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Would you like to exchange best
practices and ideas to improve care,

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enhance operational efficiency,

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and address financial
challenges with your peers?

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Becker's Healthcare is facilitating these
conversations at their eighth annual

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health IT digital health and RCM meeting.

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You can check your eligibility for
complimentary attendance at the Lincoln,

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the description. We are excited
to welcome you in October.

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This is Laura Deda with the
Becker's Healthcare Podcast.

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I'm thrilled today to be
joined by Dr. Conroy Gleer,

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assistant Professor of medicine and
associate director of Data Analytics at

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University of Rochester Medical Center,

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and Kathleen Fear Director of data
Analytics at the Health Lab. Dr. Gleer.

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Kathleen, thank you so
much for being here today.

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Thank you.

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Thank you.

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Now I know we've got a lot
to talk about and you know,

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I'm really excited to learn more
about what you're doing, um,

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there at the University of Rochester.
But before we dive into my questions, I,

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I would just love to have you
both introduce yourselves,

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tell us a little bit about your
backgrounds and then, you know,

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it'd be great to, to color the rest
of our conversation. So, uh, Kathleen,

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let's start with you.

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Sure. Uh,

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my name is Kathleen Fear and I'm the
director of data and analytics at the U

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Health Lab,

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which is the innovation incubator at the
University of Rochester Medical Center.

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Um,

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I've been at the health lab for about
five years now and at U M C for a little

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bit longer than that. Um, my background,
my PhD is in information science,

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uh, really focusing on
how people understand,

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trust and make decisions from data.

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So a lot of my work now is focused on
how do we not just make data accessible

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but usable, you know, how do we build
the right products, the right tools,

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the right culture that
enables data-driven,

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like strategic and
everyday decision making?

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And I'm Conrad Gleer. She mentioned
my titles briefly there, but I,

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I came to know Kathleen very
well from the health lab and

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I landed in the health lab kind of
cuz of not just my medical education,

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but I went to business school and
I'm sort of one of those jack of all

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trades people who have jumped
in and out of different worlds.

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But I'm now currently very heavily
set in the data and analytics world.

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Um,

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I learned most of these things
through necessity since most of

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healthcare requires a certain
degree of knowledge in this space.

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And I came to work in the innovation
lab to hopefully make things better.

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Absolutely. Well, fantastic. And you know,

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I'm excited to have our conversation
today because I know there are so many

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different ways that technology
and innovation are really
becoming a big part of

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healthcare and healthcare
delivery now for,

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I guess I wanted to start off with
thinking through what are some of the big

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opportunities that you are seeing right
now today and then the headwinds you

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have your eye on. So Kathleen, I'd love
to hear from you and then Dr. Kleber,

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I'd love your perspective as well. You.

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Know,

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I think generative AI has the potential
to really revolutionize the way people

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interact with data and
information. You know,

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in the way that the microprocessor
democratized access to computing power,

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the way the internet democratize access
to information generative a AI really

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has the potential to democratize
access to insight generation. You know,

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especially in an academic medical center,

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one of our greatest
resources is curiosity.

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Where an organization that's full of
really smart people who have chosen to

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practice in this setting because they
want and should have the space to ask

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questions, do research, innovate.

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But there are so many barriers between
asking a question and getting to an

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answer,

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especially if it's like an exploratory
blue sky kind of question that maybe

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doesn't rise to the level of a
full-on like IRB approved study or,

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or or doesn't.

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Yet generat generative AI can really
give people a running start on their

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questions and by facilitating experimental
exploratory work and by giving people

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the ability to use and apply and apply
powerful models without even know needing

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to know how to col code,

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it really has the potential to free
people up to do the kind of higher order

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thinking that our
organizations really need.

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And crucially to build a
culture of experimentation.

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I was recently listening to another
podcast with Lucas Premier, um,

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who's really an expert in how to scale
innovation and experimentation in

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organizations. And he said something
that really resonated with me,

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which is that experimentation has
to permeate everything, you know,

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in an organization it's not about
just making a few great decisions,

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it's about making many,
many slightly better ones.

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And so to me the data and
technology opportunities to
really keep an eye out for

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are the ones that help
facilitate that. You know,

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the things that help build an environment
where everyone's empowered to ask

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questions and seek answers where it's
okay to be wrong or uncertain and where

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people have, uh, have the ability
to like think and try and test.

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I can sort of key off that and
sort of come behind and generative

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AI is the new hot topic that
probably everyone's gonna want to

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talk about until there's a,

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an elephant in the room sort of
product that comes out from G P t.

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I think it stems from the large
opportunity is the democratizing

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as Kathleen said,

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but that's also in the setting
of what was happening beforehand.

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Our institution is not alone,

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but a lot of the data and analytics done
at large medical institutions was very

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siloed and work was
often repeated and people

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with different levels of skills we're
trying to analyze data in multiple

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different ways all at
the same institution.

