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This is Laura Dierda with the Becker's Healthcare

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podcast. I'm thrilled today to be joined by

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doctor Justin Koren, who is an analytics executive.

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Justin, it's a pleasure to have you on

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the podcast today.

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Thank you so much for having me. Exciting

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for this discussion.

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Absolutely. Well, Justin, I am excited that we

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have you here as well because I know

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you've had a lot of experience at a

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variety of health systems across the country and

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certainly

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in-depth knowledge on analytics

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in the IT side. And so this will

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be a great perspective to share with our

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audience. I I know things are moving so

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rapidly, especially in the artificial intelligence space. So

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I think I without further ado, let's get

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started here.

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Before we begin, could you introduce yourself and

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tell us a little bit more about your

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background?

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Absolutely.

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So, I am, as you stated earlier, an

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analytics executive

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and, been a former chief analytics officer in

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health care systems. I'm also a data scientist

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and innovator with over twenty years of experience

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in the health care industry.

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In 2024,

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I won the AI 100 award, which is

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given to the top 100 executives in the

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country that conduct AI ML

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operations

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within their companies.

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And,

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overall, I've held multiple adjunct professor positions in

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medical schools. And I I would classify myself

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as, you know, just having this ongoing passion

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for solving complex problems

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and integrating applied analytics into company's operations.

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Well, that's great to hear. You you know,

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and and certainly,

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really a lot of experience I know you

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you bring.

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So I guess we'll dive right in. As

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an analytics executive, what are the most successful

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projects you've led at Health Systems that yielded

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the best results?

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Sure. Actually, one of my earlier projects is

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still yielding,

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results. And at this point, it's gone global.

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So I created a machine learning

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algorithm to help the risk risk stratify patients,

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in the population health space and within value

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based care. And what that algorithm did is

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it found the patients that were at the

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greatest need of care.

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And when facing limited human resources in a

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large health system and when they have a

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lot of patients,

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under a population health umbrella.

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The question is, how do you get the

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sickest patients to the doctors as fast as

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possible?

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And so, when I conducted that work earlier

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on in my career,

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those patients overall, that proportion of patients were

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responsible for about $1,200,000,000

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in spend.

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And by using that algorithm and being able

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to bring the sickest patients into care and

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to the attention of care coordinators,

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we were able to save $200,000,000,000

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A lot of that money

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was in the employee health care plan within

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that health system, which meant there was a

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huge return on investment.

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And now,

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multiple health systems across the world use

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that algorithm or a customized version of it

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today to help with their population health efforts

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Some of my other work that I've done,

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is,

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several work in ai and ml space and

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detecting undiagnosed disease And how can we find

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the patients

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that either are currently under care and undiagnosed

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and get those patients to primary care or

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to the appropriate specialist,

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so that we can address

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those variations in care or that potential undiagnosed

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disease as fast as possible.

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Other things that I've done more recently is

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a lot of technology

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consolidation,

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building modern cloud infrastructures

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and really enabling

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what I call data and analytic governance,

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within health care systems and other organizations.

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And that really sets the foundation for being

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able to do applied analytics or even moving

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into the artificial intelligence and machine learning world,

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being able to integrate those type of products

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with success into a company's ongoing operations.

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That's amazing to hear. You know, it it

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really cool to understand that real world example

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of not only using the AI algorithms to,

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provide better care and more targeted care, but

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then also

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realize those types of savings. I mean, that's

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significant, especially in a time right now where

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so many hospitals and health systems are tightening

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their belts and and seeing thinner margins than

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before, being able to have the right kind

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of expertise in,

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AI models in place, it seems like is

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a huge, huge benefit.

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It's critical in in today's world. And and

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as we know, health care costs are very

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hard to control,

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and seem to continue to go up year

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over year. And so being able to leverage

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a company's information to make better clinical decisions

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or even better administrative decisions if we're not

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just talking about clinical applications,

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It's gonna be critical over the next five

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years,

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as health systems become more analytically mature and

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start looking at different enabling technologies to achieve

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efficiencies in their core business.

