088
00:29:00,720 --> 00:29:01,760
is all protected data
1089
00:29:01,760 --> 00:29:03,480
with all the right
1090
00:29:03,520 --> 00:29:04,960
measures that are taken,
1091
00:29:04,960 --> 00:29:06,960
we also have a complete digital footprint
1092
00:29:06,960 --> 00:29:07,920
of the customer journey.
1093
00:29:07,920 --> 00:29:09,400
So he went to this particular portal.
1094
00:29:09,400 --> 00:29:11,440
He kind of clicked these two-three links
1095
00:29:11,440 --> 00:29:13,200
and then there was a transaction failure.
1096
00:29:13,200 --> 00:29:14,480
I get almost that information
1097
00:29:14,480 --> 00:29:16,080
because I have the digital
1098
00:29:16,080 --> 00:29:17,040
footprint right.
1099
00:29:17,040 --> 00:29:18,360
I can also see whether this guy
1100
00:29:18,360 --> 00:29:19,400
had actually completed
1101
00:29:19,400 --> 00:29:20,760
his journey or not.
1102
00:29:20,760 --> 00:29:22,040
Then I would also have
1103
00:29:22,040 --> 00:29:24,240
the renewal information example
1104
00:29:24,240 --> 00:29:25,600
if I have a health policy
1105
00:29:25,600 --> 00:29:27,880
information that I have taken
1106
00:29:27,880 --> 00:29:29,160
in April of last year,
1107
00:29:29,160 --> 00:29:30,720
and if I'm calling you now,
1108
00:29:30,720 --> 00:29:31,840
in most probability
1109
00:29:31,840 --> 00:29:32,280
it could be
1110
00:29:32,280 --> 00:29:33,840
because I might just want to renew it
1111
00:29:33,840 --> 00:29:34,680
or I probably have
1112
00:29:34,680 --> 00:29:36,320
a challenge in renewing it.
1113
00:29:36,320 --> 00:29:38,120
So if I'm able to actually
1114
00:29:38,120 --> 00:29:39,720
take this entire data
1115
00:29:39,720 --> 00:29:40,960
that we are talking about
1116
00:29:40,960 --> 00:29:42,640
and I'm able to do
1117
00:29:42,640 --> 00:29:45,560
and apply my AI analytics over here,
1118
00:29:45,920 --> 00:29:47,880
how easy it is for my advocates
1119
00:29:47,880 --> 00:29:49,200
say – “Hey, how are you?
1120
00:29:49,200 --> 00:29:51,160
I see that your renewal is up?
1121
00:29:51,160 --> 00:29:53,600
Are you facing any kind of an issue.
1122
00:29:53,600 --> 00:29:55,080
Can I help you with anything?
1123
00:29:55,080 --> 00:29:56,160
How does that sound?”
1124
00:29:56,160 --> 00:29:57,680
versus “How are you?
1125
00:29:57,680 --> 00:29:59,400
How can I help you today?” Right.
1126
00:29:59,400 --> 00:30:00,480
So what we are trying to do
1127
00:30:00,480 --> 00:30:03,000
as we are trying to leverage ML
1128
00:30:03,000 --> 00:30:04,800
to identify the probable reason
1129
00:30:04,800 --> 00:30:06,360
why customer is calling
1130
00:30:06,360 --> 00:30:08,040
and we want to proactively address
1131
00:30:08,040 --> 00:30:09,000
that particular issue.
1132
00:30:09,000 --> 00:30:10,800
And this also becomes an input
1133
00:30:10,800 --> 00:30:12,640
for all of our other guys
1134
00:30:12,640 --> 00:30:13,840
to potentially see
1135
00:30:13,840 --> 00:30:15,120
whether there's a technology issue
1136
00:30:15,120 --> 00:30:16,200
that can be handled
1137
00:30:16,200 --> 00:30:17,440
or is that any other issue
1138
00:30:17,440 --> 00:30:18,640
that can be handled?
1139
00:30:18,640 --> 00:30:21,120
And it typically gets into our backlog.
1140
00:30:21,240 --> 00:30:22,560
That is how seamlessly
1141
00:30:22,560 --> 00:30:23,760
that we are working.
1142
00:30:23,760 --> 00:30:25,080
Being customer obsession
1143
00:30:25,080 --> 00:30:26,240
is all about this.
1144
00:30:26,240 --> 00:30:27,000
Being customer
1145
00:30:27,000 --> 00:30:28,480
centricity is all about it.
1146
00:30:28,480 --> 00:30:30,320
We are kind of putting your customer
1147
00:30:30,320 --> 00:30:32,400
at the center of all of your solutions
1148
00:30:32,880 --> 00:30:33,680
and what are the business
1149
00:30:33,680 --> 00:30:34,720
benefits that we have derived.
1150
00:30:34,720 --> 00:30:36,960
We are essentially talking about,
1151
00:30:36,960 --> 00:30:39,720
our NPS has increased by 30 points,
1152
00:30:40,000 --> 00:30:40,960
and that's a feat
1153
00:30:40,960 --> 00:30:43,200
that we could actually achieve over here.
1154
00:30:43,200 --> 00:30:47,080
And more importantly, you have 40% lesser
1155
00:30:47,720 --> 00:30:49,640
repeat calls that we are talking about
1156
00:30:49,640 --> 00:30:50,560
and the first time
1157
00:30:50,560 --> 00:30:52,120
resolutions that we are talking about,
1158
00:30:52,120 --> 00:30:52,680
which are better
1159
00:30:52,800 --> 00:30:53,840
resolutions
1160
00:30:53,840 --> 00:30:55,800
have increased by leaps and bounds.
1161
00:30:55,800 --> 00:30:57,360
So that is one
1162
00:30:57,360 --> 00:30:58,200
of the case studies
1163
00:30:58,200 --> 00:31:00,240
that I wanted to talk about on how we are
1164
00:31:00,280 --> 00:31:03,000
being very patient obsessed.
1165
00:31:05,000 --> 00:31:06,800
Now let's look into the other case
1166
00:31:06,800 --> 00:31:10,080
study that we are talking about right.
1167
00:31:12,120 --> 00:31:13,600
Okay,
1168
00:31:13,600 --> 00:31:15,760
just give me a second.
1169
00:31:17,280 --> 00:31:17,880
Okay.
1170
00:31:17,880 --> 00:31:19,960
So if you look at it again,
1171
00:31:19,960 --> 00:31:20,880
this particular industry,
1172
00:31:20,880 --> 00:31:21,480
I was telling you
1173
00:31:21,480 --> 00:31:23,720
that we receive a lot of data, right?
1174
00:31:23,720 --> 00:31:26,480
We receive a lot of data
1175
00:31:26,480 --> 00:31:27,960
in terms of unstructured data.
1176
00:31:27,960 --> 00:31:30,120
Some of these are like handwritten forms.
1177
00:31:30,120 --> 00:31:31,240
Some of these
1178
00:31:31,240 --> 00:31:32,880
that we are talking about are PDFs
1179
00:31:32,880 --> 00:31:35,040
and the PDFs are not of the same format
1180
00:31:35,040 --> 00:31:36,880
and everything that we are talking about.
1181
00:31:36,880 --> 00:31:38,680
So you get a lot of information,
1182
00:31:38,680 --> 00:31:40,760
you get a lot of unstructured data.
1183
00:31:40,760 --> 00:31:42,200
And what we have to do is
1184
00:31:42,200 --> 00:31:42,960
how do we look
1185
00:31:42,960 --> 00:31:44,040
at some of this particular
1186
00:31:44,040 --> 00:31:45,520
information itself?
1187
00:31:45,520 --> 00:31:48,120
So we have two different types
1188
00:31:48,120 --> 00:31:50,400
of problems that we are talking about,
1189
00:31:50,400 --> 00:31:51,960
which is one is a scanned document,
1190
00:31:51,960 --> 00:31:53,680
the other one is the handwritten document
1191
00:31:53,680 --> 00:31:56,080
that we are essentially referring to.
1192
00:31:56,080 --> 00:31:58,080
If you look at some of the you know,
1193
00:31:58,080 --> 00:31:59,400
we talked about operational cost,
1194
00:31:59,400 --> 00:32:01,960
what do we mean by operational cost?
1195
00:32:02,240 --> 00:32:02,720
Today,
1196
00:32:02,720 --> 00:32:03,120
we have what
1197
00:32:03,120 --> 00:32:04,280
thousand of our employees
1198
00:32:04,280 --> 00:32:05,600
who are actually sitting there
1199
00:32:05,600 --> 00:32:07,680
and doing the data entry job, right.
1200
00:32:07,680 --> 00:32:08,920
And all of this data
1201
00:32:08,920 --> 00:32:10,160
entry job has to be done
1202
00:32:10,160 --> 00:32:11,440
so that all of the data
1203
00:32:11,440 --> 00:32:13,680
is coming into some standard formats
1204
00:32:13,680 --> 00:32:14,040
that can
1205
00:32:14,040 --> 00:32:17,520
then be used by other applications
1206
00:32:17,520 --> 00:32:18,480
or processes
1207
00:32:18,480 --> 00:32:20,840
to actually handle all of this data.
1208
00:32:21,760 --> 00:32:22,800
And then what is this solution
1209
00:32:22,800 --> 00:32:24,800
that we are talking about here? Right.
1210
00:32:24,800 --> 00:32:25,720
What we have done is
1211
00:32:25,720 --> 00:32:27,360
you have got all of these technologies
1212
00:32:27,360 --> 00:32:28,520
that are already available,
1213
00:32:28,520 --> 00:32:29,800
which is your OCR,
1214
00:32:29,800 --> 00:32:31,040
image processing,
1215
00:32:31,040 --> 00:32:33,040
templatization, contouring,
1216
00:32:33,040 --> 00:32:34,400
object detection.
