-: There can be sizable gaps
between people working directly
with technology and people working in product teams.
Today's episode with Slawek Kierner,
Senior Vice President, Digital Health and Analytics
at Humana,
illustrates how diverse experience can come together
in novel ways, to build value with AI.
Hello, and welcome to "Me, Myself and AI",
a podcast on artificial intelligence in business.
Each episode, we introduce you to someone innovating
with AI.
I'm Sam Ransbotham, Professor of Information Systems
at Boston College.
I'm also the guest editor for the AI and Business Strategy,
Big Idea Program at MIT Sloan Management Review.
-: And I'm Shervin Khodabandeh, Senior Partner
with BCG and I co-lead BCG's AI practice in North America,
and together BCG and MIT SMR have been researching AI
for four years, interviewing hundreds of practitioners
and serving thousands of companies on what it takes
to build and deploy and scale AI capabilities
and really transform the way organizations operate.
-: We had a great discussion with Prakhar Mehrotra,
from Walmart last time.
Prakhar himself has a fascinating background,
from organizations like Twitter and Uber
to his current job at Walmart.
There's quite some differences between those organizations.
It's also interesting to learn some aspects
that are similar.
Check out our last episode for the fun details.
So for today, let's go on to something new.
Shervin I'm really looking forward to today's episode.
Slawek, thanks for taking the time to talk with us today.
Part of our focus is on you, really.
Can you introduce yourself,
tell us what your current role is and we'll go from there?
-: My name is Slawek Kierner,
and I'm a Senior Vice President in charge
of Data and Analytics at Humana.
Humana is a Fortune 60 company,
really focused on healthcare and helping our members live
longer and healthier lives.
I have up to 25 years of experience in data and analytics
across consumer technology and healthcare industries.
And I've always been interested in data.
My first money, I spend on a, on a PC and got MATLAB
and Simulink loaded there,
and I would create all kinds of simulations,
get them run overnight and see what's coming out in
the morning and use all kinds of ways to visualize data,
that time I was fascinated with process simulations
and the autonomous control systems and adaptive control,
And this fascination got me a job with a P&G,
Procter and Gamble,
and the good thing is I could continue to use my passion,
so there was a lot of a permission space to innovate
and bring advanced algorithms to these spaces,
and that's how I started, and it was also fascinating time
because you could start to have feedback
from your algorithms, from your AI early AI based systems.
And with this moment I got hired by Nokia.
It was the moment when iPhone was launched and
that was another fascinating transformation,
and as you probably know the story
that part of business was acquired by Microsoft,
and I moved all of my team and I was running data
and analytics for Nokia that time
to Microsoft looked around for awhile, helped run some
of the same operations across Microsoft Retail and
the Devices Business, and then move
to the cloud and AI unit,
and that got me to Humana.
So roughly one and half years ago,
I got to this moment when I thought,
like I built quite a bit of experience in data and analytics
and can make money with it,
but now let's try to use it for some good purpose.
And that's something that we can do in healthcare where next
to having a lot of data, we also have a very noble purpose.
-: I think you've almost run the table
at almost every sort of industry
and every sort of application.
It sounds like you're just right ahead
of the whatever's happening next.
-: That's what I hope so, just I'm looking
for these challenges.
-: This is pointless, but I have to follow up.
Were you a chemical engineer back in at the P&G days?
-: I worked very close to chemical engineers
by the way, so part of my projects were
to actually rewire large chemical factories
and think about really, really huge chemical operations
that P&G has in major cities.
But to your point, I actually did two majors,
one was in Mechatronics,
but essentially at that time in a fascination
with AI in my part of the world
and the other one was in Business Management.
-: I just mentioned that 'cause both Shervin
and I were Chemical Engineers back in the dark ages
and thought we'd found a compatriot in the whole thing,
'cause I actually got interested in you mentioned simulation
as your beginning as well,
and that's where I started too, was you didn't have
to build a plan you could just simulate it
and just really show some of the opportunities from data.
And it sounds like you saw some of those same things.
-: Exactly and you mentioned simulations of factors
that's exactly what I was doing.
So it's interesting because that fascination
that I had early on during my startings
with simulation, like I could bring to P&G.
-: So a factory is very different than you are in
Humana, that's a completely different...
Chemicals don't mind if they sit for a while in a vat,
but patients do.
How did the things you've learned from
that past experience influence your current?
-: I think there were a number of things
that actually you can learn in supply chain
and chemical processes where they actually do apply
to healthcare.
It's fascinating, but let me just list a few,
so first of all, the whole basic setup of process
and process control, I think applies to chemical factory,
and when you think about market, it's very similar,
you can apply so much marketing and,
when you do too much, of course you're wasting money,
not enough even going to give you
the results, so you kind of meet in a similar problem,
and I think troubles that you have,
with keeping chemical processes in control.
Also actually we see in healthcare so for example,
if chemical control cause a lot of lag so that,
the time from when you apply certain force or temperature
to a time when you start seeing the result of it on
the output of a process, the more,
this lag is the more difficult it is to keep
the system in control.
