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This is Laura Dierda with the Becker's Healthcare
Podcast. I'm thrilled today to be joined by
Michael Hasselberg, chief digital health officer at UR
Medicine and professor of psychiatry, clinical nursing, and
data science at the University of Rochester. Michael,
it's a pleasure to have you on the
podcast today.
Thank you for having me, Laura.
Looking forward to our discussion.
Absolutely. I am too. I I know it's,
you know, got a lot going on there
at UR Medicine, and and certainly something that,
we'd love to hear more about how you're
thinking about the future as well. But before
we dive in, can you take a minute
and tell us a little bit about UR
Medicine and what makes it unique?
Yeah. I'd love to. So those of you
who are not familiar with UR Medicine, we
are
a large academic health system in Western
Part of New York State. We are
the largest health system outside of New York
City in the in the state of New
York. So we have a very large
geographic reach. Essentially the whole Western half of
the state is,
our market
that we serve. So very diverse
patient population. Everything from the patients you would
see in in an inner city of a
mid sized city to
you go 30 miles outside of the city
limits and it's fairly rural.
What's really special
about,
UR Medicine is
our relationship with our university
which is
very very unique
for
academic health systems today.
We are one of the
few academic health systems in the country that
is fully integrated into our parent university. Our
parent university essentially owns the health system and
our CEO
and president of the health system reports right
up to the president of the university.
Our
quaternary hospital sits right on the university's campus.
And you know what makes that special for
us and we actually look at as a
differentiator
is we have access to some of the
most brilliant minds on our academic campus across
engineering,
across
computer science and data science,
even a world renowned school of music,
and business school and education school.
And we get to leverage that expertise and
apply it to healthcare. And so we have
a true
innovation team at the University of Rochester supported
by the university
to help move the strategic priorities of the
health system forward. Not a traditional
research shop where you know our faculty
the expectations
aren't to get as much NIH funding
as possible and publish papers. It's truly to
innovate
to support health system priorities. So under one
roof we have faculty
from all of our schools.
Again engineering and computer science and data science
and even the the School of Music under
the same roof as faculty of the medical
school, dental school, and nursing school. So having
that really eclectic group of,
minds allows us to come up with some
really exciting solutions to some of our pressing
problems in the health system side.
That's great to hear. And what a unique
opportunity to truly, truly be able to focus
on the patient care side and and being
innovative within health care delivery.
I'm curious. Looking in the last year or
so, what accomplishments are you most proud of?
Yeah. So I I the the accomplishment that
I'm most proud of, for our university is
maybe not the sexiest,
accomplishment,
especially, you know, when you think of,
all the great opportunities
and innovation in health care right now.
You know, our biggest accomplishment
was the setting up
of an artificial intelligence governance
across the health system
and actually
university wide. You know, we we felt that
having a governance that,
could help us think through, you know, how
do you deliver,
this really exciting technology in in a safe
and responsible way across all of our missions
at the university,
but also set it up in a way
that to allow us to be nimble and
to,
accelerate,
the advances using this technology
was not a small task to do.
And it took us, you know, easily six
six months to a year to really get
the governance structure
right.
But we were able to accomplish that. And,
you know, that that I think has really
set the foundation for us to continue to
be leaders
around the use of technology and specifically
artificial intelligence,
not only in health care, but in education
and and and our research missions also.
That's great to hear. And, you know, I
I know looking at the governance process is
not an easy one or a simple one
at all. So many health systems, right now
are still trying to figure it out and,
create the the right policies
and bringing the right people in order to
make those decisions. Could you go a little
bit deeper in there in terms of,
how do you did you develop the governance
process, especially around AI?
Who should be involved, and and how do
you, really then cascade the message once you've
got,
policies ready to go?
Yeah. So,
our governance
starts right up at the top of the
university. And so we have what we call
an an artificial intelligence
council
that reports right up to the president of
the university and the in the president's cabinets.
And
the artificial intelligence council
has lots of different
stakeholders on it including
chairs and representatives
from
the AI committees, which I'll talk about next.
But it also has stakeholders
from privacy and legal,
and compliance
from
IT
to ethics,
to,
some of our our our content experts, our
deep technical experts,
a and AI.
But we also have experts around thinking about
AI use in our community and and how
we can make it equitable
across our our our community. Below the AI
Council,
we have what we call domain committees and
we have five domain committees.