1217
00:32:34,400 --> 00:32:35,480
The complexity
1218
00:32:35,480 --> 00:32:37,160
of all of this image processing
1219
00:32:37,160 --> 00:32:38,200
and the templatization
1220
00:32:38,200 --> 00:32:39,960
and everything was primarily required
1221
00:32:39,960 --> 00:32:41,400
because of the handwritten notes
1222
00:32:41,400 --> 00:32:42,840
that the talking about.
1223
00:32:42,840 --> 00:32:45,440
Predominantly we use all the OCR
1224
00:32:45,960 --> 00:32:48,920
effectively and we also use the transfer
1225
00:32:48,920 --> 00:32:51,800
learning to cut down on some of the model
1226
00:32:52,120 --> 00:32:53,360
training time
1227
00:32:53,360 --> 00:32:54,480
as well as the training data
1228
00:32:54,480 --> 00:32:55,600
requirement itself.
1229
00:32:55,600 --> 00:32:58,440
Now currently we also use manual
1230
00:32:58,480 --> 00:32:59,400
data labeling
1231
00:32:59,400 --> 00:33:01,200
and we are investigating
1232
00:33:01,200 --> 00:33:02,520
some of the automated data
1233
00:33:02,520 --> 00:33:04,200
labeling approaches also.
1234
00:33:04,200 --> 00:33:06,120
So this is a work in progress.
1235
00:33:06,120 --> 00:33:07,960
But as we keep
1236
00:33:07,960 --> 00:33:09,840
working on this particular model,
1237
00:33:09,840 --> 00:33:10,680
one of the things that
1238
00:33:10,680 --> 00:33:12,200
we've identified is
1239
00:33:12,200 --> 00:33:14,320
this is where it is a repeated process.
1240
00:33:14,320 --> 00:33:15,760
Every application
1241
00:33:15,760 --> 00:33:17,640
has this kind of an issues.
1242
00:33:17,640 --> 00:33:18,680
So what we have done is
1243
00:33:18,680 --> 00:33:21,000
there are several use cases
1244
00:33:21,000 --> 00:33:22,200
of this particular solution
1245
00:33:22,200 --> 00:33:23,280
that were deployed,
1246
00:33:23,280 --> 00:33:25,240
which has led to a lot of
1247
00:33:25,240 --> 00:33:27,320
savings in our operational cost itself.
1248
00:33:27,880 --> 00:33:29,280
So that is another use case
1249
00:33:29,280 --> 00:33:31,680
that we wanted to actually talk about.
1250
00:33:32,160 --> 00:33:33,360
Now let's deep
1251
00:33:33,360 --> 00:33:34,920
dive a bit on the scaling
1252
00:33:34,920 --> 00:33:37,760
in the health care part of it.
1253
00:33:37,840 --> 00:33:40,160
So, what is scaling AI in health care
1254
00:33:40,160 --> 00:33:41,800
that we are referring to.
1255
00:33:41,800 --> 00:33:43,880
As I said, some time back,
1256
00:33:44,880 --> 00:33:46,240
because of the complexites
1257
00:33:46,240 --> 00:33:48,200
and because of the number of players
1258
00:33:48,200 --> 00:33:49,320
that are there in the health care
1259
00:33:49,320 --> 00:33:50,800
ecosystem itself,
1260
00:33:50,800 --> 00:33:52,200
we need to build partnerships
1261
00:33:52,200 --> 00:33:54,320
to come up with solutions.
1262
00:33:54,680 --> 00:33:55,920
So there are four different parts
1263
00:33:55,920 --> 00:33:56,600
of partnerships
1264
00:33:56,600 --> 00:33:58,120
that you're essentially talking about,
1265
00:33:58,120 --> 00:33:59,720
people and experiences.
1266
00:33:59,720 --> 00:34:00,480
Now, what do we mean
1267
00:34:00,480 --> 00:34:02,280
by people and experiences right?
1268
00:34:02,280 --> 00:34:05,120
An innovation should actually focus on
1269
00:34:05,120 --> 00:34:06,560
the unmet demands
1270
00:34:06,560 --> 00:34:07,920
of the health care providers
1271
00:34:07,920 --> 00:34:08,880
and where do you get these
1272
00:34:08,880 --> 00:34:11,520
unmet demands itself?
1273
00:34:11,520 --> 00:34:14,120
By co-creating solutions with the health
1274
00:34:14,120 --> 00:34:15,360
care workers itself.
1275
00:34:15,360 --> 00:34:16,960
The actual people who are going to work
1276
00:34:16,960 --> 00:34:18,080
on this particular solution,
1277
00:34:18,080 --> 00:34:20,120
if you are able to work
1278
00:34:20,120 --> 00:34:20,880
or partner
1279
00:34:20,880 --> 00:34:21,280
with them
1280
00:34:21,280 --> 00:34:22,120
early in the stage
1281
00:34:22,120 --> 00:34:24,040
of the development of the solution,
1282
00:34:24,040 --> 00:34:24,840
you see that
1283
00:34:24,840 --> 00:34:26,200
you actually get very
1284
00:34:26,200 --> 00:34:27,840
meaningful solutions
1285
00:34:27,840 --> 00:34:28,600
at the end of the day.
1286
00:34:28,600 --> 00:34:29,560
These are solutions
1287
00:34:29,560 --> 00:34:31,440
that have a higher adoption
1288
00:34:31,440 --> 00:34:32,360
where your doctors
1289
00:34:32,360 --> 00:34:33,200
and your nurses
1290
00:34:33,200 --> 00:34:34,160
and all the health care
1291
00:34:34,160 --> 00:34:35,400
workers are more than happy
1292
00:34:35,400 --> 00:34:36,720
to use this solution
1293
00:34:36,720 --> 00:34:38,400
because it is addressing
1294
00:34:38,400 --> 00:34:39,480
one pain point
1295
00:34:39,480 --> 00:34:42,000
that they got in a very seamless fashion.
1296
00:34:42,240 --> 00:34:42,880
And in fact,
1297
00:34:42,880 --> 00:34:44,280
if you look at it health care
1298
00:34:44,280 --> 00:34:46,240
workers already know what they want.
1299
00:34:46,240 --> 00:34:48,160
It is that we need to use technology
1300
00:34:48,160 --> 00:34:49,320
as an enabler to do it
1301
00:34:49,320 --> 00:34:50,560
so people and experiences
1302
00:34:50,560 --> 00:34:52,800
is one of the biggest areas
1303
00:34:52,800 --> 00:34:54,560
that we need to partner with
1304
00:34:54,560 --> 00:34:55,080
in order
1305
00:34:55,080 --> 00:34:57,360
to develop these meaningful solutions.
1306
00:34:57,360 --> 00:34:58,600
Then we are talking about data
1307
00:34:58,600 --> 00:34:59,400
and technology.
1308
00:34:59,400 --> 00:35:03,000
Developing AI enabled solutions
1309
00:35:03,200 --> 00:35:06,440
always depends on high quality, properly
1310
00:35:06,440 --> 00:35:07,360
curated data.
1311
00:35:08,520 --> 00:35:09,920
And today, you know, we were
1312
00:35:09,920 --> 00:35:11,920
talking more than several times today
1313
00:35:12,120 --> 00:35:14,600
where the data resides in silos
1314
00:35:14,600 --> 00:35:15,480
in different systems
1315
00:35:15,480 --> 00:35:16,920
and different databases
1316
00:35:16,920 --> 00:35:18,600
which don't talk to each other.
1317
00:35:18,600 --> 00:35:19,960
There is no common language
1318
00:35:19,960 --> 00:35:21,200
that these guys can actually
1319
00:35:21,200 --> 00:35:22,040
refer to also,
1320
00:35:22,040 --> 00:35:23,760
and these are all disconnected systems
1321
00:35:23,760 --> 00:35:25,360
in a lot of instances.
1322
00:35:25,360 --> 00:35:26,480
This is where we
1323
00:35:26,480 --> 00:35:28,680
are going to have a problem in fostering
1324
00:35:28,800 --> 00:35:30,120
AI into this particular
1325
00:35:30,120 --> 00:35:32,160
because of the data and technology.
1326
00:35:32,160 --> 00:35:34,160
So what we are essentially talking about
1327
00:35:34,160 --> 00:35:36,520
is look at various interconnected
1328
00:35:36,520 --> 00:35:38,160
and interoperable platforms
1329
00:35:38,160 --> 00:35:39,400
that are available
1330
00:35:39,400 --> 00:35:40,480
or infrastructure
1331
00:35:40,480 --> 00:35:42,640
that is available for us to collect,
1332
00:35:42,640 --> 00:35:43,920
combine, analyze
1333
00:35:43,920 --> 00:35:46,640
data across various settings, etc..
1334
00:35:46,640 --> 00:35:47,800
The next conversation
1335
00:35:47,800 --> 00:35:49,880
that we are going to have about is the
1336
00:35:49,880 --> 00:35:51,680
governance and the trust.