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Absolutely. That's such a great point. Now I'm

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curious looking into that future. Where do you

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see ai and machine learning headed in health

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care? What are some of those best opportunities

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for hospitals? And what should they watch out

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for?

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So I think we're gonna see multiple categories

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of AI and ML across the company. And

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then let me define that a little bit

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better. So category number one is the question

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of where is AI and ML going, let's

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say, within the clinical space, and to aid

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clinical operations,

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physicians, nurses, and other health care workers.

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The biggest boom we're seeing right now is

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the integration of AI and ML within electronic

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health record systems.

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Definitely, we're seeing Epic as one of the

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leaders here where they've got over three dozen

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types of AI products that they're bringing into

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the Epic ecosystem.

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Oracle has announced that they're doing some very

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similar things on the integration of AI within

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their electronic health record product.

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And then also within that clinical domain, it's

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not just EHRs that are benefiting from the

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integration of AI and ML, but we're already

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seeing medical devices

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utilize it more frequently than they ever have

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before. And what's interesting in the medical device

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space is MRIs were utilizing machine learning technologies

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for decades at this point. And so some

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of the earliest machine learning models were actually

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used in radiology and in the construction

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of different types of radiological

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equipment. But we're now seeing AI and ML

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and medical devices

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that are related to cardiology,

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neurology. Of course, radiology

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is still a pioneer in the space. And

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then we're even seeing some examples in pathology.

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And that's just a few to name many,

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of the different innovations that are occurring across

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the country.

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Also,

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so if we talk a little bit about

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the clinical domain, then we have the administrative

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domain. And that's a really an interesting one

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because I think a lot of times in

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health care, we first gravitate towards the clinical

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side and how can we first help our

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physicians and our nurses.

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But on the administrative side, we're now seeing

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a rise in AI that can have what

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what some of the data scientists and practitioners

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call a force multiplier effect. And what that

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means is we're looking at being able to

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have people be more efficient in the work

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that they're doing day to day. And if

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you can utilize an AI tool to do,

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let's say, two or three times the amount

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of work but not have to include any

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additional hours in your day, then AI didn't

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replace people. What AI is really doing is

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taking the existing work staff and

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multiplying the amount of work they can do

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with the same amount of effort. And then

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that's what we mean by a force multiplier.

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Some of this we're seeing within tools like

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Microsoft Copilot,

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where, you know, in traditional day, you might

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have a person that would have to sit

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in a board meeting or in other critical

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committee meetings within an organization,

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and they would take notes or potentially transcribe

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the meeting minutes. Now we have ambient technology

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that can just listen and automatically

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transcribe

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those meetings for us. That type of technology

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is also existing on the clinical side where

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we can have ambient technology listening to a

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physician patient encounter and then helping that doctor

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transcribe that activity directly into

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documentation within the electronic health record system.

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Also, on the administrative side, I think we're

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gonna see a lot of innovation in revenue

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cycle management in the finance area, especially within

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the automation space.

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And then somewhere, let's call it maybe five

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years down the road because there's only about

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three dozen health systems nationwide that are really

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doing this today. But we're gonna start seeing

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a lot of in house innovation take off

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where a health system or even another company

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could even be outside the health industry Is

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starting to build their own ai models their

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own ai agents or modifying a large language

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model that is specific and customized to health

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care

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And we definitely see we're gonna think we're

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gonna see a huge Proliferation

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in that type of work, you know anywhere

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from five to ten years from now And

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the reason that could take a long time

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is it requires a company to have a

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very strong data foundation

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and an analytic foundation, a governance program in

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place where you can have standards and policies,

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your technology and your environments that house your

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data needs to be consolidated

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so that if you're training an AI model,

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or an application that it can source that

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data from the smallest number of locations possible

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in order to speed the development,

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of that product. And there's a lot of

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health systems that are still doing a lot

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of work on their foundation. But as they

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catch up, to let's say the, you know,

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the top 20, top 36 health systems that

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might already be doing this today,

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we're gonna start seeing that exponential effect again

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of in house talented teams

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being able to customize these products alongside doctors,

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nurses, and health care workers.

