Hello, everyone, and welcome to Becker's Healthcare podcast.
I'm Scott King. Thrilled today to be joined
by two very special guests.
First, we'll start with Rick Peng, digital ventures
lead in Memorial Sloan Kettering Cancer Center's office
of entrepreneurship
and Commercialization.
And we also have Natalia Somerville,
director of decision intelligence at Memorial Sloan Kettering
Cancer Center. Thank you both so much for
joining. I know we have a a lot
planned for a for a great,
discussion on all the innovative things you're up
to over at Sloan Kettering. How are you
both doing?
Doing good. Very excited to be here. Thank
you so much for having us. And, yeah,
definitely,
very happy to share some of our experiences.
Yes. No. Thanks so much to to both
of you for joining, and and we'll go
ahead and get started here with this, important
discussion.
Now now, Rick, I'll I'll get started with
you. I know, you
know, MSK is is often seen as a
leader in applying AI in clinical settings.
How is AI currently being used across
the system, and where do you where are
you seeing the greatest
impact in regards to patients today?
Yeah. So AI is definitely being applied across
the board here at MSK.
It's being applied in not only clinical care
but also in the research space.
It's,
in the clinical spaces, it's it's used in,
use cases such as assisting
clinicians in their visits with patients. Key examples
are partnership with companies like Abridge that help
us, manage
a transcription documentation
of, patient visits.
In the research space, we we do a
lot of internal innovation,
as far as, AI,
development models that can predict responses to therapy,
and that assist in the the development and
discovery of new,
therapeutics in oncology.
We also partner with external companies in this
space as well to co develop new AIs
that can be applied in these situations. And,
of course, we have a large network of
pharma partnerships that we that, can leverage these
technologies to accelerate the development and bringing to
market new drugs that serve unmet needs in
cancer.
And of course, on top of that, we
also apply
AI as tools across the enterprise,
to help, staff at MSK,
outside of just clinicians and researchers to to
do their jobs as effectively as possible as
well. So really across the board.
Yeah. Appreciate the details there on all the
AI usages, Rick. Now, Natalia, I know you
you co create MSK's
AI governance
operating model, which is obviously a huge accomplishment,
but what problem was MSK trying to solve
when you were developing the framework?
Yeah.
So a couple years ago,
here at MSK,
our,
leadership
created what was called the AI task force,
where kinda given all the movement and,
progress
happening,
AI and health care,
our leadership wanted to understand what are the
important aspects and what should we be looking
for, what are the valuable use cases. So
one of the work groups was governance.
And, that work group was led by by
our
chief informatics officer, doctor Pitstetson,
at the time.
And, during that group, I was supporting the
lead,
and we had conversations with
important,
players,
let's say within the hospital, like
legal and,
clinicians who have been working ethics
and,
technologists.
And, that's where there was obviously
kinda an understanding between everybody that
we are starting to deploy more and more
AI,
and we need to be able
to make sure that it's done safely,
and also that we can,
scale it up because we totally understand that
if maybe a couple years there was a
couple models deployed,
this is
growing exponentially now. So we wanna be able
to do that,
responsibly and support
that innovation
and that,
scaling up of AI technologists.
Absolutely.
And, Rick, I want to ask you about
the AI governance as well. You know, in
in a high stakes environment like cancer care,
why is governance
it's not optional when it when it comes
to,
AI adoption,
and why is that?
Yeah. I mean, I I I think there
there's obvious,
you know, ramifications
on, you know,
how patients receive care, how research is conducted,
not only in terms of, like, the clinical
impact on patients, but also,
you know, everything from,
you know, ethics and, scientific integrity as far
as all that work that we do. And
I and I think, you know, broadly, like,
the the need for governance in AI is
particularly
important,
as we think about, you know, organizations like
MSK are very large. There's a lot of
different stakeholders involved,
including external collaborators.
And I think, you know,
AI requires
connectivity across all these different groups, whether they're,
you know, at the points of, applying the
AI, at the points of developing the AI,
at the points of supplying the inputs to
AI, including,
data,
where, you know, we really need all these
pieces to be, you know, working in coordination
with each other to make sure that what
comes out the other end
is,
created,
in a way that,
optimizes its intended performance as well as as
being applied responsibly,
across all the different groups that need to
be using it.
Natalia, what qualify what what qualities I'm sorry.
Should health systems look for an AI governance
partner to ensure responsible
adoption while still supporting
some innovation?
