In a time that’s been challenging for everyone, perhaps no other industry has been hit harder than healthcare. Anthem, one of America’s leading health benefits companies, needed to be ready to answer member questions at a moment’s notice. In this episode of Smart Talks, Malcolm talks to Anil Bhatt, Senior Vice President and CTO of Anthem, and Glenn Finch, Managing Partner of Global Business Services at IBM, about how AI is helping people interact with healthcare in a whole new way.
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Episode Transcript
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Speaker 1 (00:05):
Hello, Hello, Hello. This is Smart Talks with IBM, a
podcast from Pushkin Industries, High Heart Media and IBM about
what it means to look at today's most challenging problems
in a new way. I'm Malcolm Gladwell. Today I'm chatting
with a Neil Bout, the senior vice president and Chief
Technology Officer of Anthem, one of the most prominent health
(00:28):
insurance companies in the United States. We have been now
pivoting the more around. Okay, we are building these capabilities,
we are building these solutions. How are they fundamentally changing
and improving the lives of our members, our communities and
really making a difference to the people we saw? And
Neil has been with Anthem for over thirteen years and
his spearheaded efforts to improve customer experience and members needs.
(00:53):
I'll also be chatting with Glenn Finch, Managing Partner of
Global Business Services at IBM. How you deal with empathy
in an AI system. It's all based on the choice
of words that you use and the verbal inflections that
are present when you have a voice response. Then is
(01:14):
a twenty five year IBM veteran. His work focuses on
the most challenging and transformative engagements at IBM. I'm excited
to share my conversation with the Neil and Glenn about
artificial intelligence and how it's influencing customers to interact with
their healthcare in a new way. Al Right, guys, let's
(01:37):
get started. Hi everyone, Thanks guys for joining me today.
Why don't we start with the two of you just
introducing yourself, tell me, tell me what you do as
(02:00):
great I'm glad to be here today, Thanks for hosting us.
I basically lead the technology and practice here at Anthem
as a CTO, managing all the roadmaps for technology, making
sure that we're building solutions that are meeting our business
needs on a day to day basis, making sure that
we are catering to the needs of our members. So
overall technology roadmap, making sure that we work with partners
(02:23):
like IBM to bring new technology to the forefront. And
how how long have you been with Anthem. I've been
with Anthem for thirteen years actually, and the company has
evolved while we are in the healthcare business. Our focus
has been more members centric now, so really understanding how
a big organization like Anthem can make sure that we
(02:43):
pivot from being a normal traditional listed company which definitely
is meeting the expectations of the stockholders, but also catering
to the need of our members and the communities that
we serve in. Yeah, why don't you introduce yourself. I'm
Glenn Finch. I look after data in AI and the
services side of the IBM company. We take a lot
(03:04):
of wicked cool technology and bring it to life of
clients like Anthem and Uh, you know, I get the
great pleasure of working with a Neil on a daily
basis to really fundamentally change the member experience using artificial intelligence,
so usually on the cutting edge of things, and just
just love coming to work every day. Yeah, so you
(03:26):
said something. The two of you have been working together
for some time. When did you guys me me to
first know each other. As I said, the industry has
been evolving a lot, Malcolm, So a couple of years back.
We basically we're kind of figuring out as the consumer
experience changes, as people get so much used to Netflix
(03:48):
and Amazon and the way they do their day to
day shopping, the way they experienced things. We were looking
for a partner where we could really explore the power
of AI, really use our data in a way wherein
we can create these personalized experiences. So that's where Glenn
and I actually talked a little bit and we figured
out that there is a possibility of us partnering ib
(04:11):
AM bringing its UM technology, and basically that's when we
kind of figure out there's there's a definite role to
play and partner on this journey together. And it's been great.
Over the last two years, we have been able to
deliver on some great, exceptional experiences for our members and
and we are now moving beyond to other constituents and
really making sure that UM we make it awesome for
(04:34):
for members to connect with us. Yeah, Glenn, had you
worked with an in an insurance provider before? Yea, So
we have a variety of clients around the world, so yes,
but Anthem is special to my heart. We started thinking
through this because when you work with Anthem, this concept
(04:56):
of member and member experience, you need to show up
every day with that front and center in your mind.
So there are other clients who focus on cost or
technical debt or something like that, but that's not true
at Anthem. You need to show up front and center
every day with how are you going to radically improve
(05:17):
the member experience first, and fas say, but it's the
relationship between the two companies and the two of you
goes back so far that I'm really curious to get
a sense of how the kinds of questions you've been
asking and problems you've been trying to solve have evolved
over that time. Tell me about ten years ago, what
(05:37):
were you guys talking about. So I think the ten
years back, the conversation is more and more around Okay,
how many sellers do we have in our data center,
how many licensing points that we're going to be spending
this year? What will be our footprint? What is our
network speed? Are we able to manage the new capability
that we're delivering? And it really was very technologically focused
conversation that we used to have. And what has happened
(06:00):
over the years, Malcolm, is that you know, we have
been now pivoting too more around Okay, we are building
these capabilities, we are building these solutions, how are they
fundamentally changing and improving the lives of our members, our
communities and really making a difference to the people we serve.
So as we looked at technology and engineering, we kind
(06:21):
of pivoted from that to more platform and a product
that we are building for our constituents. And as that
pivot happened, you know, I would say around three or
four years back, the conversation then evolved to more around Okay,
how are we improving the experience? How are we making
sure that we're making it easier for the members? And
and it pivoted from being reactive and kind of what
(06:43):
I called sick care management to more wellness oriented conversation
how do we keep our members healthy? And that's where
the overall pioneering of personalized experiences, predictive and proactive health
care management kind of started. And as we had interactive
that IBM, we knew that they had the technology and
they had the real backbone, which good. So the needs
(07:04):
that we wanted to kind of bring forward and talk
about that pivot. I'm curious what's driving it. Did you
go to a NIL and say, look, you have an
opportunity to do so much more here? Does a neil
come to you and say I don't want to be
just focused on technology. Are members are telling us X,
Y and Z or take me back to that transformative
moment when you start thinking about this project in a
(07:26):
different way. There's been a a massive shift at the
IBM company in general to shift away from pure technology
and move towards technology on behalf of a workflow. When
you think about artificial intelligence and you are trying to
have a conversation with someone, right, you don't need just
(07:49):
deep artificial intelligence programmers. You need to have people attached
to that that know how to have a conversation with
people and what sequence some words are gonna elicit a response,
and how that experience feels. To remember, that's a very
different type of program then just dropping in a chatbot
(08:12):
and hoping it works right to answer the twelve questions
that you get most of the time, right, And you
mentioned this concept of personalization, right, just making sure we
put the right people together on the program is half
the battle, right, And that's a shift that IBM has
made very consciously, started about five years ago. You know,
we really in Earnest called out intelligent workflows about two
(08:35):
or three years ago and that's when we started doing
this together. M. It was tough, it was ambitious as
compared to anything else that we had done out here.
