Ginni Rometty Keynote at HIMSS 2017

Ginni Rometty Keynote at HIMSS 2017


[ APPLAUSE ] ROMETTY: So, it is my great pleasure to be
with you this morning, and this is a topic that is very important to IBM
but it is very personal to me. You saw a little bit about my
background and some of the boards I’m on. And hopefully you did not count
years or anything in that thing. They could have made that number
a little smaller on that page. But this is to me…oh, you’ll see I hope
by the time I’m done how special it is, because when I became CEO five years
ago, it was a really great moment. It was a moment that we got to
introduce something called Watson. This idea of cognitive learning to the
world in a game show called Jeopardy!. And at that time I had said to the IBM folks,
I said, look, this will be our next moonshot. I said I would never be arrogant enough to
believe we could change healthcare by ourselves, but we could change some small piece
of it and be a part of that solution. And with a lot of friends and partners and
all of you here, I felt this was the moment. And we’ll do some things ourselves,
maybe some grand challenges like oncology we’ll contribute to. And then it was two years ago at this
conference, while I wasn’t here we did get to announce something called Watson Health. And it’s now…well, it’s more than 7,000
people strong, and we announced it at the time with Medtronic, with Apple,
with Johnson & Johnson. But now this is another special moment. You know, and on one hand it’s a very
serious moment, as Dr. Mike talked about when he introduced, where there’s
much societal, political changes out there that will have a great impact on
healthcare one way or the other. Government, regulatory — so, whether
it’s ACA or mergers, acquisitions — and those are all really
serious issues to be dealt with. But on the other hand, I also feel this is a
profoundly hopeful moment in time for all of us, and that is something I want to talk
about, because I think we’re at a moment when we can actually transform
many parts of healthcare. We can reinvent many pieces and we can change
things like whether it’s personalized medicine, epidemiology, wherever we go, it is a moment. And I think it’s within our power and as a group
or a team — however you want to look at it — that we can change the world for the better. And this thing that’s in front of us that
I wake up, and when you said, Dr. Mike, find the joy in what you do, and I think
about this era, and it actually is an era that will play out in front of us,
which is what we call the cognitive era. So, I hope to persuade you this
early morning of three things: one, that this idea of cognitive healthcare,
systems that learn, that this is real and it is mainstream and it is here and it
can change almost everything about healthcare. The second thing, I’m going to, and
many of you…or, all; not many, almost everyone’s an IT professional. I think you’re going to make
three key architecture decisions in the next year to two years. And I think they will impact the world
healthcare for the next two decades. And I think healthcare will be the
industry impacted more than any. And in the third, is like
I said, this is an era. And the third thing I want to persuade
you of is cognitive as an era could usher in what people call a golden age. But it’s if we shape it wisely. And I also believe healthcare could be the
leader for the world in showing what it means for an industry to shape an era wisely. So, my first one, I’m going
to try to run through them. My first is this idea that cognitive healthcare, artificial intelligence is
mainstream and it’s real. Now, if I asked you, and this is a great big
group to ask, so I’m at no risk of being wrong. [ LAUGHTER ] So, how many people, if I said
how many of you are trying to digitize something, raise your hand. I’m going to give you some exercise. Okay. Almost everyone. Doesn’t matter what industry I’m in. I can ask…I could ask this at home
to my husband, I will win on this one. So, this idea that digital is
the foundation for everything. But this idea that a competitive advantage
is going to come from being cognitive. This idea of systems that
learn, all of this massive data. Now, there’s a land rush around
artificial intelligence right now. And the kind I’m talking about for
what we do is not consumer oriented. I don’t mean speech to text on
the front of a search engine. I need something that really augments the
intelligence of everyone in healthcare. And so, real application in healthcare,
financial services, retail and the like. So, I thought I’d start with five
lessons that we have learned. We’ve now been at this years,
so five lessons we’ve learned in applying AI, cognitive, to healthcare. It’s thousands of clients, lots
of partners and five things. One is those who are successful, they end
up applying a range of cognitive services — that it is not enough to just think of
artificial intelligence or natural language; you need vision, you need deep
learning, you need many more algorithms. Second, and I think we’re going to come
back to this a lot with healthcare. You have to provide transparency:
who trained it, what data was used, how do you have confidence in the insights, because this is an industry
full of professionals. The third thing we’ve learned, it’s
got to be there for domain specific — so, trained in healthcare
by healthcare physicians. And then fourth, it will be cloud
based to make it ubiquitous. But a cloud that is built for big data,
built for security; and one, by the way, that’s going to have to be hybrid,
because otherwise you can’t connect. This is such a wide ecosystem, whether it’s
an academic center to a community hospital, you’ve got to be able to connect people. And the fifth learning is it’s got to be an
open platform, because this is an industry all about innovation, and an innovation
that doesn’t come from one person. And that’s like I said when we started,
it’s about an ecosystem of people. Now, you would not be surprised if I told you
that this is what we based all of IBM Watson on the IBM Cloud and it’s why it’s
emerged as “the” AI platform for business. And in fact, those of you that met Watson
years ago I would tell you today 45 countries, 20 different industries, nine languages,
and we’re on a path to touch a billion — a billion consumers — by the end
of this year through many of you. And so what I thought in this being persuasive
that it’s mainstream, let me just use us as an illustration, and there’s
others with solutions, but us as an illustration what
does a world in healthcare look like with Watson, with cognitive? So there’s a few dimensions of healthcare
that I think are ripe to change. So, one of them is you’ll be able to go and discover actually new
medicines and new solutions. So, one of the most recent examples, Barrow