Yeah. I think it's definitely flexibility,
because
as we have learned and you just mentioned,
we have to be able to balance those
two goals,
be able to deploy AI responsibly, but also
making sure that
we are not seen and we are not,
blocking
any any innovation
performed by our clinicians. Our clinicians do amazing
research, and that is
partially why our institution is so well known
because of the research they do. So we
wanna make sure to encourage that research.
So the way we we partner and the
way we we see these processes
is making sure that there are different paths,
that if there is an AI
model or AI system
that,
the governance
committee is looking at a time that we
have different paths, that we are flexible,
understanding that the the research initiatives
may have a faster path. They are working
towards
proof of concepts and, developing the science,
versus tools that may go into workflow
workflows,
right away,
let's say predicting mortality
and we wanna deploy that within two months,
then we definitely wanna have a different path,
much more focused on reviewing the details.
So, yeah, I believe that that flexibility
on adjusting
the processes
and,
our AI life cycles
across the different types of implementation
is is key.
Natalia, how does MSK's
AI governance
operating model help ensure that AI tools are
safe and
accountable and aligned with clinical standards?
Mhmm. Yeah. That's that's a great question.
So over the last,
I wanna say two years,
we developed,
these processes
and,
an AI life cycle. So this is a
tool that,
kinda
ground the grounds
the
AI
research and deployment
that happens.
An AI life cycle is the the,
the the sequence of steps that you follow
such as,
you have an idea, you have to validate
that the idea,
has,
you know, value within the organization,
and then design, deployment, testing, all all those
different steps.
I do wanna call out that, actually, two
years ago, our our life cycle was,
our original life cycle. We did use,
Duke Health Systems life cycle. They are very
advanced in this area, and then we modified
it what makes sense for us. But so
what I was mentioning is this life cycle
allows us to create steps
depending
on what stage the AI system is.
So for example,
if the AI system
that we are,
looking into or,
governing at the moment is
in, pilot mode, then there are certain requirements
that we ask. We ask for,
the pilot,
plan to be very explicit and presented to
the committee. We're asking for a monitoring plan
in advance of the deployment
versus
if, the AI system is earlier. Let's say
they're just kind of finished design and going
into development.
There's other sorts of questions that we we
ask. So depending on the life cycle,
we have these different touch points
with the model owners and model sponsors
to make sure that it goes through,
in a in a safe way, and we're
able to
suggest any modifications
as early as possible if needed.
And and, Rick, what risks do health systems
face if they deploy AI tools, you know,
without a clear strategy or framework in place?
Yeah. So I think, like, for for health
systems, if AI isn't
isn't deployed in a way that's strategically directed,
I think there's there's a couple levels of
risks.
One is,
an operational
risk of,
have impacting the adoption of these technologies.
If there isn't a clear idea of what
workflows or what stakeholders,
need to pick this up and, you know,
build it into practice,
it can really limit,
the extent to which the the AI is
being applied in the first place.
I think, ultimately, longer term, it can impact
the ROI of these types of solutions, especially
if there, you know, if there's significant investment
in them, whether it's through purchasing external
solutions, or,
you know, investing in internal development of capabilities
if if there isn't the right adoption or
if it's not being applied in the most
impactful use cases
and reduce your ROI, which I think, you
know, can,
not only,
hamper the the the impact of the particular
AI solution in question, but also just AI
across the board as well.
And and one special consideration for for organizations
like MSK as well is, like, MSK definitely,
you know,
it's it's point of pride that we are
one of the leading cancer centers in The
US and in the world. And so I
I think, you know, application of AI also
needs to be done in a way that
fully takes advantage of our the differentiated
expertise that MSK has.
I think a lot of times where that
comes into play especially is,
you know, in terms of using AI solutions,
ensuring that there's,
the appropriate level of governance, but also input
the development
of those AI tools, whether it's through leveraging
a,
MSK's,
unique know how expertise of its
clinical and research experts, to really to really
to really, you know, infuse the AI solutions
that we do apply,
with the with the sort of secret sauce
at MSK so that, you know, when applied,
it really reflects the level of of quality
that,
applied, it really reflects the level of of
quality that, MSK has come to be known
for in the world of cancer care and
research.
Yeah. Thanks so much, Ray. And maybe diving
into that that secret sauce,
Natalia, that the process with AI over at
MSK, you know, you know, even after tools
are approved and deployed, the work's not done.
Right? So why is continuous monitoring
of AI systems essential?
Yeah. That is it's it's very important as
you mentioned, and, I the way I talk
about monitoring is actually twofold.
