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This is Laura Dierda with the Becker's Healthcare
podcast. I'm thrilled today to be joined by
doctor Ashish Atresha,
who's a professor of medicine and gastroenterology
at UC Davis Health, as well as a
venture partner with Global Ventures X. Doctor Atresha,
it's a pleasure to have you on the
podcast today.
Thank you so much, Laura, for having me.
Now I I'm excited for our conversation because
I know you've had quite a a great
career and a lot of different experiences. So
I'm wondering, first and foremost, can you introduce
yourself and tell us a little bit about
your background?
Yeah. Happy to. In fact, I, often say
that this is my fifth career life. I'm
getting into very lucky to have that.
And my career journey started as a physician
and a public health scholar. I did master's
in public health and then residency in Cleveland
Clinic. And,
because of my experiences both on the clinical
side,
as well as on the research side, we
were the first ones in Cleveland Clinic to
implement
Epic after Pfizer,
and I could do my research study right
there from the data that came out of
it. And, so that prompted me actually to
take the second part of my career, which
was informatics.
I did informatics fellowship and helped implement the
inpatient,
epic, at Cleveland Clinic and also associate program
director for informatics. So so my journey went
from
seeing clinicians to being informatics to scale what
we do and bring efficiency into that manner.
And I quickly realized that, in my GI
world as I was doing my fellowship,
informatics was mostly at that stage covering
electronic medical records, the physician part, the clinician
part.
But 99.9%
of the time which patients spend is in
their home or workplace,
and we were not touching them. So all
this chronic disease, all these health behaviors,
everything that could have really made an impact
on patient outcomes,
we were not there as physicians guiding the
patients.
I remember,
when I was managing patients with IBD, inflammatory
bowel disease, they came from West Virginia,
And this patient came with 20 bowel movements,
for twenty
years. And she was not managed properly, but
she didn't have access to resources. And we
didn't have a monitoring tool to know if
we make a difference,
did she get better or not.
So I managed her. But when she went
back, I realized six months later,
she had a surgery, and there was a
gap I felt in monitoring the patients at
their home.
So that started
a big innovation drive in me, and I
was lucky, meaning,
to be,
getting a word for innovation for virtual paging.
So I knew that we, as clinicians, when
we face a problem,
we can, if we have the right tools,
informatics and then digital health, we can be
cocreators
in problem solving. So a big part of
my journey was going beyond informatics
to really take on digital health. And I
got recruited at Mount Sinai to be chief
innovation officer
in medicine
where we established the App Lab, and that's
where I've been called as an app doctor.
So so we build apps across all specialty,
basically solving problem of patient
engagement and patient outcomes,
the, what I call as the 99.9%
of the problem.
This had been amazing journey. We built the
first platform to prescribe apps and AI bots,
right from electronic medical record. We were able
to pattern that, and then Mount Sinai was
able to commercialize that. It ended up touching
thirty to thirty million lives through health systems,
and then UnitedCare was using for 60,000,000 patients.
So
that was amazing. As a doctor,
one to one, if I would have provided
care, I would have touched 5,000, 10 thousand
patients in my life.
But here through technology,
we were able to touch, you know, 30
to 90,000,000 patients.
So UC Davis Health then hired me
as a CIO and chief digital health officer,
the inaugural chief digital health officer for UC
System,
basically to see how we bring innovation at
scale really lead to transformation.
And it has been amazing five four years
there,
where we made UC Davis the first health
system in California to be digital health most
wired level 10, partnered with AWS from cloud
innovation center, a lot of cutting edge technology,
including AI. But we started finding
that generative AI coming two and a half
years ago is just a completely different set
of technology that we need to master,
and no health system has
the skills, talent, or resources to do it
alone. So we formed a community called Valid
AI, the South fifty eight organizations.
And at the next part of my journey,
I'm going beyond one organization to support multiple
organization
kind of, and as a venture partner to
to kind of say, how do we really
create a solve an ecosystem problem of health
system, health plan, and pharma
using generative AI as,
as a tool and a platform,
that can help us all if we do
it the right way and bring convergence.
That's amazing to hear. You know? And and
really cool to hear, especially what you've been
able to do, whether it's at Cleveland Clinic
or,
UC Davis Health and and,
bringing generative AI. I I know a lot
of hospitals and health systems right now are
trying to troubleshoot that and figure out the
best way that they can, incorporate the technology,
given some of the challenges as well,
in in a safe and secure manner. I'm
curious from your perspective, what are the opportunities
and headwinds that you have your eye on
right now, especially knowing how many health systems
are trying to figure out how to use
AI appropriately?
Yeah. Happy to answer. And we started our
AI journey four, five years ago in a
much more meaningful manner at UC Davis Health.
And one of the things we are finding
is, Laura, is this problem of plenty.
But there's so many
AI providers, like AI algorithm providers.
There's so many cloud providers. There's so many
products.
Every or many of them have AI component,
and there's so many solutions you can build
on your own as well. So
how do we choose what is the right
one? And there is a fatigue happening
because our resources are getting more and more
limited, especially with the federal indirect cuts that's
happening. And the the margins are so poor
or negative for many of us.