Neurological, they’re in Arizona, with Watson, they rank ordered 1,500 genes
in the end discovered five that had never been before connected to ALS. Now, but what was important, all of the evidence
based prediction was all illuminated as again as to why that’s what matters but new medicines. You will do what Dr. Mike
talked about, personalized care. Oncology, one of the ones close to my own heart. In 2012, I think there were
14 million new cancer cases. We’re on a path for that soon to be
on the range of 22 million new a year. And I was just in India last…well, it wasn’t
just last week; it was like three days ago. They all run together. And 70 percent of the new cases, they’re
all in these developing countries. When I happened to be there, it’s
one oncologist for 1,600 patients. I mean, this is around the world. We are blessed with the number
we see in countries like this. And so, we’re rolling out now, it’s a
world with personalized care everywhere. So Watson for Oncology is rolling
across hospitals in China, across India, across Thailand, across Finland,
and some wonderful examples. So, I was in India, Manipal Hospitals, one of
the big private hospital chains, rolling out, they see 200,000 patients a year,
published now multiple studies a) and something, again, Dr. Mike… You said, one simple thing, allowing
a doctor with the help of Watson, what took him 20 minutes
to gather, 20 seconds now. Big difference. And so the time, because that’s what they want,
to spend the time doctoring in what they do. But here Juniper Medical System, which is a community hospital system
that’s going to be rolling out as well. So, a world, when I say personalized
care, already we see tens of thousands of cancer patients being served. But it will also be a world
of precision medicine. Now, I think all of us think of precision
medicine, we think about genomics — genomics, your environment, your Fitbit. In fact the other day I had
my Fitbit and a watch. Does anyone else wear two? [ LAUGHTER ] There are a few of you? Okay. I had never been before asked
that question, by the way. I had two of them on the other…or, I
had the watch on and the person said, I’ve never seen that before,
why would anyone ever do that? I sort of looked at it and I
thought, yes, why would I do that? No… [ LAUGHTER ] And I said that watch was
a gift from my husband. That’s my answer and I’m sticking to it, so. That was it. But this idea that an innovative approach, when
I say precision medicine I do think genomics and I think about all this and
our environment and how we live. So, I hope you caught an example. University of North Carolina,
the Lineberger Cancer Center, 60 Minutes did a segment not very long ago. The doctors there, 1,000 cases. And this to me is the great promise
of the era we’re working in — a thousand cases, and these
were not standard cases. In almost 100 percent of the cases, doctor
and Watson matched, but in 30 percent, Watson found more clinically actionable items. I mean, that to me is what this is all about. And so, we’re scaling genomics now. So, Watson for Genomics is
with Quest Diagnostics. So, we will reach and could reach
genomics sequencing, advanced stage cancer for almost any patient in the United States. And Illumina, who many of you know is
one of the greatest sequence producers, is now inventing Watson for Genomics. And then today Atrius Health, we
are going ahead and announcing, and they’re very well known I think to all of
you for value based care, really puts Watson in the front end and they’re going to be putting
cognition right into the EMR that’s there. So, precision medicine to me is
like just now going to come alive. Then, the idea of people getting
to manage chronic disease, another part of the world that changes. I think the work Medtronic has done. Sugar IQ, the ability to predict a
hypoglycemic event three hours in advance, 90 percent accuracy, never before done. Not enough. You look and then say the
ability that care managers, really vulnerable populations,
where a lot of help is needed. I was looking at some statistics
over the weekend. We now serve in 50 different health
and human services departments, service areas around the world,
agencies across 18 countries. And that, to me, is this
ability that you are going to be able to take and analyze these cases. In fact, we’re analyzing today
44 percent of all Medicaid cases. So, that idea to help in all those
dimensions to me is a different world. That is a different world. It’s at the beginning and it
is already starting to happen. And that’s what I mean, it’s
mainstream and it’s here. Okay. So, now, but all of us are professionals are at
the intersection of health and IT in some way. So, three decisions I want to just at
least plant with you that you’re going to have a great influence on and I think it
will make the difference on whether this scales, is secure, it’s reliable, into the future. You’re going to make a decision on three
platforms: you’re going to pick a cloud; you’re going to pick a data platform;
and, you’re going to pick an AI platform. Now, let’s talk a little bit about what
you’d look for in each one of them, and maybe in the Q&A Steve
and I will talk a little bit about what’s the downside if you don’t. So, first is on the cloud. You’re going to have to have
a cloud that’s optimized for all this cognitive data
— so, data and cognition. Hybrid because it’s a reality
and security because it must be. And I must say this is, if
I put one more plug in here, this is why when we did the IBM Watson
Health Cloud, GXP, HIPAA compliant, and the only one out there with
a quality management system. So, healthcare ready. So, you’re going to pick one. In fact, actually we recently announced
with the FDA it’s not just the cloud. Again, I hope in Q&A we’ll talk a little
bit about what can blockchain layer on here and do here, but with the FDA we’re working
on blockchain and oncology together. But you’ll pick a cloud platform
with those attributes. Then a data architecture. And I think it’s been a long time
since we talked really robustly about changing a data architecture. But it matters now, because you’re going to
have to pull together so much different data, a full spectrum, unstructured, structured,
to do that world I just described. And in medicine, 90 percent, 90 percent of
the data is image; and images, you know, heretofore have been difficult
to garner out of it but we can. So, not only do you want to have any kind
of data; you want to have control and I have to say you’ve got to be sure you have control
of the insights that come out of that data. Eighty percent of the data in the
world is not searchable on the Web. Think about that. That is where the greatest
insights are, and it’s yours. And so this idea that you have a data platform
that allows you to combine data from all places, put it together but the insights are yours,
