This is Laura Dirdle with the Becker's Healthcare
podcast. I'm thrilled today to be joined by
doctor Chris Kelly, associate chief medical information officer
for data and analytics at MultiCare Health System.
Doctor Kelly, it's a pleasure to have you
on the podcast today.
Hey. Thanks, Laura. I appreciate being on.
Now I'm excited to speak with you a
little bit more about just all the transformation
happening in the health care space and particularly
some of the cool things you're doing with
artificial intelligence at MultiCare. But before we dive
into that discussion, can you tell me a
little bit more about yourself and your background?
Yeah. Sure. So I am currently the, ACM
IO for data analytics at MultiCare.
We're a 13 hospital health care system in,
Washington state.
I I got into this through informatics
about, 10, 12 years ago, when my hospital
went on EPIC. And I really didn't have
a lot of background in this, but I
found I really enjoyed the work, became a
physician builder. I went on getting
training in medical informatics. And,
ultimately, you know, what I I realized what
so what we really need to do is
is make use of the data that we
spend so much time entering into the EMR.
So I ended up getting a master's degree
in data science, and that led to this
job I have here. And I've been doing
a lot of the work around,
implementing artificial intelligence and evaluating,
the performance of artificial intelligence
at MultiCare.
That's really been a major focus on what
I've been doing over the past year or
so.
Oh, that's amazing to hear. You know, what
a great story in terms of how you've,
just really navigated getting into the informatics space.
I I really appreciate that. Now if you
tell me a little bit about what you're
most excited on, and, you know, what what's
top of mind for you right now?
Well, you know, we've been doing a lot
of stuff. You know, the the
there's a lot of angles on artificial intelligence.
I really think that where AI is gonna
be going in health care, it's gonna be
transformative. I think the next decade is going
to be
as if not more transformative than any decade
in the history of health care. I I
really see a lot of opportunity.
But one of the things that that I
really been, you know, that our organization has
been this has really been trying to do
is find ways to utilize AI to make
the experience better for providers.
So that's a lot of what we've been
doing so far.
We've implemented an ambient note generation platform.
We've implemented
EPYC's
automated response technology for in basket messages. We're
working on EPYC's note summarization
approach.
There's a number of different algorithms that we're
introducing to improve patient care as well, but
those are really the angles that we're looking
at to try to improve how providers,
are able to,
utilize the the EMR more efficiently
and how we can take better care of
our patients.
That makes a lot of sense. And, you
know, really, it is a great way to,
it seems like begin getting into AI,
and
having it actually produce those results that you're
looking for both for the care providers as
well as patients.
You know, when you look at those projects
that you're doing today,
is there anything that you've learned along the
way as you were,
implementing some of these things? What have you,
leaned into more so? Is there anything that,
you know, you've had to pivot away from?
Well, you know, that's a really good question
because I think that,
you know, 2 years ago, it was almost
2 years ago now when,
ChatGPT
came out, everybody was really caught by surprise,
I think. I shouldn't say everybody, but a
lot of folks were caught by surprise at
how incredibly powerful it seemed. And I think
a lot of people
really gravitated towards it and thought this could
be transformative
for
a human intensive,
industry
like health care where we really have to
do a lot of 1 on 1, can
we
improve the quality of our interactions, reduce the,
you know, delays and, you know, just improve
patient satisfaction overall with this? But I think
it's proven to be a little more elusive
than people realized initially.
I think we're past that, wow, isn't this
cool, isn't this magic phase of AI? And
we really have to be
thinking about how we critically evaluate these investments
and understand the ones that are really delivering,
the value
that we hope they're going to deliver. And,
you know, at MultiCare, we're really trying to
take a
multidimensional
approach to value, not just
financial return on investment. Although, obviously,
you know, these tools, you know, tend to
be fairly expensive,
but we also have to think about it
is, you know,
does it improve the quality of patient care?
Can we demonstrate
that it actually improves the quality of patient
care? Does it improve patient experience and does
it improve the physician experience? Does it reduce
the time that doctors actually spend in EPIC?