And
there is so many so much solutions out
there, many find and click solution for each
different disease. There's a different
AI algorithm and multiple
that we don't even know how to start
and really execute in a meaningful manner. So
I see this problem of plenty
and both a challenge and an opportunity.
One of the things we are augmenting that
and not solving it in the right manner
is that we are doing our entire governance
to AI adoption roadmap, to strategy,
to implementation science,
to impact measurement and monitoring
separately. Each health system is doing it separately.
And that means
we are duplicating each other work.
And in time of precious resources, that's exactly
opposite of what we should be doing. So
there's a there's a big problem to solve,
and we are trying to solve alone in
each organization,
why we should be solving this together
as a as a scientific
and,
implementation
community. Yeah. And that's exactly what led to
ValleyDI being formed.
And now we are trying to take it
to the next level on execution at scale.
Yeah.
That's fascinating to hear. You know? As you're
getting to that next level, as you're finding,
ways that you you can scale the projects
that work and and then really have that
strong
bend towards execution. How do you make sure
your culture is ready for that? The people
within the organizations are,
supported and educated in the right way so
that this transition is as seamless as possible.
I know it's never easy, but,
how how can you set yourself up for
the best type of implementation or transition, for
new digital technologies?
Yeah. This is such a great question. And
and kind of the route started when I
was at Sana and really strengthened at UC
Davis.
We started a champions group, innovation champions group,
and I was at Mount Sinai. And we
did the formal,
leadership program called digital Davis leadership program.
So I went to the chairs,
through the dean's office,
and asked them, hey. Identify the young faculty
member who's interested in this space. We got
20 faculty members across 20 departments from cancer
to heart failure. And and we trained them
over a period of a year, every two
weeks, very hands on, experientially based, project based
learning, gave them the tools.
And that had been amazing
to see the impact. Now they're five, ten
grants out of it, 10 abstracts,
you know, around 12 papers.
And many of them those people have become
thought leaders within their own specialty
in that. So I do believe,
there's a very big need.
In fact, we need to prime the pump
before we bring technology
because there's a lot of misunderstanding
around that as well. So I think part
of it is we need to do a
double work of
some basic education for everyone.
And and then we also need to have
targeted
deeper education for people who want to go
much more advanced in that regard.
So that's a enormous,
kind of
a need that we we feel.
And we started doing executive education at Valley
Day, which had been super successful. We meet
biweekly and share and learn between each other
the best practices.
But I believe we need,
a different layer completely to take an enterprise
wide approach.
And we are kind of in early stage
of thinking how do we bring this
Gen AI and AI Academy,
to really train people at masses,
but not in a kind of boring compliance
based manner than what we see on the
the portals or learning management system,
but in a transformative manner, expediential manner, empowerment
manner. And that may mean a combination of
in person
launch days,
vision days to,
to project based learning,
with some synthetic data or something to play
with. So we call it everyone gets benefit,
and some people can really take it much
more deeper if they have to. So it's
precision based as well.
Got it. That's helpful to know. Thank you
for walking us through that. Now looking ahead
and into the future, how do you see,
thinking about growth and developing and and continuing
to add value,
to hospitals and health systems as well as
the physicians who are, you know, really interested
in learning more and digging deeper into the
AI technology driven application space?
Yeah. I think one thing is clear. We
have a affordability crisis here.
What is being offered even if we want
it, then we just can't.
So there's a very famous quote that the
AI technologies are becoming cheaper, but the people
to implement that are becoming more and more
expensive or not available.
So we did some kind of internal
assessment,
from the members, and what we found was
to really bring in a very trustworthy, responsible,
secured manner and to build a team that
understands that how to bring it and monitor.
That's an effort of 5 to $10,000,000
a year, at least to start.
Right? Because you for example, like UC Davis
has health has 800 applications, IT. Four hundred
of them will have some kind of automation
AI technology component
to for me to validate all of them,
assess their equity play,
see if they don't have biases,
and, they have repeatable replicable in their recommendations,
that's a huge amount of burden I'm adding
in addition to cybersecurity.
So so we are finding the the really,
the the elephant in the room is affordability
crisis for us to transform.
Now now the good thing is the other
part of that is
the use cases have to be brought, so
the impact has to be measured as well.
So a lot of our effort, and that's
why we are kind of shaping what we're
calling the transformation collective to the next stage
of valid AI,
where multiple organizations with valid
AI who are ready on a maturity level
and have the right leadership,
in place
to take the next stage of transformation
where
we can find the best use cases,
but we are also able to, through share
team,
decrease the cost. And and really kind of
no cost comes we actually look at the
impact first, problem first approach
rather than what's happening in the market is
a problem of plenty and solution based approach.
Oh, we have so many solutions. Which one
I want as a point and click solution?
Worse if these are the five strategic problems
we have in our health system, Let's work
backwards to say, fine.
Maybe a singular platform can solve many of
those issues and create economies of scale, economies
of affordability,
and economies for impact. So I think it's
a I see that as a very big
opportunity
to view the lens of problem based learning
here.