is going to be an important question to ask. And again, when we built the platform
on the Watson Health platform, 300 million medical records, 30 billion images,
40 million PubMed articles, every patent. But all the secondary use rights for you to
combine data and then the insights go to you. So, you’re going to make a decision
on a data architecture platform. And then last is a decision on
that AI in the cognitive platform. And I would tell you to look for a
platform that has a range of services, because just like I said, you
are going to need a range. I saw a great description of the
difference between machine learning, artificial intelligence and cognitive. And if you had a picture of a brain,
you’d start machine learning as the basic; artificial intelligence to the
front of your lobe; cognitive. And those are all different kind of
algorithms and services that you use. So, you’re going to want a
platform with many kinds. You won’t be surprised you’d
hear me say you want one that you want transparency
into, how was it trained? You know, when we did oncology, this has
now been trained by the best oncologists in the world — Memorial Sloan Kettering,
Cleveland Clinic, the Mayo Clinic — and they’ve ingested all the medical
literature of the world on this topic. And then you’re going to want
to have that transparency. I have never — again we’ll talk a little bit
in the Q&A on this — that this is an industry, in fact, many of the ones in B to
B they’re full of professionals. This is not a black box, they want to
understand why and how did that answer come up. And you want your doctor to understand
that, you want to understand that. So, these are the things I’d look
for in this kind of platform. So, you’re going to make a
decision on cloud, on data and AI. And part of why those platforms will matter,
like I said, you are an industry of ecosystems. And we had the pleasure today to announce
a couple different new ecosystems. One is the central New York Care Collaboration. And think of this as the first
population health management platform that pulls together almost 1,500-plus providers
with all of what would be the Medicaid patients in that area, but it’s integrating data from
whether it’s primary setting, acute, community, all together and giving a holistic view. Again, back to this platform thought
and what you could do with it. So, that’s a world, as I described, I think one
that is really just on the cusp of changing, one that you’re going to
have tremendous influence on these decisions you’re going to make. And as I said my last point,
though, is it could be a golden era. It really could be a golden era. And you know, with every new
era, at least in my experience, they come with amazing aspiring dreams,
but they also come with questions. And it was one of the reasons
there’s the Davos Economic Forum, I returned back there this year;
IBM hadn’t gone in decades. I returned back because this was the year there
was much discussion about AI and was it good or bad for the year, what would be its
impact, what would be its impact on jobs. And I feel very confident in a way to answer
those kinds of questions, because I think all of us that have such an impact on
this, we have to take it seriously. And that when a new era comes —
and they don’t happen often — it’s our responsibility to both
guide that technology into the world in an ethical and in a really enduring way. So, IBM, I prefer to call 105
years young — not old; young — I feel like we’ve always in our time and we
have been blessed to work with many of you, but we’ve been blessed to work on what have
been some of the world’s toughest problems. And it’s ranged from the Excimer Laser
technology that we worked on for laser surgery; to the DNA resistor; or, to systems like the
United States Social Security system; to, the reason I called it for us a moonshot is we
really did work on landing the man on the moon. So, I thought I’d have a little fun. I want to show you a clip about this. How many people might have seen a
new movie called Hidden Figures? Okay. And for an IT tech, if you haven’t, well,
I’m not like a producer or something of it. So, this is…and I’m not
advocating it for personal reasons. But it is about the dawn
of the NASA space program. So, let me just first just show you a
clip and I’ll tie this back together. It’s a clip from the film out now. [VIDEO VIGNETTE]>>What’s your name again?>>Dorothy Vaughan.>>We need the IBM for Glenn’s launch.>>We got a job to do. Whatever happens up there, it’s in God’s hands.>>My guys are ready.>>They can do the work.>>Ladies…>>We’ve been reassigned. [ MUSIC ]>>…jobs and created value for… [ MUSIC ]>>Funny what we think of as
computers now and what they were then. [ MUSIC ] [END VIDEO VIGNETTE] ROMETTY:
You could hear it, right? From where I stand, you couldn’t hear it. So, that was…okay. So, it is a great movie. Now, look, I’m a little biased on it, so we
didn’t have to pay for that advertising… [ LAUGHTER ] …but it’s one of those things. But what we have done is participated in every
manned space effort that’s been out there. And so I feel when I say we believe
in moonshots, I really believe in it; and this idea, yes, you have to have
technology, but you have to have values. And so I go back to what I
said about returning to Davos, and part of why I went we
introduced last month something that I hadn’t done for a decade in IBM. And it was a letter to the whole community about
something, and it was called The IBM Principles for Transparency and Trust in the Cognitive Era. And I want to end on this thought about
it being a golden era, if we usher it in, if we birth it right, because I think these
are principles that I’d love to see you embrace and adopt as well, and I’m going
to be sure all of IBM does. And there’s three of them, and it’s
in everything we build and what we do and what we bring to the
world around this technology. The first one is its purpose — to make no mistake that what we
are doing is building technology to augment human intelligence, not replace it. We’re here to augment what man does. This is not man versus machine;
this is man and machine. And a doctor, this is his
aide, this is his colleague, to help him do what he struggles…or a
nurse or a practitioner, it doesn’t matter, a radiologist, an IT person,
in security, it doesn’t matter. It is here to actually support them
and augment their intelligence. It is not about fear here, and
everything we build is built that way. The second one is a principle
around transparency. We will be clear with you and with our
clients when they’re being touched by AI, how it was trained and who trained it. And the business model. Don’t believe in a world
where all of the insight and all of the intellectual
property ends up with one person. This is a world where we’ll