And one thing which Malcolm was very very beautiful and
and has been very important for us learning on the goal.
When you have so much data that you're capturing, when
(08:55):
you have a technology that really can give you in
a nanosecond the response to what exactly is happening. The
beauty of it is that you can pivot and kind
of change on the fly. The agility that you build
into our system, the agility that you build into our
operations is a key and that's what we have been
able to do. And unfortunately at Anthem, we have been
(09:15):
really at the forefront of that, investing the right dollars
and bringing the agility, bringing the way we can kind
of pivot to what is more important to the concerns.
That has been a great thing that has been happening here.
Let's go through some some very specific examples. So, I
am a I'm a member of Anthem, I am on
(09:37):
your website. I would like to accomplish something. Tell me
a specific thing that an expectation a member might have,
and how you have said about trying to satisfy that expectation,
and let's get let's get super specific. Give me a scenario,
a tough a tough scenario. Yeah, yeah, well, I think
(09:58):
I can give you a comparison to the past. Right,
So when you were enrolled as a member, we probably
would send you an ID card which was a hard
piece of paper, a very good piece of paper which
costs us a lot. Then there was nothing that we
would let you know other than that, hey, if you
want a register on our website, please, you're welcome, right,
(10:18):
And then that's where our first interaction with you as
a member used to happen. And frankly, there was nothing
after that. There was a vacuum, and then you would
probably try to understand your benefits. You will make sure
that you know what your co pay is, and then
we will not hear from you for a long time,
and all of a sudden, someday, unfortunately somebody is tack
(10:41):
in your family and then you pick up the card,
go to a provider and basically have a visit um
there and then you go from there. So that's the
traditional experience that somebody would have had. Right now we
have totally revamped that. So as a member, when you
enrolled with us, we send you a welcome kit which
sent you a digital well kit. We send you an
(11:01):
ID card which is available on your phone. We send
you a link to our Sydney have tapp which basically
you can download, you can register in a minute, but
if you've been an existing member, you will get a personalized,
curated news feed which is specific to you based on
your prior experience and based on your claims, history and
(11:21):
other things that we know about you. We work with
IBM around the AI chat part, which is basically a
Watson enabled chat board which you can ask the questions
from what is my copay? You don't have a call us,
you don't have to send us an email. You can
really ask a question there itself. You can ask for
what are the providers near me? And we'll match a
provider to you based on your past history. And that's
(11:43):
where AI comes in that what do we think Malcolm's
age group, Malcolm's prior history tells us who should be
the right provider for him to take care of things.
So that interactive, more personalized, more engaging experience is what
is different. Let me give you an example. I'd love
(12:04):
for both of you the way on this. So I'm
fifty seven years old. It is indicated for someone at
my age that I get a shingles vaccine. I didn't
notice never occurred to me. A friend of mine got shingles.
It was like the worst experience of his life. He
lost three weeks. It was like so painful, and he's like,
whatever you do, Malcolm, you need to get a shingles
vaccine right now. So I went out and got my
(12:26):
shingles vaccine and then I had to get the booster.
I remember the booster and blah blah blah. Now when
you're talking about Sydney and about about drawing on past experience,
if I was a long time Anthem subscriber, would you
reach out to me and say, Malcolm, you gotta get
your shingles vaccine? Would you do? Is that what you're
thinking about? Exactly? Exactly not only we will tell you
(12:49):
that you need to take shingles vaccine, will tell you
exactly which provider probably is the right one for you.
And that is what the beauty is right now, that
not to care really that the care gap that we have.
How does the data tell us that these are the
care gaps in Malcolm's journey? You know, you you pay
a lot for your insurance company to take care of you,
and how do we make sure that we take care
(13:11):
of you? We be your advocate, We be your journey partners.
Rather than just allowing for you to come to us
when you feel that you're sick. So this this AI
system is called Sydney. First of all, who came up
with Sydney actually loved the name? But is that who?
Whose decision was it to call this system Sydney? Actually
you know, uh, it was it was our team. We
(13:34):
did some research in terms of what could be a
very neutral name that we can keep out there. And
and Malcolm, I can tell you that I love the
name so much that the beginning of when we had
COVID hit us, my daughter was asking for a dog
for a long time and we got a dog, and
actually we named the dog Sydney. So that's how how
much how much I care about the name and how
(13:55):
much I love the name. But thank you very much
for that's just so we true you Sydney. I'm getting
the AI assistum, but not your dog, That's all I
want to be clear. That's what you know we make.
We may supply you with a picture of Sydney on
when you when you come to the come to the app,
but yeah, you're getting the A. Yeah. So what we
find is that to build trust in AI systems and
(14:17):
to build the willingness for remember to go along a
journey experience. There's some things we have to do at
the table stakes level, at the grassroots level, that we
have to get right inexorably. And I'm going to go
back to a Neil's comment about the I D card.
