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This is Laura Dierda with the Becker's Healthcare
podcast. I'm thrilled today to be joined by
doctor Justin Koren, who is an analytics executive.
Justin, it's a pleasure to have you on
the podcast today.
Thank you so much for having me. Exciting
for this discussion.
Absolutely. Well, Justin, I am excited that we
have you here as well because I know
you've had a lot of experience at a
variety of health systems across the country and
certainly
in-depth knowledge on analytics
in the IT side. And so this will
be a great perspective to share with our
audience. I I know things are moving so
rapidly, especially in the artificial intelligence space. So
I think I without further ado, let's get
started here.
Before we begin, could you introduce yourself and
tell us a little bit more about your
background?
Absolutely.
So, I am, as you stated earlier, an
analytics executive
and, been a former chief analytics officer in
health care systems. I'm also a data scientist
and innovator with over twenty years of experience
in the health care industry.
In 2024,
I won the AI 100 award, which is
given to the top 100 executives in the
country that conduct AI ML
operations
within their companies.
And,
overall, I've held multiple adjunct professor positions in
medical schools. And I I would classify myself
as, you know, just having this ongoing passion
for solving complex problems
and integrating applied analytics into company's operations.
Well, that's great to hear. You you know,
and and certainly,
really a lot of experience I know you
you bring.
So I guess we'll dive right in. As
an analytics executive, what are the most successful
projects you've led at Health Systems that yielded
the best results?
Sure. Actually, one of my earlier projects is
still yielding,
results. And at this point, it's gone global.
So I created a machine learning
algorithm to help the risk risk stratify patients,
in the population health space and within value
based care. And what that algorithm did is
it found the patients that were at the
greatest need of care.
And when facing limited human resources in a
large health system and when they have a
lot of patients,
under a population health umbrella.
The question is, how do you get the
sickest patients to the doctors as fast as
possible?
And so, when I conducted that work earlier
on in my career,
those patients overall, that proportion of patients were
responsible for about $1,200,000,000
in spend.
And by using that algorithm and being able
to bring the sickest patients into care and
to the attention of care coordinators,
we were able to save $200,000,000,000
A lot of that money
was in the employee health care plan within
that health system, which meant there was a
huge return on investment.
And now,
multiple health systems across the world use
that algorithm or a customized version of it
today to help with their population health efforts
Some of my other work that I've done,
is,
several work in ai and ml space and
detecting undiagnosed disease And how can we find
the patients
that either are currently under care and undiagnosed
and get those patients to primary care or
to the appropriate specialist,
so that we can address
those variations in care or that potential undiagnosed
disease as fast as possible.
Other things that I've done more recently is
a lot of technology
consolidation,
building modern cloud infrastructures
and really enabling
what I call data and analytic governance,
within health care systems and other organizations.
And that really sets the foundation for being
able to do applied analytics or even moving
into the artificial intelligence and machine learning world,
being able to integrate those type of products
with success into a company's ongoing operations.
That's amazing to hear. You know, it it
really cool to understand that real world example
of not only using the AI algorithms to,
provide better care and more targeted care, but
then also
realize those types of savings. I mean, that's
significant, especially in a time right now where
so many hospitals and health systems are tightening
their belts and and seeing thinner margins than
before, being able to have the right kind
of expertise in,
AI models in place, it seems like is
a huge, huge benefit.
It's critical in in today's world. And and
as we know, health care costs are very
hard to control,
and seem to continue to go up year
over year. And so being able to leverage
a company's information to make better clinical decisions
or even better administrative decisions if we're not
just talking about clinical applications,
It's gonna be critical over the next five
years,
as health systems become more analytically mature and
start looking at different enabling technologies to achieve
efficiencies in their core business.
Absolutely. That's such a great point. Now I'm
curious looking into that future. Where do you
see ai and machine learning headed in health
care? What are some of those best opportunities
for hospitals? And what should they watch out
for?
So I think we're gonna see multiple categories
of AI and ML across the company. And
then let me define that a little bit
better. So category number one is the question
of where is AI and ML going, let's
say, within the clinical space, and to aid
clinical operations,
physicians, nurses, and other health care workers.
The biggest boom we're seeing right now is
the integration of AI and ML within electronic
health record systems.
Definitely, we're seeing Epic as one of the
leaders here where they've got over three dozen
types of AI products that they're bringing into
the Epic ecosystem.
Oracle has announced that they're doing some very
similar things on the integration of AI within
their electronic health record product.
And then also within that clinical domain, it's
not just EHRs that are benefiting from the
integration of AI and ML, but we're already
seeing medical devices
utilize it more frequently than they ever have
before. And what's interesting in the medical device
space is MRIs were utilizing machine learning technologies
for decades at this point. And so some
of the earliest machine learning models were actually
used in radiology and in the construction
of different types of radiological
equipment. But we're now seeing AI and ML
and medical devices
that are related to cardiology,
neurology. Of course, radiology
is still a pioneer in the space. And
then we're even seeing some examples in pathology.
And that's just a few to name many,
of the different innovations that are occurring across
the country.
Also,
so if we talk a little bit about
the clinical domain, then we have the administrative
domain. And that's a really an interesting one
because I think a lot of times in
health care, we first gravitate towards the clinical
side and how can we first help our
physicians and our nurses.
But on the administrative side, we're now seeing
a rise in AI that can have what
what some of the data scientists and practitioners
call a force multiplier effect. And what that
means is we're looking at being able to
have people be more efficient in the work
that they're doing day to day. And if
you can utilize an AI tool to do,
let's say, two or three times the amount
of work but not have to include any
additional hours in your day, then AI didn't
replace people. What AI is really doing is
taking the existing work staff and
multiplying the amount of work they can do
with the same amount of effort. And then
that's what we mean by a force multiplier.
