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the description. We are excited to welcome you in October.
This is Laura Deda with the Becker's Healthcare Podcast.
I'm thrilled today to be joined by Dr. Conroy Gleer,
assistant Professor of medicine and associate director of Data Analytics at
University of Rochester Medical Center,
and Kathleen Fear Director of data Analytics at the Health Lab. Dr. Gleer.
Kathleen, thank you so much for being here today.
Thank you.
Now I know we've got a lot to talk about and you know,
I'm really excited to learn more about what you're doing, um,
there at the University of Rochester. But before we dive into my questions, I,
I would just love to have you both introduce yourselves,
tell us a little bit about your backgrounds and then, you know,
it'd be great to, to color the rest of our conversation. So, uh, Kathleen,
let's start with you.
Sure. Uh,
my name is Kathleen Fear and I'm the director of data and analytics at the U
Health Lab,
which is the innovation incubator at the University of Rochester Medical Center.
Um,
I've been at the health lab for about five years now and at U M C for a little
bit longer than that. Um, my background, my PhD is in information science,
uh, really focusing on how people understand,
trust and make decisions from data.
So a lot of my work now is focused on how do we not just make data accessible
but usable, you know, how do we build the right products, the right tools,
the right culture that enables data-driven,
like strategic and everyday decision making?
And I'm Conrad Gleer. She mentioned my titles briefly there, but I,
I came to know Kathleen very well from the health lab and
I landed in the health lab kind of cuz of not just my medical education,
but I went to business school and I'm sort of one of those jack of all
trades people who have jumped in and out of different worlds.
But I'm now currently very heavily set in the data and analytics world.
Um,
I learned most of these things through necessity since most of
healthcare requires a certain degree of knowledge in this space.
And I came to work in the innovation lab to hopefully make things better.
Absolutely. Well, fantastic. And you know,
I'm excited to have our conversation today because I know there are so many
different ways that technology and innovation are really becoming a big part of
healthcare and healthcare delivery now for,
I guess I wanted to start off with thinking through what are some of the big
opportunities that you are seeing right now today and then the headwinds you
have your eye on. So Kathleen, I'd love to hear from you and then Dr. Kleber,
I'd love your perspective as well. You.
Know,
I think generative AI has the potential to really revolutionize the way people
interact with data and information. You know,
in the way that the microprocessor democratized access to computing power,
the way the internet democratize access to information generative a AI really
has the potential to democratize access to insight generation. You know,
especially in an academic medical center,
one of our greatest resources is curiosity.
Where an organization that's full of really smart people who have chosen to
practice in this setting because they want and should have the space to ask
questions, do research, innovate.
But there are so many barriers between asking a question and getting to an
answer,
especially if it's like an exploratory blue sky kind of question that maybe
doesn't rise to the level of a full-on like IRB approved study or,
or or doesn't.
Yet generat generative AI can really give people a running start on their
questions and by facilitating experimental exploratory work and by giving people
the ability to use and apply and apply powerful models without even know needing
to know how to col code,
it really has the potential to free people up to do the kind of higher order
thinking that our organizations really need.
And crucially to build a culture of experimentation.
I was recently listening to another podcast with Lucas Premier, um,
who's really an expert in how to scale innovation and experimentation in
organizations. And he said something that really resonated with me,
which is that experimentation has to permeate everything, you know,
in an organization it's not about just making a few great decisions,
it's about making many, many slightly better ones.
And so to me the data and technology opportunities to really keep an eye out for
are the ones that help facilitate that. You know,
the things that help build an environment where everyone's empowered to ask
questions and seek answers where it's okay to be wrong or uncertain and where
people have, uh, have the ability to like think and try and test.
I can sort of key off that and sort of come behind and generative
AI is the new hot topic that probably everyone's gonna want to
talk about until there's a,
an elephant in the room sort of product that comes out from G P t.
I think it stems from the large opportunity is the democratizing
as Kathleen said,
but that's also in the setting of what was happening beforehand.
Our institution is not alone,
but a lot of the data and analytics done at large medical institutions was very
siloed and work was often repeated and people
with different levels of skills we're trying to analyze data in multiple
different ways all at the same institution.
It happens to be one of the main issues with trying to create an effective
data culture and make decisions around data.