What happens if you've lost your I D card. You
don't want to wait on the phone for anybody to
(14:41):
get a replacement a D card. You'd like to be
able to do that once and done on the web
or the mobile. Might have to ask a couple of
questions and have it done lights out right. So there's
this combination of doing the more routine things with absolute
decision writes out complete ease of member, and then that
(15:05):
builds this trust to have this more longitudinal journey to
answer your questions or to recommend to you about shingles,
vaccine right, or a variety of other things based on
you know your your health challenges. So it's a it's
kind of a double edged sort of taking care of
the table stakes and taking people along the journey. Your
(15:25):
point is you start with the very prosaic stuff and
you build a trust in the system, and then you
can move to the more high end stuff. Tell me
about how you build an AI system like this. This
is not a trivial accomplishment. What went into building Sydney. Yeah,
so I think you know, Malcolm, Traditionally, we have a
(15:49):
lot of data over the years that we have accumulated
for every member, and we have eighty million lives, multiple
petabytes of data which is sitting on our systems, and
that data basically allows us to learn. You know, the
data is data. As long as you don't touch it,
you don't do anything. But once you start really using
(16:11):
technologies and and when when we call AI, these are
mathematical models that you can run on this data to
give you insights. And those insights are the key at
the end of the day. And as we get those insights,
we have to make sure that we have a way
to use those insights to make a difference in the
in the life of any member that we have or
(16:31):
any constant. Actually, you know, our sales experience for our
brokers are providers. Getting to know exactly what they need
to know is very very important. So we are making
sure that this data and the minding of this data
is constant. So when we talk about the partnership with IBM.
We're talking about ability for us to mind this data
(16:52):
on the fly at a very very quick speed, and
that is what is key. Then we're able to use
AI in a different text. And I'm going to give
you example of something that really we're bringing to the
forefront of of what we call as a nutrition tracker.
So imagine that you have your phone in front of you,
You have a plate of food that came in front
(17:14):
of you, and you can open Sydney and show the
food of plate to Sydney. Sydney can tell you based
on what it plate. You take a picture of the
photo and Sydney looks at the photo and says, why
are you loading up on carbs? I mean, is that
what we're talking about exactly? That's what I'm talking about.
So you know, this is a great partnership we have
with one of our ecosystem partners. And actually this isn't
(17:35):
pilot with our house account, which is eighty thousand members
right now, and it can tell you. It can you
can show it a cup and it can tell you
this is a coffee with no milk, and it's going
to be seventy calories and it keeps track of what
you're eating and basically that's how we build the healthy
habits out there. So the advancement in the in the
(17:56):
field of technology and how do we make sure that
we move away from that leg thee information technology to
really the exponential technology that is in front of us
is the key. We needed to take all of that
AI and persist a conversation with a member, right And
and that's where Watson came in to help Sydney persist
(18:20):
conversations with members, right Because crunching through data and and
knowing about your claim is one thing, but being able
to talk to you about that claim and understand your
responses back, whether you're on a keyboard, whether you're speaking,
whether you're doing whatever. That's kind of where Watson came
in to help augment Sydney and again designing those conversations.
(18:42):
I don't know if you've been in a in a
situation where you're sitting next to somebody and they're talking
and you say, oh my god, I can't believe they
said that. Well, you have to engineer that out of
the conversations that you have with members so that you know,
all of the members are delighted and one of the
things I'm proudest of is when by our work together,
(19:05):
we have members that are thanking Sydney when we're working
with them with artificial intelligence, responding to their questions, just
as if Sydney was a fully human worker, right, And
that that's what I get delight from is when we've
been able to change a member experience and work through
(19:27):
that all of the things members might ask, Now, are
you taking real life conversations, looking at them and feeding
them to Sydney and saying okay? In the last two years,
these are all the These are all the phone conversations
we've had with our members. These are the kinds of
things they ask. Is that where it starts? We build
(19:50):
what we call the anthology of the conversations? You know,
how are we making sure that as we get the
interactions noted down for our members or provide us into
our system, whether it's a phone call, whether it's a chat,
whether it's basically even they came to the website and
they clicked through specific things, right, so we are noting
(20:11):
those down. We are kind of creating a what we
call a graph model and a flow of When a
member asked this, the next question possible level, it's going
to be this. If you give up yes to that
answer or not to that answer, they're gonna probably ask
you this. So that kind of slow Sydney can be
thinking two in three steps ahead exactly. So Sydney is
(20:31):
thinking two or three steps ahead and making sure that
the anticipation of what you're going to be doing and
and beyond. Sydney, our overall system is thinking two or
three systems steps ahead and predicting proactively those conversations as
well as those interventions that we need to give to
the members. So really using ai UM you know Watson
(20:52):
as a back backbone to this, Sydney is basically what
we call the human centered, designed, focused Interaction and Engagements
system that sits on top of the backbone of the
AI as the data at the bottom, So that basically
is layered away. How Sydney is able to answer the
question that we have it aspects you of what type
of question it is because our intology of the data
(21:14):
as well as the AIS that we have built is
very very dock solid. And that is and the good
thing is that it's it's a gift that keeps giving
because the more data we collect, the more the system
it gets Yeah, wait, can you Stumps, Sydney, can you
ask it a question? You can? I'm sure it's possible too.
And then you know, when we when we get into
(21:37):
that situation, what we want to do is we want
to bring the member to a human agent so that
the member satisfied seamlessly right, so that there's there's no
daylight at all regardless of how the member has wants
to connect with the human agent. A lot of members,
you know, are are dealing with time challenges and they
(21:59):
don't want to call up anymore. They just want somebody
to you know, be able to chat with. We try
and respond to all that, and then if if somebody
needs a human agent, then we go there. Yeah. Yeah,
what's what did your what did your members tell you about,
either explicitly or implicitly about what they wanted? You know,
(22:22):
we've been through this. We've just been through a year
and a half of craziness, you know, where everything is
being turned upside down. I'm curious, what have you what
have you learned from them over the last stretch is
what a member wants today very different than it was
two years ago. Definitely, Malcolm. If you look at that,
you know, the terms that you use in healthcare are
(22:44):
very very complex, and it's very difficult for people to
understand what my cope is. What is an out of network,
what is it in network? What does a claim uh
that that needs a pre authoriation mean to me? So
if you look at the conversation that we were having before,
they were really very hardcore health care oriented conversations and
(23:06):
and the transparency to to what I'm going to pay
was not there. So this was this was industry where
in you know, you're going to buy insurance and you're
going to buy a product without really understanding what I'm
going to get. At the end of the day, what
we did and basically what our customers actually demanded from
us is that irrespect you of the channel that they
(23:27):
come to us. Um what we call here at Anthem
connected experiences. We want to build the connected experiences, whether
they come to us from a phone call, whether they're
chatting with us, they're having a web in traction, whether
they're in the provider's office. How do we make sure
that we connect the experience end to end. Now, once
we connect the experience, we want to make sure that
we are building a very human centered design way of
(23:51):
answering their questions. So it is as simple as making
sure that we provide them a nudge on probably this
is what you're looking for, and that clicks with them
and they say, yeah, that's what I was looking for.