Does it reduce the time that people spend
outside of scheduled hours continuing to chart? And
I think we really have to be fairly
rigorous about our approach to this in order
to understand
what's really effective and what's not effective. I
think that's going to be our our main
hurdle going forward is trying to understand
where our time and resources are best spent.
That's such an excellent point, you know, and
really,
makes a lot of sense as you're, you
know, trying to be innovative, but then also
understand exactly what success looks like for some
of these types of projects,
you know, in the results that you're looking
for.
When looking ahead, how are you thinking about
growth and in particular with AI,
continuing to evolve and add value to to
multicare overall?
You know, it because that's really the
that's really the key that that I think
you hit on is how do we add
value overall?
And, you know, fortunately, I we have an
analytics governance team,
and and one of my responsibilities
is, you know, with
few other folks to evaluate
the incoming opportunities
for AI.
And, you know, how do we
understand
what they can offer? How do we understand
how we're going to measure,
whether they actually do add value? And, again,
what we need to think about value in
in a multidimensional
way. We're we're we're health care system and,
you know, part of what we do are
good.
You know, part of what we do is
we need to make sure the finances work,
but our ultimate mission is to take better
care of our patients. Well, how do we
measure that? You know, how do we understand
whether these tools which seem so incredible
are actually improving
the patient care that we deliver?
And and that's not always easy to do,
you know, given the limitations that we have.
It's, you know, it's funny. We've been having
some conversations around this recently, and,
you know, I think the conclusion that a
lot of us have reached is that the
the AI algorithm
is actually the easy part.
And I don't mean to say that it's
easy, and these your algorithms are incredibly sophisticated.
But taking that AI algorithm and actually incorporating
it into clinical workflows,
making sure that you've standardized those workflows across
an incredibly
complex health care environment,
and then being able to then go back
and and measure the performance of the model
and the the impact that the model has
on the delivery of care,
that's a real challenge. And,
I think that's really where,
we're going to be seeing some,
real need for investment in coming years. There's
a lot of opportunities. There's literally
dozens of these that that are coming up
every month, but,
they all seem so promising. But we really
need to have a good understanding about what
really is going to work,
showing what isn't. And and I don't think
we're there yet. I think that's really where
we need to put our our energies.
That makes a lot of sense. And, you
know, it'll just be fascinating to see how,
you you're able to parse out those things,
you know, that really do add value in
those that work and are important to continue
investing in, for the future. So, you know,
I I think that just seems really, really
critical and helpful.
Are there any headwinds or roadblocks that you
see potentially coming down the pipe or or
something that you and your leadership team is
just are just keeping an eye on as
a trend that might unfold in the future?
Well, so, you know, this is the the
difficulty that we have,
I think, in health care
is
really understanding
how these models
work,
you know, not on an abstract dataset, but
in
a clinical
or operational context.
You know, there's a lot of different ways,
for example, to document
pieces of information on a patient. You can
put it in a flow sheet. You can
store it as discrete data. You can store
it as
free text. And there may be, you know,
different ways to do it across a complex
health care system where there's multiple different hospitals.
There may be different ways to do it
even within a hospital. And and these
problems or the way that this documentation is
done isn't just arbitrary. There's usually very good
reasons for why it's done this way. But
if we don't have that level of standardization
across a hospital or across the system, it
gets very difficult
to implement
these algorithms in such a way that you
can actually make a difference with them. You
know, the thing that I think it's a
little tricky
from a data standpoint is that,
you know, there's this assumption that we have
this this, you know, clean set of health
care data now that we're on electronic medical
records. But the data that get entered into
the EMR don't get entered
to later build algorithms off of. The data
get entered to take care of patients,
and we're doing it in the way that
we think we can best take care of
the patients given the
unique environment and the unique problems and the
unique workflows that we have.
