July 28, 2020 • 49 mins
COVID-19 has shined a spotlight on the already pressing disparities in public health. In this episode of Tech Stuff, Jonathan Strickland chats with Dr. Kyu Rhee, Chief Health Officer at IBM, and Dr. Irene Dankwa-Mullan, Deputy Chief Health Officer of IBM Watson Health, about how data and technology are helping to address these issues during the pandemic and beyond.
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Episode Transcript
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Speaker 1 (00:00):
This is a very real problem that directly affects millions
of people around the world. If we expand that to
look at the indirect effects, such as how disparity creates
enormous stress on communities around the world, we're talking about
billions of people who are impacted by this issue. And
(00:21):
for me at least, this was a really big problem
where using technology wasn't an apparent path. If you were
to ask me how technology could help address an issue
like the disparity among different populations when it comes to
health care access, I would be at a loss. But
as it turns out, technology can play an important role
(00:45):
in that effort. As we'll learn, tech is really one
piece of it. Real solutions to eliminating disparity will require
much more than technology. I spoke with Dr q Ree,
Chief Health off Sir at IBM and Dr Irene don
Qua Mullen, Deputy Chief Health Officer IBM Watson Health about
(01:06):
this issue. Both doctors have dedicated an enormous amount of
time and effort as positions and data experts to create
a more equitable access to health services across all communities.
They helped me get a deeper understanding of the challenges
we face, how they impact millions of people, and the
way technology plays a part in addressing the problem. Thank
(01:31):
you both for joining me today. We have a lot
of ground to cover and a big topic to talk about,
but before we really dive into that, I wanted to
hear a little bit about your personal story, about your
journey and how you got to where you are today
and your personal reflections upon the really big challenge of
(01:52):
disparities and access to health and health care services. Dr Rey,
would you care to share a little bit of your
background owned and your personal experience. Sure, now, I appreciate that.
So I currently serve as the chief Health Officer for
IBM and Watson Health. I'm a physician by training and
(02:14):
also UM have some background in in health policy. But
if I were to reflect on how my journey in
health and healthcare started, in some ways, it might have
started since I was born. UM. I was born in Soul, Korea.
My I was the eldest child of of my generation
(02:40):
and UM I got very sick at a very young age.
When I was several months old, I was having UM
what was known as failure to thrive and I wasn't
able to gain weight and the the health care system
at the time, UM wasn't able to find a solution,
(03:01):
and my my parents, UM we're told as go go home,
and and likely that I wasn't going to UM make it,
and so UM, in a fascinating way, things things changed.
I was able to gain weight, and my mother, who
(03:21):
was a nurse, UM, you know uh uh supported my
my growth. And then we immigrated to the US, a
country of extraordinary opportunity to to start a new life
with my dad who's an economist in the World Bank,
and my mom is a nurse. And UM was fortunate
(03:43):
enough as an immigrant to to really have a supportive family,
plus an extraordinary education that helped me recognize, you know,
the value of health and and the role that we
could play UM in advancing and improving the health of populations.
I trained as a physician in internal medicine and pediatrics
(04:05):
to take care of families. I had the the good
fortune of taking care of a lot of families, many
of whom were immigrant families UH in d c. And Baltimore,
and many from communities of color and poverty. And then
UM had the good fortune to work in the federal
(04:28):
government as a health policymaker and look at health disparities
and the challenges of health and healthcare across the country domestically,
and even play a small role in the health policy
around the Affordable Care Act, which played a very significant
role in expanding care for underserved populations. And now have
(04:51):
the good fortune nearly a decade working for IBM and
looking at global public health and and looking at ways
in which data, analytics and AI can support the health
of the populations of the clients and partners we serve.
Fascinating a doctor don Qua Mullin, can you tell us
a little bit about your background and your journey. Yes, absolutely,
(05:11):
um so I also a Service Deputy Chief Health Officer
UM at IBM Watson Health and Chief Health Equity Officer,
and I have primary responsibility for science, data and evidence
research and evaluation. And I also as Chief Health Equity
(05:32):
Officer UM basically helps to ensure AH equity, health, health equity,
diversity and inclusion UM working with Q and the brilliant
team at IBM Watson Health. In terms of my journey,
(05:53):
am I grew up in Ghana. I knew a woman
at our heart who was also called Irene Um. She
was a dentist UH and I actually called an anti
Irene even though there were no relation because she was
At that time, there were very few women doctors in
Ghana that I knew, and I was really inspired by
(06:16):
hair so Um. To be honest, I was also motivated
by the idea of being thought as someone smart, intelligent, caring,
and a dedicated physician who was also a woman. So
I really really wanted to have those qualities that I
saw in in anti Irene. Um. In addition to growing
(06:39):
up in Ghana, which was which is quite different right
from the US, there was a lot of illness that
I saw that were chronic um and diseases that were
easily preventable with either vaccination or it's cleaning and early detection. UM.
I actually remember getting moms. I remember, UM. I don't
(07:02):
remember getting measles, but I was told I had measles
as a child. Um. You know, a health care system
was was overburdened, health care was rationed and I and
I experienced and I saw all of this. But I
was also drawn by this opportunity to pursue a deeper
um scientific understanding of the human body, right the physiology
(07:25):
of the disease, why it occurs, UM and UM I
sort of really knew that there was disease outside of
just UM clinical care because of what I saw with
you know, lack of nutrition and hydration UM in in
growing up, and so as I entered I came here
(07:47):
UM actually for college after I finished high school UM I,
and when I entered medical school and residency and learn
more about determinants of health, it was sort of on
aha moment and UM in medical school, I went into
public health as well, so I did a double public
(08:07):
health medicine degree and UM MY and becoming a primary
care physician UM was what I wanted to do. And
I realized really, yes, I wanted to care for vulnerable
and socially disadvantage populations. I wanted to be more compassionate,
you know, listen and understanding and value UM their culture
(08:31):
and beliefs. UM. But but I also had that motivation
about changing the way medicine was always focused on clinical
care in the US, you know, seeing the sick chronically ill,
to focus on also addressing determinants and disparities and and
supporting interventions around what we know as social determinants, and
(08:55):
you know, both need to work together. So I had
do you just like UM Dr v i UM worked
in public health and also had the opportunity to work
at the National Institutes of Health the health care side.
Can you talk a bit about what it is IBM
is doing in that space, what are your teams actually pursuing.
(09:19):
Health is so foundational and essential to the value proposition
that I hope and I believe IBM has played and
will play during this crisis and beyond UM. If you
think about UM information, data, analytics, and and and the
opportunities around artificial intelligence and machine learning and analytics and
(09:42):
predictive analytics UM, there's such an important role to address
health UM for the what I would call the multiple
stakeholders in a health ecosystem. If you think about how
data and even care or money flows in healthcare, which
(10:05):
in the US represents one in five dollars and in
most developed countries one in ten dollars and in most
developing countries one and twenty. The the nature of health
and health care is that it typically you've got a patient,
a citizen, a consumer who comes in with a challenge
on an issue. UM. We know that of that spend
(10:31):
of that challenge in terms of costs is related to
chronic diseases like diabetes, like heart disease, like asthma, like COPD,
like depression, like arthritis, like cancer. And there is a
encounter that I that Irene had, you know, have the
good fortunate as physicians to take care of in that
(10:51):
privileged moment to take care of a patient you know,
and and and and offer support in those short you know,
five to ten minutes sometimes twenty minutes conversations and interactions,
and and that data flows in a certain way. It
flows to a pay er a health plan. It flows
to an employer who often is the one who pays
(11:16):
those bills for for their workforce and their family members.
