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This is Laura Dierdo with the Becker's Healthcare
podcast. I'm thrilled today to be joined by
doctor Yasser Terabishi,
chief medical informatics officer at Ovation and chief
health AI officer at MetroHealth. Doctor Terabishi, it's
a pleasure to have you on the podcast
today. Thank you. It's a pleasure to be
here.
Absolutely. Now I'm excited to have you here
because I know there's so many innovative and
cool things that you're doing, especially on the
digital health side at, MetroHealth system. But before
we dive into that, can you tell us
a little bit more about yourself and the
organization? What makes it unique?
Sure. So, I'll start with my role at
MetroHealth. So I'm chief health AI officer at
MetroHealth.
And in in that role, it's really to
guide the responsible
implementation
and strategic planning around AI initiatives within within
the organization.
And for reference, MetroHealth is Cleveland's safety net
health care system that serves a diverse patient
population
in Cleveland and and beyond.
The other hat that I wear is I
am CMIO
of Ovation. And Ovation is actually a virtual
care start up that's spun out of MetroHealth
and our partners
in South Carolina, namely the Medical University of
South Carolina.
The goal behind this platform is to create
a single solution that integrates into several different
partner settings where patients can get integrated
primary and urgent care. And so what that
means specifically is we can take care of
connecting patients
to on the ground brick and mortar solutions
at the partner organization and their home organization
in a way that other point solutions that
exist today do not.
That's really fantastic to hear. You know, cool
to hear about that kind of collaboration
between,
MetroHealth and,
and USC. It certainly seems like a great
opportunity
to, work on a a digital technology that
is really meaningful and beneficial for patients and
can make a big difference for the health
system as well in getting for the right
people at the right places more quickly. How
did that partnership come about?
Actually, it's been, several years in the making,
mostly a collegial,
friendship
and collaboration
that started off at the highest levels of
each organization,
namely the executives at the time. And it
was a it was a bit of a
natural, I think, collaboration.
So MetroHealth
is known for being very innovative in the
digital and informatics space. And I would say
we're scrappy innovators. We we generally punch above
our weights for for the organization that that
we're
at, the, you know, the size of our
organization
and and the fact that we're a safety
net system that's typically more resource resource strapped
than the average,
you know, health care system.
And MUSC really
similar patient population that they serve.
They also are
relatively well known in the virtual care space.
They have a very strong,
asynchronous
care program, actually, which is nationally recognized. And
so the leadership on that side felt that,
the leadership on this side
could work together to achieve
a share a common goal, really, a shared
platform here between both organizations.
And I think the the really cool thing
about that opportunity
is, you know, if you bring two organizations
together that are trying to solve virtual care,
integrated virtual care within their systems,
When we start comparing the problems we're trying
to solve,
we start to align on shared problems and
start to actually co solution.
And what that does is create a platform
or an environment where we start solving problems
and providing solutions,
that are much more scalable.
Right? So we're pouring in resources together, co
solutioning, collaborating,
and we're creating a platform and a system
that we can take to other partners. And
so that's that's kind of our opportunity there
to scale this out to a to a
higher, you know, to a higher level across
different states and different
organizations.
Our solution is built
on the Epic platform and takes advantage of
all of the things that the Epic platform
provides. And we've built on top of that
with Epic and in addition,
internally with our developers
to kind of complete that experience so that
it really is as integrated and seamless as
can be even though it it's truly a
separate
entity.
That's fascinating to hear. Thank you so much
for explaining that to us. Now I'm curious.
What is the accomplishment that you're most proud
of from the last year?
Right. So I'll say, you know, I'll I'll
use my two hats here to to go
through these.
When I think about my c m I
CMIO hat innovation, so we have successfully launched
a virtual care service for for patients that
are in Ohio that are attributed to the
MetroHealth system, both established and new.