bring insight, you bring insight. And the training, though? Those algorithms, they go to you. It’s a world of transparency. But then there’s the third — and again,
Dr. Mike alluded a little bit to this. With every new era that I can
remember and back in time, with the new era is going to come new jobs. Some jobs will change, jobs will be new; and
therefore, it’s going to be incumbent upon all of us to train for those eras,
to train for those skills. In fact, we coined a word called new collar. It’s not blue collar, it’s not white collar. It’s a future job that’s going to bring data,
technology to almost anything that you do there. And so we can all start to do our job by
preparing our kids coming out of school, our retraining, all around what this world does. So, three tenets around purpose,
transparency and skills. So, I conclude where I began. I mean, I couldn’t be more excited to be here. It’s probably a historic moment
that I will always remember. And it is a world of forces in our control
some, but some outside of our control. But I would just, if I could make one
recommendation, don’t be tentative. This is a time to play offense,
because you could build this world. And I think a world, a world that I’m
talking about, this cognitive world, it will be a healthier world,
it will be more secure. It will in the end be less wasteful. It will be more productive, more personalized. And in the end, it’s a fairer, more
diverse, and I believe more just world. And I think that’s the world
we all want to live in. So, I thank you for letting me keynote this
morning, and I wish you a great conference. [ APPLAUSE ] My cheering section. [ APPLAUSE ] Oh, that’s… [ APPLAUSE ] …all my relatives are seated
right there, okay? [ LAUGHTER ] Now, so I get to be joined here by Steve
Lieber, who, while this is HIMMSS ’17 — I think I have this right —
this is Steve’s 17th year, too. Did you guys know that? Anybody been here 17 years? My same cheering section, okay. [ LAUGHTER ] So, Steve where are you? Am I going to ask myself questions? [ MUSIC ] LIEBER: I’m here. ROMETTY: Good to see you up here. LIEBER: Good to see you. ROMETTY: All right. LIEBER: Thank you so much. Let’s have a seat and first let me start
off adding to Mike’s Zaroukian’s comments, a great big welcome to all
of you here in Orlando. It’s so great to you see you again and I
really appreciate you being with us today. You kept me busy back stage taking notes of
all the things we wanted to talk about in Q&A, so let’s see what we can do in terms of covering
some of those topics, because you really have, you’ve introduced a number of really
thought-provoking subjects there. But let me start out with just IBM in general,
especially, and I didn’t see the hands of how many people have been here 17 or more
years, but I know there a bunch of them. The landscape’s littered with companies
that said I can solve healthcare. And they’re not here anymore. So, why healthcare for IBM? ROMETTY: This was a big decision and for me
back in the moment when we decided not only to make it a moonshot but to say, look, we
really want to invest, we’ve invested heavily in this area, billions and billions of
dollars now, because I am old enough to remember many times that I’ve
worked on it throughout the years and it was never the right moment. But I think it is the right moment. And I’ll tell you why. a) there’s no bigger grand challenge
that is important to this world, and I think that’s why this
conference has 42,000 people in it. But b) there is a confluence of things
that could make it be the right time. So, where the technology is at
right now I think is so much, because health is not a system, per se. It’s not a formal system, and that’s
always been the challenge with it. And with many of these new companies or
companies over the years that have sprouted up, many of them have never been
really products; they’re features, so you have all these little islands everywhere. But now I think the biggest contribution
can be made through all of this data. And it is now at a time when
we can pull it together. It can be done cost effectively. There’s a mechanism called the cloud,
artificial intelligence is out of its winter. And you can pull all this together, and it’s a
moment when the whole system focused now more on value and a type of technology
that can actually free a practitioner to do what he wants to do or she wants to do. So, when I pull together the confluence of the
size of the problem, the type of technology, its cost point and where it’s at and this
ability to now do things you couldn’t do before, because I think a really important part, what we’re doing here we’re
not replacing necessarily, this is not about replacing
healthcare professionals; this is about making them do what they love
to do and allowing them to do it in a way. So, this has really been one of the
important learnings we’ve had over the years, I’ve watched it, because many people’s first
reaction was I don’t want to…you know, it’s going to be a replacement, I’m threatened. But what I’ve watched in
hundreds and hundreds of doctors, it’s both a very collegial relationship… And Toby Cosgrove from the Cleveland Clinic,
when he and I have talked about this a lot, he says, you know, we’re now on a path that data
is going to double in healthcare in 75 days. It’s impossible, and everyone in this
profession wants to do their very best. So, this idea that you can augment, and
I watched that happen in the technology that that point, I think that will be
what unlocks this, this ability to augment and allow people to do what they do
better will make the difference in time. LIEBER: I saw that a couple of years ago,
and the years do kind of run together quickly and so it may have been more than that. But early days in terms of the first
rollout of Watson and you had oncologists from Memorial Sloan Kettering and
Anderson and Cleveland Clinic, Mayo, I was struck by their engagement
in what was being developed. And I say this was early days;
it was not a commercial product. And I thought about it then in
terms of exactly what you just said about I’m surprised they’re
not feeling threatened. And so I’m just kind of curious of
how did you get the clinician buy-in, how did you get to the point where… ROMETTY: Well, look, we’ve
had many, many learnings. There’s no straight road and all trees don’t
grow straight to heaven, and so this is, we’ve had many, many learnings along the way. But because it was built for them, with
them, and this idea, what I learned, you know it already because you face it every
day, but what I saw so clearly was the point about workflow and that you have to be in
a workflow at the right opportune moments for these technologies to be adopted. If this is something that goes and
rides side saddle, it just doesn’t work. They won’t do it. And by the way, it’s something