And this is something that Kathleen and I run into all the time. I mean,
our biggest opportunity that we see is setting up playgrounds and sandboxes
and virtual environments to allow
anyone to do data exploration, uh,
allow people to explore clinical data,
not with the mindset of they have to be very careful
about what decisions they're making, about how the data is used.
We set them up in a way that they can explore to their heart's content
and come to advanced conclusions.
And that can be greatly enhanced by generative AI as you can simply
ask the question of the AI and it can help you obtain an answer or at least set
you on the right path.
Absolutely.
That's such a great point and it's fascinating to see how that generative ai,
uh, has really, um, come into its own, uh, over the past several months,
especially, um, in the healthcare space as well as overall.
And it will be exciting to see how it evolves in the future.
Now when you're looking at the technology and, and really, um,
the opportunities to continue to add value to, um, organizational overall,
what does that look like for you? Where do you really see, um,
the most potential for technology and uh, um, your, both your roles,
um, in innovation, being able to continue to add value going forward?
Yeah, I think maybe, maybe I'll start with kind of a cautionary note,
which is I think especially with things like, you know,
shiny new tools like chat pt, but you know,
even around any other technology that that we look at, it's, it's,
it's important to not fall into the trap of sort of technology wishful thinking.
You know,
it's really easy to look at any of these tools and just hope it's gonna be a
silver bullet that is gonna solve every little problem we have. You know,
whether it's generative ai, you know,
giving everybody the ability to do whatever they want whenever they want or even
something like, you know, our our,
our new enterprise data warehouse is just gonna solve all our access problems
or, or whatever. Um,
but like fundamentally healthcare is a really complex sociotechnical system and
the hard part is often not the technical part,
it's the socio part that can really be the kicker. You know, I think our,
our toughest problems are the things that are operational implementation
systems and and people kinds of problems.
Yes.
I mean a lot of times we will have these great ideas and Kathleen and I will
work on them and then we go to the implementation phase and it's often three
times longer than the innovation phase into figuring out how
to use these tools.
I mean one of the biggest opportunities we have and sort of the fun part of
where we are and basically why I'm here is we have
so many new products coming along be them data products or technology products,
but they are being pushed in some one sort of general direction.
It's that the bottom line is we're looking for technologies that help doctors
and nurses be doctors and nurses rather than dealing with the
surrounding stresses. I mean,
the burden of being in clinical practice as a doctor or nurse is
growing and people are choosing to leave it despite the value
there. And if we can find technologies and ways to make it better,
it is an unbelievable opportunity.
So there's a staffing crisis with nurses and doctors that's probably most of the
people on this that listen to this podcast institutions are all feeling.
And the product that comes out that helps that burden
and helps with that crisis is the one that's gonna survive being part of
the healthcare market in general.
We just love to see the ideas come from generative AI because that will sort of
push a lot of people to make products that will help us.
But what Kathleen said is true,
there's no silver bullet cuz every workflow is different,
but even these small improvements to a workflow can be additive as much as
they're subtractive when one extra burden is laid on clinical practice.
That makes a lot of sense. You know, and it's really fascinating to hear,
I know as you mentioned,
so many different organizations are trying to figure out their, uh,
staffing challenges and shortages and,
and make sure that they're moving in the right direction,
whether it's using technology and automation as much as possible and then to,
um, you know,
connecting with their teams and trying to retain that the staff that they have
and team members that they have with um, eliminating burnout. So it's just,
it's a fascinating time right now for innovation in the staffing of workforce
space. Now as we've been talking about,
I know that resources are limited and especially whether it's financial or um,
human capital. So, you know, what is important to still invest in, in, um,
risks that are still worth taking right now to put, set yourself up, uh,
for growth and success in the future,
even given some of the challenges that we're seeing today?
I think one of the best investments that health systems can make is to invest
into their own people. You know,
invest in the people who are already engaged in the system.
Give them time and space to grow and to do the things that are rewarding to
them. You know, the health lab, we, we set out to be an innovative place,
you know,
to develop new technologies and do impactful things for the health system.
And one of the impacts that we have had but didn't anticipate having is
actually on faculty retention. You know,
we have clinicians who work with us and who came to us when they were so burned
out and ready to walk away,
but by giving them the resources to explore the things that are meaningful to
them, we've helped 'em stay engaged. Um, and of course like there's, there is,
this is an investment, there's a cost to this, you know,
people need protected time and like the health lab is entirely funded or
mostly funded by overhead. But I think the,
the dividends that our work has been able to pay in terms of like
not just provider job satisfaction but like technical staff satisfaction, um,
I think have been really important.