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It happens to be one of the main issues
with trying to create an effective

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data culture and make
decisions around data.

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And this is something that Kathleen
and I run into all the time. I mean,

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our biggest opportunity that we see is
setting up playgrounds and sandboxes

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and virtual environments to allow

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anyone to do data exploration, uh,

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allow people to explore clinical data,

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not with the mindset of
they have to be very careful

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about what decisions they're
making, about how the data is used.

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We set them up in a way that they
can explore to their heart's content

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and come to advanced conclusions.

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And that can be greatly enhanced
by generative AI as you can simply

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ask the question of the AI and it can
help you obtain an answer or at least set

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you on the right path.

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Absolutely.

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That's such a great point and it's
fascinating to see how that generative ai,

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uh, has really, um, come into its
own, uh, over the past several months,

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especially, um, in the healthcare
space as well as overall.

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And it will be exciting to see
how it evolves in the future.

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Now when you're looking at the
technology and, and really, um,

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the opportunities to continue to add
value to, um, organizational overall,

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what does that look like for
you? Where do you really see, um,

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the most potential for technology
and uh, um, your, both your roles,

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um, in innovation, being able to
continue to add value going forward?

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Yeah, I think maybe, maybe I'll
start with kind of a cautionary note,

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which is I think especially
with things like, you know,

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shiny new tools like
chat pt, but you know,

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even around any other technology
that that we look at, it's, it's,

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it's important to not fall into the trap
of sort of technology wishful thinking.

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You know,

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it's really easy to look at any of these
tools and just hope it's gonna be a

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silver bullet that is gonna solve
every little problem we have. You know,

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whether it's generative ai, you know,

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giving everybody the ability
to do whatever they want
whenever they want or even

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something like, you know, our our,

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our new enterprise data warehouse is
just gonna solve all our access problems

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or, or whatever. Um,

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but like fundamentally healthcare is a
really complex sociotechnical system and

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the hard part is often
not the technical part,

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it's the socio part that can really
be the kicker. You know, I think our,

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our toughest problems are the things
that are operational implementation

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systems and and people kinds of problems.

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Yes.

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I mean a lot of times we will have these
great ideas and Kathleen and I will

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work on them and then we go to the
implementation phase and it's often three

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times longer than the innovation
phase into figuring out how

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to use these tools.

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I mean one of the biggest opportunities
we have and sort of the fun part of

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where we are and basically
why I'm here is we have

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so many new products coming along be them
data products or technology products,

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but they are being pushed in some
one sort of general direction.

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It's that the bottom line is we're
looking for technologies that help doctors

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and nurses be doctors and nurses
rather than dealing with the

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surrounding stresses. I mean,

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the burden of being in clinical
practice as a doctor or nurse is

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growing and people are choosing
to leave it despite the value

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there. And if we can find technologies
and ways to make it better,

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it is an unbelievable opportunity.

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So there's a staffing crisis with nurses
and doctors that's probably most of the

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people on this that listen to this
podcast institutions are all feeling.

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And the product that comes
out that helps that burden

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and helps with that crisis is the one
that's gonna survive being part of

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the healthcare market in general.

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We just love to see the ideas come from
generative AI because that will sort of

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push a lot of people to make
products that will help us.

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But what Kathleen said is true,

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there's no silver bullet cuz
every workflow is different,

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but even these small improvements to
a workflow can be additive as much as

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they're subtractive when one extra
burden is laid on clinical practice.

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That makes a lot of sense. You know,
and it's really fascinating to hear,

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I know as you mentioned,

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so many different organizations
are trying to figure out their, uh,

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staffing challenges and shortages and,

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and make sure that they're
moving in the right direction,

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whether it's using technology
and automation as much
as possible and then to,

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um, you know,

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connecting with their teams and trying
to retain that the staff that they have

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and team members that they have with
um, eliminating burnout. So it's just,

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it's a fascinating time right now for
innovation in the staffing of workforce

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space. Now as we've been talking about,

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I know that resources are limited and
especially whether it's financial or um,

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human capital. So, you know, what is
important to still invest in, in, um,

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risks that are still worth taking
right now to put, set yourself up, uh,

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for growth and success in the future,

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even given some of the challenges
that we're seeing today?

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I think one of the best investments that
health systems can make is to invest

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into their own people. You know,

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invest in the people who are
already engaged in the system.

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Give them time and space to grow and
to do the things that are rewarding to

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them. You know, the health lab, we,
we set out to be an innovative place,

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you know,

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to develop new technologies and do
impactful things for the health system.