1337
00:35:51,680 --> 00:35:53,320
In order for your public
1338
00:35:53,320 --> 00:35:54,520
to trust your solution,
1339
00:35:54,520 --> 00:35:54,960
in order
1340
00:35:54,960 --> 00:35:56,440
for the public to actually say,
1341
00:35:56,440 --> 00:35:56,840
okay, I'm
1342
00:35:56,840 --> 00:35:58,120
going to give you my information
1343
00:35:58,120 --> 00:35:59,760
so that you know, you can apply
1344
00:35:59,760 --> 00:36:01,120
AI part of it,
1345
00:36:01,120 --> 00:36:02,680
you have to understand that, you know,
1346
00:36:02,680 --> 00:36:03,840
to strengthen the public
1347
00:36:03,840 --> 00:36:05,280
and professional trust,
1348
00:36:05,280 --> 00:36:06,600
we need to go hand-in-hand
1349
00:36:06,600 --> 00:36:08,200
with appropriate governance,
1350
00:36:08,200 --> 00:36:09,720
along with the data privacy,
1351
00:36:09,720 --> 00:36:12,400
security and ethics of data itself.
1352
00:36:12,680 --> 00:36:13,120
Right.
1353
00:36:13,120 --> 00:36:16,280
With that in 2019,
1354
00:36:16,280 --> 00:36:19,240
we have launched a program
1355
00:36:19,240 --> 00:36:21,040
which is focused on responsible
1356
00:36:21,040 --> 00:36:23,240
use of AI/ML systems and models,
1357
00:36:23,600 --> 00:36:25,240
along with various advanced
1358
00:36:25,240 --> 00:36:27,560
analytics and algorithms product itself.
1359
00:36:28,080 --> 00:36:29,960
This program has since evolved
1360
00:36:29,960 --> 00:36:31,360
into an enterprise
1361
00:36:31,360 --> 00:36:33,480
AI/ML responsible use program,
1362
00:36:33,800 --> 00:36:35,520
which is aimed at establishing
1363
00:36:35,520 --> 00:36:37,560
and maintaining a governance model
1364
00:36:37,560 --> 00:36:38,040
of what
1365
00:36:38,040 --> 00:36:39,200
using AI/ML
1366
00:36:39,200 --> 00:36:40,200
responsibly
1367
00:36:40,200 --> 00:36:42,720
within our organization itself.
1368
00:36:42,720 --> 00:36:44,400
Then we are talking about partnerships
1369
00:36:44,400 --> 00:36:45,760
and new business models.
1370
00:36:45,760 --> 00:36:46,920
So this is an area
1371
00:36:46,920 --> 00:36:49,040
where a lot of innovation is happening,
1372
00:36:49,040 --> 00:36:51,200
especially on the business models itself.
1373
00:36:51,520 --> 00:36:52,800
So they are going to be like, you know,
1374
00:36:52,800 --> 00:36:55,040
partners, ecosystem integrations
1375
00:36:55,040 --> 00:36:55,800
or new business
1376
00:36:55,800 --> 00:36:56,360
models like,
1377
00:36:56,360 --> 00:36:57,600
you know, SaaS based software
1378
00:36:57,600 --> 00:36:59,520
marketplaces are becoming
1379
00:36:59,520 --> 00:37:01,720
one of the most important part
1380
00:37:01,720 --> 00:37:03,080
where they’re
1381
00:37:03,080 --> 00:37:05,160
bringing in AI into clinical practices
1382
00:37:05,160 --> 00:37:05,960
itself.
1383
00:37:06,000 --> 00:37:07,520
Let's just take,
1384
00:37:07,520 --> 00:37:07,800
you know,
1385
00:37:07,800 --> 00:37:08,640
as an example,
1386
00:37:08,640 --> 00:37:10,680
you have got an India Policy Bazaar.
1387
00:37:10,680 --> 00:37:11,280
Right?
1388
00:37:11,280 --> 00:37:12,000
So similarly,
1389
00:37:12,000 --> 00:37:13,560
there are more and more aggregator
1390
00:37:13,560 --> 00:37:15,160
platforms that are coming in
1391
00:37:15,160 --> 00:37:15,960
where you are able
1392
00:37:15,960 --> 00:37:17,800
to actually create more sales
1393
00:37:17,800 --> 00:37:18,720
and marketing models
1394
00:37:18,720 --> 00:37:20,520
for all of your product itself.
1395
00:37:20,520 --> 00:37:22,920
So that is where the entire partnerships
1396
00:37:23,280 --> 00:37:24,400
are going to work
1397
00:37:24,400 --> 00:37:25,320
and that is the way
1398
00:37:25,320 --> 00:37:26,480
that we can actually scale
1399
00:37:26,480 --> 00:37:28,440
AI in health care space.
1400
00:37:28,440 --> 00:37:32,640
Now as we're talking about, you know,
1401
00:37:33,240 --> 00:37:35,200
every story has got two sides,
1402
00:37:35,200 --> 00:37:37,920
every coin has got two parts of it.
1403
00:37:38,280 --> 00:37:40,480
So every hype has got a downside of it,
1404
00:37:40,480 --> 00:37:41,800
the flip side of it.
1405
00:37:41,800 --> 00:37:43,680
So we need to be very, very clear
1406
00:37:43,680 --> 00:37:45,800
about data privacy and security,
1407
00:37:45,800 --> 00:37:46,680
because you are dealing
1408
00:37:46,680 --> 00:37:48,600
with a lot of patient information.
1409
00:37:48,600 --> 00:37:50,720
And some of this data has to be protected
1410
00:37:50,720 --> 00:37:52,600
from unauthorized access
1411
00:37:52,600 --> 00:37:53,520
so that the patients
1412
00:37:53,520 --> 00:37:54,400
have the right control
1413
00:37:54,400 --> 00:37:56,280
on how their data is being used.
1414
00:37:56,280 --> 00:37:57,600
So we need to ensure
1415
00:37:57,600 --> 00:37:59,280
when we are developing those solutions,
1416
00:37:59,280 --> 00:38:01,160
we think about the data privacy
1417
00:38:01,160 --> 00:38:01,920
and security part of it.
1418
00:38:01,920 --> 00:38:04,440
The next part of it is
1419
00:38:04,440 --> 00:38:06,280
the lack of transparency. AI
1420
00:38:07,240 --> 00:38:07,920
solutions are
1421
00:38:07,920 --> 00:38:10,000
actually considered to be black boxes.
1422
00:38:10,000 --> 00:38:11,040
No one really knows
1423
00:38:11,040 --> 00:38:12,400
what actually happens inside this.
1424
00:38:12,400 --> 00:38:14,560
It is like a magic box.
1425
00:38:14,560 --> 00:38:15,280
You give some input
1426
00:38:15,280 --> 00:38:17,280
and there's an output that is coming out.
1427
00:38:17,280 --> 00:38:19,080
The people don't understand
1428
00:38:19,080 --> 00:38:21,120
how that particular output is coming in.
1429
00:38:21,360 --> 00:38:23,120
It is going to become more magic
1430
00:38:23,120 --> 00:38:24,120
than actually something
1431
00:38:24,120 --> 00:38:25,240
that people would believe in.
1432
00:38:25,240 --> 00:38:28,240
The transparency part of it
1433
00:38:28,240 --> 00:38:29,600
is going to actually hit
1434
00:38:29,600 --> 00:38:31,240
some of the health care solutions
1435
00:38:31,240 --> 00:38:33,280
that we are going to eventually build.
1436
00:38:33,280 --> 00:38:34,560
We are talking about regulations
1437
00:38:34,560 --> 00:38:37,000
and governance, as we talked about.
1438
00:38:37,000 --> 00:38:38,560
You know, on one hand,
1439
00:38:38,560 --> 00:38:39,880
the HIPPA compliance
1440
00:38:39,880 --> 00:38:40,560
and the protected
1441
00:38:40,560 --> 00:38:41,760
data regulations
1442
00:38:41,760 --> 00:38:42,800
that we have got
1443
00:38:42,800 --> 00:38:43,800
are going to tighten
1444
00:38:43,800 --> 00:38:45,720
on some of the regulations part of it.
1445
00:38:45,720 --> 00:38:47,640
But there's also a clear
1446
00:38:47,640 --> 00:38:49,800
lack of regulations and governance
1447
00:38:49,960 --> 00:38:51,000
or guidelines
1448
00:38:51,000 --> 00:38:52,160
of the use of
1449
00:38:52,160 --> 00:38:54,320
AI within the industry itself.
1450
00:38:54,600 --> 00:38:55,800
So this is also going to
1451
00:38:55,800 --> 00:38:57,000
make it very, very difficult
1452
00:38:58,080 --> 00:38:58,960
for us.
1453
00:38:59,040 --> 00:39:01,200
The next one is unrealistic expectations.
1454
00:39:01,200 --> 00:39:03,840
If you might have actually read or,
1455
00:39:03,840 --> 00:39:04,560
you know, recently
1456
00:39:04,560 --> 00:39:07,080
I came across an article
1457
00:39:07,080 --> 00:39:07,840
which is a Forbes article
1458
00:39:07,840 --> 00:39:08,400
that we
1459
00:39:08,400 --> 00:39:09,720
are talking about field problems
1460
00:39:09,720 --> 00:39:12,280
or a case of unrealistic expectations.
1461
00:39:12,520 --> 00:39:14,040
So you hear a lot of people
1462
00:39:14,040 --> 00:39:16,040
complaining that I have leveraged AI
1463
00:39:16,040 --> 00:39:17,520
but this has failed me.
1464
00:39:17,520 --> 00:39:18,480
I don't think it
1465
00:39:18,480 --> 00:39:19,640
the AI that is failing
1466
00:39:19,640 --> 00:39:21,760
but just that unrealistic expectation
1467
00:39:21,760 --> 00:39:22,680
about what
1468
00:39:22,680 --> 00:39:23,760
AI can achieve
1469
00:39:23,760 --> 00:39:25,080
because it needs to have the right data,
1470
00:39:25,080 --> 00:39:26,720
it needs to have the right training.
