Hello, everyone. This is Jacob Emerson with the
Becker's Digital Health and Health IT podcast. Thrilled
today to be joined by doctor Mark Townsend,
who is the chief clinical digital ventures officer
at Bon Secours Mercy Health. Doctor Townshend, thank
you so much for taking the time to
sit down with me on the podcast today.
Sure thing, Jacob. I think, thanks for having
me. Yeah. Absolutely. And before we dive into
everything we wanna talk with you about, can
you tell us a little bit more about
yourself, your background in health care, and what
it is that you're doing today at Bon
Secours?
Yeah. Sure. So I'll break that into three,
I guess. So beginning, with myself,
serving
as chief clinical digital ventures officer,
is,
the job of a lifetime. It's a super
cool job, and I'll describe that in a
little bit of detail.
I I think it would make sense to
begin with my background.
And, so my background,
began as a physician, obviously,
intent on on practicing internal medicine pediatrics,
which allowed me to go into adult congenital
cardiology and pediatric cardiology. So,
four boards later,
I I had the honor and privilege of
of being part of a specialty that was
essentially up and coming adult congenital cardiology,
which put me on the front end of
program growth, and development and, actually, technology growth
and development as well,
which which really led to my current role.
So,
as you mentioned, I serve at Bon Secours
Mercy Health, and, we are the fifth largest
Catholic health care system in the country.
50 facilities, 10 regional markets.
And we have a company called Accrete, which
is our digital ventures arm.
And so,
my job as an operator is representing essentially
the the core of of BSMH and knowing
the operators and the pain points
of our boots on the ground people, if
you will, in the health system.
They then come forward,
with technology,
you know, requests specific to individual pain points
or use cases.
And then what we've been able to do
through Recruit is put together acceleration funding. So
an operator comes forward with a problem,
and,
and they will create, for example, an offset
in their budget to support a pilot. What
we then do
is then provide majority
funding in real time, if you will, so
without having to wait for a budget cycle
to go by.
And then we can accelerate some of these
these relationships and technologies, and we can scale
them throughout our organization. So,
again, I'm blessed to be in this role.
Super cool space to be in, and,
and and, definitely sets us up for for
a good conversation today.
Yeah. Absolutely. Well, I appreciate that overview, Mark.
And the first thing we wanted to talk
with you about is something that we're hearing
about from health systems all over the country
all the time, and it's how they're increasingly
using
real world data. So,
my my first question for you is what
are some of the main challenges and opportunities
for health systems in terms of integrating unstructured
data into their clinical workflows and and ultimately
improving care
delivery at scale?
Yeah. Sure. Great question.
And you point out there the difference between
structured and unstructured data, which is which is
a key part of the conversation as well.
So
challenges and opportunities, quite frankly, can be synonymous
here.
Data acquisition is changing rapidly,
changing as we speak, and we'll we can
talk a little bit about that.
And and so, you know, specific to our
approach to data,
the opportunity for us, and I like to
say I'm standing on the shoulder of giants
here who actually built this, but we built
a an intentional
approach to data aggregation,
a data mart. And, and and so that
data repository
essentially empowers us then, for example,
to then, formalize relationships,
and with technologies that can can help us.
And so one of the technologies I'd like
to highlight today is Omni.
And so OmniHealth,
is an ecosystem
and, again, that they partner through a group
for for BSMA for Bontecore Mercy Health. So
OmniHealth,
you know, itself,
pulls in 1,000,000,000 patient encounters,
three and a half billion unstructured clinical notes,
and and really is helping us with our
approach to unstructured
documentation. So the difference, of course, being structured
documentation
turns clinicians into data entry technicians. Right? And
we have to enter data into specific,
specific fields or discrete fields,
unstructured data. And and and this is kind
of an interesting time to be having that
conversation because ambient documentation is growing.
And and I think anecdotally,
we're seeing that the length of the the
clinical documents
increasing.
We're capturing more problems,
and, arguably, we're capturing more unstructured data. And,
you know, it's been
a a a a hot topic in in
health care to to really implement these technologies
with ambient dictation.
So so back to then, you know, the
the making sense, if you will, of all
of this data.
