This is Laura D with the Becker Health
care podcast.
I'm thrilled today to be joined by Doctor.
Y T mitsubishi, Chief Medical Informatics officer an
interim medical director for Ovation and Director of
clinical research informatics at the Metro health system.
Doctor. T she she's a pleasure to have
you on the podcast today.
Thank you for having me. I'm really looking
forward to our discussion and diving a little
bit deeper to what you're doing at both
ovation as well as the Metro health system,
but before we dive in, I was wondering
if you could tell us a little bit
more about yourself in your background?
Sure. So I am a pulmonary and critical
care physician and position in
Who's been doing a lot more of the
latter in my career.
I've been at Mental Health now for almost
9 years.
And
increasingly
doing
things in the data science and clinical decision
support space as well as more recently in
the virtual care space
through this ovation venture, which is a metro
health and
Collaboration. So multi health care system
virtual care platform, which is built frankly on
interoperability
and system integration as a differentiating factor.
That's really cool to hear. And and definitely
exciting to see how the technology can really
inform and influence the things you're doing at
the health system level as well. And and
given your perspectives in both fronts, what are
some of the biggest issues that you're following
in healthcare today?
Sure. So I think
artificial intelligence is
obviously on everybody's in mind.
When I think about
healthcare systems through the
clinical informatics lens, a lot of it
least from our perspective right, sits on this
cushion of data
and how we can turn data,
data input into meaningful outputs to help drive
patient care.
And so as you think about kind of
the evolution of where we've been and what
we've been able to achieve in the health
informatics space
around
really getting everybody on an electronic health records,
standardizing
data to the degree that we've been able
to do so and increasingly more looking at
interoperability
I think that's created an incredible
really charged atmosphere or
bigger and better things that we can do
with this data
including obviously implementing Ai intervention.
And so
I think that that
really becomes the key driver
of where we go next? We can talk
a lot about some of those aspects. So
I think about things like
how does data standards and interoperability
drive
evolution in the Ai
clinical and support space,
and that comes down to things like being
able to build and scale and fed
learning and models
in a way that really is meaningful
and externally valid.
And also how do we bring those insights
and models
and knowledge back into our healthcare system. And
so talking about things like
Ai assurance labs and how do we scale
those? Like how do we know that models
approved or sanctioned by other organizations or entities?
Really come back into our system and make
a difference. So a lot of what I
focused this on is
thinking about Ai
implementation
at the local level
and us kind of reached this conclusion that
really Ai is a implementation, frankly is a
local
concept
and the value of a model
is only really
made
obvious when it's implemented and valid at the
local setting.
And so I like to think about how
all of these
tools in these ecosystems
evolve to
support that mission.
And I get we can get into some
of the details, but I think that's both
the opportunity and I think the excitement in
this space.
I think about the challenges it's
really the hype
and
making sure as an informatics as a physician
executive, you're not making the wrong
decisions and choices
when it comes to which solutions to implement,
right? So the obvious 1 being saying away
from point solutions and making good solid
decisions that enhance your digital ecosystem and infrastructure
and that can
allow you to build on top of that
and develop
the Ai driven analytics that are going to
propel your organization and healthcare forward.
So very high level generic
take on the Ai space.
But I'm happy to get into the details
if you're interested.
Yeah. Absolutely. And I I really appreciate your
analysis here because you know, I I know
some money leaders and organizations are trying to
figure out the best way to move forward
with Ai and technology and the different types
of investments that they're trying to make. Now
when you talk about artificial intelligence especially for
organizations that, you know, may have started to
work through some of that and and figure
out what
their their mission is and how Ai can
support their mission. How do you then grow
into more sophisticated space? What you need from
a team level as well as a technology
level that, will really make a difference and
propel you quickly into the future.
Yeah.
So
I'll say that a lot of
the infrastructure that you need to embrace this
evolving technology.
Now at the end of the day, it's
a tool. Obviously, it's a different tool
with
unique implications
and considerations from
several perspectives, right? So like I said, this
notion of truly validating that a model is
going to work for your patient's place in
time that it's going to be fair and
unbiased and even beyond that actually being able
to implement it in a way that actually
does not
exacerbate disparities, which is especially important for a
place like metro health where we're safety healthcare
care systems that we see patients that are
at risk
minorities and soc economic
deprivation being a common theme here. So
when you think about
what is the skills set required?
Right? To bring that technology in.
I think the foundation
is fairly similar to really everything we do
in the informatics space.
Those understanding systems,
understanding that any technology you bring in enters
a
person process
a complex person process associated with technical space,
right that you need to
appreciate and understand
and
the implications too of change,
innovation,
implementation science,
quality improvement. So all of these things that
we we've been doing, right, from the outset,
implementing all these technologies in a thoughtful and
meaningful way.