So that input, simple interaction really helps to make sure
that you make the member feel good. Having the ability
to text, having an ability to get dancers while you're
(24:13):
cooking your dinner and you can text and say that, hey,
could you please tell me what will cope for the
next visit? I have a daughter X And you go
ahead and start cooking your dinner and when you come back,
you have a text back out there which tells you
exactly what it is. And the beauty of it is
that we had a very constant loop out there, you know,
the technologies that we use that that allowed us to
(24:34):
have a constant feedback on those complex interactions that we
were having. And that's where IBM team and we work
together and kind of figured out, Okay, what will be
our game plan. What did you learn from working with
other people on the Watson platform that helped a Neil
and Anthem? What did you bring to them? So from
what you've learned from others. So what we what we've
(24:55):
tried to do with Watson, Well, Watson first started, we
thought that everybody wanted of a spoke suit, and so
we kind of go on a journey together to make
up a spoke suit. And what what we found the
clients really wanted was well, look, I want you to
show up with the suit partially done to answer some
(25:20):
of the basic things, and then I want to make
it my own, right, So so show up ready to
go so that we can get into production answering questions
in a few months, and then we will work together
to radically customize and taylor that experience. That's been my
biggest learning, right, So whether it was UM in financial services,
(25:46):
or healthcare or Telco or you know, there's about seven
or eight dominant industries. UM. We tried to make a
series of industry specific cartridges so that Watson came kind
of pre trained, right, so that we were ready to
go quickly. And then the second learning was we needed
(26:06):
to show up with the right people because remember you're
creating a conversational interaction with someone, right, so you've got
to make sure that people are designing the words correctly
and the user experience right. Those are the two things
I think that you know, we brought that tried to
help Anthem accelerate. I mean, and Neil said something that
(26:28):
I thought was fascinating. You're talking about designing a system
with empathy, and I'm curious what does First of all,
what does empathy look like in an AI system? And
b have you has anyone ever, has any non healthcare
player ever asked you, Glenn to put empathy in the system. UM.
(26:51):
Clients outside of healthcare are less focused on empathy. They
are focused more on making sure to get UM, you know,
the information out there correctly, especially in highly regulated industries. Right.
How you deal with empathy in an AI system, it's
(27:13):
all based on the choice of words that you use
and the verbal inflections that are present when you have
a voice response, right, and you you and I UM
when we're talking right now, was Malcolm with Annal with whomever.
We can just by the words that somebody chooses, we
can know whether it matters to them about what we're
(27:34):
talking about, right, And so we try and build a
lot of those human characteristics into all the responses as
compared to just getting the information right. Just what's you know?
Usn't just telling people you're sorry, right, Those are the
types of things that you have to engineer in as
compared to just being flawlessly precise about the answer. Yeah.
(27:58):
Um wait, one last question for for both of has
been such a fun conversation. We talked about ten years
ago when you guys started talking and then this sort
of this transition moment five years ago. Now let's go
five years in the future. So let's imagine it's twenty
and we're three of us are talking again. I want
(28:18):
to know what problems you're trying to solve. Then the
problems we're trying to solve at that time would would
would definitely be much different on where we are. But
what I can tell you before I get there is
that you know, we want to make sure that in
the next five years, anthem Is is treated like a
(28:39):
platform company which is focused on creating these solutions with
the help of our partners that really meet the need
of the members in the journey that they have from
a healthcare perspective, and we do want to pivot from
a from a sick care to more proactive and predictive
care and wellness for members. So we're gonna will down
(29:00):
and keep working on that because it's a it's something
that never ends and it's going to keep keep going
in the years to come. I'm still processing this fantastic
idea about taking a photo of your meal, if your
plate of food, and getting instantent feedback and analysis on that.
(29:20):
So Sydney starts gets all these pictures of my food,
gets it gets a sense of what I'm eating over
the course of the given day. Is the idea that
so I'm thinking about this five year from now conversation.
So five years from now I might be taking a
photo of everything, and then at the end of every day,
Sydney text me and says, Malcolm, you should be aware
(29:43):
of the fact that you're nutritional patterns of the last
few days. You need to eat a few more vegetables
or you'll be useful to have some. Is that what
we're talking about here? But I think this idea is
fantastic because there is no we have no way of
making any nutritional sense of the stuff we unless you
spend two hours on a on Google before you make
(30:04):
your dinner. How do you know whether the sum total
of the things you eat in a given day is
going to be um is optimum? I love this. I
want this with this now? Can I do I have
to wait five years? Can I have? Guys, It's been
a really, really fun conversation. I really appreciate you taking
the time, Neil Glenn have a wonderful day, and that
(30:26):
the future cannot come fast enough, at least for me,
So bring it on. I'm waiting for it. Thank you
very much for Malcolm. Oh yeah, awesome, Thanks Malcolm. Bye, guys.
Understanding customer needs has become even more important in the
wake of COVID nineteen. Companies like IBM and Anthem are
(30:48):
learning to leverage technology to deliver a more personal experience,
a crucial part of our evolving healthcare system. Thanks again
to a Neil Bot and Glenn Finch for talking king
with me, I learned a lot Smart Talks with IBM
is produced by Emily Rosteck with Carlie Migliori, edited by
(31:08):
Karen Shakergee engineering by Martin Gonzalez, mixed and mastered by
Jason Gambrell and Ben Tolliday. Music by Granmoscope. Special thanks
to Molly Sosha, Andy Kelly Mia, Label, Jacob Weisberg, Heather Faine,
Eric Sandler, and Maggie Taylor and the teams at eight
Bar and IBM. Smart Talks with IBM is a production
(31:31):
of Pushkin Industries and I Heart Media. You can find
more Pushkin podcasts on the i Heart Radio app, Apple Podcasts,
or wherever you like to listen. I'm Malcolm Gladwell, See
you next time.