Some of this we're seeing within tools like
Microsoft Copilot,
where, you know, in traditional day, you might
have a person that would have to sit
in a board meeting or in other critical
committee meetings within an organization,
and they would take notes or potentially transcribe
the meeting minutes. Now we have ambient technology
that can just listen and automatically
transcribe
those meetings for us. That type of technology
is also existing on the clinical side where
we can have ambient technology listening to a
physician patient encounter and then helping that doctor
transcribe that activity directly into
documentation within the electronic health record system.
Also, on the administrative side, I think we're
gonna see a lot of innovation in revenue
cycle management in the finance area, especially within
the automation space.
And then somewhere, let's call it maybe five
years down the road because there's only about
three dozen health systems nationwide that are really
doing this today. But we're gonna start seeing
a lot of in house innovation take off
where a health system or even another company
could even be outside the health industry Is
starting to build their own ai models their
own ai agents or modifying a large language
model that is specific and customized to health
care
And we definitely see we're gonna think we're
gonna see a huge Proliferation
in that type of work, you know anywhere
from five to ten years from now And
the reason that could take a long time
is it requires a company to have a
very strong data foundation
and an analytic foundation, a governance program in
place where you can have standards and policies,
your technology and your environments that house your
data needs to be consolidated
so that if you're training an AI model,
or an application that it can source that
data from the smallest number of locations possible
in order to speed the development,
of that product. And there's a lot of
health systems that are still doing a lot
of work on their foundation. But as they
catch up, to let's say the, you know,
the top 20, top 36 health systems that
might already be doing this today,
we're gonna start seeing that exponential effect again
of in house talented teams
being able to customize these products alongside doctors,
nurses, and health care workers.
That was fascinating to hear. You know? And
and, so truly, truly important to have that
right mix of, you know, outside expertise that
you're working with as well as building those
in house teams to meet the need needs
of the organization overall. And I'm curious as
we get closer and closer to that space
of of,
more AI, more of those models, more technology
that's supporting the in person teams,
What does that look like for the IT
leaders? How are their teams transforming? What type
of talent or or skill sets do they
need to bring in in order to prepare
for that future?
Sure. So data engineering is gonna be critical.
And, you know, sometimes that comes into,
a buy versus a build type strategy. Do
you partner with a third party that can
really help with your data management and and
data engineering environments? Think of, like, cloud based
environments and going from potentially fragmented data centers
or multiple,
technology platforms into just one just to enable
that work to be done.
Regarding
inside talent and I think this is another
reason why health care is gonna be a
little bit slower to adopt
a build strategy or even being able to
innovate at innovate at scale is because you
really need to have the right mix of
talent. And that means the combination
of engineers,
data scientists, analysts, and potentially even software developers
all being able to work together with a
common goal,
for that organization. And historically,
most health systems,
especially if they're not an academic health system,
because we do see this quite a bit
in academic health systems,
don't have that same mix of talent or
that mix of expertise in house today. The
other challenge is this field is growing exponentially,
where one of my key pieces of advice
to any analytic executive, but this can even,
be a piece of advice to CIOs and
CTOs,
is to master the art of self directed
learning
because the field is evolving so quick year
over year. And that could be based on
technologies that are advancing high performance computing.
This could be brand new programming languages or
innovations within popular programming languages such as Python
and even just the availability of large language
models. You know, several years ago, there was
only maybe a dozen. Today, there's over 350.
And being able to be that strong self
directed learner will allow you to stay ahead
of the curve and to really be able
to understand what's coming down in the pipeline.
But even more important is how do you
filter out some of that noise? Where if
you're being bombarded with, you know, 100 to
200 new ideas a day, how do you
figure out what the top 10 or top
20 most important are that you should focus
your time on and then really start thinking
of what does that mean for my operations
or even for my company?
Got it. That makes a lot of sense.
And, you know, it is really helpful specifics
in terms of looking at how different organizations
are thinking about this and going about this.
Obviously, short and long term,
you know, looks different for everybody, but to
have that kind of skill set and understanding
of what's possible and how quickly things are
changing in the technology side is really, really
helpful. Before we wrap up here, I'm curious,
what advice do you have for emerging health
care leaders, especially in the analytics field?
Yep. Specific to analytics executives, one of the
top pieces of advice that I could provide
is partner very closely
with your chief information officers, your chief technology
officers,
and even your your vice presence of IT.
Analytic operations
cannot reach its full potential if analytic leaders
operate in a silo.
It requires
collaboration
across the company, but even stronger collaboration
between
your IT department operations and your analytic operations.
And that's specific no matter where analytics sits.
In some comp in many companies, analytics sit
in the IT department, and and you might
have a natural hierarchical relationship
to those leaders, your CIO, your CTO, or
other VPs.
But there are other companies where analytics sits
outside of IT.
And
it is critical that even in those business
models
that they partner very closely, they should have
a seat at the table both in the
governance
program of of how you're governing data and
using analytic products. But even in the innovation
space and the technology space of what technology
do we need in the company to enable,
the analytics strategy, and what does that look
like if we do wanna create something new
in house within the company?
Those members should have a seat at the
table during all of those discussions and be
very close partners.
That's amazing to hear. Justin, thank you so
much for joining us on the podcast today.
This has been a really amazing discussion, and
I look forward to connecting with you again
soon.
Thank you very much for having me. It
was a pleasure.