But
if it's all done in different ways, if
there's not a lot of standardization, it becomes
increasingly difficult to make use of the data,
to normalize the data, to make sure we
have a good understanding
of how,
we're going to be able to make sense
of this going forward. So I think the
first big challenge that we have to do
is,
operationalize
these models in such a way that we
can really,
deliver the impact that we think they're going
to have,
in health care.
As you know, a fascinating point you brought
up there and especially just looking at data,
it seems like a very much colossal challenge
to get that organized in a way that,
makes sense for those algorithms and and for
an AI informed,
healthcare system. And so I I can imagine
that, you know, we'll take a huge mind
share, in the future.
I'm wondering, you know, are there any other
opportunities that you see for growth or development,
as you really move forward?
So, you know, my training has been in
data science and,
you know, addition to to the informatics side
of things. And I really think that what
we're going to
see is an increased demand
for the use of people, not just to
report on things, but to really
make sure we understand
from a,
causal or hypothesis testing
way of, actually trying to understand,
you know, what the
reasons for various outcomes are.
There's some
health care is is so multifaceted and so
complicated,
that it gets really tricky to try to
actually make sense of
how a specific workflow
is,
working well in one environment and not working
well in another environment. The number of different
hypotheses that you can throw out, but you
need to be able to test them, and
you need to be able to have people
on the data side of things who can
abstract,
you know, the data from the electronic medical
record, from the various databases that we have,
and then be able to
use data science techniques to,
do some inference testing to to try to
understand
this
relationship. Is there a correlation here? Can we,
you know, try to figure out some way
to infer causation?
But it's challenging, and I I think there's
a lot of folks who understand data science.
But understanding data science is only part of
the problem. You really need to understand that
health care environment.
And I really think that the next, you
know, few years, we're going to see increasing
need for people who who have this
ability to have one foot in the clinical
world and one foot in the the data
science world to help us move to that
next level of of understanding and and appreciation
for how
nuanced with so much of the work we
do is.
Absolutely. I I think that really, you know,
is a helpful reminder of just the complexity
within the health care system and having as
you do, you know, such a a great
background,
within health care delivery as a physician as
well as, you know, your data science training
and the technology side of it. You can
really have a unique
position there to see where,
there are huge challenges, but also I can
imagine great ways to solve problems and try
to really
get to
a point where you can create that value
in the future as the technology evolves as,
you know, get to a point where you've
got more and more data in a great
space. And then 2, from the health care
side of it, just really can see what
is emerging as most
beneficial,
for patients as well as caregivers.
Of course, that's my bias. Yeah. That's my
bias.
But, of course, that's that's my perspective. I
won't say that's necessarily universal, but I I
you know, that's the way I think we
can really approach these types of problems.
Absolutely. Well, I think that's just very helpful.
Doctor Kelly, is there anything else you wanted
to leave our our audience with? Anything that,
you know, would be helpful for our folks,
you know, other health care leaders and hospital
health system leaders as they're thinking about and
trying to figure out out what their AI
strategy is gonna be, how, you know, they
need to prepare in order to really, you
know, create robust programs in the future?
No. I think the I guess the way
I end this is to say that
the real challenge is going to be there's
gonna be so much opportunity. There's so many
amazing tools and amazing technologies coming down the
line very quickly. What we really have to
be able to do is to understand
how these different tools can
be implemented and then how the effectiveness of
the tools can be measured. That's not an
easy thing to do. And and right now,
I think we're we're still in that that
early phase of adoption. It's going to be
that next level where we can really say,
this is the tool that makes a difference
and these 6 tools don't. This is where
we need to put our priorities. You know,
those are the kinds of things that we
need to be able to work with with
3rd party vendors on and be able to
say, listen. This is a nice product, but
it's not delivering what we need it to.
And and those are hard things to do.
But I think if we can get that
in place, it's going to take that that
level of integration to really move,
health care artificial intelligence forward.
That's amazing. Doctor Kelly, thank you so much
for joining us on the podcast today. This
has been such a a wonderful discussion. I
feel like I I've learned a lot in
the last few minutes here, and I look
forward to connecting with you again soon.
I appreciate it, Laura. Thank you.