Hello, Hello, Hello. This is Smart Talks with IBM, a
podcast from Pushkin Industries, High Heart Media and IBM about
what it means to look at today's most challenging problems
in a new way. I'm Malcolm Gladwell. Today I'm chatting
with a Neil Bout, the senior vice president and Chief
Technology Officer of Anthem, one of the most prominent health
(00:28):
insurance companies in the United States. We have been now
pivoting the more around. Okay, we are building these capabilities,
we are building these solutions. How are they fundamentally changing
and improving the lives of our members, our communities and
really making a difference to the people we saw? And
Neil has been with Anthem for over thirteen years and
his spearheaded efforts to improve customer experience and members needs.
(00:53):
I'll also be chatting with Glenn Finch, Managing Partner of
Global Business Services at IBM. How you deal with empathy
in an AI system. It's all based on the choice
of words that you use and the verbal inflections that
are present when you have a voice response. Then is
(01:14):
a twenty five year IBM veteran. His work focuses on
the most challenging and transformative engagements at IBM. I'm excited
to share my conversation with the Neil and Glenn about
artificial intelligence and how it's influencing customers to interact with
their healthcare in a new way. Al Right, guys, let's
(01:37):
get started. Hi everyone, Thanks guys for joining me today.
Why don't we start with the two of you just
introducing yourself, tell me, tell me what you do as
(02:00):
great I'm glad to be here today, Thanks for hosting us.
I basically lead the technology and practice here at Anthem
as a CTO, managing all the roadmaps for technology, making
sure that we're building solutions that are meeting our business
needs on a day to day basis, making sure that
we are catering to the needs of our members. So
overall technology roadmap, making sure that we work with partners
(02:23):
like IBM to bring new technology to the forefront. And
how how long have you been with Anthem. I've been
with Anthem for thirteen years actually, and the company has
evolved while we are in the healthcare business. Our focus
has been more members centric now, so really understanding how
a big organization like Anthem can make sure that we
(02:43):
pivot from being a normal traditional listed company which definitely
is meeting the expectations of the stockholders, but also catering
to the need of our members and the communities that
we serve in. Yeah, why don't you introduce yourself. I'm
Glenn Finch. I look after data in AI and the
services side of the IBM company. We take a lot
(03:04):
of wicked cool technology and bring it to life of
clients like Anthem and Uh, you know, I get the
great pleasure of working with a Neil on a daily
basis to really fundamentally change the member experience using artificial intelligence,
so usually on the cutting edge of things, and just
just love coming to work every day. Yeah, so you
(03:26):
said something. The two of you have been working together
for some time. When did you guys me me to
first know each other. As I said, the industry has
been evolving a lot, Malcolm, So a couple of years back.
We basically we're kind of figuring out as the consumer
experience changes, as people get so much used to Netflix
(03:48):
and Amazon and the way they do their day to
day shopping, the way they experienced things. We were looking
for a partner where we could really explore the power
of AI, really use our data in a way wherein
we can create these personalized experiences. So that's where Glenn
and I actually talked a little bit and we figured
out that there is a possibility of us partnering ib
(04:11):
AM bringing its UM technology, and basically that's when we
kind of figure out there's there's a definite role to
play and partner on this journey together. And it's been great.
Over the last two years, we have been able to
deliver on some great, exceptional experiences for our members and
and we are now moving beyond to other constituents and
really making sure that UM we make it awesome for
(04:34):
for members to connect with us. Yeah, Glenn, had you
worked with an in an insurance provider before? Yea, So
we have a variety of clients around the world, so yes,
but Anthem is special to my heart. We started thinking
through this because when you work with Anthem, this concept
(04:56):
of member and member experience, you need to show up
every day with that front and center in your mind.
So there are other clients who focus on cost or
technical debt or something like that, but that's not true
at Anthem. You need to show up front and center
every day with how are you going to radically improve
(05:17):
the member experience first, and fas say, but it's the
relationship between the two companies and the two of you
goes back so far that I'm really curious to get
a sense of how the kinds of questions you've been
asking and problems you've been trying to solve have evolved
over that time. Tell me about ten years ago, what
(05:37):
were you guys talking about. So I think the ten
years back, the conversation is more and more around Okay,
how many sellers do we have in our data center,
how many licensing points that we're going to be spending
this year? What will be our footprint? What is our
network speed? Are we able to manage the new capability
that we're delivering? And it really was very technologically focused
conversation that we used to have. And what has happened
(06:00):
over the years, Malcolm, is that you know, we have
been now pivoting too more around Okay, we are building
these capabilities, we are building these solutions, how are they
fundamentally changing and improving the lives of our members, our
communities and really making a difference to the people we serve.
So as we looked at technology and engineering, we kind
(06:21):
of pivoted from that to more platform and a product
that we are building for our constituents. And as that
pivot happened, you know, I would say around three or
four years back, the conversation then evolved to more around Okay,
how are we improving the experience? How are we making
sure that we're making it easier for the members? And
and it pivoted from being reactive and kind of what
(06:43):
I called sick care management to more wellness oriented conversation
how do we keep our members healthy? And that's where
the overall pioneering of personalized experiences, predictive and proactive health
care management kind of started. And as we had interactive
that IBM, we knew that they had the technology and
they had the real backbone, which good. So the needs
(07:04):
that we wanted to kind of bring forward and talk
about that pivot. I'm curious what's driving it. Did you
go to a NIL and say, look, you have an
opportunity to do so much more here? Does a neil
come to you and say I don't want to be
just focused on technology. Are members are telling us X,
Y and Z or take me back to that transformative
moment when you start thinking about this project in a
(07:26):
different way. There's been a a massive shift at the
IBM company in general to shift away from pure technology
and move towards technology on behalf of a workflow. When
you think about artificial intelligence and you are trying to
have a conversation with someone, right, you don't need just
(07:49):
deep artificial intelligence programmers. You need to have people attached
to that that know how to have a conversation with
people and what sequence some words are gonna elicit a response,
and how that experience feels. To remember, that's a very
different type of program then just dropping in a chatbot
(08:12):
and hoping it works right to answer the twelve questions
that you get most of the time, right, And you
mentioned this concept of personalization, right, just making sure we
put the right people together on the program is half
the battle, right, And that's a shift that IBM has
made very consciously, started about five years ago. You know,
we really in Earnest called out intelligent workflows about two
(08:35):
or three years ago and that's when we started doing
this together. M. It was tough, it was ambitious as
compared to anything else that we had done out here.