I learned not just with doctors; I watched it in every profession we’ve worked
on, because we work on underwriters, financiers, cybersecurity professionals,
sales professionals. Everyone’s a professional. You’ve got to be in workflow. Everyone’s like, hey, I don’t have
time to change my workflow here, right, to get adoption and I’m very empathetic to that. So, that’s, a lot of our learnings
and where I see greatest adoption is where people understand how to fit that in. By the way, it’s why we had to
go build out big design centers and all sorts of things to get that going. So, that was one big way of how to get adoption. But I will tell you another. I was with a group of physicians, cancer
specialists, and we were talking about this. And I said, well, but what’s going
to make you…why are you doing this? Why are you guys giving all your time? And some of these, they’ve been
training for tens of thousands of hours; I said, why are you doing this? And one of them said, she
said something really astute. She said, because the next generation
of patients will demand this. And I think that is another
equally motivating reason. LIEBER: There’s no question about it I think
we all, especially those of us in this space and as close to it as we
are, we recognize potential. We recognize where this is going
in terms of personalized medicine, that the challenge is being able to, and I think
you touched on it with your three platforms. ROMETTY: To hit a tipping point here, the scale. LIEBER: You need these pieces and all to bring
it together to start to address other things. So, talk a little bit more about the three
platforms, and you said there may be a downside or other, an alternative approach or view? ROMETTY: I think the three and why, because to
get this to scale, and many of you, I’m sure, got both things you’re doing or
trying or experimenting or rollout. And but I do now when people say “why do you
believe so hard” I say because I have the data. I mean, now I see the results. I actually, this is out there with tens of
thousands, hit a billion people at the end…so, it’s moving, so it is reaching those
tipping points by what’s happening. And but the inhibitors are those platforms,
because I was mentioning to Steve, I was out last week with a group, and
they were talking about the challenge. He said in the business they’d
had all these people trying lots of different artificial intelligence. He goes, now I’ve got a problem. I’ve got all these little islands everywhere. I can do nothing with this stuff. I can’t even match the learnings
up with each other. And it was very reminiscent to me of the
computing days when everyone did sort of a little bit of thing and then we sort
of had to pull all that back together. So, I think you can get ahead of
it now, and that’s what I meant. You can get ahead of it by, thoughtfully. On one hand you want lots of experimentation,
and then there will become a day, though, that you want to put platforms in
for that experimentation to happen. And so you’ll pick one or two of these
kinds of platforms to get that moving. And I think it may be for a group like this one
of the most important things they start to think about it if you all as a platform not
just as an individual point solution, because that’s where the
power’s going to come especially because it’s an ecosystem to pull together. LIEBER: Yes. In your remarks, you just
mentioned the word blockchain… ROMETTY: Oh, yes. LIEBER: …you really didn’t go deep. ROMETTY: …because I could
go like an hour on that. LIEBER: And so, it is, it’s sort
of part of sort of next phase. ROMETTY: Yes. LIEBER: It’s the things we need to
start thinking about and understanding. Talk to us a little bit. ROMETTY: How many people are looking at, think
blockchain has an application to this industry? Okay. The front of the room. And how many of you have
businesses in that, too? No, I’m just kidding. [ LAUGHTER ] That would be me, too. So, but the reason, this is one
of those…and it’s not an era in and of itself; it’s an approach. And most people heard of blockchain first
with Bitcoin, and that was the thing that kind of put it sideways to many people. But what I mean when I say
blockchain, it’s the technology that was actually underneath the basic
technology that could be underneath a Bitcoin. And all it simply says is you could safely,
securely, trusted exchange anything. And if you think of it that way,
man, it applies to so many things. And whether it’s clinical trials or regulatory
compliance, because we’re doing a lot of work in compliance and financial services
and you’re just right behind in this. And so, the idea that you could exchange. So, why I’m so optimistic about it,
I have myself in our own company, we’ve now got in production many
blockchain production applications. I have a whole financing business; we
put the whole ledger up on blockchain and now we’re doing all our dispute resolution. I have a services business; all
the parts are flying around. I mean, you have medicine. You have parts flying all the way
through the supply chains of healthcare. And we’re both sides, it is unbelievable
the productivity that you can get and the authenticity and
transparency into the data. Food safety with Walmart, we’re doing
food safety, and they had issues in China, so this is pork in food safety, so,
from…I was going to say from farm, well, whatever…from the pig thing to wherever the
plate goes, you know, the whole way across. And so this idea of what you can do with it. Now, there’s a couple things about it,
though, and I’ll…those of you exploring, I would recommend highly, this is the Linux
Foundation has something called the open ledger, the Hyperledger Project. This is the fastest growing
open source project in history. We’re one of the contributors,
but there’s hundreds of companies contributing into the code. I have 50 full-time folks
contributing, this is the fabric. And this would be the fabric that’s free,
it’s open source, and then everybody’s going to build applications on top of it. But if you don’t have one standard,
blockchain will never take off. And that’s why the Internet took off. There was a standards group not owned by
one company, an open standards group in WWW, ITEF there were groups that set
the standards for the world. That’s what these groups can be
for blockchain and the ability to then securely transfer even health records
for you to determine your health record, who should have a view of it, longitudinal, 360. I mean, the opportunities to me
for productivity are endless. We did a study, it’s the quote of
productivity alone forget about the value, was in hundreds of billions of
what could come with blockchain. So, anybody who’s not yet
looking at where to apply it, there’s very basic things to start with. Simple stuff is a supply chain
for maintenance or a supply chain of anything, would be a simple way to start. But I think it’s going to have