1471
00:39:26,720 --> 00:39:27,720
I think we need to have
1472
00:39:27,720 --> 00:39:28,880
a realistic expectation
1473
00:39:28,880 --> 00:39:29,960
on all the
1474
00:39:29,960 --> 00:39:31,960
AI solutions that we are building in.
1475
00:39:31,960 --> 00:39:33,640
The last but not the least,
1476
00:39:33,640 --> 00:39:34,920
which is the most important
1477
00:39:34,920 --> 00:39:36,040
part of it is,
1478
00:39:36,040 --> 00:39:37,120
for organizations
1479
00:39:37,120 --> 00:39:39,200
to invest in these companies,
1480
00:39:39,560 --> 00:39:40,640
they need to see
1481
00:39:40,640 --> 00:39:42,720
how can they monetize that opportunity.
1482
00:39:43,080 --> 00:39:46,200
While we can be very socialistic
1483
00:39:46,200 --> 00:39:46,920
in our approach
1484
00:39:46,920 --> 00:39:48,960
of providing solutions and everything,
1485
00:39:48,960 --> 00:39:49,920
but for the scaling,
1486
00:39:49,920 --> 00:39:51,120
it actually requires
1487
00:39:51,120 --> 00:39:52,480
any kind of money investment.
1488
00:39:52,480 --> 00:39:53,920
Then that would happen
1489
00:39:53,920 --> 00:39:55,360
only when the companies would invest
1490
00:39:55,360 --> 00:39:56,520
and the companies would invest
1491
00:39:56,520 --> 00:39:57,680
only when they look at the
1492
00:39:57,680 --> 00:39:59,280
monetization opportunities.
1493
00:40:00,360 --> 00:40:00,680
Now with
1494
00:40:00,680 --> 00:40:02,520
that, let's come to the last topic
1495
00:40:02,520 --> 00:40:03,840
that we have on our hand,
1496
00:40:03,840 --> 00:40:05,000
which is on the health care
1497
00:40:05,000 --> 00:40:08,120
professional skillset itself.
1498
00:40:08,160 --> 00:40:10,080
So I'm going to concentrate on
1499
00:40:10,080 --> 00:40:11,880
what are some of the critical skillsets
1500
00:40:11,880 --> 00:40:12,960
that we need to develop
1501
00:40:12,960 --> 00:40:15,120
as health care professional itself.
1502
00:40:15,120 --> 00:40:17,480
Now, why do I need to talk about this?
1503
00:40:18,240 --> 00:40:19,400
It's not the technology
1504
00:40:19,400 --> 00:40:21,240
that makes the differentiator for you.
1505
00:40:21,240 --> 00:40:23,760
I think what makes it over here is
1506
00:40:23,760 --> 00:40:23,920
that, do
1507
00:40:23,920 --> 00:40:25,800
you have the compassion
1508
00:40:25,800 --> 00:40:28,040
and empathy to be in this industry?
1509
00:40:28,040 --> 00:40:29,320
You have to understand
1510
00:40:29,320 --> 00:40:30,880
that you are always surrounded
1511
00:40:30,880 --> 00:40:33,120
by people who are a probably in pain.
1512
00:40:33,120 --> 00:40:34,520
The high moment comes in
1513
00:40:34,520 --> 00:40:36,120
many of solve that particular problem.
1514
00:40:36,120 --> 00:40:37,880
But if you look at the value of process
1515
00:40:37,880 --> 00:40:38,640
itself,
1516
00:40:38,640 --> 00:40:41,280
you're constantly surrounded by problems.
1517
00:40:41,280 --> 00:40:42,760
You are constantly surrounded
1518
00:40:42,760 --> 00:40:44,280
by sickness and illness.
1519
00:40:44,280 --> 00:40:45,600
And that requires
1520
00:40:45,600 --> 00:40:46,680
a lot of compassion
1521
00:40:46,680 --> 00:40:47,200
and empathy
1522
00:40:47,200 --> 00:40:48,840
for us to actually develop solutions
1523
00:40:48,840 --> 00:40:50,400
in this industry.
1524
00:40:50,440 --> 00:40:51,360
And the second thing
1525
00:40:51,360 --> 00:40:53,840
is a constant exposure to this kind of
1526
00:40:54,360 --> 00:40:55,440
illness or,
1527
00:40:55,440 --> 00:40:57,320
you know, this kind of
1528
00:40:57,320 --> 00:40:58,480
a problem statements
1529
00:40:58,480 --> 00:40:59,400
that we are talking about,
1530
00:40:59,400 --> 00:41:00,240
emotional stability
1531
00:41:00,240 --> 00:41:01,800
is something that we really,
1532
00:41:01,800 --> 00:41:03,200
really look out for in people.
1533
00:41:03,200 --> 00:41:06,080
Great and excellent communication,
1534
00:41:06,080 --> 00:41:06,800
because this kind of
1535
00:41:06,800 --> 00:41:08,040
makes you a very effective
1536
00:41:09,360 --> 00:41:10,200
team player.
1537
00:41:10,200 --> 00:41:11,520
Being honest,
1538
00:41:11,520 --> 00:41:12,360
having strong
1539
00:41:12,360 --> 00:41:14,040
ethics, is
1540
00:41:14,040 --> 00:41:16,440
one of the non-negotiable things
1541
00:41:17,160 --> 00:41:18,200
that we are essentially
1542
00:41:18,200 --> 00:41:19,560
talking about, right?
1543
00:41:19,560 --> 00:41:21,600
Because we are we are dealing with lives.
1544
00:41:21,600 --> 00:41:23,200
We are dealing with health care,
1545
00:41:23,200 --> 00:41:25,320
which is one of the most important things
1546
00:41:25,320 --> 00:41:26,040
of our lives itself.
1547
00:41:26,040 --> 00:41:28,240
Proactive and
1548
00:41:28,320 --> 00:41:30,160
being responsible for your work
1549
00:41:30,160 --> 00:41:32,400
and also having certain
1550
00:41:32,400 --> 00:41:34,000
control over your time management,
1551
00:41:34,000 --> 00:41:34,320
where you have,
1552
00:41:34,320 --> 00:41:35,480
what all the time management
1553
00:41:35,480 --> 00:41:36,400
skills and everything
1554
00:41:36,400 --> 00:41:37,840
are the critical skills
1555
00:41:37,840 --> 00:41:39,360
that we are talking about.
1556
00:41:39,360 --> 00:41:39,960
Now let's look
1557
00:41:39,960 --> 00:41:41,400
at some of the secondary skills.
1558
00:41:41,400 --> 00:41:41,840
The reason
1559
00:41:41,840 --> 00:41:43,160
I call them secondary skills is,
1560
00:41:43,160 --> 00:41:45,480
these are skills that can be learnt.
1561
00:41:45,680 --> 00:41:47,760
These are the skills that can be evolved.
1562
00:41:47,760 --> 00:41:48,920
I think the first skills,
1563
00:41:48,920 --> 00:41:49,920
what we are talking about,
1564
00:41:49,920 --> 00:41:50,600
the compassion
1565
00:41:50,600 --> 00:41:51,160
and empathy
1566
00:41:51,160 --> 00:41:51,680
is something
1567
00:41:51,680 --> 00:41:53,800
that has to come internal itself.
1568
00:41:54,280 --> 00:41:56,520
So, you know, you've got a lot of things.
1569
00:41:56,520 --> 00:41:58,800
Again, AI is a very, very vast area.
1570
00:41:59,080 --> 00:42:00,440
But I wanted to kind of,
1571
00:42:00,440 --> 00:42:01,680
you know, call down,
1572
00:42:01,680 --> 00:42:03,400
especially at Optum,
1573
00:42:03,400 --> 00:42:04,520
what are the things that we are
1574
00:42:04,520 --> 00:42:05,880
kind of looking at?
1575
00:42:05,880 --> 00:42:07,520
These are machine learning experts
1576
00:42:07,520 --> 00:42:09,280
who have got expertise in Scala,
1577
00:42:09,280 --> 00:42:10,640
Python and Java.
1578
00:42:10,640 --> 00:42:12,400
We look always for, you know,
1579
00:42:12,400 --> 00:42:13,120
data scientists
1580
00:42:13,120 --> 00:42:14,640
play a very important role in our
1581
00:42:14,640 --> 00:42:16,400
AI journey itself.
1582
00:42:16,400 --> 00:42:17,280
BI developer,
1583
00:42:17,280 --> 00:42:18,440
so the BI developer,
1584
00:42:18,440 --> 00:42:19,200
we are not looking at
1585
00:42:19,200 --> 00:42:20,240
who is going to develop
1586
00:42:20,240 --> 00:42:21,640
just the dashboards,
1587
00:42:21,640 --> 00:42:23,640
but who can convey a story.
1588
00:42:23,640 --> 00:42:24,960
End of the day,
1589
00:42:24,960 --> 00:42:26,480
your dashboard should tell the story
1590
00:42:26,480 --> 00:42:27,360
on whether you're doing
1591
00:42:27,360 --> 00:42:28,680
well or not doing well.
1592
00:42:28,680 --> 00:42:30,320
What are the areas of improvement?
1593
00:42:30,320 --> 00:42:31,320
And that's the story
1594
00:42:31,320 --> 00:42:33,080
that we're kind of looking at from the BI
1595
00:42:33,080 --> 00:42:33,160
developer itself.
1596
00:42:33,160 --> 00:42:35,280
Research scientist
1597
00:42:35,280 --> 00:42:37,160
because we have a lot of patents.