It flows potentially to a farmer company as they think
about studies and trials um. It flows to a government
if you think about all the testing that's happening with
COVID nineteen now and and and the challenges of contact
tracing and treatment in isolation and quarantining. So there's an
(11:41):
ecosystem here as it relates to health and the impact
that health has on has on you know, people, communities, families,
and the role that data analytics and AI and the
expertise of people at IBM who you know, are experts
in data science, are expert into and computing, our experts
(12:01):
into you know, Watson and machine learning to bring those
worlds together of tech and health and healthcare. I mean,
what better endeavor than to try to improve the health
of populations. Fantastic And this kind of also brings us
to the discussion at hand for today. This is a
huge topic disparity in access to healthcare, to health services,
(12:26):
to health information, and that it has as of itself.
It's it's such an enormous thing and it has so
many facets. It's challenging to talk about because there are
so many different ways we could go at it. We
could look at it along aspects of socio economic levels, regions,
we could look at it by race, and it is
(12:49):
a complicated issue. Can you talk a little bit about
the overall concept of health disparity. We have a challenge
globally domad e stickally in communities all across this country
and all across the globe where there are members of
our family who are ill, who suffer disproportionately from illness
(13:11):
from those chronic diseases like diabetes, like heart disease, like cancer,
like asthma, like depression, And we have an opportunity and
responsibility in terms of our values to find ways to
bring those folks back to better health. And unfortunately, many
of the factors that represent how those family members are
(13:33):
ill are are are based on risk factors that are
associated with things like race or ethnicity, or sexual orientation
or socio economic status or you know, education or employment.
And so it's so essential for us to to to
(13:53):
think about this as a society, to think about the
values that we believe are essential for us to be
you know, you know, to support our family members, but
also you know, create a dynamic where we we we
we bring those those health disparity populations back up in
terms of health. So that's how I you know, simplified
(14:15):
or think about it in terms of health disparities. And
to me, equity represents that hope that all members of
our family are are healthy and how do we achieve that? Yes,
and I can add to UM the concept of health
disparities and and even share a story UM as an example.
(14:38):
So there are health differences and their health disparities, and
when we the health difference for example, a health difference
for example is UM the elderly population having more you know, diseases,
or morbidity than the younger population. Right, So that's that's
(15:00):
a health difference. And when we talk about health disparities,
we are referring to that particular type of health difference
that is linked with a social or economic or environmental disadvantage.
So UM, in terms of address and health disparities, we
(15:23):
try to understand the root causes of why they exist
because they are complex. UM. Disease and illness are complex,
not just from one factor or due to one single factor,
but due to multiple structural policy decisions that we make
as a society. UM. For example, having access to clean,
(15:45):
safe and healthy environment, having access to healthy food UM,
and overall UM not being breadened by everyday stresses. UM.
There's there's a lot of stress from being for low
income or unemployed UM. And so the stresses that are
(16:06):
experienced disproportionately as he was mentioning by people with social
disadvantage or by those who have experienced racism or discrimination
UM are all as impact health and and are seen
as health disparities. And so solutions for health disparities are
(16:29):
not always just medical or clinical UM. And I and
in terms of a story that I wanted to share,
UM sort of a close and personalities around racial disparities
in maternal UM and infant health or pre timbers or
(16:51):
infant mortality. I think, UM, you know, there's a lot
of literature and scientific evidence UM around the huge, huge
disparity GAS between African American UM blacks and white um
in in in maternal mortality, in infan mortality UM as
well as pre timbers UM or premature new natal birds.
(17:16):
There's ample evidence from studies that show that cettain maternal
risk factors UM that explained these disparities are mostly from
racism or racial disparities, and the experience of systemic racial bias,
not not just raise itself, can compromise health UM in
(17:40):
In Ghana, infant mortality was was high, I mean, and
it was seen mostly with women from loyal socio economic
status or regions with very very poor health care infrastructure.
But however, in the US, in the United States UM
(18:01):
in fun mortality or blacks, US blacks across all socio
economic status have poor maternal health outcomes and infant mortality
outcomes compared to their UM white counterparts, even in the
same socio economic status UM. And in fact, there's a
study that showed that Black woman with a higher education
(18:24):
like college level or graduate level degree have similar rates
compared to similar rates to whites who only have a
high school education in terms of UM in fun mortality.
So a lot of African American families, for example, I mean,
so this is all due to UM, some underlying systemic
(18:49):
UM or determinants or social determinants of health. UM. And
of course there's ongoing research to really tease out UM
why that is the case. And I myself, you know,
surprisingly experienced the high risk pregnancy. UM. I had a
pretember UM and I thought I was doing everything right.
(19:12):
I mean, for myself, I was eating well. I thought
being educated put me at least risk for any adverse outcomes. UM.
Deadly in Ghana, it would have so UM, you know,
being in the u United States environment, being in this
probably you know, I'm not saying, I'm not sure exactly
(19:32):
what calls it, but you know, I, as an example,
was quite surprised. Um. Of course everything has turned out fine.
My daughter is will turn eighteen at the end of
this month, and UM, she's off to college. So it's
think think everything works out, work down well. So that's
sort of my story that I wanted to share UM
(19:53):
that yes, you know, this is an example of the disparities.
And I believe it's safe to ay that the current
COVID nineteen crisis has really highlighted disparities in a lot
of ways. That we've seen this play out tragically with
the response to to COVID nineteen within certain communities. How
(20:18):
has COVID nineteen impacted or affected this issue? Oh goodness, UM,
COVID has definitely shaped our world. UM. It has exposed
huge inequalities and health UM in healthcare UM, the burden
of underlying disease and access outcomes UM from from COVID nineteen.
(20:44):
What we see and then hearing actually from the front
lines are UM glaring inequalities not only in the US,
but also globally. UM. In the United States, we're actually
seen the spread is from COVID across race as well race, ethnicity,
(21:04):
and geography, right, so mostly UM along socio economic lines. UM.
The disparities that we're seeing are in you know, testing
rates and infection, severe disease, illness, UM, hospitalization and even
I see you UM outcomes And so basically what we're
(21:27):
seeing is something really structural and systemic. In addition to
what we know are higher COOL mobilities in low sitio
economic status UM minorities as well as with with racio
ethnic minorities or people from communities of color. UM. We've
(21:49):
come to find out that it's also UM higher among
language minorities. So the COVID positive cases or infection UM
is not also only occurring along racial ethnic clients, but
UM also according to the we see that along the
(22:12):
impact of we've seen the impact of racial segregation, I
would say, UM, working class UM, the lack of home
ownership or wealth, and how people living in these communities
really are impacted by the labor market right UM. These
are we've seen individuals who really have to go to work,
(22:34):
you know that to pay their bills, leading to higher
exposure dr read What are some of the most challenging
racial and ethnic disparities in public health and health care
as you see it? One quick truth to recognize is
that health is so much more than health care. That
doesn't mean health care isn't essentially it is, but if
you think about the determinants that play a role in
(22:58):
health outcomes, it's almost you could think one, two, three, four,
which adds up to UM. The the broader recognition. When
you talk about disparities and inequities, you have to recognize
the ten percent which is clinical genomics and and and
(23:20):
some of that's connected to this this common history UM.
You know, family history of chronic diseases makes you a
higher risk of having a chronic disease, so UM. And
then the thirty social, environmental, and behavioral and so so
many of our communities are challenged where the statement that
(23:42):
your zip code is more important than your genetic code
is really true. Place matters, UM. You can in the
same we use this UM reference in in d C.
When I worked serving and underserved communities in DC. You know,
you could go on the metro and you can go
from one stop to another stop and literally span twenty
(24:05):
to thirty years of life expectancy. So I want to
highlight that. So when you look across almost every health condition,
there are disparities that exists, and there's opportunities for equity.