We've we've had, at this point, well over
10 providers that we've hired on. We've seen
thousands of patients, and we've had just fantastic
results, raving reviews,
a lot of emphasis on the ease of
access,
lot of a lot of good positive feedback
on the integration.
So, you know, I'll I'll give you an
example.
You have a fever.
You call into our services.
We figure out that you really ought to
get swabbed for COVID,
and flu, and we can go from a
virtual visit
to a swab in a brick and mortar
facility that's a MetroHealth facility,
to the prescription
and even delivery, home delivery,
for instance, Paxlovid,
if if you had COVID,
within several hours,
which is just amazing compared to the usual
experience of going into an express care, an
emergency room and waiting for much longer.
And so it's been really rewarding to see
that integration
play out.
The other, you know, so I'd say that's
a really big win we've had over the
last year that's
developed and evolved. And actually, we're hoping to
go live in South Carolina
shortly
actually to to match the level of service
that we've achieved in Ohio.
And so that's been a phenomenal
opportunity,
lots of growth potential,
for Ovation and also MetroHealth and MUSC in
that collaboration. So that's really coming to fruition.
We also have several other partners that are
lined up thinking about, you know, launching in
other states and other systems. So I think
that the value of what we're developing
has been well received.
As far as the AI work,
that I do at MetroHealth, so,
you know, I'm a physician informaticist first. I'm
a pulmonary critical care doc at MetroHealth.
And
my growth into the AI space really came
from several implementations
of AI solutions that maybe didn't always have
something to do with my specialty,
but got a lot of attention for being
really pragmatic, responsible, ways to
validate, vet, and implement a solution.
And the takeaway from that has always been,
you know, there's a large responsibility
in implementing these solutions, especially in in very
diverse, different environments where these models may not
have been validated or developed.
We always want to make sure we are
improving,
care for all of our patients. We wanna
make sure that we are not
worsening disparities that we know exist in all
aspects of care.
And, you know, beyond that,
thinking strategically about how do we bring these
solutions in, you know, how do we make
sure that they're actually having an impact? And
I'll say that a lot of our learning
is consistent with with others and that the
vast majority of the value
of any AI or tech implementation for that
matter
is more to do with the people in
the process and the why and less to
do with the actual solution. So we've been
building predictive models and analytics for really decades
at this point in medicine.
And our opportunity has really been harnessing them
in a way that makes sense
at the point of care
and measuring and understanding that impact, both in
terms of positive impact and also unanticipated
consequences.
And there are several examples that actually that
we have published.
You can find in the literature today if
you looked.
So, you know, we've taken that framework and
we've built governance on top of that. So
we have healthy,
multidisciplinary
AI governance in our in the form of
our AI advisory committee.
And I'll say one thing that I thought
we've done relatively well is not jump onto
the wagon too quickly. And so we did
not want to be at the extreme cutting
edge of these solutions.
You know, we don't have that much flex
money to spend on
experimental concepts.
And so we waited just long enough to
know that, for instance, ambient AI needs to
happen, and we already know who the top
players in the space are. And so we've
made a selection now for for our first
partner in the ambient AI space without having
to do six or seven pilots like other
sites were were doing.
And, you know, from the the the rest
of the generative AI perspective,
we've just partnered with,
a relatively mature but still evolving
vendor
who will be
conducting general advice summarization
within the electronic health records. So it's not
a native solution,
but we found an opportunity where we could
partner and collaborate and build
for our own, you know, our own purposes
in a relatively,
I would say, affordable way that both feeds
what we hope to do with the core
product as it exists and also gives us
opportunity
to develop what we think we need as,
you know, as MetroHealth individually
or independently.
That's great to hear. And really exciting on
both ends, especially as you mentioned, just building
out that AI infrastructure and how you're able
to,
understand the advisory council, looking at the ways
that, you know, it will be most meaningful
to bring AI and generative AI into,
the broader organization, I think, is something that
so many hospitals and health systems are still
trying to work through today and, you know,
much a position like yourself and looking at,
you know, what partnerships makes the most sense,
what products then you know, what applications are
gonna be most meaningful for them. Given this
background you you've, shared with us in terms
of the digital transformation,
where do you see some of the big
growth opportunities over the next twelve months or
so?