1598
00:42:37,160 --> 00:42:38,280
We also want to look at,
1599
00:42:38,280 --> 00:42:38,560
you know,
1600
00:42:38,560 --> 00:42:40,000
parallel computing,
1601
00:42:40,000 --> 00:42:41,520
distributed computing, quantum
1602
00:42:41,520 --> 00:42:42,920
computing benchmark
1603
00:42:42,920 --> 00:42:44,360
and a lot of other things that we look
1604
00:42:44,360 --> 00:42:45,720
from a research scientist itself.
1605
00:42:45,720 --> 00:42:47,200
Then of course,
1606
00:42:47,200 --> 00:42:48,960
we have the big data architect.
1607
00:42:48,960 --> 00:42:51,240
And if you look at predominantly
1608
00:42:51,240 --> 00:42:52,960
a lot of our work gets done
1609
00:42:52,960 --> 00:42:55,240
from Gurgaon, Hyderabad and Bangalore at
1610
00:42:55,240 --> 00:42:56,040
this point of time.
1611
00:42:57,600 --> 00:42:58,640
So, with that I
1612
00:42:58,640 --> 00:43:00,480
come to the end of our presentation
1613
00:43:00,480 --> 00:43:02,400
and we are open for questions.
1614
00:43:02,400 --> 00:43:05,040
But I wanted to actually
1615
00:43:05,040 --> 00:43:06,960
reiterate again that we want to improve
1616
00:43:06,960 --> 00:43:08,240
the lives of others
1617
00:43:08,240 --> 00:43:10,440
while doing our life’s best work
1618
00:43:10,600 --> 00:43:20,560
and that's at Optum.
1619
00:43:20,840 --> 00:43:21,760
I see some questions, Jyo.
1620
00:43:21,760 --> 00:43:23,040
Let me read the questions
1621
00:43:26,040 --> 00:43:32,640
that we can answer.
1622
00:43:33,000 --> 00:43:34,920
Is there any parametric test
1623
00:43:34,920 --> 00:43:35,760
data available
1624
00:43:35,760 --> 00:43:38,880
till today, to diagnose any
1625
00:43:40,080 --> 00:43:41,640
disease as earlier predictions
1626
00:43:41,640 --> 00:43:43,440
at any stage?
1627
00:43:44,160 --> 00:43:45,520
I think those are some of the things
1628
00:43:45,520 --> 00:43:47,480
that we have been talking about, right?
1629
00:43:47,480 --> 00:43:49,320
We are talking about a skin lesion.
1630
00:43:49,320 --> 00:43:51,440
We are talking about lung cancer.
1631
00:43:51,440 --> 00:43:52,280
We are also talking
1632
00:43:52,280 --> 00:43:53,480
about the spinal surgery
1633
00:43:53,480 --> 00:43:55,080
and various other things.
1634
00:43:55,080 --> 00:43:57,480
So this is an evolving area.
1635
00:43:57,480 --> 00:43:58,520
If you are asking me
1636
00:43:58,520 --> 00:43:59,520
have we figured out
1637
00:43:59,520 --> 00:44:00,800
that particular formula
1638
00:44:00,800 --> 00:44:03,120
by which we could definitely do
1639
00:44:03,120 --> 00:44:04,560
early detection of this,
1640
00:44:04,560 --> 00:44:05,480
I wouldn't say
1641
00:44:05,480 --> 00:44:07,120
that it's a definite answer
1642
00:44:07,120 --> 00:44:08,680
because it's in a work in progress
1643
00:44:08,680 --> 00:44:11,280
and we are continuously improving
1644
00:44:11,280 --> 00:44:12,520
some of the algorithms
1645
00:44:12,520 --> 00:44:14,080
as a health care industry itself,
1646
00:44:14,080 --> 00:44:16,160
we kind of continuously working on it.
1647
00:44:16,440 --> 00:44:18,440
But yes, there are several use cases
1648
00:44:18,440 --> 00:44:20,520
that we are able to actually point out
1649
00:44:20,520 --> 00:44:22,440
and where the accuracy is,
1650
00:44:22,440 --> 00:44:24,400
you know, closer to what the health care
1651
00:44:24,400 --> 00:44:25,560
professionals are able to
1652
00:44:25,560 --> 00:44:26,400
detect at this point of time.
1653
00:44:29,360 --> 00:44:30,320
The next question,
1654
00:44:30,320 --> 00:44:31,480
what are the challenges
1655
00:44:31,480 --> 00:44:32,880
you see in integrating
1656
00:44:32,880 --> 00:44:35,560
AI into existing health care systems
1657
00:44:35,880 --> 00:44:37,440
in companies like UHG?
1658
00:44:37,440 --> 00:44:39,840
Can you repeat that?
1659
00:44:39,840 --> 00:44:41,440
What are the challenges
1660
00:44:41,440 --> 00:44:44,440
we foresee in integrating AI into
1661
00:44:44,440 --> 00:44:46,040
existing health care systems
1662
00:44:46,040 --> 00:44:47,760
in companies like UHG?
1663
00:44:47,760 --> 00:44:49,160
So, as I told, you right
1664
00:44:49,160 --> 00:44:50,280
all the health care systems
1665
00:44:50,280 --> 00:44:52,200
that you've got a very disparate,
1666
00:44:52,200 --> 00:44:54,240
and then it is very siloed information
1667
00:44:54,240 --> 00:44:55,080
that we are talking about.
1668
00:44:55,080 --> 00:44:56,760
The first and foremost part of it
1669
00:44:56,760 --> 00:44:58,840
is getting curated data itself,
1670
00:44:58,920 --> 00:45:00,240
and then also creating
1671
00:45:00,240 --> 00:45:01,200
that particular layer
1672
00:45:01,200 --> 00:45:02,160
where we are able
1673
00:45:02,160 --> 00:45:03,240
to bring all the data
1674
00:45:03,240 --> 00:45:05,200
onto the same language,
1675
00:45:05,200 --> 00:45:05,520
when I say the same language
1676
00:45:05,520 --> 00:45:07,360
I am not really talking about a Java
1677
00:45:07,360 --> 00:45:08,640
or a JavaScript
1678
00:45:08,640 --> 00:45:10,840
kind of a thing, but more importantly,
1679
00:45:10,840 --> 00:45:12,360
what the systems understand
1680
00:45:12,360 --> 00:45:14,440
or they have structured data
1681
00:45:14,440 --> 00:45:16,640
that is interpreted in the same way.
1682
00:45:16,960 --> 00:45:17,760
So I think this is
1683
00:45:17,760 --> 00:45:19,440
one of the most important channels
1684
00:45:19,440 --> 00:45:20,920
not to spend optimizing,
1685
00:45:20,920 --> 00:45:21,720
but I think it is
1686
00:45:21,720 --> 00:45:23,280
something that is global
1687
00:45:23,280 --> 00:45:24,760
that we are essentially talking about.
1688
00:45:24,760 --> 00:45:28,320
The next one,
1689
00:45:28,320 --> 00:45:30,520
ChatGPT is recently an AI chatbot,
1690
00:45:31,320 --> 00:45:32,680
whether it can be implemented
1691
00:45:32,680 --> 00:45:33,440
in health care,
1692
00:45:33,440 --> 00:45:34,080
for example,
1693
00:45:34,080 --> 00:45:35,480
minor symptoms like fever,
1694
00:45:35,480 --> 00:45:37,200
health problems.
1695
00:45:37,200 --> 00:45:40,360
You know, I think very frankly
1696
00:45:40,360 --> 00:45:41,240
it is too early for
1697
00:45:41,240 --> 00:45:42,760
any one of us to actually talk
1698
00:45:42,760 --> 00:45:44,280
about the potential
1699
00:45:44,280 --> 00:45:46,720
or the use cases that ChatGPT
1700
00:45:46,840 --> 00:45:47,880
can actually deliver
1701
00:45:47,880 --> 00:45:48,920
in the health care space,
1702
00:45:48,920 --> 00:45:50,520
because I think it is too early
1703
00:45:50,520 --> 00:45:50,880
for us
1704
00:45:50,880 --> 00:45:53,040
to actually have the data
1705
00:45:53,040 --> 00:45:55,720
that is going to support our claim,
1706
00:45:55,720 --> 00:45:57,960
whether it is for or against the ChatGPT
1707
00:45:58,280 --> 00:45:59,600
that we are talking about.
1708
00:45:59,600 --> 00:46:01,200
I would like to reserve my judgment
1709
00:46:01,200 --> 00:46:02,480
for a later date
1710
00:46:02,480 --> 00:46:04,200
when we have actually experimented
1711
00:46:04,200 --> 00:46:04,800
with some of the
1712
00:46:04,800 --> 00:46:05,960
ChatGPT use cases
1713
00:46:05,960 --> 00:46:06,960
and we have a concrete answer
1714
00:46:06,960 --> 00:46:08,480
that we can talk about.
1715
00:46:08,480 --> 00:46:10,080
I think it is premature on my part,
1716
00:46:10,080 --> 00:46:11,560
to answer any of those right now.
1717
00:46:13,880 --> 00:46:15,920
The next one, how can,
1718
00:46:15,920 --> 00:46:18,720
how can one explore or apply
1719
00:46:19,120 --> 00:46:21,680
for data science jobs at Optum?
1720
00:46:21,680 --> 00:46:23,760
See, you know, let me
1721
00:46:26,160 --> 00:46:27,280
tell you this
1722
00:46:27,720 --> 00:46:28,200
one.