And one thing which Malcolm was very very beautiful and
and has been very important for us learning on the goal.
When you have so much data that you're capturing, when
(08:55):
you have a technology that really can give you in
a nanosecond the response to what exactly is happening. The
beauty of it is that you can pivot and kind
of change on the fly. The agility that you build
into our system, the agility that you build into our
operations is a key and that's what we have been
able to do. And unfortunately at Anthem, we have been
(09:15):
really at the forefront of that, investing the right dollars
and bringing the agility, bringing the way we can kind
of pivot to what is more important to the concerns.
That has been a great thing that has been happening here.
Let's go through some some very specific examples. So, I
am a I'm a member of Anthem, I am on
(09:37):
your website. I would like to accomplish something. Tell me
a specific thing that an expectation a member might have,
and how you have said about trying to satisfy that expectation,
and let's get let's get super specific. Give me a scenario,
a tough a tough scenario. Yeah, yeah, well, I think
(09:58):
I can give you a comparison to the past. Right,
So when you were enrolled as a member, we probably
would send you an ID card which was a hard
piece of paper, a very good piece of paper which
costs us a lot. Then there was nothing that we
would let you know other than that, hey, if you
want a register on our website, please, you're welcome, right,
(10:18):
And then that's where our first interaction with you as
a member used to happen. And frankly, there was nothing
after that. There was a vacuum, and then you would
probably try to understand your benefits. You will make sure
that you know what your co pay is, and then
we will not hear from you for a long time,
and all of a sudden, someday, unfortunately somebody is tack
(10:41):
in your family and then you pick up the card,
go to a provider and basically have a visit um
there and then you go from there. So that's the
traditional experience that somebody would have had. Right now we
have totally revamped that. So as a member, when you
enrolled with us, we send you a welcome kit which
sent you a digital well kit. We send you an
(11:01):
ID card which is available on your phone. We send
you a link to our Sydney have tapp which basically
you can download, you can register in a minute, but
if you've been an existing member, you will get a personalized,
curated news feed which is specific to you based on
your prior experience and based on your claims, history and
(11:21):
other things that we know about you. We work with
IBM around the AI chat part, which is basically a
Watson enabled chat board which you can ask the questions
from what is my copay? You don't have a call us,
you don't have to send us an email. You can
really ask a question there itself. You can ask for
what are the providers near me? And we'll match a
provider to you based on your past history. And that's
(11:43):
where AI comes in that what do we think Malcolm's
age group, Malcolm's prior history tells us who should be
the right provider for him to take care of things.
So that interactive, more personalized, more engaging experience is what
is different. Let me give you an example. I'd love
(12:04):
for both of you the way on this. So I'm
fifty seven years old. It is indicated for someone at
my age that I get a shingles vaccine. I didn't
notice never occurred to me. A friend of mine got shingles.
It was like the worst experience of his life. He
lost three weeks. It was like so painful, and he's like,
whatever you do, Malcolm, you need to get a shingles
vaccine right now. So I went out and got my
(12:26):
shingles vaccine and then I had to get the booster.
I remember the booster and blah blah blah. Now when
you're talking about Sydney and about about drawing on past experience,
if I was a long time Anthem subscriber, would you
reach out to me and say, Malcolm, you gotta get
your shingles vaccine? Would you do? Is that what you're
thinking about? Exactly? Exactly not only we will tell you
(12:49):
that you need to take shingles vaccine, will tell you
exactly which provider probably is the right one for you.
And that is what the beauty is right now, that
not to care really that the care gap that we have.
How does the data tell us that these are the
care gaps in Malcolm's journey? You know, you you pay
a lot for your insurance company to take care of you,
and how do we make sure that we take care
(13:11):
of you? We be your advocate, We be your journey partners.
Rather than just allowing for you to come to us
when you feel that you're sick. So this this AI
system is called Sydney. First of all, who came up
with Sydney actually loved the name? But is that who?
Whose decision was it to call this system Sydney? Actually
you know, uh, it was it was our team. We
(13:34):
did some research in terms of what could be a
very neutral name that we can keep out there. And
and Malcolm, I can tell you that I love the
name so much that the beginning of when we had
COVID hit us, my daughter was asking for a dog
for a long time and we got a dog, and
actually we named the dog Sydney. So that's how how
much how much I care about the name and how
(13:55):
much I love the name. But thank you very much
for that's just so we true you Sydney. I'm getting
the AI assistum, but not your dog, That's all I
want to be clear. That's what you know we make.
We may supply you with a picture of Sydney on
when you when you come to the come to the app,
but yeah, you're getting the A. Yeah. So what we
find is that to build trust in AI systems and
(14:17):
to build the willingness for remember to go along a
journey experience. There's some things we have to do at
the table stakes level, at the grassroots level, that we
have to get right inexorably. And I'm going to go
back to a Neil's comment about the I D card.
