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This is Gracelyn Keller with the Rutgers Healthcare
Podcast, and we are live at the 9th
Annual Health IT Digital Health and RCM Conference.
I'm currently joined by Michael Hasselberg, who is
the chief digital health officer at UR Medicine.
Michael, thanks so much for joining me today.
We'd love to have you introduce yourself a
little bit further as we get started. Well,
thanks for having me. Excited to be here.
Hello, everyone. Michael Hasselberg.
First and foremost, I'm a nurse, a psychiatric
nurse practitioner.
I started my career in a very rural
part of New York state where I was
the only
psych provider for about 6 counties. So became
very passionate about how do we get high
quality care out to underserved patient populations. Ended
up going on
to get a doctorate, then a business background
after that, and I I currently serve as
the chief digital health officer for University of
Rochester Medicine.
Those who are not familiar with UR Medicine,
we are a large academic health system,
in upstate New York, the largest health system
outside of the New York City. And in
my role, I oversee the digital transformation and
innovation strategies.
Wonderful. Well, thanks for being here. And we're
gonna start our conversation
with AI adoption. This is
exploding in health care, as you know. So
in your view, what is the most significant
or promising application of this technology,
and how is that informing your organization's innovation
strategy?
Love the question. I don't think we could
ever go with a conversation these days without
talking about AI, so thank you for for
bringing it up. You know, one of the
things that I think is super
exciting in terms of,
AI applications
are the actual generative AI foundation models themselves.
So not any specific tool,
not any specific
application
off of these foundation models, but just the
foundation models. You know? I think
that having access as a health system to
these foundation models is essentially a game changer.
It's it's level setting. It's never been easier
for a health system like mine to develop
our own AI tools using our own data
building off of these pretrained
large models that were already trained by
big industry corporations.
And so, you know, for an innovation team
like mine, you know, we are now developing
our own AI tools
in a development
time that has gone from 6 months to
a year to essentially days at this point.
And switching gears just slightly
over to data,
we're seeing health care leaders managing greater volumes
of data and more devices on a growing
number of care settings and population. So this
complex environment that has kind of popped up,
I'd love to know what your clinical data
integration tools or practices that you're seeing drive
improvements in patient outcomes, and if you could
share an specific example of that as well.
Yeah. You know, I think, you know, one
of the the biggest wins thus far, specifically
let's just stay with AI for a second,
generative AI. It's forced health systems to make
investments in their data infrastructure. And, you know,
not to say that we didn't
have investments in data infrastructure prior, but now
it's even more important that we have a
robust enterprise data warehouse
that, you know, we have cleaned and curated,
and that we have a governance process,
over our data. So in terms of kind
of best practices,
you know, we have spent a lot of
time specifically over this last year,
you know, setting up what our data governance
process is and, you know, essentially then on
top of that, putting an an
AI governance council together starting at the university
level then down to the health system. And
that's allowed us to really unleash our data
and drive insights into our health system.
And as we discuss data and AI and
technological innovation,
I'm curious your thoughts on how health care
organizations
can better support both IT and clinical teams
as we're carrying out innovation efforts. And what
are some common pitfalls that you've seen in
this?
You know, I'll I'll start with the pitfalls.
You know, one of the things that,
is, I think, pretty common across health care
is silos.
You know, we have a lot of,
disciplines,
you know, that work in silos,
and what silos create is essentially groupthink. And
groupthink is
the downfall of all innovation,
in my opinion. You know, one of
the things that is unique about University of
Rochester Medicine is we're one of only a
handful of academic medical centers left in the
country that is still fully integrated into our
parent university. And so when we think of
innovation,
we have a very diverse
group of thinkers around the table where we've
essentially broken down those silos. And I have,
you know, faculty from my engineering school and
computer science department and data sciences to even
faculty from my music school
sitting
elbow to elbow with faculty from my medical
school, dental school, and nursing school. And it's
when you have that diverse thought process that
we can really come up with some out
of the box
innovations to solve some of the most pressing
problems within our health system.
And as we wrap our conversation today, I'd
love to know your top piece of advice
for health care leaders as we prepare for
further advancements in technology and greater demands for
care.
You know, be okay with failure.
Just be able to fail fast. You know,
this technology is
is moving at such rapid pace, and there's
so much that we don't know about it.
And when we go in and test these
tools and applications within the health system,
we're bound to have failures. But to be
okay with that, because it's from learning from
those failures that we can really develop the
solutions that are are are gonna drive improvements
to our patient outcomes
and outcomes for our clinicians delivering that care.
Absolutely. Well, thanks so much for joining me
today on the Becker's healthcare podcast. Again, we
are live at the 9th annual health IT
digital health and RCM conference.
Thank you for having me. It's a lot
of fun.