Hi and welcome to our video cast
today we are talking about mammography
and how AI can help accelerate productivity
and patient care on your moderator, Rhonda
Blaschke and with me I have two great
resources from both Change Healthcare
and I CAD.
And one of the things we want to talk about today is how
the healthcare industry continues to learn more
about the value of learning and AI
and how we empower those radiologists.
We want to talk about how artificial intelligence
in particular and mammography is
helping speed up breast cancer detection,
improving the workflows of the radiologist,
analyzing pattern pattern recognition
and obviously most importantly helping us really
all work towards providing better patient
care. Um so today
I have on our team, Archie Mariani
the chief product officer of enterprise imaging
for Change for Change healthcare. And
in her role Archie provides product
leadership for global teams building
the next generation cloud, native clinical and workflow
solutions and she is passionate about
healthcare And has devoted more than
20 years to the field.
Archie, do you want to say hi
thank you, Rhonda, I am so happy to be here
with you and Rodney
thank you And Rodney, we
just mentioned you but you're definitely one of our
key product presenters here today. So
Rodney Hawkins, the vice president of AI
solutions that I can incorporated and
for the past six years Rodney has led Ike
has a I product strategy creating
a market leading portfolio breast
AI solutions
that are being used worldwide to benefit both
providers and women in the global fight
against breast cancer.
He spent over 25 years focused
on developing and introducing innovative
technology to improve clinical
care and efficiency and diagnostic
imaging and we couldn't be happier
to have Rodney and the team and I can
as partners with us Rodney do you want to say
hello, great to be here. Hello everyone and
thanks for inviting me,
artie. I'm gonna start with you first on our discussion
and I'm gonna ask you a little bit about what
your personal background is and your expertise
and and what drives you. Why is
mammography so important for radiology
customers and how we're actually
helping to drive the digital transformation
and healthcare from an enterprise imaging perspective.
Can you tell us a little bit about that?
Thank you Rhonda. Yes, for sure. I mean I think
a lot of people don't know but I um
I actually have a personal story
of you know why even you know change
and and how it actually,
you know, it's connected to my mom
and her experience with breast cancer
Um personally, you know, I have
like you said, you know devoted 20/20
years to healthcare and and been
in health tech for most of my life.
And you know the reason is that I've always
had this idea of you know finding
passion with purpose and healthcare
always allows you to do that.
My mom got diagnosed with breast cancer
and we really wanted a second opinion.
She was in India and I wanted her
to get a DBT done
um which she did and I really wanted
to get a get sort of you know send
the image across so that you know, we could get a second
opinion here in the United States and
I couldn't believe that
back in 2019, there
wasn't a way for her to be able to do that
when we could, you know, when we talk
about all of the big data and the big
tables at Google, we didn't
have that ability to share um
to share an image. So for me it was a personal
challenge to be able to
develop an enterprise imaging platform
so that you know, you can truly democratize,
you know patient data and
expedite care in the best
way possible. I think any
journal that you pick up today,
you're going to find, you know true
adoption of Ai really
has only been in the space of tomography
and you know partners
like Ichat innovators like ICHAt
are truly making that possible when you talk
about radiologists
For them having to do 17,000
different things for one study,
one read
To be able to elevate that
productivity and reduce the burnout
by 70%. Those
are sort of you know, even conservative numbers that
we look at, you know of any study out there. It
doesn't matter whether it's an Ichat
um you know algorithm or something else.
So to be able to truly
focus a on our patients,
you know, to expedite care when they really
needed and be on our providers
who are continuously burned out
to be able to make them read, you
know faster and more effectively,
really focusing on those high,
you know, highly complex cases.
I think that I genuinely feel
like AI has really arrived in the right
place at the right time.
So Archie that's such an important point.
And I think one of the things that we really need
to understand is how the workflow really
is a key driver and the ability
to provide um great
patient care. And so Romney, looking for your perspective
on what are some essential elements
that providers should look for when they're adopting
AI to streamline that clinical
workflow and really improve clinical
outcomes. Can you talk to us a little bit about what you've
seen and what your perspective is?
Sure. And that's a great question because
not all AI is created equal.
Um
you know, first is is how well does the AI
do what it's supposed to do in other words,
how well does it perform?
And there's there's two ways to think about performance.
First is
what we call standalone performance, which is,
you know, how well the AI performs on its
own. Um and this is typically
measured by running the AI on datasets
with known outcomes known
cancers to measure sensitivity,
known normal cases to measure specificity.
The second and perhaps more importantly,
is how well the radiologist
performs when using the Ai.
Um This is typically measured with
a reader study where radiologists read cases
both with and without A. I. Uh
and the results are compared ideally
the AI should improve radiologist performance
at least one of three areas sensitivity.
Or does it help them detect the disease
in the case of mammography. Ai does it help them
detect cancers
uh specificity or does
it help them reduce false positives and
ultimately unnecessary recalls
um or efficiency. And does it
help the reader do their job faster?
It's really difficult to find any
kind of technology that will improve
performance in all of these areas.
Um But but we were pleased,
very pleased with the results of
the profound AI reader study which
showed radiologists improvement in cancer detection
rates, reduction in the number of
false positives and recall rates as well
as a significant reduction and reading time.