You know, it it is important to have
a data strategy as a health system. But
then,
the tools to essentially help us,
parse that data, to read that data, to
mine it, to look for a needle in
a haystack essentially
are,
you know, really the the value propositions that
empower relationships like the one we have with
Omni.
Certainly. And let's talk a little bit about
your your most recent partnership with OmniHealth.
You're you're partnered with them to unlock valuable
insights from unstructured
clinical notes. You just mentioned the rise of
ambient listening all at health systems all over
the country,
Mark. So can you expand a little bit
upon that in terms of what what you
see as some of the biggest opportunities
in leveraging this type of data for improving
the care to your patients and then clinical
decision making within the broader health system?
Yeah. Sure.
You know, we have talked for, I feel
like, a decade or more about,
you know, the growing interest in genomics and
precision medicine, etcetera.
And and,
again, you know, with with that kind of
needle in the haystack construct,
you know, the the difficulty that we've all
encountered, I think, as health systems is how
do you wrap care around an individual,
and and how do you customize care to
an individual?
It's difficult
when, you know, an individual,
data set specific to a a given patient
is part of a very large ecosystem. Right?
And so, you know, being able to parse
down and find a specific patient,
with a specific
clinical presentation,
or with a very specific,
you know, component in their their problem,
statement or their their past medical history, that's
that's an example of that in a very
granular sense. If you could then start there
and say, okay. Well, what we're addressing then
essentially are care gaps,
then you start to, you know, think a
little bit more broadly.
And, in in addressing care gaps, for example,
we are all,
pulling in social and behavioral determinants of health,
right, as part of our screening
of of all of our our patients and
our clinical populations.
As we do that,
for example,
we are picking up on,
gaps in care, not only just gaps in
clinical care, but, for example, gaps in resources.
And so we've really doubled down on addressing
resource,
constraints as well for our patients, again, wrapping
care around them.
So, you know, real world examples,
housing insecurity,
BSMH,
as a Catholic health system, has a housing
initiative. And, in some of our markets, we
can actually help with that.
Transportation limitations, for example, are very real world
examples.
And, capturing that in an
unstructured
format, you know, just in a conversation with
a patient,
allows us then to close those gaps. So
we have partners,
in that space as well that can really,
service our patients and and close those those
need gaps.
And then taking all of,
those individual
resource constraints essentially
and then pulling them into an after visit
summary. You know, they're very real, use cases,
and technologies that we're partnering with there as
well.
And so all of that now,
you know, brings us full circle to the
to the construct of, big data. Right? And
so big data
then allows us to look,
at a population level for insights, and population
based insights,
you know, are really kind of exciting. And
and,
you know, whether or not we're talking about,
you know, congestive heart failure
as a KPI or, you know, whether or
not, again,
we're we're looking to match a specific patient,
with a specific tumor
with a clinical trial. Right? So all of
those lend themselves to technology,
again, empowered by data as an asset.
Certainly. And just listening to you talk about
this market, it seems like there's so much
potential for this data. So I think the
the natural follow-up question is, how do you
foresee the role of health systems evolving in
terms of using this type of data to
drive innovation and to improve the outcomes for
their patients?
Yeah. Well, key term evolving.
So it is evolving. It's evolving very quickly.
And and I think
you know, the expectations of our patients and
the populations that we serve are evolving as
well. Right? So, you know, I referenced essentially
how they receive care,
you know, how we match,
you know, specific constraints in care delivery to
to specific needs.
The evolution,
of of essentially using data to to drive
innovation.
Again,
you know, it's it's worth doubling down on,
for example,
specific disease processes. So
so the email I wrote right before jumping
on this this call with you, was specific
to a new structural cardiologist
in our Kentucky market,
which is awesome. And,
as he as he joins, you know, his
first question is, alright. So I want to
essentially parse datasets
specific to the application
of clinical guidelines,
and and I want to find patients who
have care gaps.
And and so I was able to write
back. I was like, well, sure. We can
do that. So beginning with aortic stenosis, for
example, I mean, we can take,
guidelines, and guidelines, for example, used to live,
for example, in the brains of my partners,
my cells, and, you know, the the,
the resources that we use to to get
our patient care, you can take those guidelines
now as an algorithm essentially, and you can
use them to curate a list of patients
who have a care gap,
around a very specific disease state. State. Again,
my background in cardiology. So using this example,
aortic stenosis, you know, thickening of the aortic
valve, give me a list of patients who
meet the clinical criteria for intervention
but who have not received appropriate care. Right?