One is monitoring,
the the the safety. You know? Has there
been any adverse events, for example? Because,
as
as Rick was mentioning, there's,
kinda several
aspects that could potentially go wrong. So we
wanna be able to monitoring
any, AI adverse,
adverse,
events,
as well as kinda the the technical
model itself. Is there any drift?
For example, if there is a pattern change
in the data,
then we need to be able to acknowledge
that in the model if it doesn't do
it automatically.
So depending on the type of model, we
need to be able to,
see that every
quarter, every month. It it really depends on
how the model is is structured.
At the same time, the second part that
is also very important that sometimes
it's talked about less actually than the technical
monitoring
is precisely what Rick was mentioning. We also
want to make sure that the tool is
actually being used.
And beyond being used, that the tool is
bringing the expected impact.
If we are deploying,
an AI tool
to support,
for example, discharges,
the the nurses with their discharge efforts,
and the original goal was to reduce the
patient length of stay such that they can
go home as soon as possible,
and enjoy kinda more, comfort settings,
then we wanna see that the length of
stay is actually decreasing.
So we wanna be able to see those
metrics that we are targeting,
as well as usage. We don't wanna be
maintaining
tools that nobody's using. So those are the
two aspects, and they are typically monitored,
differently and in kinda different
platform and visualizations, but both are
key to be able to say that the
AI is being used properly.
And, Rick,
government regulation is always obviously a big topic
in health care, and it doesn't escape AI.
So with AI regulations
now evolving at the state level, how does
an organization like MSK
streamline compliance without slowing progress or innovation?
So I I think a big part of
that is,
creating visibility across all different stakeholder groups. Part
of that is, on an operational level, the
creation of the appropriate governance committees with, multidisciplinary,
multifunctional
representation,
which, Natalia and her colleagues have really spearheaded
over the past few years here at MSK.
A a part of that as well is
just facilitated
through through technology solutions that, enable us to
do this on an efficient basis. And so,
having the tools for these,
these these individuals
within these committees to effectively communicate, have the
right information at their fingertips so that when,
there need to be conversations or decisions around,
the the these AI solutions that that's handled.
And I think, like, at MSK, I'm not
directly embedded in these functions, but we also
have very robust functions around
regulatory compliance. And I think a big part
of it is MSK
overall, you know, prides ourselves in, you know,
staying abreast of all the latest developments, not,
in in all the regulatory and legal spaces
that impact the work that we do. And
so,
our our processes are, and our our stakeholders
in those functions are, ever evolving to to
meet
the the the the latest requirements,
and make sure that we we stay not
only up to speed, but also think ahead
in terms of where we need to be,
not just this year, but also next year.
Yeah. And if I if if that's okay,
I can, jump in as well as as
definitely, as Rick is mentioning, the the
the other aspect is also kinda
being
involved with and in communication with other organizations
like us
that are also working and implementing their governance
processes,
and part of CHI. For example, MSK is
part of CHI,
and that is a way that we try
to,
keep,
keep present in kinda the latest developments
and,
as well from the
government or legal aspects,
any
new technology developments.
I do believe that it's kinda important that
all these organizations that we are,
sharing with each other and kinda progressing together
in this way.
Yeah. Yeah. As you as you share what
you've learned through this experience, Natalia, like, what
lessons do you do you think from MSK's
approach to AI governance do you think can
be applied to other
hospitals or or health care organizations?
Yeah. Definitely. I love this question because
one thing that,
is often
also
not talked about is actually how
operationally
heavy
is
to
manage the govern the the governance process.
Because as I mentioned at the beginning, the
AI, kind of the the AI
implementations and systems and development is,
growing exponentially.
But
if we want to have a touch base
with every model, every system that is medium
to high risk, there's a lot of operational
tasks that need to be done, you know,
kinda check with the model owner, have them
register their systems
and verify they are pilots and perform risk
assessments. And so it's actually operationally
very, very heavy.
And,
so partnering
with somebody who can help with that,
those sort of operations
can be very, very helpful.
So for organizations that are beginning to look
into this AI governance systems, I will definitely
recommend two things. One is take a look
at what has been done already.
Many many of us has have published
some of our systems. We are also always
happy to talk about that, kinda implement some
of the processes,
and guidance that other organizations have done already
and also look at platforms that can help
with that operationalization
of the governance.
That is typically
bypassed
at the beginning and,
that can really delay
any sort of meaningful
scaling that you can do with your governance
process.
Well, Rick and Natalia, thank you so much
for joining the podcast and just for a
great discussion
on MSK's, you know, innovative use of of
AI. I look forward to working with you
both again soon.
Thank you so much for having us.
Yeah. Thank you so much, Scott. It was
great to be here.