Philips is a health tech leader focused on
innovation that improves the health and well-being of
people. Our health care technology and informatics solutions
help care teams diagnose, treat, and manage more
patients with greater precision, speed, and confidence across
the care journey. With Philips, clinicians are empowered
with streamlined insights in the moments that matter
for every patient. Better care for more people.
Philips.
This is Haley Recker with the Becker's Health
Care Podcast. We are live at the 9th
annual health IT Digital Health and RCM conference.
And today, I am joined by Nabil Shahadi,
who is the chief clinical transformation officer at
MetroHealth.
Nabil, thank you so much for speaking with
me today.
To get us started, can you please share
a bit about yourself, your background, and your
role at your organization?
Thank you, Haley, for hosting this podcast today.
I'm a a physician, a urologist by training.
I am the chief clinical transformation officer at
MetroHealth,
and MetroHealth is a super safety net hospital
in Cleveland, Ohio.
We are about $2,000,000,000
on revenue,
5 hospitals,
pre emergency room,
and more than 30 outpatient,
clinic. We serve more than
60% of our patient are governmental patients, which
means Medicaid, Medicare patients, and uninsured.
Alright. Thank you so much for sharing. So
for my first question, I wanted to ask
about AI adoption, which is currently exploding in
health care. So in your view, what's the
most significant or promising application of this technology
right now, and how is this informing your
organization's innovation strategy?
Thank you for this great question. There's no
easy answer for it. There's a lot of,
potential excitement,
but potentially also so many potential disappointment,
as well.
So how do we tackle this?
I think few things we we can,
zoom
into and and have a conversation about.
1 is,
into
data
and,
predictive data analytics.
How can you leverage AI
into streamlining,
that data and and predict a disease before
it becomes,
a much bigger problem?
How do you use it to help manage
chronic disease
on an outpatient setting?
How do you use it
within the inpatient setting to predict a potential
bad outcome before it happens? Like potential very
well known,
sepsis
sepsis prediction
or fall,
prediction on the nursing unit. So how do
you help your nurses and clinical staff
to prevent
a catastrophic
event before it happens? And can you have
early warning signs that can help you in
the management,
of those,
potentially disastrous,
health outcomes for your patient?
So that's on the,
structured
data
side.
However,
on imaging,
and diagnostic,
there is a
lot to
be wishing for,
with AI. Can AI help us into,
some of the images in radiology,
MRI, x-ray, CT scans, etcetera?
And
can
AI
accelerate the efficiency
of the provider reading those x rays?
Can it,
unravel
some subtle
differences in the images, etcetera, that might lead
to a
different diagnostic,
outcome.
But the same can apply for pathology,
reading slides for,
cardiologists
reading their own images,
even dermatologists lesion.
And can can can you look at patterns
that the naked eye cannot, truly see? And
can you really help AI with those diagnostic
tools? Absolutely.
You mentioned the intersection of AI and data
in the very beginning there, and so I
kinda wanna pivot the conversation to data specifically.
On a daily basis, health care leaders are
managing greater volumes of data and more devices
across a growing number of care settings and
populations.
In this complex environment, what clinical data integration
tools or practices are you seeing drive improvements
in patient outcomes and operations? And then can
you share an example?
Sure. That's probably
the biggest,
conundrum and issue that we have to solve
too. All AI tools
are based on what kind of data we
have,
and bad data will lead to bad outcome.
Biased data
will lead to biased
decision support tool,
that we have today.
So the opportunity,
as I stated before, to get a lot
of, predictive analytics, etcetera,
only
if your
basic
data gathering is clean.
And what we struggle today
as a health system,
not just integrating
data
that come to us from all kind of
different sources from outside of our organization.
Because on an average,
a patient that sees us at MetroHealth will
only get 50% of their care at MetroHealth,
but that the other 50% is somewhere else.
So how can you account for that data?
Also, the payers so we're the health system.
There's the payer, and the payer have the
those data. And can you
bring,
that data and integrate it with your,
electronic medical records so you have that nice
base of data that that you can
use to build your your AI tools on?
But that's only one part of the equation.
The other part of the equation
is within your own health system.
There are all those peripheral
and devices
just like EKGs
or or monitors on the inpatients,
that are really collecting data?
How do you interface all of those and
bring them into a a a nice,
data warehouse? And finally,
are the wearable,
with the consumers?
And this is one of the biggest challenge.
Every consumer
is collecting
data
and wants to share that data with their
provider, but that's gonna bring a lot of
noise about,
useful data that you can act on it
or not, but that's probably where
we're innovating the most, trying to curate and
try to figure out what is
the most important
data element,
that you're receiving from your patient wearable that
a provider
a a team of provider and their nurses
and assistants can act upon and make a
difference.
In your view, how can health care organizations
better support IT and clinical teams as they
carry out innovation efforts, and what are the
common pitfalls that you see here?
Another good question.
It's all about governance.
I truly believe
you have to have a strong,
governance
internally to your health system
in order to streamline
what kind of,
application you're bringing in.
A lot of those application
promises
they promise a lot,
but most of the time, they've not proven
their outcome.
And without a
true governance in place, how you can streamline,
how you can allow small pilot to happen
before you commit your organization into 6, 7
figure
expenses
without any
data to prove that the health outcome is
better
or actually your cost has improved and your
efficiency have improved.
I've seen it so many
time that there is a rush to bring
in the excitement,
but then
you spend a lot of
resources
and the outcome is not better and sometimes
it's worse. So you gotta do baby steps.
So that's that's one of my biggest thing.
The other one is you don't wanna over
govern and not allow innovation to happen.
You wanna
be very mindful that
if you have a good governance structure,
if
you allow them to innovate, but take those
baby steps, you are more likely to figure
out what is the best that you're gonna
ultimately invest and and and deploy system wide.
Alright. Thank you so much for sharing. And
to wrap up our conversation today, what's your
top piece of advice for health care leaders
as they prepare for further advancements in technology
and greater demands for care?
Don't be afraid
of the technology,
but
you gotta also be cognizant that within your
health system,
people are gonna use technology whether you allow
it or not. So
embrace it,
but embrace it in a very calculated way.
Have a very strong governance,
not just of what you adopt and what
you don't adopt and what you invest in
and not invest in, but also be very
cognizant about your data infrastructure
and invest into a
good data warehouse. So
it is not,
bad data in
bad outcome, bad decision. You want good data,
and you want data that is unbiased so
you don't create a health disparity while you're,
relying on AI.
Thank you so much for sharing your insight
with us. Once again, we are at the
9th annual health IT digital health and RCM
conference.
Nabil, thank you so much for being here.
Thank you, Hailey. Thank you.