1723
00:46:28,200 --> 00:46:29,600
We have LinkedIn posts
1724
00:46:29,600 --> 00:46:30,480
that actually go in
1725
00:46:30,480 --> 00:46:32,200
because we feel that
1726
00:46:32,200 --> 00:46:33,720
one of the best places
1727
00:46:33,720 --> 00:46:35,920
where we can recruit our employees
1728
00:46:35,920 --> 00:46:37,360
or our potential employees
1729
00:46:37,360 --> 00:46:38,600
would be on LinkedIn.
1730
00:46:38,600 --> 00:46:40,440
So if you can actually subscribe
1731
00:46:40,440 --> 00:46:43,320
to our Optum LinkedIn
1732
00:46:43,320 --> 00:46:45,120
channel that we are talking about,
1733
00:46:45,120 --> 00:46:46,360
you will keep seeing a lot of
1734
00:46:46,360 --> 00:46:47,880
our advertisements
1735
00:46:47,880 --> 00:46:49,520
are required for the post
1736
00:46:49,520 --> 00:46:50,760
actually get in.
1737
00:46:50,760 --> 00:46:52,120
One of the other best ways,
1738
00:46:52,120 --> 00:46:53,480
look out for,
1739
00:46:53,480 --> 00:46:54,800
you know, your connections
1740
00:46:54,800 --> 00:46:56,040
who are there at Optum.
1741
00:46:56,040 --> 00:46:57,840
We have an internal job portal
1742
00:46:57,840 --> 00:46:58,960
where we kind of post
1743
00:46:58,960 --> 00:47:02,960
all the openings for our Optum, in India
1744
00:47:02,960 --> 00:47:03,880
and U.S. also.
1745
00:47:03,880 --> 00:47:05,280
It's a global portal
1746
00:47:05,280 --> 00:47:06,560
that we are talking about,
1747
00:47:06,560 --> 00:47:08,480
that is a very good source of information
1748
00:47:08,480 --> 00:47:08,960
in terms of
1749
00:47:08,960 --> 00:47:09,680
what are the roles
1750
00:47:09,680 --> 00:47:11,040
that are actually available.
1751
00:47:11,040 --> 00:47:12,840
And this particular roles are two ways
1752
00:47:12,840 --> 00:47:13,680
that we are talking about.
1753
00:47:13,680 --> 00:47:15,840
One is the internal reference itself,
1754
00:47:15,840 --> 00:47:16,800
where a person
1755
00:47:16,800 --> 00:47:18,120
who is working within Optum
1756
00:47:18,120 --> 00:47:19,760
can actually look for a change and move
1757
00:47:19,760 --> 00:47:22,120
because we totally
1758
00:47:22,120 --> 00:47:23,520
encourage talent mobility
1759
00:47:23,520 --> 00:47:25,160
within the organization itself.
1760
00:47:25,160 --> 00:47:25,960
But the second one
1761
00:47:25,960 --> 00:47:26,880
that we are essentially
1762
00:47:26,880 --> 00:47:29,320
talking about is also how do you,
1763
00:47:29,760 --> 00:47:30,480
you know,
1764
00:47:30,480 --> 00:47:32,240
network with your individuals
1765
00:47:32,240 --> 00:47:33,000
or your connections
1766
00:47:33,000 --> 00:47:34,200
within Optum to understand
1767
00:47:34,200 --> 00:47:35,160
what are the roles?
1768
00:47:35,160 --> 00:47:36,200
If I'm not wrong, I'm
1769
00:47:36,200 --> 00:47:38,160
not like 100% sure about this,
1770
00:47:38,160 --> 00:47:38,920
but I think that is
1771
00:47:38,920 --> 00:47:40,320
an external facing portal
1772
00:47:40,320 --> 00:47:41,760
also which talks
1773
00:47:41,760 --> 00:47:43,080
about the various Optum
1774
00:47:43,080 --> 00:47:44,080
openings that are there.
1775
00:47:49,960 --> 00:47:57,720
What is the scope of
1776
00:47:57,920 --> 00:47:59,240
career if I wanted to transition
1777
00:47:59,240 --> 00:48:02,480
to an AI product manager for
1778
00:48:05,880 --> 00:48:06,360
process
1779
00:48:06,360 --> 00:48:09,000
improvement consulting?
1780
00:48:09,600 --> 00:48:11,280
I think you're already there.
1781
00:48:11,280 --> 00:48:12,880
If you're already looking at a process
1782
00:48:12,880 --> 00:48:14,480
improvement part of it,
1783
00:48:14,480 --> 00:48:16,200
you are already playing a product
1784
00:48:16,200 --> 00:48:17,920
owner role, in my opinion,
1785
00:48:17,920 --> 00:48:19,200
because you are essentially trying
1786
00:48:19,200 --> 00:48:21,240
to see how you can enhance and experience
1787
00:48:21,560 --> 00:48:22,040
all you can,
1788
00:48:22,040 --> 00:48:23,720
how you can make a particular process
1789
00:48:23,720 --> 00:48:25,040
efficient itself.
1790
00:48:25,040 --> 00:48:26,120
In just,
1791
00:48:26,120 --> 00:48:26,520
you know,
1792
00:48:26,520 --> 00:48:28,880
I think you need to acquire
1793
00:48:28,920 --> 00:48:30,680
a little bit more practical knowledge
1794
00:48:30,680 --> 00:48:31,120
in terms
1795
00:48:31,120 --> 00:48:32,120
of what a product owner
1796
00:48:32,120 --> 00:48:33,360
would require like the tools
1797
00:48:33,360 --> 00:48:35,160
and technologies that are required.
1798
00:48:35,160 --> 00:48:36,240
You're talking about a Jira
1799
00:48:36,240 --> 00:48:39,200
or Rally or RAS kind of thing
1800
00:48:39,440 --> 00:48:40,400
and also bring in the rigor
1801
00:48:40,400 --> 00:48:42,760
that is required for you to document
1802
00:48:42,760 --> 00:48:44,040
what is your ask?
1803
00:48:44,040 --> 00:48:45,120
And as I was talking about,
1804
00:48:45,120 --> 00:48:46,920
experience led development. Right.
1805
00:48:46,920 --> 00:48:47,880
One of the other things
1806
00:48:47,880 --> 00:48:49,240
that you can look at is
1807
00:48:49,240 --> 00:48:51,240
how do you develop prototypes
1808
00:48:51,240 --> 00:48:52,960
or wired framing or storyboarding
1809
00:48:52,960 --> 00:48:54,440
of your requirements in terms of the
1810
00:48:54,440 --> 00:48:55,200
what is the process
1811
00:48:55,200 --> 00:48:57,120
requirements that are need to be done
1812
00:48:57,120 --> 00:48:59,120
and how do you actually coordinate
1813
00:48:59,120 --> 00:49:00,800
and improve the communication
1814
00:49:00,800 --> 00:49:01,760
along with the developers
1815
00:49:01,760 --> 00:49:03,720
to convert your vision into a product
1816
00:49:03,720 --> 00:49:05,120
that is deployable?
1817
00:49:05,120 --> 00:49:07,240
I think of getting into a process
1818
00:49:07,240 --> 00:49:09,320
improvement to a product owner is not
1819
00:49:09,640 --> 00:49:10,720
something that is like
1820
00:49:12,280 --> 00:49:13,160
vastly different.
1821
00:49:13,160 --> 00:49:13,960
I think you
1822
00:49:13,960 --> 00:49:15,320
should be able to do that
1823
00:49:15,320 --> 00:49:17,160
with a little bit of focus and rigor
1824
00:49:17,160 --> 00:49:18,960
and a bit of guidance over there.
1825
00:49:18,960 --> 00:49:22,320
And good functional knowledge of it.
1826
00:49:23,320 --> 00:49:24,160
Yes.
1827
00:49:24,840 --> 00:49:28,000
The next one is,
1828
00:49:28,000 --> 00:49:30,720
how AI/ML is helping
1829
00:49:30,720 --> 00:49:33,040
it is the problem of medical adherence,
1830
00:49:33,120 --> 00:49:35,880
as Optum deployed any
1831
00:49:35,880 --> 00:49:37,200
AI/ML algorithms to address
1832
00:49:37,200 --> 00:49:39,440
medical adherence and improving
1833
00:49:39,440 --> 00:49:41,040
patient experience?
1834
00:49:41,040 --> 00:49:42,680
So I think we have addressed
1835
00:49:42,680 --> 00:49:43,800
some of these questions
1836
00:49:43,800 --> 00:49:44,640
in the presentation
1837
00:49:44,640 --> 00:49:46,080
that we have talked about,
1838
00:49:46,080 --> 00:49:47,400
the medical adherence
1839
00:49:47,400 --> 00:49:49,680
and the data security and privacy
1840
00:49:49,680 --> 00:49:50,560
that we are talking about
1841
00:49:50,560 --> 00:49:51,680
or the governance part of it.
1842
00:49:51,680 --> 00:49:52,760
The entire regulations
1843
00:49:52,760 --> 00:49:54,280
and everything that is there.
1844
00:49:54,280 --> 00:49:57,120
We have been using AI extensively
1845
00:49:57,120 --> 00:49:57,680
to ensure
1846
00:49:57,680 --> 00:49:58,560
that we are being
1847
00:49:58,560 --> 00:50:00,120
very compliant with that.
1848
00:50:00,120 --> 00:50:02,680
But if your question is more in-depth,
1849
00:50:02,680 --> 00:50:04,000
we can probably take it offline.
1850
00:50:04,000 --> 00:50:21,400
Government policies keep changing,
1851
00:50:21,640 --> 00:50:24,840
how do you keep your solutions up to date
1852
00:50:25,240 --> 00:50:27,880
in accordance with government policies?