What happens if you've lost your I D card. You
don't want to wait on the phone for anybody to
(14:41):
get a replacement a D card. You'd like to be
able to do that once and done on the web
or the mobile. Might have to ask a couple of
questions and have it done lights out right. So there's
this combination of doing the more routine things with absolute
decision writes out complete ease of member, and then that
(15:05):
builds this trust to have this more longitudinal journey to
answer your questions or to recommend to you about shingles,
vaccine right, or a variety of other things based on
you know your your health challenges. So it's a it's
kind of a double edged sort of taking care of
the table stakes and taking people along the journey. Your
(15:25):
point is you start with the very prosaic stuff and
you build a trust in the system, and then you
can move to the more high end stuff. Tell me
about how you build an AI system like this. This
is not a trivial accomplishment. What went into building Sydney. Yeah,
so I think you know, Malcolm, Traditionally, we have a
(15:49):
lot of data over the years that we have accumulated
for every member, and we have eighty million lives, multiple
petabytes of data which is sitting on our systems, and
that data basically allows us to learn. You know, the
data is data. As long as you don't touch it,
you don't do anything. But once you start really using
(16:11):
technologies and and when when we call AI, these are
mathematical models that you can run on this data to
give you insights. And those insights are the key at
the end of the day. And as we get those insights,
we have to make sure that we have a way
to use those insights to make a difference in the
in the life of any member that we have or
(16:31):
any constant. Actually, you know, our sales experience for our
brokers are providers. Getting to know exactly what they need
to know is very very important. So we are making
sure that this data and the minding of this data
is constant. So when we talk about the partnership with IBM.
We're talking about ability for us to mind this data
(16:52):
on the fly at a very very quick speed, and
that is what is key. Then we're able to use
AI in a different text. And I'm going to give
you example of something that really we're bringing to the
forefront of of what we call as a nutrition tracker.
So imagine that you have your phone in front of you,
You have a plate of food that came in front
(17:14):
of you, and you can open Sydney and show the
food of plate to Sydney. Sydney can tell you based
on what it plate. You take a picture of the
photo and Sydney looks at the photo and says, why
are you loading up on carbs? I mean, is that
what we're talking about exactly? That's what I'm talking about.
So you know, this is a great partnership we have
with one of our ecosystem partners. And actually this isn't
(17:35):
pilot with our house account, which is eighty thousand members
right now, and it can tell you. It can you
can show it a cup and it can tell you
this is a coffee with no milk, and it's going
to be seventy calories and it keeps track of what
you're eating and basically that's how we build the healthy
habits out there. So the advancement in the in the
(17:56):
field of technology and how do we make sure that
we move away from that leg thee information technology to
really the exponential technology that is in front of us
is the key. We needed to take all of that
AI and persist a conversation with a member, right And
and that's where Watson came in to help Sydney persist
(18:20):
conversations with members, right Because crunching through data and and
knowing about your claim is one thing, but being able
to talk to you about that claim and understand your
responses back, whether you're on a keyboard, whether you're speaking,
whether you're doing whatever. That's kind of where Watson came
in to help augment Sydney and again designing those conversations.
(18:42):
I don't know if you've been in a in a
situation where you're sitting next to somebody and they're talking
and you say, oh my god, I can't believe they
said that. Well, you have to engineer that out of
the conversations that you have with members so that you know,
all of the members are delighted and one of the
things I'm proudest of is when by our work together,
(19:05):
we have members that are thanking Sydney when we're working
with them with artificial intelligence, responding to their questions, just
as if Sydney was a fully human worker, right, And
that that's what I get delight from is when we've
been able to change a member experience and work through
(19:27):
that all of the things members might ask, Now, are
you taking real life conversations, looking at them and feeding
them to Sydney and saying okay? In the last two years,
these are all the These are all the phone conversations
we've had with our members. These are the kinds of
things they ask. Is that where it starts? We build
(19:50):
what we call the anthology of the conversations? You know,
how are we making sure that as we get the
interactions noted down for our members or provide us into
our system, whether it's a phone call, whether it's a chat,
whether it's basically even they came to the website and
they clicked through specific things, right, so we are noting
(20:11):
those down. We are kind of creating a what we
call a graph model and a flow of When a
member asked this, the next question possible level, it's going
to be this. If you give up yes to that
answer or not to that answer, they're gonna probably ask
you this. So that kind of slow Sydney can be
thinking two in three steps ahead exactly. So Sydney is
(20:31):
thinking two or three steps ahead and making sure that
the anticipation of what you're going to be doing and
and beyond. Sydney, our overall system is thinking two or
three systems steps ahead and predicting proactively those conversations as
well as those interventions that we need to give to
the members. So really using ai UM you know Watson
(20:52):
as a back backbone to this, Sydney is basically what
we call the human centered, designed, focused Interaction and Engagements
system that sits on top of the backbone of the
AI as the data at the bottom, So that basically
is layered away. How Sydney is able to answer the
question that we have it aspects you of what type
of question it is because our intology of the data
(21:14):
as well as the AIS that we have built is
very very dock solid. And that is and the good
thing is that it's it's a gift that keeps giving
because the more data we collect, the more the system
it gets Yeah, wait, can you Stumps, Sydney, can you
ask it a question? You can? I'm sure it's possible too.
And then you know, when we when we get into
(21:37):
that situation, what we want to do is we want
to bring the member to a human agent so that
the member satisfied seamlessly right, so that there's there's no
daylight at all regardless of how the member has wants
to connect with the human agent. A lot of members,
you know, are are dealing with time challenges and they
(21:59):
don't want to call up anymore. They just want somebody
to you know, be able to chat with. We try
and respond to all that, and then if if somebody
needs a human agent, then we go there. Yeah. Yeah,
what's what did your what did your members tell you about,
either explicitly or implicitly about what they wanted? You know,
(22:22):
we've been through this. We've just been through a year
and a half of craziness, you know, where everything is
being turned upside down. I'm curious, what have you what
have you learned from them over the last stretch is
what a member wants today very different than it was
two years ago. Definitely, Malcolm. If you look at that,
you know, the terms that you use in healthcare are
(22:44):
very very complex, and it's very difficult for people to
understand what my cope is. What is an out of network,
what is it in network? What does a claim uh
that that needs a pre authoriation mean to me? So
if you look at the conversation that we were having before,
they were really very hardcore health care oriented conversations and
(23:06):
and the transparency to to what I'm going to pay
was not there. So this was this was industry where
in you know, you're going to buy insurance and you're
going to buy a product without really understanding what I'm
going to get. At the end of the day, what
we did and basically what our customers actually demanded from
us is that irrespect you of the channel that they
(23:27):
come to us. Um what we call here at Anthem
connected experiences. We want to build the connected experiences, whether
they come to us from a phone call, whether they're
chatting with us, they're having a web in traction, whether
they're in the provider's office. How do we make sure
that we connect the experience end to end. Now, once
we connect the experience, we want to make sure that
we are building a very human centered design way of
(23:51):
answering their questions. So it is as simple as making
sure that we provide them a nudge on probably this
is what you're looking for, and that clicks with them
and they say, yeah, that's what I was looking for.