We have a clinical domain committee,
a research domain committee and education domain committee
and administrative
domain committee and a marketing and communications
domain committee. And those committees have
a diverse group of stakeholders that sit on
those committees and the purpose of the AI
Council is to really enable
the committees
at the domain level to
be autonomous
to to be able to identify what are
the specific artificial intelligence
drivers that align with the values of that
domain,
support those committees to develop their own guidelines,
procedures, and in some cases, like in our
clinical domain committee, actual policies,
to support
regulatory
and compliance and safety
concerns.
But also, you know, allow those domain committees
to inform the council about where we should
be making strategic investments,
at the university level,
within and within AI. And so at the
council level,
you know, it's it's it's that
group's job is to try to standardize where
we can across all of the domains on
our guiding principles,
and again guidance and and policies
to make sure that we have
safe,
and internally
improved,
AI tools that are equitable across our students,
staff, and and and faculty.
And, really serve
as a group,
sounding board for the the AI committees if
they run into,
issues that they need further strategic
guidance on. And then within that governance structure,
we have workflows,
identified
for
the submission of new AI tools that,
individuals within our university and health system would
like to
try or use.
And so we've we've built a kind of
a nimble workflow to get those tools vetted.
And then we've also, you know, developed,
a new contract addendum guidance for some of
our long standing vendors who are starting to
introduce
AI functionality into their platforms to make sure
that we have that partnership with our vendors
and they are,
you know, protecting our data,
to the standards that we,
hold our ourselves to at the university and
that they are also thinking about what are
the ethical
bias,
accuracy, reliability
implications
of these tools, especially on the generative AI
side.
That's helpful for understanding. Thank you, Michael, for
going through that with us and especially, like
you mentioned, getting into generative AI. I know
that's a whole new, can of worms opening
in some ways. So,
very much appreciated. Now I'm curious looking ahead,
where do you see some of the big
growth opportunities over the next twelve months or
so?
Yeah. Well, let's, Laura stay on generative
AI. You know,
we we as a as a health system,
you know, we were one of the
early,
users
of,
secured private instance of, GPT four, that specific
foundation model.
You know, we we also have other secure
private instances
of, public foundation models, and and we also
have a supercomputer where we have
a smaller,
open source,
model sitting on. And
for a university,
academic health system like ours,
it has never been easier for us to
develop our our own AI tools
rapidly
to solve our own problems by essentially,
doing some prompt engineering,
of these foundation models with our own data
or fine tuning these foundation models on our
own data.
And so, like, we're really
very narrowly focused in on where are those,
manual
administrative
tasks across the system
that if we were able to automate those,
using,
generative AI tools,
where what what would have the biggest, not
only return on investments,
but,
you know,
decreasing,
mitigation risks and and have the biggest impact
really on our employees,
our our clinicians,
and our staff? That's where we're
focusing
our efforts on and we're making a lot
of headway. We are cranking out tools in,
you know, days to weeks. Tools that typically
would have taken our engineering
and data science teams
six months to a year to build. So
it's it's been really exciting and quite powerful,
you know, our ability to to develop internally
by having
access to these these massive foundation models.
That's great to hear. And, you know, really
cool to have that opportunity to speed things
up and, like you mentioned, just,
do more and more as time goes on,
with degenerative AI. I know a lot of
opportunity there, but I can imagine also challenges.
What are some of the big, challenges that
you're anticipating
heading into the future as well?
Yeah. You know, I think,
one of the biggest challenges
is understanding
what the regulatory
landscape may be around the use of of
of specifically generative AI on on the health
care side. And, you know, we know that
there is a lot of changes happening at
the federal level,
in this space and trying to understand that.
But when push comes to shove,
you know, as a as a health care
institution,
you know, we
our primary business is providing high quality care
to our patients.
And so, like, that is that's the the
essence of what we do. And so it
doesn't really matter, you know,
what
regulations come out or maybe lack of regulations
that may come out. We still have to
hold ourselves accountable
of
of making sure that these technologies
are safe
and trustworthy,
when,
delivered within clinical care settings. And we need
to make sure
that we can create assurances
for our clinicians using these tools that they
can trust,
the the the output,
that that we're seeing
from from the AI. And so, you know,
I think,
you know, really trying to
get a handle on that and and really
trying to
figure out how do we create,
pre deployment validation opportunities
of of these generative tools and
post deployment
auditing and monitoring,
systems,
to watch what these tools are doing.