1853
00:50:28,120 --> 00:50:30,600
Okay, so you know, in U.S.
1854
00:50:30,600 --> 00:50:32,240
it gets even more complicated
1855
00:50:32,240 --> 00:50:33,440
because you are not just talking
1856
00:50:33,440 --> 00:50:35,160
about a global government policies,
1857
00:50:35,160 --> 00:50:36,080
but you're talking about
1858
00:50:36,080 --> 00:50:38,280
statewide government policies also.
1859
00:50:38,280 --> 00:50:40,200
So every state has got its own legal
1860
00:50:40,200 --> 00:50:41,640
and governance policies
1861
00:50:41,640 --> 00:50:44,320
that are mandatory for us to adhere.
1862
00:50:44,400 --> 00:50:45,040
But then, you know,
1863
00:50:45,040 --> 00:50:46,160
you also have to understand
1864
00:50:46,160 --> 00:50:47,200
that the government
1865
00:50:47,200 --> 00:50:48,520
also ensures that we are
1866
00:50:48,520 --> 00:50:49,480
given enough notice
1867
00:50:49,480 --> 00:50:50,200
to be compliant
1868
00:50:50,200 --> 00:50:51,360
with all of those procedures
1869
00:50:51,360 --> 00:50:53,760
and policies and regulations and so on.
1870
00:50:53,760 --> 00:50:55,520
So when you kind of,
1871
00:50:55,520 --> 00:50:55,720
you know,
1872
00:50:55,720 --> 00:50:57,120
when we are talking about the health care
1873
00:50:57,120 --> 00:50:58,720
domain expertise itself,
1874
00:50:58,720 --> 00:51:00,960
these are the areas as a product owner or
1875
00:51:01,640 --> 00:51:03,600
the person was defining or
1876
00:51:03,600 --> 00:51:05,080
designing the solution itself,
1877
00:51:05,080 --> 00:51:06,960
you can kind of make it
1878
00:51:06,960 --> 00:51:07,720
a flexible
1879
00:51:07,720 --> 00:51:08,520
enough for you
1880
00:51:08,520 --> 00:51:09,840
to kind of take care
1881
00:51:09,840 --> 00:51:11,640
of the governance,
1882
00:51:11,640 --> 00:51:12,960
regulations and policies
1883
00:51:12,960 --> 00:51:14,400
while you can't really predict
1884
00:51:14,400 --> 00:51:16,480
what kind of policies are coming in.
1885
00:51:16,480 --> 00:51:18,560
You can make the solution
1886
00:51:18,560 --> 00:51:19,720
in a configurable way.
1887
00:51:19,720 --> 00:51:20,800
That is what we do it
1888
00:51:20,800 --> 00:51:21,840
for a certain extent.
1889
00:51:21,840 --> 00:51:22,360
I wouldn't say
1890
00:51:22,360 --> 00:51:24,600
our solution is totally configurable,
1891
00:51:24,600 --> 00:51:26,360
but we try to, given that expertise,
1892
00:51:26,360 --> 00:51:27,840
we try to anticipate
1893
00:51:27,840 --> 00:51:28,560
what are the things
1894
00:51:28,560 --> 00:51:30,560
that would likely change in future
1895
00:51:30,560 --> 00:51:32,160
and we try to make that as flexible
1896
00:51:32,160 --> 00:51:33,120
as possible.
1897
00:51:33,120 --> 00:51:35,400
You want to add anything?
1898
00:51:35,400 --> 00:51:37,280
That's good,
1899
00:51:37,280 --> 00:51:38,400
that's good in a way
1900
00:51:38,400 --> 00:51:41,280
that probably I would have missed it.
1901
00:51:41,280 --> 00:51:44,480
Talking about, you know,
1902
00:51:44,720 --> 00:51:48,000
I think I was focusing on something else.
1903
00:51:48,000 --> 00:51:52,200
Good, also I see a number of questions on
1904
00:51:53,800 --> 00:51:56,480
the skills needed for
1905
00:51:57,200 --> 00:51:58,400
the spaces needed
1906
00:51:58,400 --> 00:52:01,080
and how they can apply for jobs at Optum?
1907
00:52:01,080 --> 00:52:03,560
We do have portals
1908
00:52:03,640 --> 00:52:04,240
and external portals,
1909
00:52:04,240 --> 00:52:08,040
as also mention that we also have
1910
00:52:08,520 --> 00:52:10,640
the LinkedIn post that keep coming.
1911
00:52:10,640 --> 00:52:13,240
And I see people who are looking for
1912
00:52:14,920 --> 00:52:19,160
change or career at Optum.
1913
00:52:19,160 --> 00:52:25,040
There are lots of opportunities as well.
1914
00:52:25,120 --> 00:52:25,440
Okay,
1915
00:52:25,440 --> 00:52:26,080
So probably
1916
00:52:26,080 --> 00:52:28,160
I won't be going into those details.
1917
00:52:32,040 --> 00:52:32,720
Okay.
1918
00:52:35,160 --> 00:52:36,360
Are there any cloud
1919
00:52:36,360 --> 00:52:38,040
based solutions
1920
00:52:38,040 --> 00:52:40,680
in pipeline in this domain?
1921
00:52:40,680 --> 00:52:42,120
So most of our solutions today,
1922
00:52:42,120 --> 00:52:43,760
if you look at it, is already on health
1923
00:52:43,760 --> 00:52:44,840
care cloud
1924
00:52:44,840 --> 00:52:46,480
that we are essentially talking about.
1925
00:52:46,480 --> 00:52:48,240
So we are kind of migrating
1926
00:52:48,240 --> 00:52:49,640
to the cloud infrastructure.
1927
00:52:49,640 --> 00:52:51,560
Some applications are already moved,
1928
00:52:51,560 --> 00:52:52,640
but the rest of it
1929
00:52:52,640 --> 00:52:54,080
are actually in progress.
1930
00:52:54,080 --> 00:52:55,800
So we have a very solid pipeline
1931
00:52:55,800 --> 00:52:57,560
of ensuring all our key
1932
00:52:57,560 --> 00:52:59,360
or strategic and blue chip programs
1933
00:52:59,360 --> 00:53:01,080
that actually moving on to the cloud.
1934
00:53:01,080 --> 00:53:02,880
Maybe just to add to that,
1935
00:53:02,880 --> 00:53:05,560
we have huge initiatives in Optum,
1936
00:53:05,640 --> 00:53:08,360
towards making the data,
1937
00:53:09,400 --> 00:53:11,560
storing the data
1938
00:53:12,880 --> 00:53:13,560
and making it
1939
00:53:13,560 --> 00:53:15,640
available seamlessly
1940
00:53:16,680 --> 00:53:19,240
and then making through a robust channel.
1941
00:53:19,560 --> 00:53:21,640
You know, we are leveraging
1942
00:53:22,480 --> 00:53:24,280
cloud data platforms
1943
00:53:24,280 --> 00:53:25,560
like Snowflake,
1944
00:53:25,560 --> 00:53:27,080
Azure Cosmos DB
1945
00:53:27,080 --> 00:53:29,120
and those are things to heavily explored
1946
00:53:29,200 --> 00:53:31,560
in fact we have a lot of key programs
1947
00:53:31,600 --> 00:53:33,400
towards those.
1948
00:53:37,400 --> 00:53:39,640
So what are the research
1949
00:53:39,680 --> 00:53:41,720
opportunities?
1950
00:53:42,960 --> 00:53:44,000
I mean,
1951
00:53:45,080 --> 00:53:46,360
are you're talking about
1952
00:53:46,360 --> 00:53:48,800
the research opportunities within
1953
00:53:48,880 --> 00:53:52,000
AI/ML space you know?
1954
00:53:52,000 --> 00:53:56,160
As Jyothsna mentioned earlier,
1955
00:53:56,160 --> 00:53:59,040
the research opportunities are endless.
1956
00:53:59,360 --> 00:54:02,800
Any health care problem where automation
1957
00:54:03,080 --> 00:54:04,040
and machine learning
1958
00:54:04,040 --> 00:54:06,280
can help
that's an opportunity
1959
00:54:07,200 --> 00:54:08,840
is what we see.
1960
00:54:08,840 --> 00:54:11,000
So yeah, in fact
1961
00:54:13,560 --> 00:54:14,840
there is so much focus
1962
00:54:14,840 --> 00:54:17,400
on identifying the problems
1963
00:54:17,400 --> 00:54:19,560
and then finding the solutions.
1964
00:54:19,560 --> 00:54:21,680
And we are talking about zero distance
1965
00:54:21,680 --> 00:54:23,920
from the
1966
00:54:23,920 --> 00:54:24,880
business problems,
1967
00:54:24,880 --> 00:54:25,400
in this case
1968
00:54:25,400 --> 00:54:27,520
the health care problems to the solutions
1969
00:54:28,080 --> 00:54:29,080
that we provide.
1970
00:54:29,080 --> 00:54:31,480
So just to add on top of it, right.
1971
00:54:31,480 --> 00:54:33,160
I think there are a lot of
1972
00:54:33,160 --> 00:54:34,680
research opportunities
1973
00:54:34,680 --> 00:54:35,680
that exist in the health
1974
00:54:35,680 --> 00:54:37,520
care domain itself.