And it requires requires us to understand the determinants. It
requires us to bring in the right data and then
(24:29):
to influence the decision makers. So I'm on this three
D commission, we call it Determinants Data Decisions that's sponsored
by the Rockefeller Foundation and UM and the School Public
Health at BEU. And this is very important to think
about because we've we've got an opportunity now with COVID
(24:50):
and all the data and the determinants science we know
to influence decisions in the disparities that exist in public
health and healthcare. UM. COVID is making us more aware
of mental illness, their disparities there UM in depression and
anxiety disorders and psychotic disorders and addictions that exists that
(25:12):
need to be addressed and flatten those curves have to
be flattened as well, there are chronic disease curves that
need to be flattened. UM. That's worsening, especially for communities
of color and poverty. So you know, all the chronic
diseases of already referenced from diabetes, are heart disease to
(25:32):
asthma to SELPD, to depression, to arthritis to cancer. And
then the other curve that we have to really address
and and be really you know, open about, is the
curve of inequities and the role of structural racism and
discrimination in our in our systems, and how can we
address that UM and really invest in diversity and inclusion
(25:58):
and equity so UM. I mean, there's so much opportunity
here for us to take this moment with COVID nineteen
to be upfront about what we have, what we need
to do, and what we want to build in terms
of the health system of the future. It sounds like
(26:20):
there's a great deal of work to be done. I'm
very curious to learn more about what it is that
IBM and more specifically what IBM what's in health are
doing in an effort to try and address these challenges.
I mean, clearly, this is something bigger than what is
going to take you know, a tech solution. Dr you
(26:42):
mentioned that earlier, it's going to require a lot of
different work. But what is IBMS peace in this What
are what are you guys doing in your efforts to
kind of address the issue of health disparity. So we
see r ole by leveraging technology, but ultimately by by
(27:04):
looking at trust. I think so much of health and
healthcare is still foundationally about relationships and trust, and so
as you think about a journey with IBM, it is
working with life science companies, hospitals, health systems, governments, um
(27:27):
and employers and businesses and health plans in a partnership
with what I would call shared expertise. You know, some
of the brightest minds and the smartest minds who are
very global and very diverse across the globe in data
science and AI and health and healthcare, like like we've
(27:48):
got with Dr Danko Mullen and and others on our
our team to work with those other partners and clients
and to evolve that shared expertise into conversations about data.
How do we connect these unique data sets, How do
we protect these data sets because so much about data
(28:08):
is about trust UM, And then how do we bring
them together and apply analytics and artificial intelligence and advanced
analytics to bring insights that better predict, that better personalized,
and that better prevents bad outcomes and promote good outcomes.
(28:29):
And so, you know, we're very proud of the work
we've done with so many different clients to deliver that
value and that shared expertise UM and those insights from
the data, analytics and AI. Dr don Komlin, I have
a question for you about data and analytics. I mean,
(28:51):
we we know that data is important, but obviously data
doesn't matter so much unless you're able to do something
actionable with it. So, how can organizations actually apply data
and analytics to make better decisions or to create better
outcomes for themselves? What are some actual processes that you
(29:16):
look at. Data is actually a very powerful tool UM.
I've heard someone saying how data is actually a lifeline. UM.
It's it's very important. And so organizations can definitely you know,
(29:38):
incorporated health equity UM lens when applying data and analytics,
especially for decision making UM, in order to really see
improved outcomes or or better outcomes UM. And they of
course they also need to ensure that while using the
(30:01):
data that we are addressing any transparency UM bias as
well as UM ethical issues that are usually at the
core of UM data use. UH. You know, in terms
of our even the current strategy to address COVID response
M recovery and even preparedness for for a potential wave
(30:26):
or increase in cases, we really need sort of a
considered coordinated effort using accurate data or complete data UM
that that is informed by UM health equity and integrated
into all of our all of our policies, all of
(30:46):
our interventions UM in scientific evidence. So I would basically
UM say that the use of data for and technology
for we're feel good. UM. It's what organizations UM need
and so that we can make better informed decisions and
(31:11):
produce better outcomes as well as at risk of health
disparity scale. We've talked a lot about what IBM is doing,
and I know there are a lot of people out there,
whether they are currently having issues accessing health services, maybe
they've been affected by disparity. Do you have any advice
for people who want to work to eliminate health disparities?
(31:33):
What can the average person do that can be helpful.
It starts with your people, UM, as I was saying,
and the diversity that you need to respect amongst your people. UM.
It also then goes to what I would call the
data and the nature in which you collect data, how
you build trust, and the diversity of your data sets
(31:54):
and the transparency you have about your data, but also
the protections you have, the privacy protections because as I
said earlier, you can't you can't you can't trust data
or share data. You don't share data with people you
don't trust. So that's a very key piece I think
in this this clash between the culture of tech and
(32:14):
healthcare and public health. You need companies that you can
trust who will protect and secure that data, and that
our values based. It then is about analytics, and we
were very proud to be doing analytics that support what
I would call this concept of equity dashboards, where people
can see the disparities that exist in the populations they serve,
(32:39):
whether you're a hospital, whether you're an employer, whether you're
a health plan, whether you're a government UM. And then
the last piece is AI, and I think ethical, transparent,
and equitable AI is going to be so essential. Many
companies want to create what I would call a black
box for AI, and you know, you know, we believe
(33:00):
that you need transparency. UM. You need to know who
trains these AI systems because in many ways, the biases
that they may have might be continued or extrapolated if
you're not transparent. And people need to know how they're trained,
they need to know the data sets they're trained on,
and they need to recognize the limitations of AI. And
(33:21):
we've always suggested that the value prop is not humans
or AI, it's humans plus AI to make better decisions.
And a part of making those better decisions is to
reduce the role of bias UH in those decisions by
by taking advantage the best of technology and the best
(33:44):
of human expertise together. Do you have advice for people
who want to work to eliminate health disparities? What can
the average person do that can be helpful. What I
love about IBM as a company and in our role
we have in society is we can catalyze these conversations
as we're doing today. And so there's so much anyone
(34:05):
can do to address health disparities and health inequities. Number One,
you you educate yourself, You make yourself aware UM as
it relates to the challenges as relates to you know,
the members of a community that are are facing these
disparities and and and and to me, I'm a big
(34:28):
believer in we uh, you know, and how we think
about our society and and and UM data brings people
together in many ways. UM and and analytics and and
companies like IBM can play an important role in that.
So think about, you know, educating yourself about these disparities
(34:48):
that exist, learn about them, and think about how you
can bring attention to that UM and and and more
knowledge and awareness. UH. A lot of this starts with
with what I call data and trust. It's it's it's
this idea. If you think about part of this journey
(35:08):
starts with how the data is collected. When you're in
UM a hospital, or you're you're an employer, and where
you're in the census, and you you share data about
yourself and and you're transparent about maybe you're limited English proficiency,
or you're transparent about your race, ethnicity, or your country
of origin. You're transparent about you know, topics like sexual orientation.
(35:33):
These these factors play a very important role in the
future of reducing those disparities. If the data isn't collected
accurately and then the disparities aren't identified, and then you
can't close those gaps. So there's a big conversation about
trust and how you how data is shared and how
it's used. And you should be comfortable asking those questions
(35:57):
like what is this data for and how's it being used,
but but challenging that you know, hopefully that you're willing
to share that data. UM. In my view, whenever we
analyze anything, I mean, you should look at disparities. You
should look at factors of race and socio economic status.
When you're running reports, when you're doing whatever you do,
(36:18):
you know, ask that question. You know, we know, for example,
blacks make fifty nine cents you know, compared to whites
in terms of income. We know that in terms of wealth,
they make ten cents for every dollar of wealth that
a person who's white makes. You know, we I know
my daughters, you know, when they grow up there, you
know right now they're competing in an environment where they'll
(36:41):
make seventy cents on the dollar that a man will make.