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That was fascinating to hear. You know? And

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and, so truly, truly important to have that

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right mix of, you know, outside expertise that

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you're working with as well as building those

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in house teams to meet the need needs

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of the organization overall. And I'm curious as

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we get closer and closer to that space

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of of,

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more AI, more of those models, more technology

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that's supporting the in person teams,

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What does that look like for the IT

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leaders? How are their teams transforming? What type

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of talent or or skill sets do they

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need to bring in in order to prepare

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for that future?

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Sure. So data engineering is gonna be critical.

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And, you know, sometimes that comes into,

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a buy versus a build type strategy. Do

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you partner with a third party that can

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really help with your data management and and

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data engineering environments? Think of, like, cloud based

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environments and going from potentially fragmented data centers

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or multiple,

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technology platforms into just one just to enable

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that work to be done.

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Regarding

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inside talent and I think this is another

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reason why health care is gonna be a

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little bit slower to adopt

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a build strategy or even being able to

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innovate at innovate at scale is because you

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really need to have the right mix of

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talent. And that means the combination

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of engineers,

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data scientists, analysts, and potentially even software developers

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all being able to work together with a

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common goal,

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for that organization. And historically,

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most health systems,

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especially if they're not an academic health system,

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because we do see this quite a bit

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in academic health systems,

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don't have that same mix of talent or

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that mix of expertise in house today. The

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other challenge is this field is growing exponentially,

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where one of my key pieces of advice

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to any analytic executive, but this can even,

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be a piece of advice to CIOs and

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CTOs,

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is to master the art of self directed

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learning

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because the field is evolving so quick year

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over year. And that could be based on

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technologies that are advancing high performance computing.

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This could be brand new programming languages or

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innovations within popular programming languages such as Python

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and even just the availability of large language

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models. You know, several years ago, there was

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only maybe a dozen. Today, there's over 350.

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And being able to be that strong self

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directed learner will allow you to stay ahead

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of the curve and to really be able

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to understand what's coming down in the pipeline.

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But even more important is how do you

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filter out some of that noise? Where if

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you're being bombarded with, you know, 100 to

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200 new ideas a day, how do you

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figure out what the top 10 or top

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20 most important are that you should focus

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your time on and then really start thinking

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of what does that mean for my operations

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or even for my company?

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Got it. That makes a lot of sense.

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And, you know, it is really helpful specifics

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in terms of looking at how different organizations

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are thinking about this and going about this.

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Obviously, short and long term,

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you know, looks different for everybody, but to

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have that kind of skill set and understanding

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of what's possible and how quickly things are

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changing in the technology side is really, really

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helpful. Before we wrap up here, I'm curious,

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what advice do you have for emerging health

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care leaders, especially in the analytics field?

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Yep. Specific to analytics executives, one of the

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top pieces of advice that I could provide

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is partner very closely

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with your chief information officers, your chief technology

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officers,

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and even your your vice presence of IT.

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Analytic operations

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cannot reach its full potential if analytic leaders

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operate in a silo.

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It requires

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collaboration

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across the company, but even stronger collaboration

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between

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your IT department operations and your analytic operations.

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And that's specific no matter where analytics sits.

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In some comp in many companies, analytics sit

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in the IT department, and and you might

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have a natural hierarchical relationship

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to those leaders, your CIO, your CTO, or

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other VPs.

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But there are other companies where analytics sits

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outside of IT.

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And

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it is critical that even in those business

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models

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that they partner very closely, they should have

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a seat at the table both in the

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governance

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program of of how you're governing data and

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using analytic products. But even in the innovation

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space and the technology space of what technology

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do we need in the company to enable,

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the analytics strategy, and what does that look

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like if we do wanna create something new

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in house within the company?

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Those members should have a seat at the

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table during all of those discussions and be

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very close partners.

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That's amazing to hear. Justin, thank you so

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much for joining us on the podcast today.

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This has been a really amazing discussion, and

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I look forward to connecting with you again

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soon.

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Thank you very much for having me. It

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was a pleasure.