Do you have to take all of that
solid base that you developed
and add this new tooling to it
and the stretch that you have to make
when you're getting to that point.
Is you need somebody who understands enough about
these models
and the implication,
right to bring it into the space.
Because the converse
unfortunately is it's not true, right? So you
can have amazing Ai and amazing technology,
but shoving it into a healthcare system.
Is not always the best way, right? That's
just a clear path of failure.
And so
I think that the model that I
I think speaks to this
is this concept of a like quan
and that somebody who
understands enough about the technology
implications and potential, right, to bring it into
their environment
and then bring it into their environment into
this complex healthcare system
in the way that it actually
feeds and enables people to do the things
that they're setting out to do, which obviously
in this space primarily is around
providing excellent care.
Understand that the implications understands the risk and
biases that they have to address.
And so you obviously need some
that informatics type individual coming out from healthcare.
With enzyme understanding into the data science and
the artificial intelligence,
also obviously paired with somebody who can do
to data science.
I think we're slowly moving away from the
need to have
hardcore data scientists.
I'm also optimistic that
as the tools evolve
that these vendors that are producing these solutions,
package
their solutions with ideally open source tools that
have been
really kind of taken through their paces,
standardized
vetted,
allowing these organizations then to kind of flex
up with whatever resources they have to reach
into that space. And bring that solution in.
And so that way you can start to
think about how you're democrat
the Ai to the smaller healthcare organization. We
don't have the money and the resources and
the personnel
to individually develop their own models
and their own Ai expertise. Now those places
exist, obviously
academic centers
obviously, I could name some organizations,
we all know which ones they are. It's
the same organizations who are leading a lot
of The charge and this color is the
rest of the discussion that I I kind
of brought in earlier.
I think
this notion of Ai assurance,
right, that an organization can tell this model
looks good because we tested in our data
as someone who's independently implemented and validated Ai
solutions at the point of care.
Models are messy inherently biased and they don't
always work well for your patient population. And
this has been true throughout time. It doesn't
not be a sophisticated model to do that,
even models at pretty risk
like heart disease for instance. When we bring
them into a system like Metro health
and more diverse and our risk coa population,
we can see that the model actually tends
to under predict risk of cardiac, disease and
poor outcomes
which case, we actually could be doing our
patients on disservice,
a model like that is. So this notion
of rules and models being biased
just the world we live in and it's
not new.
And I think
when you take these models out from systems
that they've been developed, we bring them into
systems
like metro health or
F
or other similar systems.
There's always that fair and concern that you're
actually going to be doing and disadvantage. And
so some of the work that we do
for instance,
we've done
implementation of
potentially challenging, predictive models like we predicted the
risk that a patient when to show up
to a clinic.
And what that brings with it is obviously
more often than not more likely, you're actually
going to be targeting. Whatever intervention you build
on that, you're going to be targeting
patients that are at risk, right? So usually
minorities.
And actually did the implementation through a study
where we decided we're not going to over
book because we think that that actually worsen
disparity in this but we're actually going to
provide more resources for outreach to bring patients
that risk not showing up into the system.
And then doing so actually reduced d show
rate in our Black patients by 15 percent.
And so we kind of hold that up
as an example of how an
organization
can responsibly
validate implement
a model that could otherwise worsen disparities in
a way where not only do all new
tied with all boats, but
really you were able to generate a benefit
to your patient population
in a very equitable way. We're actually the
outside effect help build that were less fortunate.
So that's
kind of the work that I do that's
where I find myself
spending a lot of time thinking
about these problems and thinking about how to
bring these
solutions in. And I think the opportunity like
I said earlier
is the tooling needs a lot of work,
but I'm excited that
with better standards, data standardization and interoperability,
getting people on the same
kind of playing field where I can share
my model and my experience
and data
in a way where I can validate your
model
right? On my data for instance, Having that
interchange happen
in a way where I get it's
protected,
no particular information is passed,
I think that's incredibly powerful. So I see
this as a continuum of
everything that we've been doing in informatics
making that world just so much much more
potentially powerful and empowering
some of the organizations with western resources is
to join the Ai revolution that
otherwise, may just be relegated to, you know,
the the large academic centers.
That's fascinating to hear. Thank you so much
for going through that with us dig you
1 bit deeper, especially on Ai. Now what
are you most excited about for the future?
And what makes you nervous?
So
I'm excited about
the opportunity
that we have,
I think
maybe even sufficiently excited about
the dying down of the Ai hike.
So the more pragmatic
folks rate that weather the storms of new
technologies
and the hype cycles, right? We'll tell you
that we really just want to know what
works, right and what works for our patients
and what enable us to provide the care
that we'd like.
So
I think that the current energy
is around
trying to validate an Fda regulate and even
create
assurance
bodies to basically sanction
certain models as
usable
or safe.