So if you don't include equity in your analytics, and
you don't include these factors, then in some ways you
you you're oblivious to the problem or the gaps that
you want to reduce. So we should ask and demand
to have those measures, you know, analyze and tracked, and
(37:01):
then ask questions about the root cause and how do
we reduce those gaps. Um I also believe so much
in the diversity of people. Like think about your own team,
think about the people you interact with, Think about how
you recruit your people, how you retain people. You know,
how are you thinking about diversity? Um in in in
(37:22):
your in your processes of of who you listen to
and who you recruit, who you retain. You know, I'm
a big believer in diversity and we're very proud at
IBM for was it twenty seven years being leader in
patents in the US? I mean twenty seven years in
a row being number one in patents and I am
a strong believer. A big source of that ability to
(37:45):
be leader in patents and that type of creativity is diversity.
And so many studies have shown that diversity breeds innovation.
And there's an r O I attached to being diverse.
So I would ask each of you to challenge yourself
to to think about the community and the people you're
with and how do you embrace diversity and whatever you do.
(38:06):
Dr Dougua Mullen, would you like to chime in about
this about what the average person can do to address disparity.
Health disparities are costly. I'm not sure if everyone realizes
UM and the and it's caused by a lot of
you know, determinants of healthy UM. It's also caused by
(38:32):
the fact that data UM that we are promoting or
working on is sometimes not complete or not accurate UM.
So in terms of you know, companies or individuals, I
think we should awareness is it's key and helping to
(38:53):
drive and address inequalities by UM, you know, promoting and
having a health equity lens and promoting UM data and
and analytics or artificial intelligence for for social good or
ensuring that we're all building technologies or working on solutions
(39:16):
that voll ensure the benefits for everyone and that unfairly
disadvantage other populations would help UM. And and we've seen
that this pandemic has actually resulted in you know, a
recession and a loss of economic livelihoods UM for a
lot of people. And so what we really need to
(39:40):
also look at is sporting UM programs, building up these
programs and interventional efforts that would really improve the economic
resiliency UM or or social capital, especially for despreading populations UM,
(40:01):
marginalized or under resource communities that have been really hard
hips UM. You know, it's hard to imagine how we
we could recover. I mean, there's been a number of
communities that have been left behind, for example, by the
digital UM and AI UM revolution, and so really helping
(40:22):
I mean with resources or UM technologies can help. I mean,
for example, schools education is a really strong determinant of
health and school education and cater UM these technologies for
black and brown communities that are in you know, socially
(40:45):
disadvantage communities may not always have to uh technology or computers. UM.
And so assisting with that, and this displays a larger
role in addressing, UM, a larger role in sort of
understanding these systemic or structural inequities. UM. Until helping with
(41:07):
that is really key. UM. I think as individuals. I mean,
I'm really pleased that we are having this conversation UM
around UM equity. Uh. You know, health disporities have asisted
for really long in the United States, UM, I mean
(41:29):
around the world as well. But I am I'm sort
of pleased with a with a conversation that's ongoing. So
if health disporities have existed for long, and we know
some of the root causes and promising interventions, we need
to ask yourself, you know, what are we contributing to
(41:50):
that legacy or the science right now? UM. You know
in the past, we would often say, oh, nothing can
be done about it, UM, you know, and what could
we do? And it's and so I think we're at
the right time in our history where a lot of
people are now caring more about inequalities, about racial justice. UM,
(42:12):
We're beginning to really address these difficult things and and
just talking about it, you know, UM, asking questions and
having that dialogue is is a great start. I mean
I'm inspired by uh, you know, the current movement Black
Lives Matter UM and and I think that that helps basically,
(42:37):
I mean, where people can start talking about the people
can look at you know, how can you we can
leverage our skills, UM and expertise and for the benefit
of everyone. I mean, I think playing all of us
playing our part in leveraging our skills and looking um
(42:58):
beyond competition, collaborating in our own space for the benefit
of everyone. Help. I could not agree more. I mean,
I feel that we are entering into a time where
more and more people are either realizing the realities that
have been in place forever but or or effectively forever
(43:21):
for all of our lifetimes. But maybe they weren't aware
of them because they'd never directly experienced them, or they're
they're acknowledging them. Perhaps they were at least subconsciously aware
but had not truly reflected upon it. We're starting to
see that change. I am also, like you, inspired by that,
and I am determined to do whatever I can in
(43:43):
my role as a voice of the media to continue
that conversation and to carry it forward, to make sure
people are still talking about this and thinking about this
and thinking about the aspects of the challenges that they
can rise to meet, and and why other ways we
can look to help others and to really, through helping others,
(44:07):
help everyone. I was wondering, Dr don Quamalen, if there
were any stories you could share about the work that
your team has done during the pandemic that you you're
particularly proud of. IBM has developed there's a website aimed
specifically for all COVID nineteen researchers and so it allows
(44:30):
users to upload information from electronic medical records or from
draft trials, UM or other sources and use these algorithms
to uncover new findings. Um AND and the side to
set up so that users can also keep their data
private um AND AND or share it as long as
(44:51):
UM they have privacy laws in place. But but I
hope is that the site that has been developed allows
researchers a around the world too collaborate and gain a
better insight into the understanding of the virus and how
it's um how it reacts in different populations UM and
(45:14):
you know, apart from the disparities that we're seeing UM
and and really have precise UM treatment. I mean, so
this that there is that there's also UM. The clinical
development journey is really extensive UM. But we are looking
(45:34):
at ways in which we can accelerate UM research and
using the cloud based technology at IBM and and and
streamlining data collection UM or its integration or standardization, especially
through the clinical trial process and vaccine development. We want
(45:56):
to make sure that it's inclusive and everyone UM and
it's safe for all populations UM. So those are some
of the ongoing work UM AND and some of it
that we hope to publish soon. But I would say,
you know, this collaboration, this website UM specifically for researchers
(46:19):
and especially to conduct equity work and look at electronic
health records UM and data has been UM really helpful.
So I find that extremely inspiring and I love learning
more about this because when we hear about on the news,
we typically hear about things in the context of doctors
(46:41):
are working on this, scientists are studying it, but it
tends to be at that level and we don't get
a deeper appreciation for what that actually means, what is
going into that process. What does it mean to analyze
the effects of COVID nineteen, or potential treatments for COVID nineteen,
(47:03):
or looking at even the spread of COVID nineteen and
how it spreads. So learning a little bit more about
that gives me a much deeper appreciation for the work
that that you and others that IBM are doing in
an effort to really address not just the COVID nineteen crisis,
but the overall challenge of providing health services, making sure
(47:29):
that you help others to provide health services to address
the issues of health disparity. One thing that became clear
in my conversation with doctors Re and don Qua Mullen
is that we can't really begin to address a problem
like disparity in health care access without data. To form solutions,
(47:50):
we first have to really understand the problem. Without that step,
any solution we attempt is bound to be insufficient to
me in our needs. It is therefore critical to have
sophisticated systems in place to collect information and to analyze it.
That's where the technology really comes in. We can lean
(48:11):
on tech to sift through information so that we can
glean insight from those findings. Cognitive systems can help guide
us to potential approaches that are more likely to have
a real impact and steer us away from actions that
might intuitively seem helpful but in reality have very little effect.
And as I learned, time is a critical component when
(48:35):
you're talking about health. To learn more about how IBM
is responding to COVID nineteen, including information that business leaders
can use to form their response plans and guide decision making,
visit IBM dot com slash impact slash COVID Dash nineteen.
(48:56):
I want to thank doctors Re and Donqua Mullin once
again for coming on a show and sharing their time
with me. I found it incredibly informative, and I hope
you did too. Make certain you check out all of
our past episodes of smart Talks. You can find those
past episodes in the Tech Stuff feed and the Stuff
to Blow your Mind feed. We've had a chance to
(49:18):
talk to lots of incredible people who are using technology
in really interesting ways, so make sure you go back
and listen to those episodes as well. Thank you so much.