I think I'll focus on that specifically for a little bit.
As in some cases people view clinicians
or analysts
doing work outside their scope in a
odd way. I generally refer to this as the can't assumption.
There are groups that feel that there is an assumption that some groups should
not or cannot go and work or understand a different workflow
in Kathleen and i's world.
It's the can't assumption that Kathleen can't understand the clinical workflow
cuz she's not a clinician and it's the can't assumption that I can't understand
what a data analyst does cuz I'm not a data analyst and I think we are the
antithesis of the can't assumption.
Kathleen is very capable and does understand clinical workflows and I
understand and know how to do data analysis and
our work has been supported here. Uh,
I implore that one of the big investments that we are trying to make and that
institutions across should try and make for investing in their own is
invest in, there's two main arms.
So invest in entrepreneurship so the people who have good ideas don't
force them to leave your institution to execute the idea.
Don't make them start a startup, nurture them,
take that person aside,
get rid of all of the red tape in their way and implore them to do
the job within the institution so that both can reap benefits.
And at the same time,
part of that can be catalyzed by creating data pipelines that
help people gain the insights they need to gain a lot of these free thinking.
People need data behind their idea to know that a,
it either works or it doesn't so they can fail quickly or succeed quickly.
I mean, fast failing should be something that is desired out of
the free thinkers in an institution rather than there being a fear of failing.
So a big opportunity we see is sort of setting up data pipelines for people so
that they can explore, like we talked about before,
but also investing in the people who are already there and maybe understand the
workflows better so that the A, they stay as Kathleen said, and b,
they gained value for your institution by inventing a product that the
institution can take hold of.
I love that. And you know, what a, a great way to foster some of the,
the really important innovations that are happening internally.
I know entrepreneurship is a,
a big focus for us at Becker's here this year internally and it's just been
amazing to see how many ideas people come to the table with and things that we
can try out. And you're right a hundred percent in terms of, you know,
being in a space where you can get the data, get the feedback,
make pivots quickly, and uh,
really make the most out of the team that's with us.
So I I really appreciate um,
you bringing that up and I think it's such great insight and especially in
healthcare.
I know that can be a challenging space to try new things but you know,
it seems like now is as good a time as they need to to really embrace that.
Yeah,
now is a great time cuz there's a lot of drive now that people are seeing what's
out there more. It's, it's exciting.
Absolutely. Now before we wrap up our conversation,
and this has been so great speaking with you both,
I wanted to look to the future a little bit more. Uh,
where do you see some of the best opportunities for growth and development in
healthcare? Where do you see things going, uh, you know, with the,
your health lab and the other efforts that you are having around data analytics
and, and really, um,
using that to drive success both internally as well as with the patients in the
community?
Well, you know, I think as, as we've been talking about like our, our focus,
our,
or a huge part of our focus right now really is on generative AI and figuring
out not just what can it do technically,
but how can we effectively use it to improve our health system.
You know, where are the places that it can, you know,
improve efficiency, improve productivity, improve quality,
uh, you know, of people's experience in, in their work. Um,
so that's what we're, that's where we see a big opportunity and what we're,
what we're focused on right now.
Yes, I mean,
I work directly with Kathleen in the health lab and even in my position outside
the health lab, this is the topic of conversation. This is the best opportunity.
There are gonna be hundreds,
there's going to be a boom of companies and products and ideas that come out of
this space.
And our goal and our best opportunities are to find the ones that will help us
the most. And we are sort of pushing all of our efforts in that way.
Making sure that it's done appropriately is difficult,
but there's so much opportunity there that we have to try.
I love that. Dr. Gleer, Kathleen, thank you so much for being here today.
This has been such a, an amazing conversation and, and really, um,
very enlightening in terms of where the healthcare system is headed and thinking
about generative AI and how that can really be effective. I,
I'm looking forward to having you both as well speak at our health IT digital
Health and Revenue cycle event in October. I think, you know,
things will be accelerated even further by then and I'm really excited to see
where the discussion takes us.
Yeah. Thank you so much for having us. This, this was a great conversation.
Thank you again.
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