profound impact on healthcare. LIEBER: When you think about healthcare
data, though, and the cognitive technologies that you’re so deeply involved in, it’s very dependent upon the sharing
of information, sharing of data. ROMETTY: Yes. LIEBER: And we still struggle. I mean, if we’re really honest about
it, maybe not in front of 6,000 people, but when we’re talking one on one,
there’s a hesitancy, there’s a reticence in some places to be that open in sharing. And I think about some of
the cognitive work that you and other companies are doing, so dependent. ROMETTY: So, why do you think
the big reason is for that? LIEBER: Well, I think, one, we still have
some issues of proprietary interests. I mean, healthcare is a big business. I mean, let’s be honest. We’re in the business of caring for
patients, but it’s also a big business. There’s a lot of money here. And a perception is that data is the asset. That’s the intellectual property. And I’m not really…am I feeding
my competitor by sharing that? And so, breaking through that, and as I say I
know that especially among, and I would expect in the early days of Watson, you found such
a collaborative spirit among the oncologists and a willingness to go in and do this. But do you sense that that is
possible today in other places? What are you running into as you’re looking
beyond oncology, are you still having that same spirit of cooperation
and collaboration? ROMETTY: I think this is an incredibly
important topic, and I alluded to it in my talk, because even I think I was the
one that coined this phrase that data was the world’s next natural resource. You can have lots of it but there’s
no value unless you refine it. There’s lots of countries that have lots of oil but they’re very poor countries
where the oil actually sits. And so there’s two sides of this data point. So, one is, as an example, when I was using
our own examples of the Watson Health Cloud that we went and looked for data
that had secondary use rights. It wasn’t necessarily just to live on its own. So, 300 million patients’ worth as well as
30 billion images for training of Watson. But then, any individual company could come
and bring their data with it, combine it, but the insights from that
combination belong to you. And so there is a recognition there
are pieces that will be shared, but I think there absolutely
will be things you do not share. And I understand, many of you are
absolutely in for-profit organizations. And by the way, it isn’t just that you should
hoard the data; it is your accumulated assets that have value like any other
kind of asset in the company has. And so, I don’t even consider it legacy; I assume it is your accumulated
assets of your worth. And so, that idea, and this is why I think
it’s important when you pick a platform. We worked really hard. You know, if you were…this is one case
where…we were not born a search engine. And there are big search engines,
they are knowledge graphs. And a knowledge graph is one big pool of data with lines…it’s simply
put; it’s more than that. But you know, connecting all of the data
together so when you bring your data, you’re training a big pool
and then everybody benefits. We really worked hard on
Watson to have an architecture where there would be some data
shared, others that’s not, and the insights could stay
with the person who brought it. And so that’s why I think you’re going to live
and that’s another reason I’m quite hopeful, a world where that will break that barrier
because there are some parts you want to share, and then even then, you’ll
build things with that that you’ll monetize as well
when you do share them. So, I think you’re going to see all
different business models come out of this. Look, you see them in other
industries already starting to happen, and there are things people will
share like in cybersecurity. Just last week, general availability,
Watson for Cybersecurity, because all of you have cyber
people in your companies. I mean, my goodness they
see 200,000 alerts a day. What on earth? You know, how good…the
challenge they have is so hard. And so, in this case people will share all
of their threat data anonymously, right, and pool it together and then use it. LIEBER: We were talking beforehand
and Ginni asked me, she said, okay, what is it that this group is here
for this week in terms of the things that are really pressing on their minds. And I said, well, I’ll probably
have a better answer on Thursday after I spend some time talking with you. But I came up with three things; one is
changing political landscape and administration. None of us know what’s going to be
happening there, so we’re all here trying to pick each other’s brains to figure that out. Analytics, cognitive technology. Everybody is talking and focused on that. I was in Istanbul about three, four
weeks ago working with my colleagues in Turkey for our program there in May. And they’re asking me what can you do, Steve, in terms of bringing some
thought to Turkey about analytics? We’re really focused on moving
from where we are into that, so it’s clearly everywhere, a
major topic of conversation. The third is the one you
just mentioned, security. Everybody is deathly afraid that they’re next. ROMETTY: Yes. Well, this is one of those topics, right? It’s, you know, I have always, as we’ve been
transforming IBM and as we were building out all our new portfolios, we went and
invested in analytics, cloud, mobility; and I always said, underpinned by security. I mean, from the very beginning,
underpinned by security, when no one was… Because I said all this is great, but
if this has not got security under it, we’re going to take big steps
backwards on all sharing of data and mobility and everything about it. And so I have a strong, I was telling Steve,
I have a strong viewpoint about security and that I hope it resonates, because my analogy
is very…it’s healthcare, is the analogy on it. I said security is a big data and analytics
problem — that the average company, believe it or not, has 145 security
products across 40 vendors. And I said it would be like putting a
different alarm system on every door and window of your house and thinking that you would be
safer; which, of course, you know you wouldn’t. Right? You wouldn’t. Or, just this idea it’s a moat you could
assume if you put a deep enough moat around your company that nobody bad gets in. So, I said the reason it’s a
big data analytics problem — and I think that’s the paradigm to
use in solving it for all of you — is that you’ve got to assume
the bad guy’s in already. I mean, you have to assume there is
something…and as you know a great number, by the way, are either accidental
or inside as well, so it isn’t just external things that go bad. And so this idea in healthcare, if you