So we can do that, and then we
can essentially then,
turn that over to a navigator or a
new structural cardiologist in Paducah, Kentucky and say,
here you go. These are patients that would,
very, very much appreciate a call from you
all. And and so that that is it's
it's, evolving very quickly, but it's exciting.
And
and quite frankly,
you know, sometimes a little bit overwhelming. Right?
You know, the the technology is coming at
us pretty quickly. So,
you know, I'd like to say that that
we're keeping pace,
as a health system, and,
I don't think we're, like, bleeding edge, but
but but we we aspire to absolutely be
leading edge. Right? I mean, the early adopters.
Absolutely. And that's a great example that you
shared with us, so I really appreciate that,
Mark. And to follow-up on on your last
point there, exciting, but but certainly can be
overwhelming. So, you know, what's your advice for
the other health system leaders listening in right
now in terms of how they can better
build a data driven culture like Bon Secours
within their organizations, particularly as they also work
to integrate diverse data sources from from EHRs,
from clinical notes,
and from patient reported outcomes?
Yeah. Great question and a challenging one as
well. I think the challenge
for us as,
operators, health care administrators,
physician leaders
is is to be able to challenge our
health systems to really view data as an
asset. Right? And so,
I've I've had a hand throughout my career
in data aggregation and and fortunately, was, you
know, known as a data geek. And, hence,
the people would come and as they were
building, you know, our our first enterprise data
warehouse, for example, in a prior health system,
they would come to me and say, so
give us examples of use cases. That's where
there's a disconnect.
And, again, you can pull together
brilliant data teams, you know, who can aggregate
data. There's infrastructure
out there readily accessible,
and and that has absolutely accelerated. But but
it does need to be championed,
you know, by by someone who who can
actually understand the use cases. Right?
So so finding out whether or not you
have a data strategy, for example, whether or
not you're aggregating your data, whether or not
you've created a data mart, I think, you
know, would be a good place to start
and, you know, particularly
smaller health systems, large health systems, of course,
I think, you know, have invested in this
strategy.
But but then,
coming forward with use cases
and connecting with the clinicians who,
again,
you know, absolutely can tell you what they
are seeing in terms of the technology.
Younger physicians,
older physicians are keeping pace. They they are
constantly approached
by our vendors in in the digital world,
and, they can often point to, you know,
very specific use cases.
So I think then back to VSMH and
our strategy, we recognized that we needed to
be able to engage in real time
And building that infrastructure that, you know, again,
wasn't going to take a a a great
idea and turn it into an eighteen month
process
was was really the challenge that we needed
to address. And so being able to
use a great acceleration,
for example, and, you know, build a infrastructure
that allows us to
to, you know, to to finance and fund
some of these programs in real time, that
has been key for us. And, it really
does engage your teams as well. So
all lessons that we have learned are learning.
We've got a lot to learn. We certainly
haven't got it all figured out. I don't
think anybody has. But,
but it, again, takes, teams, and we're fortunate
to to have teams that that really support
this work.
Fantastic. Well, Mark, what else are we missing?
Any final thoughts you wanna offer our listeners
today?
You know, I often use this as the
rallying cry, which is we're at the point
where
clinicians have reached the tipping point. Right? Cognitive
assistance is required. The amount of information and
data coming at us in health care
is really beyond the human capacity, I think,
to keep up. And so
so,
we we are thankfully
living in an era where, you know, we
we can empower,
our teams,
not only with data, but then with the
tools, in many cases, AI driven tools that
that allow us to to effectively overcome the
the cognitive limitations. So this is my rallying
cry. We've got to work smarter, not harder.
And, I'll leave you with that, Jacob.
Wonderful. Well, doctor Townsend, thank you so much
for taking the time to sit down with
us and for sharing your insights with our
audience. We really appreciate it.
My pleasure. Thanks for having me. Yeah. And
if you'd like to listen to more podcasts
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