-: Classic thermostat problem.
-: That's what it is.
And when you think about healthcare,
we have exactly the same.
So we are tying all kinds of treatments and programs
for our members on the onset.
So think about diabetes or issue of let's say,
having a heart disease and then the needing to adhere
to medications, we try to convince you to do it,
but very often we need to wait very long until we see
that actually your health has improved.
And again, here we have this long lax and at this lax,
are both inherent to the process itself and clinical domain,
but quite often they also result
from just poor data interoperability.
-: I think you're making super interesting point
that there are archetypal sort of problems
in different disciplines, different industries,
different fields, all together,
but similar approaches once customized to
that particular industry or company would give a lot
of good results.
And I resonate a lot with that.
You listed some very good examples.
Do you feel that diversity across having seen different
archetypal problems in different industries
and disciplines?
Do you feel like that is a important attribute
of someone like yourself,
who's leading an AI organization for a company just coming
from sort of different backgrounds,
having seen it across very different disciplines?
-: I think it is useful for me, of course,
and I have a lot of respect for leaders
that emerged from healthcare industry,
and of course they have that background
which I am learning on it how
to really operate in a healthcare context.
But to your point, I think in a specifically in healthcare,
there is a need for people that will come
from other industries and bring knowledge
because it feels that healthcare is totally behind,
certainly from data transformation, availability of data,
certainly from usage of AI and also
from platform point of view.
-: So you mentioned several different examples through
there, can you give us some more detail about
some particular success that you've had at Humana
with particularly around obviously artificial intelligence
is what we're interested in.
Is there some story of success that you're proud of?
-: There are a few.
So we certainly are testing and learning a lot.
I think the key progress that I'm really proud of is
the fact that we have created our own internal machine
learning platform, which helps our data scientists
have access to modern technologies,
to all of the open source capabilities,
have cloud accessibility, such that,
computing power is not any more limited
by what you have in your data center,
but essentially you can tap,
and run any kind of algorithms out there.
And we are starting to see,
the benefits coming through way better,
way more accurate models
that predict retention in our business,
but help us predict Inpatient admission situations
and look forward, act hopefully way ahead
of a time when our member needs to visit ER,
or get into hospital and hopefully be there early enough so
that we can help this person stay in better health,
and avoid needing an inpatient treatment.
There's also a lot of progress in terms of usage of AI,
algorithms and sophistication of this.
We had to overcome a lack of proper security in the cloud
to handle PII and PHI data.
So as we're building those capabilities,
and helping also build those with our vendors,
we had to generate high quality testing data,
but it would be differentially private.
We are able to create a new model, an AI model
based on synthetic data, which is of similar accuracy
to the one that is created on real data.
Using genetic AI, we created a high fidelity synthetic
profiles and populations of our members
and could use Vose to ingest that to our platform,
start to learn how to use it.
we train our data scientists.
We have more than 200 PhD grade data scientists at Humana,
and we could already get access
and start using systems ahead of our readiness
for PHI and PII data handling,
which happened in the meantime
but the fact that we have a synthetic data creation
capability actually is helping us
in many other fronts as well.
-: So make sure I understand you're using this tool
to help your organization learn how to handle the real data?
So you use AI to generate synthetic data
that then lets everybody practice
and learn on before it becomes a real patient.
-: That's correct.
-: Actually, I really like that example.
Could you describe why, I might be,
I think I'm trivializing it,
but why is that an AI problem and not,
a statistical sampling problem,
like what makes AI fit there well?
-: It's a good question
and I think it became an AI problem when AI became better.
So I don't know, if you've probably have seen some
of the work of Nvidia that creates
the synthetic faces of people.
So essentially you use deep learning
to train you on network to essentially learn on how
a face of a person looks like,
and then you ask it to recreate, taking away
the original data and controlling
for our fitting in such a way that it can ensure
that none of the training pictures is recreated exactly.
So, so essentially all of the faces that
that synthetic generator generates,
are unreal and never existed.
And that field has started many years ago, but initially
this faces they kind of always had two eyes,
but an eye could be in the middle of the forehead
and so like you create it to see but it's not real,
but over the last two years we have improved so much,
that when you look at the synthetic face right now, it
looks, it's hard to recognize,
that it's not a human and we could be easily tricked.
-: So the parallel there is that you're tricking
you're performing the same trick,
but with data rather than with images.
-: Exactly, we look at a human's record at
the history, you know health history,
but also actually a complete history of the demographic
and health data.
and we recreate the same population through an approach like
this one so fully differentially, private,
very high quality,
when you look at take a physician looking at the history
of the synthetic individual and a physician cannot tell,
that it's unreal and it looks like real.
-: Who had the idea to do that?
Where did you...
I would not have thought of that.
-: So it's always a mix of people
that are sitting around the table to try to solve
a tough issue that we have.