Text Stuff is an I Heart Radio production. For more
podcasts from My Heart Radio, visit the I heart Radio app,
(49:41):
Apple podcasts, or wherever you listen to your favorite shows.
This is a very real problem that directly affects millions
of people around the world. If we expand that to
look at the indirect effects, such as how disparity creates
enormous stress on communities around the world, we're talking about
billions of people who are impacted by this issue. And
(00:21):
for me at least, this was a really big problem
where using technology wasn't an apparent path. If you were
to ask me how technology could help address an issue
like the disparity among different populations when it comes to
health care access, I would be at a loss. But
as it turns out, technology can play an important role
(00:45):
in that effort. As we'll learn, tech is really one
piece of it. Real solutions to eliminating disparity will require
much more than technology. I spoke with Dr q Ree,
Chief Health off Sir at IBM and Dr Irene don
Qua Mullen, Deputy Chief Health Officer IBM Watson Health about
(01:06):
this issue. Both doctors have dedicated an enormous amount of
time and effort as positions and data experts to create
a more equitable access to health services across all communities.
They helped me get a deeper understanding of the challenges
we face, how they impact millions of people, and the
way technology plays a part in addressing the problem. Thank
(01:31):
you both for joining me today. We have a lot
of ground to cover and a big topic to talk about,
but before we really dive into that, I wanted to
hear a little bit about your personal story, about your
journey and how you got to where you are today
and your personal reflections upon the really big challenge of
(01:52):
disparities and access to health and health care services. Dr Rey,
would you care to share a little bit of your
background owned and your personal experience. Sure, now, I appreciate that.
So I currently serve as the chief Health Officer for
IBM and Watson Health. I'm a physician by training and
(02:14):
also UM have some background in in health policy. But
if I were to reflect on how my journey in
health and healthcare started, in some ways, it might have
started since I was born. UM. I was born in Soul, Korea.
My I was the eldest child of of my generation
(02:40):
and UM I got very sick at a very young age.
When I was several months old, I was having UM
what was known as failure to thrive and I wasn't
able to gain weight and the the health care system
at the time, UM wasn't able to find a solution,
(03:01):
and my my parents, UM we're told as go go home,
and and likely that I wasn't going to UM make it,
and so UM, in a fascinating way, things things changed.
I was able to gain weight, and my mother, who
(03:21):
was a nurse, UM, you know uh uh supported my
my growth. And then we immigrated to the US, a
country of extraordinary opportunity to to start a new life
with my dad who's an economist in the World Bank,
and my mom is a nurse. And UM was fortunate
(03:43):
enough as an immigrant to to really have a supportive family,
plus an extraordinary education that helped me recognize, you know,
the value of health and and the role that we
could play UM in advancing and improving the health of populations.
I trained as a physician in internal medicine and pediatrics
(04:05):
to take care of families. I had the the good
fortune of taking care of a lot of families, many
of whom were immigrant families UH in d c. And Baltimore,
and many from communities of color and poverty. And then
UM had the good fortune to work in the federal
(04:28):
government as a health policymaker and look at health disparities
and the challenges of health and healthcare across the country domestically,
and even play a small role in the health policy
around the Affordable Care Act, which played a very significant
role in expanding care for underserved populations. And now have
(04:51):
the good fortune nearly a decade working for IBM and
looking at global public health and and looking at ways
in which data, analytics and AI can support the health
of the populations of the clients and partners we serve.
Fascinating a doctor don Qua Mullin, can you tell us
a little bit about your background and your journey. Yes, absolutely,
(05:11):
um so I also a Service Deputy Chief Health Officer
UM at IBM Watson Health and Chief Health Equity Officer,
and I have primary responsibility for science, data and evidence
research and evaluation. And I also as Chief Health Equity
(05:32):
Officer UM basically helps to ensure AH equity, health, health equity,
diversity and inclusion UM working with Q and the brilliant
team at IBM Watson Health. In terms of my journey,
(05:53):
am I grew up in Ghana. I knew a woman
at our heart who was also called Irene Um. She
was a dentist UH and I actually called an anti
Irene even though there were no relation because she was
At that time, there were very few women doctors in
Ghana that I knew, and I was really inspired by
(06:16):
hair so Um. To be honest, I was also motivated
by the idea of being thought as someone smart, intelligent, caring,
and a dedicated physician who was also a woman. So
I really really wanted to have those qualities that I
saw in in anti Irene. Um. In addition to growing
(06:39):
up in Ghana, which was which is quite different right
from the US, there was a lot of illness that
I saw that were chronic um and diseases that were
easily preventable with either vaccination or it's cleaning and early detection. UM.
I actually remember getting moms. I remember, UM. I don't
(07:02):
remember getting measles, but I was told I had measles
as a child. Um. You know, a health care system
was was overburdened, health care was rationed and I and
I experienced and I saw all of this. But I
was also drawn by this opportunity to pursue a deeper
um scientific understanding of the human body, right the physiology
(07:25):
of the disease, why it occurs, UM and UM I
sort of really knew that there was disease outside of
just UM clinical care because of what I saw with
you know, lack of nutrition and hydration UM in in
growing up, and so as I entered I came here
(07:47):
UM actually for college after I finished high school UM I,
and when I entered medical school and residency and learn
more about determinants of health, it was sort of on
aha moment and UM in medical school, I went into
public health as well, so I did a double public
(08:07):
health medicine degree and UM MY and becoming a primary
care physician UM was what I wanted to do. And
I realized really, yes, I wanted to care for vulnerable
and socially disadvantage populations. I wanted to be more compassionate,
you know, listen and understanding and value UM their culture
(08:31):
and beliefs. UM. But but I also had that motivation
about changing the way medicine was always focused on clinical
care in the US, you know, seeing the sick chronically ill,
to focus on also addressing determinants and disparities and and
supporting interventions around what we know as social determinants, and
(08:55):
you know, both need to work together. So I had
do you just like UM Dr v i UM worked
in public health and also had the opportunity to work
at the National Institutes of Health the health care side.
Can you talk a bit about what it is IBM
is doing in that space, what are your teams actually pursuing.
(09:19):
Health is so foundational and essential to the value proposition
that I hope and I believe IBM has played and
will play during this crisis and beyond UM. If you
think about UM information, data, analytics, and and and the
opportunities around artificial intelligence and machine learning and analytics and
(09:42):
predictive analytics UM, there's such an important role to address
health UM for the what I would call the multiple
stakeholders in a health ecosystem. If you think about how
data and even care or money flows in healthcare, which
(10:05):
in the US represents one in five dollars and in
most developed countries one in ten dollars and in most
developing countries one and twenty. The the nature of health
and health care is that it typically you've got a patient,
a citizen, a consumer who comes in with a challenge
on an issue. UM. We know that of that spend
(10:31):
of that challenge in terms of costs is related to
chronic diseases like diabetes, like heart disease, like asthma, like COPD,
like depression, like arthritis, like cancer. And there is a
encounter that I that Irene had, you know, have the
good fortunate as physicians to take care of in that
(10:51):
privileged moment to take care of a patient you know,
and and and and offer support in those short you know,
five to ten minutes sometimes twenty minutes conversations and interactions,
and and that data flows in a certain way. It
flows to a pay er a health plan. It flows
to an employer who often is the one who pays
(11:16):
those bills for for their workforce and their family members.
It flows potentially to a farmer company as they think
about studies and trials um. It flows to a government
if you think about all the testing that's happening with
COVID nineteen now and and and the challenges of contact
tracing and treatment in isolation and quarantining. So there's an
(11:41):
ecosystem here as it relates to health and the impact
that health has on has on you know, people, communities, families,
and the role that data analytics and AI and the
expertise of people at IBM who you know, are experts
in data science, are expert into and computing, our experts
(12:01):
into you know, Watson and machine learning to bring those
worlds together of tech and health and healthcare. I mean,
what better endeavor than to try to improve the health
of populations. Fantastic And this kind of also brings us
to the discussion at hand for today. This is a
huge topic disparity in access to healthcare, to health services,
(12:26):
to health information, and that it has as of itself.