So that input, simple interaction really helps to make sure
that you make the member feel good. Having the ability
to text, having an ability to get dancers while you're
(24:13):
cooking your dinner and you can text and say that, hey,
could you please tell me what will cope for the
next visit? I have a daughter X And you go
ahead and start cooking your dinner and when you come back,
you have a text back out there which tells you
exactly what it is. And the beauty of it is
that we had a very constant loop out there, you know,
the technologies that we use that that allowed us to
(24:34):
have a constant feedback on those complex interactions that we
were having. And that's where IBM team and we work
together and kind of figured out, Okay, what will be
our game plan. What did you learn from working with
other people on the Watson platform that helped a Neil
and Anthem? What did you bring to them? So from
what you've learned from others. So what we what we've
(24:55):
tried to do with Watson, Well, Watson first started, we
thought that everybody wanted of a spoke suit, and so
we kind of go on a journey together to make
up a spoke suit. And what what we found the
clients really wanted was well, look, I want you to
show up with the suit partially done to answer some
(25:20):
of the basic things, and then I want to make
it my own, right, So so show up ready to
go so that we can get into production answering questions
in a few months, and then we will work together
to radically customize and taylor that experience. That's been my
biggest learning, right, So whether it was UM in financial services,
(25:46):
or healthcare or Telco or you know, there's about seven
or eight dominant industries. UM. We tried to make a
series of industry specific cartridges so that Watson came kind
of pre trained, right, so that we were ready to
go quickly. And then the second learning was we needed
(26:06):
to show up with the right people because remember you're
creating a conversational interaction with someone, right, so you've got
to make sure that people are designing the words correctly
and the user experience right. Those are the two things
I think that you know, we brought that tried to
help Anthem accelerate. I mean, and Neil said something that
(26:28):
I thought was fascinating. You're talking about designing a system
with empathy, and I'm curious what does First of all,
what does empathy look like in an AI system? And
b have you has anyone ever, has any non healthcare
player ever asked you, Glenn to put empathy in the system. UM.
(26:51):
Clients outside of healthcare are less focused on empathy. They
are focused more on making sure to get UM, you know,
the information out there correctly, especially in highly regulated industries. Right.
How you deal with empathy in an AI system, it's
(27:13):
all based on the choice of words that you use
and the verbal inflections that are present when you have
a voice response, right, and you you and I UM
when we're talking right now, was Malcolm with Annal with whomever.
We can just by the words that somebody chooses, we
can know whether it matters to them about what we're
(27:34):
talking about, right, And so we try and build a
lot of those human characteristics into all the responses as
compared to just getting the information right. Just what's you know?
Usn't just telling people you're sorry, right, Those are the
types of things that you have to engineer in as
compared to just being flawlessly precise about the answer. Yeah.
(27:58):
Um wait, one last question for for both of has
been such a fun conversation. We talked about ten years
ago when you guys started talking and then this sort
of this transition moment five years ago. Now let's go
five years in the future. So let's imagine it's twenty
and we're three of us are talking again. I want
(28:18):
to know what problems you're trying to solve. Then the
problems we're trying to solve at that time would would
would definitely be much different on where we are. But
what I can tell you before I get there is
that you know, we want to make sure that in
the next five years, anthem Is is treated like a
(28:39):
platform company which is focused on creating these solutions with
the help of our partners that really meet the need
of the members in the journey that they have from
a healthcare perspective, and we do want to pivot from
a from a sick care to more proactive and predictive
care and wellness for members. So we're gonna will down
(29:00):
and keep working on that because it's a it's something
that never ends and it's going to keep keep going
in the years to come. I'm still processing this fantastic
idea about taking a photo of your meal, if your
plate of food, and getting instantent feedback and analysis on that.
(29:20):
So Sydney starts gets all these pictures of my food,
gets it gets a sense of what I'm eating over
the course of the given day. Is the idea that
so I'm thinking about this five year from now conversation.
So five years from now I might be taking a
photo of everything, and then at the end of every day,
Sydney text me and says, Malcolm, you should be aware
(29:43):
of the fact that you're nutritional patterns of the last
few days. You need to eat a few more vegetables
or you'll be useful to have some. Is that what
we're talking about here? But I think this idea is
fantastic because there is no we have no way of
making any nutritional sense of the stuff we unless you
spend two hours on a on Google before you make
(30:04):
your dinner. How do you know whether the sum total
of the things you eat in a given day is
going to be um is optimum? I love this. I
want this with this now? Can I do I have
to wait five years? Can I have? Guys, It's been
a really, really fun conversation. I really appreciate you taking
the time, Neil Glenn have a wonderful day, and that
(30:26):
the future cannot come fast enough, at least for me,
So bring it on. I'm waiting for it. Thank you
very much for Malcolm. Oh yeah, awesome, Thanks Malcolm. Bye, guys.
Understanding customer needs has become even more important in the
wake of COVID nineteen. Companies like IBM and Anthem are
(30:48):
learning to leverage technology to deliver a more personal experience,
a crucial part of our evolving healthcare system. Thanks again
to a Neil Bot and Glenn Finch for talking king
with me, I learned a lot Smart Talks with IBM
is produced by Emily Rosteck with Carlie Migliori, edited by
(31:08):
Karen Shakergee engineering by Martin Gonzalez, mixed and mastered by
Jason Gambrell and Ben Tolliday. Music by Granmoscope. Special thanks
to Molly Sosha, Andy Kelly Mia, Label, Jacob Weisberg, Heather Faine,
Eric Sandler, and Maggie Taylor and the teams at eight
Bar and IBM. Smart Talks with IBM is a production
(31:31):
of Pushkin Industries and I Heart Media. You can find
more Pushkin podcasts on the i Heart Radio app, Apple Podcasts,
or wherever you like to listen. I'm Malcolm Gladwell, See
you next time.
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