think of your immune system in your body. We all have germs in our body, lots of them. And the idea with an immune system
is if they flare up, zz-oop! The immune system kind of boxes it off so
it doesn’t infect the rest of your body. That’s what you would do with security,
and it’s how we run it in the company and that’s the portfolio we’ve built, which
is it’s constant monitoring of everything and making those connections you can never see. And it’s, I always say “the little
footprints in the sand of what’s coming.” And therefore, you have to have at
the heart of your security system and your company I think is an
analytics engine at the heart of it. I mean, forgive me, we have,
of course, built that. But that is the idea. And at the heart of it is analytics, and
you’re constantly…and there are others, you’re constantly monitoring this. And then, like…this is why I think
health is such a great idea or analogy. You guys built out things like the
CDC, the Center for Disease Control; the WHO, the World Health Organization. Why did those happen? Those were all formed because if a disease broke out in one country, not to
go to another country. And to me, that’s why information
sharing and the right kind of governance and regulatory regimes around this are so
important, that you’re now starting to see and under President Obama there was passage
of a law to have cyber, the sharing, right, because most people don’t want to share because if they share they have
a threat of a lawsuit and all. So, you have to share, and that’s
what those organizations did, right? Without impunity you could
share to get that to happen. So, I think it is that you’re
the number one industry, healthcare is the number one industry targeted. The average cost of a breach in healthcare…or,
of a medical record is about $220, which maybe doesn’t sound like a lot
until you multiply it by millions of what you might have in
each individual breach. And so, there is a lot of reasons to approach
it that way, but I am as well optimistic, because I see the impact of using these engines. I saw a client last week, great security staff. And they had gone through all those alerts
and said, no, the company is not infected. We, through Watson Cybersecurity, 30 seconds,
yes, it was about two-thirds of the company. So, and it’s not they’re
not…they’re great; you can’t see it. LIEBER: Yes. ROMETTY: Yes. LIEBER: I didn’t catch what, because I couldn’t
hear you back stage, what your reference to Hidden Figures was but I’m just going to… ROMETTY: Oh, well…hmm, okay. He must not have heard anything
I said, is what I think. Okay. [ LAUGHTER ] LIEBER: I wanted to go down a particular path but I didn’t know how much you
had already said down this path. ROMETTY: So, Hidden Figures… I’ve always said when we
started the work in healthcare and made this really formal
announcement that we’d worked up to for quite some time, it was our moonshot. So, inside to all my IBMers I’ve
always said this is our moonshot, and the reason was IBM’s time working one of
the great, and we’ve had the chance to work on many great things in the world,
but one of them was landing the men on the moon, all the manned space missions. If you look at our archives the photos
at that point in time, the space control, they’re all IBM shirts hanging, every other
two, three, with other companies but hanging on the jackets there, played a huge role. And it’s this idea that even if it’s hard,
take a grand challenge and you make, you know, you do your part in the world
to make an impact on it. And so the Hidden Figures, if you haven’t
seen the movie yet you’ve got to go see it. It is about that time in space
and the role that…well, we’re in it as a backdrop, actually. A friend of mine was producing it, he says,
hey, this movie’s got you all over it. Do you want to take a look at this? And so, I said, well, is it accurate? And it’s accurate. And so it’s a wonderful time. And it was just that analogy that when you
do things, I think incumbent on all of us, and you’re an industry that its heart,
when you do things that are so important, you have an obligation and a responsibility
to guide them into the world safely. And I feel that way about AI into healthcare,
that we have an obligation to do it safely. LIEBER: Well, I’m going to go a
different direction with the movie, because there’s certainly
another theme going on there, and that’s women succeeding
in a male dominated world. And certainly you go back to the
early days of NASA and the sixties, I mean, it was all about testosterone. And so in your career — and I would bet going
back to the early days and maybe still — you have challenges and struggles
in the corporate world. So, talk to us a little bit about your
experience, what you had to deal with, how you overcame it, how
you got to where you are. ROMETTY: Well, you know, for me, I guess we
all learn so much from how we’re brought up. And I’ve told this story at times. I mean, I was raised by a single mom. And we had had sort of a pretty normal, very
normal life, until I was in my early teens, but my siblings were all quite younger than I. And just one day my mother found us all
alone with no money, no house, nothing. And I really, I always say my brothers and
sisters and I really watched and learned just by watching her, nothing she ever said. She didn’t have a college degree, but
no money, she had to do something. And so for a while we had to go on
food stamps and all these other things. And she went back to school. And by the way, she ended up working her whole
career at Rush Presbyterian Hospital in Chicago. And but what I watched at that time and I think
what it taught us is a) you never let someone else define who you are; and b) you always
find a way to take care of yourself. And as I tell young people all the time,
I mean, in the end it turned out great. My siblings, I always say I’m the
least successful of the four of them; the other three have done superb. And my mom always says, God, what did I ever do? And she did everything. And this idea that early on, you know, you
always do what you think to take care of, you know, not only yourself,
but don’t let others define you. So, I always say to whether it’s young women
but actually young men, anyone, you know, one of the reasons I’m such a strong proponent
of STEM, of technology and science and math and particularly in this stage when I talk about
new collar, right, we’re going to have math and data riddled through
every job that’s out there. It will be the birth of new vocational schools. I think it will rebirth that in America. But I say, go to school and take math. Take math, take science. And I say, I practiced as an
engineer for just a very short time. I worked for General Motors first. And so really, when I went into engineering, but I said what I thought it did