I I think it's leading into exactly those
spaces. So if we focus on the generative
AI applications,
I'll say that there's, you know, there's a
lot that we know that this technology can
do. It's probably strongest in terms of information
retrieval.
The opportunities beyond that, I think, you know,
there's a lot of questions that come my
way as chief health officer, like why can't
we just put the entire electronic health record
into, you know, a large language model? Like
why can't you just copy and paste once
all trying to chat
in a protected setting, obviously? And those of
us that live in the space know it's
a lot more complicated. So what information we
submit to a large language model, the context,
the prompting.
Right. That's that's the hard part. That's the
part that really requires a lot of diligence
and oversight.
And so,
you know, we've gotten to a point now
where we, I would say, as a community,
have started to understand where this technology does
well, where it does not. I think the
obvious parts are, like I said,
information retrieval. So what happened to this patient?
You know, in this last hospital encounter, for
instance, or write me a discharge summary. I
think those are all fairly achievable things.
What we'd like to see more of and
I think the literature starting sport,
it's more of that kind of physician's copilot
where if I can identify,
I'll use the pulmonary and critical care example
here, but I can identify when somebody looks
like they could have an infection so that
I can determine that they may or may
not be septic.
That's information that can come from rich notes,
that can come from imaging, for instance, that
isn't immediately available in the electronic health record
today. So I think that there's opportunities to
develop phenotypes from the data
that can serve both our operational and clinical
need and then beyond that, actually, our quality
focus. And so being able to capture or,
you know, identify
potentially
reversible or preventable
conditions early in the state.
I think that's gonna be really crucial. And
another example, for instance, is someone might have
a Foley catheter or a line that does
not need to be there. It's a lot
more difficult to identify that
in real time when you're not trying to
analyze
context through documentation or a note.
So I'm excited about that prospect, and that's
the kind of development that we're going to
be working on. In addition to those things,
we always have to focus on
what can potentially improve our bottom line. So
if we're paying for this expensive technology, then
naturally after that, start asking, can we can
we use the technology for appropriate billing? Right.
So we don't miss diagnosis
to make sure that we're submitting the right
information for for CDI, for instance.
We're also using the technology for, prior authorizations,
for example. So that does help us
solve a unique health care problem faster.
Although I'm usually
really nervous about using technology
to solve broken processes.
Obviously, as a single system, we're not going
to be able to solve a lot of
these problems. But if they can help us
reduce the effort
and get the patients the outcomes that we
need, I think that's that's fair game at
this point.
Absolutely. That sounds great.
And really truly will be cool when you
have that system in place to use the
technology, use the AI,
and get the most out of,
the team members that you have in in
in really truly elevating,
throughout the space. So that's really cool to
hear about, and thank you for sharing with
us. I I know there's a lot of
opportunities, especially with technology and AI, but I
can imagine some big challenges as well. What
are you anticipating for the next year or
so? Anything that could be potential roadblocks for
you?
Yeah. I'll I'll do the the two hat
thing now. So,
when I when I think about my role
in in virtual care and CMI of Invesha,
I think
I'll just say that the the telehealth extension,
while reassuring
and, you know,
definitely a positive move,
only gives us so much runway to build
our our plat to continue to build our
platform and solution
in a comfortable way.
So I think most of us have now
reached this conclusion that virtual care is here
to stay. It's a necessary component. It's it
obviously
works best at the right patient time and
place. It's not for all care settings.
It's really can you right size it?
Patients expect
it. Clinicians enjoy it. At least, you know,
based on the surveys we've done internally,
it does provide more access. It likely is
similar or better quality in terms of keeping
patients engaged to their health care
journey. And so I would love to see
more of a realization of that,
again, at the kind of the policy level.