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And one of the impacts that we have
had but didn't anticipate having is

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actually on faculty retention. You know,

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we have clinicians who work with us and
who came to us when they were so burned

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out and ready to walk away,

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but by giving them the resources to
explore the things that are meaningful to

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them, we've helped 'em stay engaged. Um,
and of course like there's, there is,

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this is an investment, there's
a cost to this, you know,

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people need protected time and like
the health lab is entirely funded or

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mostly funded by
overhead. But I think the,

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the dividends that our work has
been able to pay in terms of like

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not just provider job satisfaction but
like technical staff satisfaction, um,

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I think have been really important.

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I think I'll focus on that
specifically for a little bit.

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As in some cases people view clinicians

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or analysts

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doing work outside their scope in a

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odd way. I generally refer to
this as the can't assumption.

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There are groups that feel that there
is an assumption that some groups should

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not or cannot go and work or
understand a different workflow

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in Kathleen and i's world.

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It's the can't assumption that Kathleen
can't understand the clinical workflow

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cuz she's not a clinician and it's the
can't assumption that I can't understand

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what a data analyst does cuz I'm not
a data analyst and I think we are the

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antithesis of the can't assumption.

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Kathleen is very capable and does
understand clinical workflows and I

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understand and know how
to do data analysis and

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our work has been supported here. Uh,

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I implore that one of the big investments
that we are trying to make and that

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institutions across should try and
make for investing in their own is

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invest in, there's two main arms.

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So invest in entrepreneurship so
the people who have good ideas don't

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force them to leave your
institution to execute the idea.

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Don't make them start a
startup, nurture them,

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take that person aside,

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get rid of all of the red tape in
their way and implore them to do

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the job within the institution
so that both can reap benefits.

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And at the same time,

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part of that can be catalyzed
by creating data pipelines that

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help people gain the insights they need
to gain a lot of these free thinking.

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People need data behind
their idea to know that a,

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it either works or it doesn't so they
can fail quickly or succeed quickly.

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I mean, fast failing should be
something that is desired out of

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the free thinkers in an institution
rather than there being a fear of failing.

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So a big opportunity we see is sort of
setting up data pipelines for people so

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that they can explore, like
we talked about before,

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but also investing in the people who are
already there and maybe understand the

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workflows better so that the A,
they stay as Kathleen said, and b,

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they gained value for your institution
by inventing a product that the

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institution can take hold of.

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I love that. And you know, what a,
a great way to foster some of the,

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the really important innovations
that are happening internally.

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I know entrepreneurship is a,

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a big focus for us at Becker's here
this year internally and it's just been

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amazing to see how many ideas people
come to the table with and things that we

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can try out. And you're right a
hundred percent in terms of, you know,

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being in a space where you can
get the data, get the feedback,

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make pivots quickly, and uh,

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really make the most out
of the team that's with us.

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So I I really appreciate um,

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you bringing that up and I think it's
such great insight and especially in

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healthcare.

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I know that can be a challenging
space to try new things but you know,

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it seems like now is as good a time as
they need to to really embrace that.

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Yeah,

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now is a great time cuz there's a lot of
drive now that people are seeing what's

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out there more. It's, it's exciting.

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Absolutely. Now before we
wrap up our conversation,

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and this has been so great
speaking with you both,

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I wanted to look to the
future a little bit more. Uh,

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where do you see some of
the best opportunities for
growth and development in

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healthcare? Where do you see things
going, uh, you know, with the,

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your health lab and the other efforts
that you are having around data analytics

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and, and really, um,

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using that to drive success
both internally as well as
with the patients in the

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community?

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Well, you know, I think as, as we've
been talking about like our, our focus,

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our,

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or a huge part of our focus right now
really is on generative AI and figuring

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out not just what can it do technically,

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but how can we effectively use
it to improve our health system.

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You know, where are the
places that it can, you know,

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improve efficiency, improve
productivity, improve quality,

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uh, you know, of people's
experience in, in their work. Um,

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so that's what we're, that's where we
see a big opportunity and what we're,

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what we're focused on right now.

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Yes, I mean,

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I work directly with Kathleen in the
health lab and even in my position outside

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the health lab, this is the
topic of conversation. This
is the best opportunity.

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There are gonna be hundreds,

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there's going to be a boom of companies
and products and ideas that come out of

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this space.

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And our goal and our best opportunities
are to find the ones that will help us

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the most. And we are sort of pushing
all of our efforts in that way.

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Making sure that it's done
appropriately is difficult,

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but there's so much opportunity
there that we have to try.

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I love that. Dr. Gleer, Kathleen,
thank you so much for being here today.

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This has been such a, an amazing
conversation and, and really, um,

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very enlightening in terms of where the
healthcare system is headed and thinking

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about generative AI and how
that can really be effective. I,

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I'm looking forward to having you both
as well speak at our health IT digital

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Health and Revenue cycle event
in October. I think, you know,

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things will be accelerated even further
by then and I'm really excited to see

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where the discussion takes us.

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Yeah. Thank you so much for having us.
This, this was a great conversation.

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Thank you again.

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