Um In addition to performance.
Uh There are other important things
to look for. Uh for example,
how much and how and the quality
of the data used to train and test the algorithm,
for example, was the data collected from multiple
sites representing a diverse population.
Um Has the AI been validated
with imaging image acquisition
technology from different vendors?
You know, if the AI only works with one
vendor's technology for example,
this can be problematic for sites with systems
from multiple vendors,
the speed of the of
the processing a case. Um If
it takes too long to process a case,
the Ai may not be available when the study
is being read, which is clearly a problem.
Uh And and finally and perhaps one of
the most important is how well the AI
integrates into radiologist
workflow.
We learned a lot over the years about the differences
and how well AI is supported
by the various packs and workstation technologies.
There isn't a one size fits all approach
and flexibility is really important.
That's why we've worked very closely with
the Change Healthcare team to ensure
an optimal workflow when using our
AI in their packs environment.
You can have the best performing AI available.
But if it doesn't easily fit
into how radiologists
do their work it likely won't be used.
That's a great point. And I think one of the biggest things
that I took away from that is
not only is it multifaceted
and what really makes effective Ai
but it is going back to how does
it really drive efficiency
patient care
being um diverse and
making sure that it has an inclusive and strong
background of the data that
selectively used to make sure that it
is accurate. Um And I think
those are great points and you know one of
the things I want to talk about a little bit and Ron young
I'll go to you first is
when you think about the opportunities and
challenges in the mammography and imaging
space.
We know that ai is extremely important
because it is more around helping us make
sure we improve in preventative
care as well as the caregiving for those that are
diagnosed.
But when the National Cancer Institute predicted
that there could be almost 10,000 excess
deaths from breast cancer and colorectal
cancers over the next decade
as a as a direct result
of delayed screening due to the pandemic.
Um But now that the mammography screens
have started to resume how
my clinicians efficiently manage
the backlog of those who need
to be screened. Um
And what is your your your thoughts on how
that can be managed and helped by technology?
Again? Great question. Um You
know our ai our Ai technology
is particularly relevant as
it relates to recovering from the impact of
covid and breast cancer screening. Um
Our product doesn't just indicate
where suspicious lesions are in the breast
but it also assigns an algorithm
derived competence scores
which indicates, you know which cases
are more likely to be malignant. Additionally,
we just recently released
our profound
Ai risk product which is a short term
risk model uh to help
identify which women who
are the women who are most likely to
be diagnosed with cancer before
their next mammogram.
You know both the Ai scores and the risk
scores can be very valuable in
helping providers identify the
women who will benefit most by not delaying
their screening appointments or those
who should be at the front of the line when it comes
to scheduling their next screening appointments,
that's really awesome. Um and as
a female, I'm very happy to hear that as well.
Um because it's definitely something that I think is of
concern with all of us. Um
so I think that's that's great to hear
before we go and I know we've asked a lot
of questions first of all, Archie, do you have any
last thoughts before we close
out this session?
I think, you know, to to what
Rodney said, it is so critical
to understand how, you know, covid
19 has really
upended, you know, radiology
and especially mammography as we understand
and as we knew it right with,
you know, sort of reduction in elective surgeries,
continuous pressures on our
radiologist to read more with
less time I guess, and fewer tools,
you know, as they as they transitioned to
working remote. There has been
extraordinary pressure
on our providers to actually
continuously provide,
you know, the diagnosis at the right time,
through the right technologies available.
And I think that's where, you know, we have to
really look at, you know, sort of these confluence
of forces that are creating, you
know, really until unreal
pressures on our radiologists and
to be able to do that, we have
to start leveraging, you know, cloud
nativity that can allow this
remote access and the
ai algorithms combined with it
should be, you know, such a great
way of, you know, talking reducing that
burnout and so with those
two forces combined, you know, or technological
advancements combined, I do
believe that you know as the elective
surgeries or government coming up, you
know the the number of radiologists
joining the workforce or in the workforce
are not increasing. And to be able
to you know sort of do the same
or more with less, we have
to start looking at how do we create
you know, these
bandwidths
of you know, productivity
for our radiologists and cloud native
enterprise imaging platform which is what
you know sort of uh for us
you know that is the changes future and
and our providers future. We're so
excited to have partnership with Rodney
and his team to be able to really allow
um sort of work for our radiologist
to work in this new normal if you will.
That's awesome.
So before we go I want to say thanks to Archie
and Romney for being part of the program.
Um I hope that all of you watching
have enjoyed the information we shared
and learned from it on how we're looking
to drive towards adoption of ai in mammography
and how we're partnering together to really drive
improved patient care. If you have
a chance to stop by in in Chicago
at RS and a change healthcare will be there
to answer any questions as well as the
eye can team. We both happen to be
located in the South Hall. We also
have a virtual booth available if you're unable
to make it in person. But for more information
also from my cat, you can visit them at icann
med dot com or in person
at the South Hall meeting suites room to
14 to 2 16.
Thank you again, Rodney and Archie really
appreciate it. Um and we look
forward to hearing from any of you on
questions you might have on how we can partner
and help you with your mammography solutions.