was teach me how to solve problems. Now, they get bigger with time, by the way. So, but whatever, no matter what you choose
to do, I think it’s a great foundation. And so that to me is what
kind of led me down this path, this path of a) don’t let someone define
you; b) go right ahead and take something that will give you that foundation that would
endure for anywhere you chose to go in your life because I spent much of my time not just
in engineering but in sales and in services and building out our services business. And it was just a wide range of things. And that would be my recommendation. LIEBER: As a liberal arts major, I
might get into a debate with you… [ LAUGHTER ] …about STEM versus liberal arts. But in all seriousness, in your role, and
you said it, you only practiced engineering for a short period of time and then there
are new skills that you have to develop. And certainly when you get to
the top of the organization, my degree in psychology might be a little
bit of a help in a situation like that. ROMETTY: No, no. People always say, they say what do
you look for when you hire people. And I always say — and I think this
is never more true than right now — the most wonderful characteristic
is someone who is endlessly curious because I saw a book the other day, one of the great characteristics is
now for everyone lifelong learning. And I think for all of us,
that’s why you’re here. Why else would people be here for three, four
days, right, other than being endlessly curious. I think that’s what you need in this day and age
because the reinvention is happening so fast. So, that’s why I’ve often said when people
say, well, did you have a mentor, a mentor? I often recommend to people you don’t want one. I think every time you’re
with someone you look at them on what can I learn, what
can I learn from Steve. So, look, Steve has been CEO of
this organization for 17 years. Do you realize how many people
are not CEOs for 17 years. There’s like one, it’s Steve. [ LAUGHTER ] So, you should…yes. [ APPLAUSE ] So, it’s true. It is, so I want to ask you a question. So, your 17 years, which this is
your…Steve and I came out front to watch the introduction,
we both wanted to see it. And I said, you know, cherish these moments,
because this will be, you’re sitting here in that role, the last time, to
watch this conference, that motion. So, what have you learned overall
of that time that you would pass on? LIEBER: What I’ve learned most — and you touched on it a moment ago
— – is the need to be flexible. One of the things I’ve talked to the HIMMSS
staff about, and I think if you ask any of them, what’s the one thing that Steve talks
about most, it’s change and flexibility. It’s what defines us. And it’s really been what’s allowed HIMMSS
to grow from where it was 17 years ago to where we are today is that willingness to be
flexible, that willingness to embrace change, that willingness to take calculated risks. Never bet the farm, but you know,
put some things out there and some of them don’t work, some of them do. But I think that’s the thing that I would say
to anyone in any setting is that you’ve got to be able to deal with whatever
gets thrown at you and be flexible and also have a solution for that. And we do. We have a mantra, don’t take a problem,
take a solution to anything that comes up. And as a result I think we’ve achieved really
a nice interpersonal kind of culture at HIMMSS, where, as I said to staff that
I was meeting with on Saturday, anyone in the organization
should feel very comfortable in calling somebody else out, including me. If the line is BS, then call
me on it and say so. And creating that environment that
allows for that degree of exchange and interaction I think very
much still comes back to that thought around be flexible, be humble. Recognize where you’ve been and
understand those sorts of things. So, I think those are the things. And it has. Some of the crowd may not be aware that
I have announced my intention to retire at the end of this calendar year. ROMETTY: Oh, they don’t all know that? [ LAUGHTER ] LIEBER: Well, it’s… [ LAUGHTER ] …out there. ROMETTY: I was not meant
to make your announcement. [ LAUGHTER ] LIEBER: No, no, no, it is public and all. This is truly a great opportunity
for me to thank all of you for what you’ve done in contributing
to my 17 years. [ APPLAUSE ] ROMETTY: See, you’ve got your fan club. LIEBER: Thank you. ROMETTY: Yes. LIEBER: So, we did, we had a little conversation
about what’s next, and so let’s close with that. What’s next for IBM and what’s next for Ginni? ROMETTY: Ah, well… LIEBER: Given that you now
have been CEO for five… ROMETTY: I’m not going anywhere. [ LAUGHTER ] LIEBER: Well, I think you said
been the CEO for five years so… ROMETTY: Just five, tat’s five… LIEBER: …you may be on
borrowed time here, so… [ LAUGHTER ] ROMETTY: No, no, no. There is, I think, look, what I think is next,
as I said, this is the beginning of an era. I cannot think…for all of us, I can’t
think of a more exciting time to be here. I really can’t. And I have been here through, you know, you gave
the dates up there, so it’s been a few decades. And this is the time that I think the
promise of what a world with — with — all of this will be like is the ability to
solve some of this world’s greatest problems. Now, as I said, you know, I’ll end
with one of my greatest learnings. I always, I’ve repeated it many times, I
say growth and comfort, it never co-exists. And so, and it’s true for a person, each of us; it’s true for a country;
it’s true for a company. And so this isn’t a straight line. But this is a moment, I think we are
really lucky at this point in time because we will solve whether it’s education,
I see the work that we’re doing with education to personalize a learning for
your child who can’t learn, and every child should have
the right to have the right way to be approached to learn and break through. You’ll see it in education. You’re going to see it in healthcare, and
you are going to see us be able to break through in ways that we never have. Not all at once, but they will come. So what’s next for me is, and us, I hope
us, because certainly Watson Health, we’re doing financial services,
we’re doing cybersecurity, we’re doing commerce and other things. But Watson Health is that moonshot
and moonshots are made to be landed. LIEBER: Well, congratulations to you
and thank you so much for being with us. ROMETTY: Thank you. My pleasure. [ APPLAUSE ]

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12 thoughts on “Ginni Rometty Keynote at HIMSS 2017

  1. Hope Obama Care/New Trump Care incorporates plan for adopting these new technologies – what is impact on "affordable health care"?

  2. Just amazing how she went from nothing into success and she has the passion to teach others, wonderful God bless her.

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