That's part of it, and quite often
we invite our partners, so in particular,
that technology came from a collaboration with a partner
from Europe who interestingly enough,
also worked with me at Nokia, so a very talented individual,
that created a synthetic data set capabilities
and synthetic data creation capabilities
and got a lot of success with this in Europe,
which as you probably know,
of course, he's much more concerned with personal privacy.
And then another set of partners we are working very closely
with right now is Microsoft and innovate advancement
of differential privacy and this space.
And then finally, we quite often connect with academia
and we have those connections as well.
-: This is a great example of how
in a real technical topic, from a different discipline,
with deep learning and image recognition makes a tangible
difference in a completely different industry,
and I could imagine maybe 20 years ago, 15 years ago,
clinicians and business folks running a company like Humana
would say, well, that's not the same.
There's no real patient.
It's all synthetic.
We can't trust it, et cetera, et cetera.
My question is: what level of education and sort of
knowledge sharing do you feel you've had to go through both
at Humana and in your prior careers to sort of bridge
that gap between the art of the possible on the technical
side and the business where
the understanding is not the same,
and do you feel like there is still a gap
and how do you bridge that and narrow it?
-: Shervin yes there is a gap
and I think there's a gap between technology
and business understanding,
and there is a gap between technology and ourselves.
We all in this particular field need to continue to learn.
Every few years things change
and sometimes they change totally on us.
So part of it, and I think the first skill is
how do you continue to learn?
It's continuous learning, but it's necessary
for all of us and us as leaders who need
to inspire our teams to do the same,
because even if you hire in PhDs in Data Science,
but have been recently trained,
they need to continue to learn,
they need to have a work bench where they can tweak data and
they can learn with others.
And then the other part of it,
which I spearheaded at Humana is a much tighter link
with product teams, invest in a large technology companies
that we collaborate with.
So we are trying to do is to make sure
that we are closely in touch with those product teams,
we follow what they're doing,
we participate to customer advisory boards
and through this both help them shape
their products and, and for us, and get excited
and hopefully drive accelerated adoption
of this new features and functionalities ourselves.
So that's one part of your question.
So how do we stay ahead?
And then of course we have a huge role
of helping our business teams
and our partners in enterprises, where we happen to work,
to also understand and the art of the possible
and help us turn this technology knowledge into reality,
that actually advances our experience both internally
and also for our members and customers.
-: My takeaway from what you're saying is hiring
the team and keeping the team at the forefront of the art of
the possible and inspiring them is one part of it.
But also organizations have to take steps to actually bridge
these gaps through all these things that you talking about
so that there is more collaboration and sort of cross
functional teaming and much closer sort of product
management with analytics, with AI, with voice of customer,
all of that so that these ideas are allowed to even incubate
and go somewhere, Is that right?
-: 100% agree.
-: Well if we've gotten a hundred percent agreement
from Slawek I think that's a great time to end.
Thank you for taking the time to talk with us Slawek.
(mellow music)
-: Shervin, Let's do a quick recap.
-: Sounds good Sam, Slawek made some very interesting points.
-: One of the things that he mentioned a lot was,
how past experiences led to his current role
and he had so many different past experiences
and yet he found ways to apply them.
I thought that was a pretty fascinating point.
-: I really resonated with that.
He talked about some archetypal problems like the chemical
engineering, the problem of system controls
and the lag in the system
and how that has to be managed,
and he likened this to a problem of marketing,
and then more importantly to the problem of managing
the care of millions of the patients,
because as they propose different treatments
for different members, there will be a lag between
what's working and what's not working.
And the ability to understand what's working
and what's not working and what that lag time is,
that's almost a standardized chemical engineering
or control systems electrical engineering problem
and his ability to see these archetypes
and sort of transcend from discipline and domain
and industry to health insurance is really,
really important.
-: Gave me a little hope
that humans will still be around for a bit.
It's interesting as well, that in a lot of these examples,
the specific industry details were obviously different
and you can't just blindly apply them
from one industry to another.
And I think there's a role too, that creativity
and being smart about what fits and what's smart about
what doesn't fit.
That again seems very human.
-: Yeah, completely.
And the other thing, building up on that is importance
of again, building trust and building trust of the humans in
the AI solutions.
And he talked about synthetic data to do synthetic tests
of different treatments.
And then he talked about the process of showing
the clinicians how that synthetic data actually mimics
the real data and how it does and so I think
that's also very important,
again building trust and building trust in areas where,
human judgment really, really matters.
-: I liked how, how pragmatic it was to...
You know the computer are gonna have problems
and so you'd much rather find those problems out
with generated data and I thought,
what was creative about his solution was using AI
to generate that data that smelled just like real data,
but they could afford to screw up with.
I like things like that,
where it makes complete sense once he says it,
but I would never have thought of it myself.
-: That's a great point.
That's all the time we have today,
but next time, join us as we talk to Gina Chung from DHL.
-: Thanks for listening to "Me, myself and AI".
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(mellow music)