It's it's such an enormous thing and it has so
many facets. It's challenging to talk about because there are
so many different ways we could go at it. We
could look at it along aspects of socio economic levels, regions,
we could look at it by race, and it is
(12:49):
a complicated issue. Can you talk a little bit about
the overall concept of health disparity. We have a challenge
globally domad e stickally in communities all across this country
and all across the globe where there are members of
our family who are ill, who suffer disproportionately from illness
(13:11):
from those chronic diseases like diabetes, like heart disease, like cancer,
like asthma, like depression, And we have an opportunity and
responsibility in terms of our values to find ways to
bring those folks back to better health. And unfortunately, many
of the factors that represent how those family members are
(13:33):
ill are are are based on risk factors that are
associated with things like race or ethnicity, or sexual orientation
or socio economic status or you know, education or employment.
And so it's so essential for us to to to
(13:53):
think about this as a society, to think about the
values that we believe are essential for us to be
you know, you know, to support our family members, but
also you know, create a dynamic where we we we
we bring those those health disparity populations back up in
terms of health. So that's how I you know, simplified
(14:15):
or think about it in terms of health disparities. And
to me, equity represents that hope that all members of
our family are are healthy and how do we achieve that? Yes,
and I can add to UM the concept of health
disparities and and even share a story UM as an example.
(14:38):
So there are health differences and their health disparities, and
when we the health difference for example, a health difference
for example is UM the elderly population having more you know, diseases,
or morbidity than the younger population. Right, So that's that's
(15:00):
a health difference. And when we talk about health disparities,
we are referring to that particular type of health difference
that is linked with a social or economic or environmental disadvantage.
So UM, in terms of address and health disparities, we
(15:23):
try to understand the root causes of why they exist
because they are complex. UM. Disease and illness are complex,
not just from one factor or due to one single factor,
but due to multiple structural policy decisions that we make
as a society. UM. For example, having access to clean,
(15:45):
safe and healthy environment, having access to healthy food UM,
and overall UM not being breadened by everyday stresses. UM.
There's there's a lot of stress from being for low
income or unemployed UM. And so the stresses that are
(16:06):
experienced disproportionately as he was mentioning by people with social
disadvantage or by those who have experienced racism or discrimination
UM are all as impact health and and are seen
as health disparities. And so solutions for health disparities are
(16:29):
not always just medical or clinical UM. And I and
in terms of a story that I wanted to share,
UM sort of a close and personalities around racial disparities
in maternal UM and infant health or pre timbers or
(16:51):
infant mortality. I think, UM, you know, there's a lot
of literature and scientific evidence UM around the huge, huge
disparity GAS between African American UM blacks and white um
in in in maternal mortality, in infan mortality UM as
well as pre timbers UM or premature new natal birds.
(17:16):
There's ample evidence from studies that show that cettain maternal
risk factors UM that explained these disparities are mostly from
racism or racial disparities, and the experience of systemic racial bias,
not not just raise itself, can compromise health UM in
(17:40):
In Ghana, infant mortality was was high, I mean, and
it was seen mostly with women from loyal socio economic
status or regions with very very poor health care infrastructure.
But however, in the US, in the United States UM
(18:01):
in fun mortality or blacks, US blacks across all socio
economic status have poor maternal health outcomes and infant mortality
outcomes compared to their UM white counterparts, even in the
same socio economic status UM. And in fact, there's a
study that showed that Black woman with a higher education
(18:24):
like college level or graduate level degree have similar rates
compared to similar rates to whites who only have a
high school education in terms of UM in fun mortality.
So a lot of African American families, for example, I mean,
so this is all due to UM, some underlying systemic
(18:49):
UM or determinants or social determinants of health. UM. And
of course there's ongoing research to really tease out UM
why that is the case. And I myself, you know,
surprisingly experienced the high risk pregnancy. UM. I had a
pretember UM and I thought I was doing everything right.
(19:12):
I mean, for myself, I was eating well. I thought
being educated put me at least risk for any adverse outcomes. UM.
Deadly in Ghana, it would have so UM, you know,
being in the u United States environment, being in this
probably you know, I'm not saying, I'm not sure exactly
(19:32):
what calls it, but you know, I, as an example,
was quite surprised. Um. Of course everything has turned out fine.
My daughter is will turn eighteen at the end of
this month, and UM, she's off to college. So it's
think think everything works out, work down well. So that's
sort of my story that I wanted to share UM
(19:53):
that yes, you know, this is an example of the disparities.
And I believe it's safe to ay that the current
COVID nineteen crisis has really highlighted disparities in a lot
of ways. That we've seen this play out tragically with
the response to to COVID nineteen within certain communities. How
(20:18):
has COVID nineteen impacted or affected this issue? Oh goodness, UM,
COVID has definitely shaped our world. UM. It has exposed
huge inequalities and health UM in healthcare UM, the burden
of underlying disease and access outcomes UM from from COVID nineteen.
(20:44):
What we see and then hearing actually from the front
lines are UM glaring inequalities not only in the US,
but also globally. UM. In the United States, we're actually
seen the spread is from COVID across race as well race, ethnicity,
(21:04):
and geography, right, so mostly UM along socio economic lines. UM.
The disparities that we're seeing are in you know, testing
rates and infection, severe disease, illness, UM, hospitalization and even
I see you UM outcomes And so basically what we're
(21:27):
seeing is something really structural and systemic. In addition to
what we know are higher COOL mobilities in low sitio
economic status UM minorities as well as with with racio
ethnic minorities or people from communities of color. UM. We've
(21:49):
come to find out that it's also UM higher among
language minorities. So the COVID positive cases or infection UM
is not also only occurring along racial ethnic clients, but
UM also according to the we see that along the
(22:12):
impact of we've seen the impact of racial segregation, I
would say, UM, working class UM, the lack of home
ownership or wealth, and how people living in these communities
really are impacted by the labor market right UM. These
are we've seen individuals who really have to go to work,
(22:34):
you know that to pay their bills, leading to higher
exposure dr read What are some of the most challenging
racial and ethnic disparities in public health and health care
as you see it? One quick truth to recognize is
that health is so much more than health care. That
doesn't mean health care isn't essentially it is, but if
you think about the determinants that play a role in
(22:58):
health outcomes, it's almost you could think one, two, three, four,
which adds up to UM. The the broader recognition. When
you talk about disparities and inequities, you have to recognize
the ten percent which is clinical genomics and and and
(23:20):
some of that's connected to this this common history UM.
You know, family history of chronic diseases makes you a
higher risk of having a chronic disease, so UM. And
then the thirty social, environmental, and behavioral and so so
many of our communities are challenged where the statement that
(23:42):
your zip code is more important than your genetic code
is really true. Place matters, UM. You can in the
same we use this UM reference in in d C.
When I worked serving and underserved communities in DC. You know,
you could go on the metro and you can go
from one stop to another stop and literally span twenty
(24:05):
to thirty years of life expectancy. So I want to
highlight that. So when you look across almost every health condition,
there are disparities that exists, and there's opportunities for equity.