You know, that that is a a space
that I I see a lot of opportunity,
but again, a lot of challenge just given
there's so much
black box or
things we don't know about the these these
large foundation models. And so, you know, that
has been the,
I think,
biggest thing that we've been
running into and thinking about. Again, never been
easier to build a point solution. Like, we
we we can we can do that really,
really quickly.
But to be able to go from pilot
to
full scale across the health system in a
safe and trustworthy way,
that has been a challenge and difficult. And,
again, it's
because of
not having that
clarity or mechanisms,
in place to to, again, monitor and audit
these tools at scale.
That's fascinating to hear. You know? And, certainly,
I can imagine,
a a challenge that others others in your
position are are seeing in health systems as
well. And I'm curious, you know, especially when
you think about, the taking pilots or some
of the small scale things that you're doing
and then seeing successes, rolling them out, and
and,
defining challenges
within that. I guess, could you share with
us any examples of needing to
kind of put the brakes on a project
or,
something that was like a a a near
miss that really kinda highlighted what you're talking
about here?
You know, not not per se,
a near miss,
yet, at least, because we are very,
thoughtful of leaving our tools that we develop
turned on outside of production for, you know,
some some cases, you know, six months after
we've built the tool and kind of watching
the tool outside of production and comparing to
what we're seeing from,
our humans,
managing
that
task,
within production.
What we have seen, however, you know, with
things of,
you know, these
these foundation models changing so quickly and getting
updated,
so quickly. We have seen
things that,
when we
deployed a tool,
again, in in outside of production, we've been
watching it. It, you know, maybe did really,
really well at,
triaging a patient message to the the right
endpoint,
or the right clinician or staff member that
that message should go to.
But, you know, as there's updates on that,
on the foundation model, we're finding that, you
know, some messages are getting caught in the
filters of the foundation model that, you know,
initially weren't getting, caught. And so,
you know, we're having to go in and
try to understand why is it getting caught
in the filter and changing some of our
prompts to make sure it gets to the
to to the right place.
You know, we're really trying to be sensitive
around
what problems we're trying to solve
that
if if the if the model,
or tool was to get it wrong,
and and and, you know, that if there
was a near miss or an actual miss,
the the risk of harm is is gonna
be much lower. And so most of the
tools that we've developed thus far,
there still
absolutely
has to be a human in the loop.
And when I say a human in the
loop, it's one of our clinicians
or one of our staff members and employees,
that validates,
the appropriateness
of of the output. And so having that
extra layer
in, I think, has,
created that safety net so we haven't had
that miss yet. I I I don't think,
you know, we're ready,
culturally,
or from a societal standpoint
yet. Or I'm not even sure if the
technology is ready yet to
be directly
interfacing with with our patients without having that,
clinician in the loop yet.
Got it. Absolutely. I think that's such helpful
perspective. It it really truly illustrates,
some of the the things that we've been
going through today. So,
I I appreciate it. Before we wrap up
here, I'm curious. What is the number one
thing that you're doing right now to set
set up your medicine for long term success?
Yeah. That's an easy question. You know, for
us, you know, it's
your medicine
is not gonna figure this out all by
ourselves.
Industry is not gonna figure this out all
by themselves.
The feds and states are not, from the
government level, are not gonna figure them figure
this out all by themselves.
It's truly gonna be through partnerships.
And so we have
really thought out of of, you know, what
are the the
the the right groups of consortiums,
of industry,
private,
public,
groups that we can join and have a
seat at the table
to kinda talk through some of these
difficult challenges and learn from others about how
they're
overcoming these challenges or the way that they're
thinking about that. And so we've made a
conscious effort and we have protected our time
of a lot of our leaders in our
organizations
to participate
in,
again, you know, these
these consortiums that are forming around AI and
health care and you know, really trying to
get out,
and travel to other health systems
and meet with our industry partners
and really understand how they're trying to tackle
these problems. Because I actually
believe that the future,
I mean, where we're really gonna be transformative
in health care in this country,
is is is gonna be more, you know,
on this sharing of ideas and almost on,
like, an open source concept of of us
across industry, across providers,
across payers,
across government,
you know, giving away some of our our
learnings and sharing it with others so others
can build on top of that. And that's
where I think we're really going to be
able to leverage technology like generative AI to
transform health care for the better of our
our patients
and our clinicians. And so again for us,
you know,
long term success is gonna be through partnerships
and making sure we find those those right
groups that have a similar why and vision
that we have.
I love that. Michael, thank you so much
for joining us on the podcast today. This
has been just a really fun conversation, and
I look forward to connecting with you again
soon.
Thank you, Laura, for having me.