Right. To continue to fund that mechanism. Because,
again, it's I think it's it's here to
stay. It's not going to go back to
COVID levels
anytime soon. But there is a time and
a place for it. And I think we
need to acknowledge that
when I put on my C H I
O hat.
The biggest concern that I have
continues to be spending money in the wrong
places and on the wrong solutions.
I still think we're figuring these things out,
so we are still treading lightly. We are
trying to, at least when we commit to
partners in the space,
come in in a way where we have
leeway. You know, our EHR vendors developing solutions
and opportunities,
when it comes to generative AI specifically
that start to conflict.
I'm really concerned about
AI scope creep. So when we pick a
solution for a particular pain point or if
it's even part of a larger platform,
it starts to grow in terms of scope.
So, you know, first, it's summarizing notes. Next,
it's listening to your conversation. Well, hold on.
I already have an AMP and AI solution.
These things are starting to bump into each
other. So I am worried about the overall
spend.
From the clinician perspective,
I think there's a lot of assumption
about the fact that all of this is
going to make clinicians' lives easier. But it's
still out. You know, the question is still
out there as to whether or not this
really decreases
a provider's cognitive burden or if it just
kind of changes the level of work that
they have to do. Right? So
writing a note versus
reviewing a note that's been written by a
generative AI solution,
looking for hallucinations,
being always worried about something that crept in
there that's not supposed to be in there
or even worse, potentially something that was that
was missed. Right? So none of these solutions
are perfect. And so we have to be
really careful about
how much more work we're throwing on our
care team and making assumptions
about whether or not we're truly, you know,
truly enhancing
their workload.
So, yeah, I think that those are those
are the things that I don't know that
they keep me up at night, but they're
definitely concerns that I have as we step
more, you know, into the space.
Absolutely. I think, definitely, it makes a lot
of sense and, you know, especially in bringing
in AI,
you know,
not really a blueprint of how that should
all work. And so I think,
it seems like you've got a a great
approach and strategy to just keep an eye
on that and understanding the the risks as
well. Before we wrap up here with our
last minute, what is the number one thing
you're doing right now to set your organization
up for long term
success? I think we've we've echoed,
a lot of these points here, but it's
it's it's worth rehashing.
So making sure we have a solid
AI governance
strategy and plan, making sure we have the
right people, a diverse set of experiences,
at the table,
and making sure that we upscale our clinicians
and what these technologies can do and what
they can't do. And also empowering them. Right.
To speak up when it's not working the
way that it's supposed to be working.
So continuing down that road,
being really careful about
the the life cycle of all of these
solutions, meaning
being ready from a financial
responsibility perspective
and from even just an overall governance perspective
to say this tool or this solution
is not working. We need to decommission it.
I think there's this natural assumption of more
technology is always good. So we have to
keep balancing that out. Right. To make sure
that we're actually literally getting what we pay
for and making sure that we're not making
anything harder than it has to be.
So those are the things that I focused
on, at least from the AI perspective.
From the
virtual care
perspective,
I think we're just keeping our heads down
and
trying to build a solid solution
or I should say building a solid solution
and a product
that really scales well. And the most important
thing I think that that I've learned in
that space is you can build the most
exquisite piece of technology and platform that is,
to a high degree,
seamlessly integrated into others, EHRs or solutions.
But really, what's driving
our
energy
and driving our positive results, it's the people
that we've hired.
And so we've
continued to, you know, make sure that people
understand people that we bring out, understand our
culture, that they're good fits for our culture.
And I think a lot of the positive
responses we've gotten from from patients of our
services really come from the stellar care that
they've that they've had independent of how easy
and, you know, seamless the integration of the
virtual care experience truly was.
That's amazing to hear. Doctor Terabishi, thank you
so much for joining us on the podcast
today. This has been such a fun and
informative conversation, and I look forward to connecting
with you again soon. Yeah. Thank you for
having me.