1975
00:54:37,520 --> 00:54:38,160
For example,
1976
00:54:38,160 --> 00:54:40,240
there's a lot of universities that are,
1977
00:54:40,560 --> 00:54:41,280
you know,
1978
00:54:41,280 --> 00:54:42,520
spending a lot of grants
1979
00:54:42,520 --> 00:54:43,560
in terms of
1980
00:54:43,560 --> 00:54:45,000
research on how the
1981
00:54:45,000 --> 00:54:46,240
health care detections
1982
00:54:46,240 --> 00:54:47,280
that you're talking about right
1983
00:54:47,280 --> 00:54:50,360
the health care, the disease detections
1984
00:54:50,360 --> 00:54:51,240
or the increasing
1985
00:54:51,240 --> 00:54:52,800
the accuracy of these detections
1986
00:54:52,800 --> 00:54:53,280
and everything.
1987
00:54:53,280 --> 00:54:53,760
I think there's
1988
00:54:53,760 --> 00:54:55,240
a lot of research opportunities
1989
00:54:55,240 --> 00:54:56,280
that are going on.
1990
00:54:56,280 --> 00:54:57,640
And if you look at that, health
1991
00:54:57,640 --> 00:54:59,000
care is also one area
1992
00:54:59,000 --> 00:55:00,040
that kind of attracts
1993
00:55:00,040 --> 00:55:01,640
a lot of patents, right?
1994
00:55:01,640 --> 00:55:03,480
And the patents are filed
1995
00:55:03,480 --> 00:55:04,600
almost like every day
1996
00:55:04,600 --> 00:55:06,160
in the health care space of it.
1997
00:55:06,160 --> 00:55:07,840
So there's always some
1998
00:55:07,840 --> 00:55:09,760
or the other research that is going
1999
00:55:09,760 --> 00:55:11,080
on, on a product
2000
00:55:11,080 --> 00:55:12,800
can be productized itself.
2001
00:55:12,800 --> 00:55:14,720
So yes, the opportunities are
2002
00:55:14,720 --> 00:55:15,600
numerous over there.
2003
00:55:16,560 --> 00:55:18,960
Yeah, multiple reporting and audit.
2004
00:55:18,960 --> 00:55:20,840
So one of the major workload,
2005
00:55:20,840 --> 00:55:25,480
do you apply AI/ML for that as well.
2006
00:55:25,560 --> 00:55:27,520
At this point of time, not really,
2007
00:55:27,520 --> 00:55:28,160
because you know,
2008
00:55:28,160 --> 00:55:29,520
we are just doing some pilots
2009
00:55:29,520 --> 00:55:30,120
in terms of
2010
00:55:30,120 --> 00:55:31,560
how effectively can we use
2011
00:55:31,560 --> 00:55:33,680
AI in terms of the audit trail
2012
00:55:33,680 --> 00:55:35,720
or the audit part of it itself.
2013
00:55:35,720 --> 00:55:37,320
But we don't have a solution
2014
00:55:37,320 --> 00:55:38,520
that it can actually talk about
2015
00:55:38,520 --> 00:55:39,520
at this point of time on
2016
00:55:39,520 --> 00:55:40,640
how we have successfully
2017
00:55:40,640 --> 00:55:43,320
used AI in terms of the auditing itself.
2018
00:55:44,760 --> 00:55:46,960
So a
2019
00:55:46,960 --> 00:55:49,000
lot of other questions, probably
2020
00:55:49,000 --> 00:55:52,080
we won't be able to cover everything,
2021
00:55:52,080 --> 00:55:52,840
but then I think
2022
00:55:52,840 --> 00:55:53,720
we can take these questions
2023
00:55:53,720 --> 00:55:54,560
offline as well.
2024
00:55:54,560 --> 00:55:57,800
Maybe
2025
00:55:57,880 --> 00:56:01,760
we will take one last question.
2026
00:56:01,760 --> 00:56:05,280
How the people looking
2027
00:56:05,280 --> 00:56:08,320
and teaching in academics can work
2028
00:56:08,320 --> 00:56:12,320
along with Optum?
2029
00:56:12,400 --> 00:56:12,560
I
2030
00:56:17,320 --> 00:56:18,880
need to get back to
2031
00:56:21,720 --> 00:56:22,840
you on that particular question.
2032
00:56:22,840 --> 00:56:24,240
Probably I'm not the right person
2033
00:56:24,240 --> 00:56:25,560
to answer that question,
2034
00:56:25,560 --> 00:56:27,600
but I can circulate back to Sukriti
2035
00:56:27,600 --> 00:56:28,360
on, you know,
2036
00:56:28,360 --> 00:56:29,440
how can we partner
2037
00:56:29,440 --> 00:56:32,120
with any of the educational institutions
2038
00:56:32,120 --> 00:56:33,560
or probably the professors
2039
00:56:33,560 --> 00:56:34,120
that are kind of
2040
00:56:34,120 --> 00:56:35,880
in that particular space, we can do it.
2041
00:56:35,880 --> 00:56:37,680
But I can call out something, right?
2042
00:56:37,680 --> 00:56:39,040
So we always look out
2043
00:56:39,040 --> 00:56:40,400
for the educationalists
2044
00:56:40,400 --> 00:56:42,480
and also the premier institutes
2045
00:56:42,480 --> 00:56:44,360
and everything to guide us.
2046
00:56:44,360 --> 00:56:46,280
For example, when the ChatGPT has come
2047
00:56:46,280 --> 00:56:47,040
in, one of the things
2048
00:56:47,040 --> 00:56:47,760
that we are working with
2049
00:56:47,760 --> 00:56:49,600
some of the colleges
2050
00:56:49,600 --> 00:56:50,960
is to see how effectively
2051
00:56:50,960 --> 00:56:51,720
can we use ChatGPT,
2052
00:56:51,720 --> 00:56:53,400
I mean what are the use cases.
2053
00:56:53,400 --> 00:56:55,360
ChatGPT is again
2054
00:56:55,360 --> 00:56:57,240
a huge trend that we are talking about
2055
00:56:57,240 --> 00:56:58,480
and it's evolving.
2056
00:56:58,480 --> 00:57:00,240
So what we actually wanted to do was
2057
00:57:00,240 --> 00:57:01,760
what are the low hanging
2058
00:57:01,760 --> 00:57:04,080
use cases that we can start implementing
2059
00:57:04,320 --> 00:57:05,400
to see the effectiveness
2060
00:57:05,400 --> 00:57:06,640
of those particular use cases
2061
00:57:06,640 --> 00:57:08,520
is something that we've already started
2062
00:57:08,520 --> 00:57:10,320
and getting in conversations in terms of,
2063
00:57:10,320 --> 00:57:10,920
you know,
2064
00:57:11,480 --> 00:57:12,840
what other complex use
2065
00:57:12,840 --> 00:57:14,040
cases can be derived
2066
00:57:14,040 --> 00:57:16,560
from ChatGPT solutions.
2067
00:57:17,200 --> 00:57:18,800
However, I need to give you
2068
00:57:18,800 --> 00:57:20,080
an exact answer on
2069
00:57:20,080 --> 00:57:21,440
how do we partner
2070
00:57:21,440 --> 00:57:23,000
that's probably something that I need
2071
00:57:23,000 --> 00:57:27,280
to revert.
2072
00:57:27,360 --> 00:57:28,440
I think we have covered.
2073
00:57:28,440 --> 00:57:29,360
So Sukriti,
2074
00:57:29,360 --> 00:57:34,000
I will give the control back to you then?
2075
00:57:34,080 --> 00:57:35,280
Thank you so much, Jyothsna
2076
00:57:35,280 --> 00:57:36,240
and thank you Pragati
2077
00:57:36,240 --> 00:57:38,200
for helping us with the Q&A
2078
00:57:38,200 --> 00:57:40,560
and a lot of positive feedback
2079
00:57:40,560 --> 00:57:41,920
from our audience today Jyothsna.
2080
00:57:41,920 --> 00:57:43,400
Everybody you know,
2081
00:57:43,400 --> 00:57:44,280
everyone in the audience
2082
00:57:44,280 --> 00:57:45,480
enjoyed the session thoroughly
2083
00:57:45,480 --> 00:57:46,800
and thank you for taking up
2084
00:57:46,800 --> 00:57:49,160
so many questions along with Pragati.
2085
00:57:49,240 --> 00:57:51,000
Thank you, Jyothsna once again,
2086
00:57:51,000 --> 00:57:51,600
thank you, everyone,
2087
00:57:51,600 --> 00:57:53,040
for joining the session.
2088
00:57:53,040 --> 00:57:53,880
We have recorded it
2089
00:57:53,880 --> 00:57:55,440
and it will be available in 24 hours
2090
00:57:55,440 --> 00:57:56,760
on the same webinar page.
2091
00:57:56,760 --> 00:57:57,360
So in case
2092
00:57:57,360 --> 00:57:58,320
if you've missed anything,
2093
00:57:58,320 --> 00:57:59,560
you can revisit the session
2094
00:57:59,560 --> 00:58:01,640
and actually get your queries sorted.
2095
00:58:01,920 --> 00:58:04,600
Jyothsna thank you so much once again.
2096
00:58:04,600 --> 00:58:07,240
Thank you Sukriti. Thank you.
2097
00:58:07,440 --> 00:58:09,520
Thank you Sukriti for this opportunity.
2098
00:58:11,320 --> 00:58:12,600
Thank you Pragati.
2099
00:58:12,600 --> 00:58:13,880
Everyone please stay safe and take care.
2100
00:58:13,880 --> 00:58:15,040
I'll see you very soon
2101
00:58:15,040 --> 00:58:17,040
with another episode of our webinar.
2102
00:58:17,040 --> 00:58:17,480
Thank you.
Watch the webinar to know more about the fundamentals of AI and its application in health care.
Key takeaways:
- Role of AI in improving access and quality of care and in reducing cost
- High-impact AI/ML use cases in health care
- Skills and roadmap to build a career in AI