And it requires requires us to understand the determinants. It
requires us to bring in the right data and then
(24:29):
to influence the decision makers. So I'm on this three
D commission, we call it Determinants Data Decisions that's sponsored
by the Rockefeller Foundation and UM and the School Public
Health at BEU. And this is very important to think
about because we've we've got an opportunity now with COVID
(24:50):
and all the data and the determinants science we know
to influence decisions in the disparities that exist in public
health and healthcare. UM. COVID is making us more aware
of mental illness, their disparities there UM in depression and
anxiety disorders and psychotic disorders and addictions that exists that
(25:12):
need to be addressed and flatten those curves have to
be flattened as well, there are chronic disease curves that
need to be flattened. UM. That's worsening, especially for communities
of color and poverty. So you know, all the chronic
diseases of already referenced from diabetes, are heart disease to
(25:32):
asthma to SELPD, to depression, to arthritis to cancer. And
then the other curve that we have to really address
and and be really you know, open about, is the
curve of inequities and the role of structural racism and
discrimination in our in our systems, and how can we
address that UM and really invest in diversity and inclusion
(25:58):
and equity so UM. I mean, there's so much opportunity
here for us to take this moment with COVID nineteen
to be upfront about what we have, what we need
to do, and what we want to build in terms
of the health system of the future. It sounds like
(26:20):
there's a great deal of work to be done. I'm
very curious to learn more about what it is that
IBM and more specifically what IBM what's in health are
doing in an effort to try and address these challenges.
I mean, clearly, this is something bigger than what is
going to take you know, a tech solution. Dr you
(26:42):
mentioned that earlier, it's going to require a lot of
different work. But what is IBMS peace in this What
are what are you guys doing in your efforts to
kind of address the issue of health disparity. So we
see r ole by leveraging technology, but ultimately by by
(27:04):
looking at trust. I think so much of health and
healthcare is still foundationally about relationships and trust, and so
as you think about a journey with IBM, it is
working with life science companies, hospitals, health systems, governments, um
(27:27):
and employers and businesses and health plans in a partnership
with what I would call shared expertise. You know, some
of the brightest minds and the smartest minds who are
very global and very diverse across the globe in data
science and AI and health and healthcare, like like we've
(27:48):
got with Dr Danko Mullen and and others on our
our team to work with those other partners and clients
and to evolve that shared expertise into conversations about data.
How do we connect these unique data sets, How do
we protect these data sets because so much about data
(28:08):
is about trust UM, And then how do we bring
them together and apply analytics and artificial intelligence and advanced
analytics to bring insights that better predict, that better personalized,
and that better prevents bad outcomes and promote good outcomes.
(28:29):
And so, you know, we're very proud of the work
we've done with so many different clients to deliver that
value and that shared expertise UM and those insights from
the data, analytics and AI. Dr don Komlin, I have
a question for you about data and analytics. I mean,
(28:51):
we we know that data is important, but obviously data
doesn't matter so much unless you're able to do something
actionable with it. So, how can organizations actually apply data
and analytics to make better decisions or to create better
outcomes for themselves? What are some actual processes that you
(29:16):
look at. Data is actually a very powerful tool UM.
I've heard someone saying how data is actually a lifeline. UM.
It's it's very important. And so organizations can definitely you know,
(29:38):
incorporated health equity UM lens when applying data and analytics,
especially for decision making UM, in order to really see
improved outcomes or or better outcomes UM. And they of
course they also need to ensure that while using the
(30:01):
data that we are addressing any transparency UM bias as
well as UM ethical issues that are usually at the
core of UM data use. UH. You know, in terms
of our even the current strategy to address COVID response
M recovery and even preparedness for for a potential wave
(30:26):
or increase in cases, we really need sort of a
considered coordinated effort using accurate data or complete data UM
that that is informed by UM health equity and integrated
into all of our all of our policies, all of
(30:46):
our interventions UM in scientific evidence. So I would basically
UM say that the use of data for and technology
for we're feel good. UM. It's what organizations UM need
and so that we can make better informed decisions and
(31:11):
produce better outcomes as well as at risk of health
disparity scale. We've talked a lot about what IBM is doing,
and I know there are a lot of people out there,
whether they are currently having issues accessing health services, maybe
they've been affected by disparity. Do you have any advice
for people who want to work to eliminate health disparities?
(31:33):
What can the average person do that can be helpful.
It starts with your people, UM, as I was saying,
and the diversity that you need to respect amongst your people. UM.
It also then goes to what I would call the
data and the nature in which you collect data, how
you build trust, and the diversity of your data sets
(31:54):
and the transparency you have about your data, but also
the protections you have, the privacy protections because as I
said earlier, you can't you can't you can't trust data
or share data. You don't share data with people you
don't trust. So that's a very key piece I think
in this this clash between the culture of tech and
(32:14):
healthcare and public health. You need companies that you can
trust who will protect and secure that data, and that
our values based. It then is about analytics, and we
were very proud to be doing analytics that support what
I would call this concept of equity dashboards, where people
can see the disparities that exist in the populations they serve,
(32:39):
whether you're a hospital, whether you're an employer, whether you're
a health plan, whether you're a government UM. And then
the last piece is AI, and I think ethical, transparent,
and equitable AI is going to be so essential. Many
companies want to create what I would call a black
box for AI, and you know, you know, we believe
(33:00):
that you need transparency. UM. You need to know who
trains these AI systems because in many ways, the biases
that they may have might be continued or extrapolated if
you're not transparent. And people need to know how they're trained,
they need to know the data sets they're trained on,
and they need to recognize the limitations of AI. And
(33:21):
we've always suggested that the value prop is not humans
or AI, it's humans plus AI to make better decisions.
And a part of making those better decisions is to
reduce the role of bias UH in those decisions by
by taking advantage the best of technology and the best
(33:44):
of human expertise together. Do you have advice for people
who want to work to eliminate health disparities? What can
the average person do that can be helpful. What I
love about IBM as a company and in our role
we have in society is we can catalyze these conversations
as we're doing today. And so there's so much anyone
(34:05):
can do to address health disparities and health inequities. Number One,
you you educate yourself, You make yourself aware UM as
it relates to the challenges as relates to you know,
the members of a community that are are facing these
disparities and and and and to me, I'm a big
(34:28):
believer in we uh, you know, and how we think
about our society and and and UM data brings people
together in many ways. UM and and analytics and and
companies like IBM can play an important role in that.
So think about, you know, educating yourself about these disparities
(34:48):
that exist, learn about them, and think about how you
can bring attention to that UM and and and more
knowledge and awareness. UH. A lot of this starts with
with what I call data and trust. It's it's it's
this idea. If you think about part of this journey
(35:08):
starts with how the data is collected. When you're in
UM a hospital, or you're you're an employer, and where
you're in the census, and you you share data about
yourself and and you're transparent about maybe you're limited English proficiency,
or you're transparent about your race, ethnicity, or your country
of origin. You're transparent about you know, topics like sexual orientation.
(35:33):
These these factors play a very important role in the
future of reducing those disparities. If the data isn't collected
accurately and then the disparities aren't identified, and then you
can't close those gaps. So there's a big conversation about
trust and how you how data is shared and how
it's used. And you should be comfortable asking those questions
(35:57):
like what is this data for and how's it being used,
but but challenging that you know, hopefully that you're willing
to share that data. UM. In my view, whenever we
analyze anything, I mean, you should look at disparities. You
should look at factors of race and socio economic status.
When you're running reports, when you're doing whatever you do,
(36:18):
you know, ask that question. You know, we know, for example,
blacks make fifty nine cents you know, compared to whites
in terms of income. We know that in terms of wealth,
they make ten cents for every dollar of wealth that
a person who's white makes. You know, we I know
my daughters, you know, when they grow up there, you
know right now they're competing in an environment where they'll
(36:41):
make seventy cents on the dollar that a man will make.