Got it. That's super helpful to know. And
and, you know, really cool to have that
opportunity now to move on to the next
level and and, really integrate into the health
system.
As you're looking at growth development, and I
know technology is moving quickly in the health
care space, what are still some of those
risks and challenges that you see,
and and what investment will it take in
order to, really realize
the overall benefit,
for some of these technologies as well?
Yeah. I think the the biggest risk we
see is the the risk of bringing technology,
which is not along with strategy.
Spending money into it and the money drives
away, so you can't really make an impact.
And, outdoing it in a manner, that actually
opens up cybersecurity risk or other kind of
risk and make your, things. So I think
those are the risks we see are very
tangible.
The main gap we see,
is the talent gap,
to really allow us from and we published
a paper, you know, five or six of
us, from different academic centers.
UC San Diego, UC Davis, we combined, and
we talked about how we need to bring
AI implementation science centers.
The variability most of people talk about model
variability,
but we truly believe the variability is more
in the real world even,
because that's when we reduce the models or
AI models to practice.
Who touches them, how they touch,
and what does the impact happens, and that
does lead to a positive outcome or not.
There's so much variability in that workflow
that 80%, sometimes, we call it as variability
at the implementation science level and not at
the model level. While everyone talks about model
validation, we believe
to really derisk
the AI adoption, we need a strategic lens.
We need to address the talent gap through
a shared service,
and we need And that by addressing that
both strategic lens and the right talent can
help
create AI implementation signs of excellence.
And then we can have a network of
those AI implementation signs of excellence,
which
support each other and complement each other and
complete each other,
rather than duplicate each other work again, and
that doesn't lead to the ecosystem evolving.
So I think, it's just a fascinating time,
where the opportunity is huge
to impact
clinicians,
like, through AI scribes,
through patients, like, through digital avatar and automation
and having a post call for them which
knows them automatically guides them through conversation layer
through operational efficiency.
So a lot of the things through agentic
workflows can be done automatically. We do not
have to repeat and manual stuff and really
liberate the data to intelligence layer, because unstructured
data, which is 80% of health system data,
can now be liberated to intelligence layer of
generating AI.
Absolutely. Wow. That's, really cool to hear and
definitely amazing to see how the different health
systems are working together, as you mentioned, just
to share information and share,
share how they're doing things. And so, you
know, you can build upon each other's work
and continue to advance in the future. Before
we wrap up here, I'm curious, where do
you see some of the best opportunities
for physicians especially,
in the future?
Yeah. I think,
one is I think we I had predicted
two, three years ago, we're gonna have AI
scribes like water. It should be ubiquitous, and
everyone should be able to use it, and
the prices will come down. And that's what's
happening because the need is so real from
our AI scribes to decrease the burden of
documentation for clinicians. And we're gonna see extension
of that intelligence layer doing more and more,
in terms of intelligence and decisions and putting
orders. So that's just an amazing,
amazing
opportunity.
And this is the first time we have
intelligence layer between a patient and a physician
that's on top of a EMR.
So the magic can happen here. We call
it headless EMR,
that that can potentially shape up. We all
dreamt of it, but this finally can become
a reality.
But I'm equally and and maybe more excited.
There are limited number of clinicians, and we
can empower them, and that's so humongous because
but they also have to serve
millions and billions of patients if you look
at worldwide lens.
How can we empower patients
to be a partner in their own journey?
And that's my lens from a public health
lens as well.
When I
I the words with my patients,
it's not just me telling them what to
do, but also empowering them so they can
learn knowledge.
And that way their behavior,
changes,
and that's
stuff they have to do, and and we
can empower them. But if we can create
digital avatars, and we started doing that in,
wellness space, to the venture studio, we're planning
to bring more of them, then they have
guidance with them 247365.
Right? We can even I give this example
from physician perspective, IVD perspective. If I can
actually take what I tell my patients, maybe
I can take best 10
physicians in inflammatory bowel disease, what they tell
their patients at certain time.
We can make our collective digital avatar, and
that can be with the patient 247, 3
60 5 answering plates in a trusted manner,
but with a layer of human in the
loop,
telling them which are
tested, repeatable, replicable,
but anything that's vague or that comes in
a different point of decision making bubbles up
to the human's level,
we can create a great way of
human led
AI transformation.
Right? And and that will
change complete care
from one to one
where we believe a clinician and a patient
has to be in the same space in
the same time
but truly one too many
where the same clinician can impact
millions of patients at once. And we had
seen that in COVID time
and earlier part with the rule based AI.
We were able to build a bot within
eight days and touch 1,000,000 patients through EMR
and 55,000
patients were being monitored.
In the age of generative AI,
we can have a hundred x, 2,000,000 x,
that kind of,
value stream if we do it the right
way.
So so super excited about that possibility,
of working together,
with with many of the partners here,
to to march towards that.
That's excellent to hear. Doctor, Otrincha, thank you
so much for joining us on the podcast
today. This has been a wonderful conversation, and
I look forward to connecting with you again
soon.
Great. Thank