I would say that really we should
I worry a
about the amount of money that we're spending
as the healthcare
organizations following that high cycle.
So I am looking forward to that dying
down, right? And for us to really to
see these the real solutions come out also
to
likely actually lead to more
or should I should say surface the more
obvious integrated solutions rather than the point solutions
that
are more hype than actual value.
So that's the concern right. We spend more
money there that concerns me. But the opportunity
again, is once the field starts to kind
of
or I should say once the weaker options
shake out, I think the path forward for
driving true value from this
Ai clinical
revolution, I think it becomes more obvious. And
that's the kind of work that many us
in this area are excited about.
Really
getting beyond the hype cycle goal and actually
starting to integrate
these
solutions into our process and the workflow
in a meaningful way.
The other parallel to that for me in
the virtual care space. So
mentioned that we're building this cross
organization
so
Metro health and Have partnered together
to build a
common virtual care platform, which is meant to
be
inter and integrated with healthcare care systems that
really want to
extend the care that they do.
And the differentiation for us there is
being able to provide the additional service in
a way where really from the patient perspective
it's a seamless which part of their journey.
That when you have reasons to come back
into and organization's brick and mortar services,
that happens
seamlessly. And so that also
common theme here being built on
great data standards
and
interoperability.
And so these are the kinds of things
that you can unlock
when we all start speaking that same language.
And that for me is very exciting. And
so to kind of bring those 2 examples
for circle,
we need to be continuing to spend more
money
on the right things
and the right things are
continuing to integrate our systems
to create an opportunity across the board for
a lot of these
initiatives and collaborations to really
to break through and really deliver on that
promise,
rather than spending money
on
shiny objects
The other thing in terms of kind of
spending money is new regulation, so that concerns
be coming around the corner. So
how is the Fda planning to regulate a
lot of this technology?
And what is that do to the innovation
space, right? So does that increase the barrier
of entry? Does it take away the ability
to democrat
the both
and
of of some of these Ai
initiatives.
It's fascinating in here. You know, and really,
cool to to think through
all the possibilities that are ahead. Before we
wrap up here, I'm wondering, could you talk
about what the most effective healthcare leaders will
need in order to be successful over the
next 2 to 3 years, especially plans as
Ai continues to evolve and in grow this
space and technology becomes an even bigger part
of health delivery and in systems
operations.
Yes. So
I'll recap that
to create change
in a healthcare environment
I think we have
already
a slew of amazing models to build off.
And so
modeling around this notion of learning health systems,
right, building
organizations and
that attempt to be agile and learn from
the data they generate to create this loop
of continuous quality improvement.
How do you
how do you change, right? And how do
you manage change And how do you implement
things that you know should be implemented and
how do you
monitor the effects from a quality improvement perspective.
I think these are the many
very
specific
areas that somebody who's in clinical informatics
is a clinician leader even needs to think
about right from an
organizational perspective.
If you want to add Ai to that,
I'll recap this notion of the Light Quan,
which is start understanding enough about the technology
to understand the implication, the limitations and the
opportunities
that allow to bring it back
into the system.
And so
what I guess I'm trying to say in
the nutshell is, we have these really great
tools, right in the informatics universe that
we leverage to do what we do
And so building on that, I think
where an informatics leader specifically
used to sit, right, 10 to 20 years
ago they were the physician champion of the
electronic health record,
right but that digital ecosystem now has expanded
And so now they need to be thinking
much more broadly
about
where we meet patients, how we meet patients
and that's exciting, right, because now we're getting
into the act I think the point of
contact
and the doors if you will have expanded
in terms of opportunity.
And so they need to be thinking about
digital care. They need to be thinking about
patients as consumers with choice.
And beyond that, they need to be thinking
less
tactically, like how do we survive the day
and more strategically,
why should we acquire and implement this platform
or technology?
How can we carry that forward? Great? How
can we how can we turn this into
a successful return our investment
for our organization? And so I really do
just feel like this
a more initially limited role of a clinical
and informatics leader
has just kind of blown up. And I
think you've seen
pieces of that right
specializations now and there's your Chief Digital Officer
and
your Chief Ai
sir and it's really just shown the need
for people to start exploring some of the
expanding tentacles of the health technology states.
I guess like conclusion really, there's
a lot that we already know about how
to create effective change and lead within healthcare
and from within healthcare, which is I think
where most of the gist are going to
happen.
It's just a matter of where you think
you need to sit in terms of driving
that change. So Ai is not for everybody.
It's going to be ubiquitous, but it's
definitely going to come to a point where
there's going some people don't understand enough about
a drive forward and the rest of us
are just basically going to have to
so that's just going to be a part
of life.
Doctor Terry. Thank you so much for joining
us on the podcast today. This has been
a fascinating discussion, and I look forward to
connecting with you again soon.
Yeah. Thank you for having. It's it's been
fun.