Latest / Becker’s Healthcare - Clinical Leadership Podcast / Dr. Conrad Gleber and Dr. Kathleen Fear of University of Rochester Medical Center

Transcript

Dr. Conrad Gleber and Dr. Kathleen Fear of University of Rochester Medical Center · Becker’s Healthcare - Clinical Leadership Podcast

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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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