So if you don't include equity in your analytics, and
you don't include these factors, then in some ways you
you you're oblivious to the problem or the gaps that
you want to reduce. So we should ask and demand
to have those measures, you know, analyze and tracked, and
(37:01):
then ask questions about the root cause and how do
we reduce those gaps. Um I also believe so much
in the diversity of people. Like think about your own team,
think about the people you interact with, Think about how
you recruit your people, how you retain people. You know,
how are you thinking about diversity? Um in in in
(37:22):
your in your processes of of who you listen to
and who you recruit, who you retain. You know, I'm
a big believer in diversity and we're very proud at
IBM for was it twenty seven years being leader in
patents in the US? I mean twenty seven years in
a row being number one in patents and I am
a strong believer. A big source of that ability to
(37:45):
be leader in patents and that type of creativity is diversity.
And so many studies have shown that diversity breeds innovation.
And there's an r O I attached to being diverse.
So I would ask each of you to challenge yourself
to to think about the community and the people you're
with and how do you embrace diversity and whatever you do.
(38:06):
Dr Dougua Mullen, would you like to chime in about
this about what the average person can do to address disparity.
Health disparities are costly. I'm not sure if everyone realizes
UM and the and it's caused by a lot of
you know, determinants of healthy UM. It's also caused by
(38:32):
the fact that data UM that we are promoting or
working on is sometimes not complete or not accurate UM.
So in terms of you know, companies or individuals, I
think we should awareness is it's key and helping to
(38:53):
drive and address inequalities by UM, you know, promoting and
having a health equity lens and promoting UM data and
and analytics or artificial intelligence for for social good or
ensuring that we're all building technologies or working on solutions
(39:16):
that voll ensure the benefits for everyone and that unfairly
disadvantage other populations would help UM. And and we've seen
that this pandemic has actually resulted in you know, a
recession and a loss of economic livelihoods UM for a
lot of people. And so what we really need to
(39:40):
also look at is sporting UM programs, building up these
programs and interventional efforts that would really improve the economic
resiliency UM or or social capital, especially for despreading populations UM,
(40:01):
marginalized or under resource communities that have been really hard
hips UM. You know, it's hard to imagine how we
we could recover. I mean, there's been a number of
communities that have been left behind, for example, by the
digital UM and AI UM revolution, and so really helping
(40:22):
I mean with resources or UM technologies can help. I mean,
for example, schools education is a really strong determinant of
health and school education and cater UM these technologies for
black and brown communities that are in you know, socially
(40:45):
disadvantage communities may not always have to uh technology or computers. UM.
And so assisting with that, and this displays a larger
role in addressing, UM, a larger role in sort of
understanding these systemic or structural inequities. UM. Until helping with
(41:07):
that is really key. UM. I think as individuals. I mean,
I'm really pleased that we are having this conversation UM
around UM equity. Uh. You know, health disporities have asisted
for really long in the United States, UM, I mean
(41:29):
around the world as well. But I am I'm sort
of pleased with a with a conversation that's ongoing. So
if health disporities have existed for long, and we know
some of the root causes and promising interventions, we need
to ask yourself, you know, what are we contributing to
(41:50):
that legacy or the science right now? UM. You know
in the past, we would often say, oh, nothing can
be done about it, UM, you know, and what could
we do? And it's and so I think we're at
the right time in our history where a lot of
people are now caring more about inequalities, about racial justice. UM,
(42:12):
We're beginning to really address these difficult things and and
just talking about it, you know, UM, asking questions and
having that dialogue is is a great start. I mean
I'm inspired by uh, you know, the current movement Black
Lives Matter UM and and I think that that helps basically,
(42:37):
I mean, where people can start talking about the people
can look at you know, how can you we can
leverage our skills, UM and expertise and for the benefit
of everyone. I mean, I think playing all of us
playing our part in leveraging our skills and looking um
(42:58):
beyond competition, collaborating in our own space for the benefit
of everyone. Help. I could not agree more. I mean,
I feel that we are entering into a time where
more and more people are either realizing the realities that
have been in place forever but or or effectively forever
(43:21):
for all of our lifetimes. But maybe they weren't aware
of them because they'd never directly experienced them, or they're
they're acknowledging them. Perhaps they were at least subconsciously aware
but had not truly reflected upon it. We're starting to
see that change. I am also, like you, inspired by that,
and I am determined to do whatever I can in
(43:43):
my role as a voice of the media to continue
that conversation and to carry it forward, to make sure
people are still talking about this and thinking about this
and thinking about the aspects of the challenges that they
can rise to meet, and and why other ways we
can look to help others and to really, through helping others,
(44:07):
help everyone. I was wondering, Dr don Quamalen, if there
were any stories you could share about the work that
your team has done during the pandemic that you you're
particularly proud of. IBM has developed there's a website aimed
specifically for all COVID nineteen researchers and so it allows
(44:30):
users to upload information from electronic medical records or from
draft trials, UM or other sources and use these algorithms
to uncover new findings. Um AND and the side to
set up so that users can also keep their data
private um AND AND or share it as long as
(44:51):
UM they have privacy laws in place. But but I
hope is that the site that has been developed allows
researchers a around the world too collaborate and gain a
better insight into the understanding of the virus and how
it's um how it reacts in different populations UM and
(45:14):
you know, apart from the disparities that we're seeing UM
and and really have precise UM treatment. I mean, so
this that there is that there's also UM. The clinical
development journey is really extensive UM. But we are looking
(45:34):
at ways in which we can accelerate UM research and
using the cloud based technology at IBM and and and
streamlining data collection UM or its integration or standardization, especially
through the clinical trial process and vaccine development. We want
(45:56):
to make sure that it's inclusive and everyone UM and
it's safe for all populations UM. So those are some
of the ongoing work UM AND and some of it
that we hope to publish soon. But I would say,
you know, this collaboration, this website UM specifically for researchers
(46:19):
and especially to conduct equity work and look at electronic
health records UM and data has been UM really helpful.
So I find that extremely inspiring and I love learning
more about this because when we hear about on the news,
we typically hear about things in the context of doctors
(46:41):
are working on this, scientists are studying it, but it
tends to be at that level and we don't get
a deeper appreciation for what that actually means, what is
going into that process. What does it mean to analyze
the effects of COVID nineteen, or potential treatments for COVID nineteen,
(47:03):
or looking at even the spread of COVID nineteen and
how it spreads. So learning a little bit more about
that gives me a much deeper appreciation for the work
that that you and others that IBM are doing in
an effort to really address not just the COVID nineteen crisis,
but the overall challenge of providing health services, making sure
(47:29):
that you help others to provide health services to address
the issues of health disparity. One thing that became clear
in my conversation with doctors Re and don Qua Mullen
is that we can't really begin to address a problem
like disparity in health care access without data. To form solutions,
(47:50):
we first have to really understand the problem. Without that step,
any solution we attempt is bound to be insufficient to
me in our needs. It is therefore critical to have
sophisticated systems in place to collect information and to analyze it.
That's where the technology really comes in. We can lean
(48:11):
on tech to sift through information so that we can
glean insight from those findings. Cognitive systems can help guide
us to potential approaches that are more likely to have
a real impact and steer us away from actions that
might intuitively seem helpful but in reality have very little effect.
And as I learned, time is a critical component when
(48:35):
you're talking about health. To learn more about how IBM
is responding to COVID nineteen, including information that business leaders
can use to form their response plans and guide decision making,
visit IBM dot com slash impact slash COVID Dash nineteen.
(48:56):
I want to thank doctors Re and Donqua Mullin once
again for coming on a show and sharing their time
with me. I found it incredibly informative, and I hope
you did too. Make certain you check out all of
our past episodes of smart Talks. You can find those
past episodes in the Tech Stuff feed and the Stuff
to Blow your Mind feed. We've had a chance to
(49:18):
talk to lots of incredible people who are using technology
in really interesting ways, so make sure you go back
and listen to those episodes as well. Thank you so much.
Text Stuff is an I Heart Radio production. For more
podcasts from My Heart Radio, visit the I heart Radio app,
(49:41):
Apple podcasts, or wherever you listen to your favorite shows.
TechStuff News
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