Latest / Becker’s Healthcare Digital Health + Health IT / Mayil Dharmarajan, Vice President of Data and Analytics at UC Irvine Health

Transcript

Mayil Dharmarajan, Vice President of Data and Analytics at UC Irvine Health · Becker’s Healthcare Digital Health + Health IT

0:02
The most important health care decisions don't happen
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in isolation.
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They happen when leaders come together.
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This is Laura Dirdo with the Becker's Healthcare
0:48
podcast.
0:49
I'm thrilled today to be joined by Myles
0:51
Demarajan, who is the vice president of data
0:53
and analytics at UC Irvine Health. Myles, a
0:55
pleasure to have you on the podcast today.
0:59
Same here, Laura. Thanks for inviting me.
1:02
Absolutely. Now I'm looking forward to our conversation
1:05
because I know UC Irvine Health has gone
1:07
going through a big transformation right now, a
1:09
lot happening, some expansion after your acquisition. So
1:12
I'm looking forward to hearing a bit more
1:14
about that. But first, can you introduce yourself
1:16
and just talk a little bit more about
1:17
UC Irvine Health?
1:19
Sure.
1:20
I'm Mile Duran. People call me Mile. The
1:22
closest pronunciation is m I l e.
1:25
So I'm vice president of data and analytics
1:27
in USA Health. So I help USA Health
1:30
in creating the data driven ecosystem so that
1:33
we can take,
1:35
authorization,
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decision based on data insights
1:39
that we develop using the metrics and other
1:42
parameters.
1:44
You see, eighteen months for me. I'm relatively
1:47
new, and I'm very excited to be here
1:50
in UCI Health.
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It's one of the, you know, popular academic
1:54
medical center in Southern California.
1:56
So we are,
1:58
close
1:59
to 350
2:00
beds,
2:01
hospital.
2:02
And recently, you know, in this,
2:05
I would say couple of months, we transform
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ourselves from
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being single hospital to seven hospital system, integrating,
2:11
you know, several tools, technology, processes, people, all
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of them together. And everything went live, you
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know, last,
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December, meaning just six weeks back.
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Good night. Wow. That's, incredible to hear. And
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I know, you know, so much,
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time and space happening. So, I guess, could
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you tell us a little bit more about
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some of the big trends you're watching currently?
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What's top of mind for you?
2:35
Yeah. I mean, some of the trends that
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we are currently, you know, watching from,
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as a health care organization, you know, including,
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being a academic medical center also is,
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how AI, like anybody else, right, can enable
2:50
some of the things that we do.
2:52
As you may
2:53
know, the health care, especially the provider side
2:55
of the business, is a very low margin,
2:57
and we are always in the financial pressure
3:00
to make sure that how we can deliver
3:02
patient care with the limited resources that are
3:05
available,
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at the same time without any compromising the
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quality of the care that we do. So
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keeping those things in mind,
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how maximum we can leverage technology that are
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available.
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So
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currently, we are looking at all the tools
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technology available that we want to use
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with our recent,
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you know, opportunity to move from shared
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EPYC or EMR instance to our own instance,
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helped us a lot of, you know, positive
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way to get there.
3:33
And also
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in parallel, we are also, you know, shifting
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ourselves moving from
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the typical AI pilot demos
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to how do we, you know, productionize
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those AI projects
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and, make sure that, you know, appropriate governance
3:48
structure put in place
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and,
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enable our, you know, people across our organization
3:55
from either its operations or the clinicians using,
3:57
you know, some of the technology like Upbridge
4:00
for the scribes, as well as the community
4:01
cycle and research,
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community,
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to make sure that though they have
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the appropriate tool that they can use
4:09
in a more effective way to do their
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day to day job.
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And, also, you know, from the
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health care data perspective,
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we also, you know, enable the technical framework
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in our new EMR.
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So what what it benefits is, you know,
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it it it also
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enabled us to leverage
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all the future functionality of the TEFCO framework
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so that we don't have to really, you
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know, dip it on a lot of HAEs
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to do certain functionality.
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We leverage the maximum
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opportunities to use a technical framework
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so it can, you know, also help us
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in cutting some of the costs related to
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the HIE technology.
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Additionally, we are also now investing,
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or looking for the opportunity to do the
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AI governance.
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In the AI governance, you know, the important
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part is to make sure that how do
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we
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manage those models in appropriate way? How do
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we have the right repository? How do we,
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you know, align with the regulatory,
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needs? And then also, obviously, the privacy and
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other concerns that we,
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have to make sure that those models are
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working appropriately to make it happen.
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So that's another, you know, important thing that
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we are investing as well as the
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the FHIR or the FHIR, the API maturity
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also helping us in
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helping solve the,
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analytics integration back into the application,
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which is, you know, previously I mean, we
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we used to have FHIR,
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APIs, but, again, it's not that much better.
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But now we have that opportunity
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to leverage that, more and more.
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And,
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in parallel, this is all, you know, mostly
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on the EMR side, you know, vendor dependence
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in certain things, including some of those,
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HIE
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stuff. But at the same time, we are
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also investing
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in modern analytics platform,
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which needs
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for our, you know, the administrative
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and other research,
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related needs. So this platform will help us
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to integrate data across, our multiple data domains,
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with the appropriate data governance structure. We also
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have an opportunity to,
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make sure that,
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the data is having all the trust
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in terms of, you know, making,
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the validation, data lineage, data metadata management, and
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other
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technology solution in the bottom,
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as well as collaborating always tightly with the
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business team to make sure that they understand,
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the the needs
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of definition of the each of the KPIs
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that they are talking about and making sure
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that those KPIs or the key performance indicators
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are defined appropriately.
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And then bringing all of them onboarded
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to understand these definitions are, you know, aligning
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with their specific needs
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and publish those information so that it there's
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there's a transparency
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in understanding and the documenting and using those,
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key performance syndicated. So why it is important?
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Once you have that particular process built
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with the tight partnership with the business,
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it will be very tough for us to,
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you know, say that, you know, this particular
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data is accurate. So the accuracy of the
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data, the trust,
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worthiness of the data is built with a
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partnership and then with the data governance structure.
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We are not there fully yet, but at
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the same time, we are kicking off, data
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governance process, collaborating again heavily with our business
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leaders,
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to make sure that we have
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each one of the step that we do
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in analytics and having that kind of a
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partnership.
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So those are the broader trends that currently
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we are, you know,
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watching and then making sure that those trends,
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are aligned with our needs as an organization.
7:40
And then how do we, you know, bring
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in those, new tools, technology, other processes, you
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know, that tends can be even in the
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process, some of the specific process improvement that
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we can do,
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embedded,
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into our day to day, you know, workflow
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and, you know, our needs for our business.
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I love that. I think it's so helpful
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to understand that technology and exciting to see
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what's possible, but also, you know, the gravity
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of everything that you have to do in
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order to integrate the technology and the data
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and make sure the data is correct and
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accurate and then continues to, function in the
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way it's supposed to. I I think one
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of the things you talked about is the
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data governance process, which I know is so,
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so critical in being able to get the
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right outcomes that you need once you've got
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the technology in place.
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So can you talk a little bit more
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about the data governance process and how, you
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know, you set it up, who's involved, and
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what you've really learned about, making sure that
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this is done in a way that helps
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you move innovation forward, but also isn't putting
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you at risk?
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Yeah. I I think, I mean, those are
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the things that we are, watching now. So
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we we, the as our association, we established
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health data governance.
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But, again, we focused mainly on certain areas.
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And, again, we want to expand that across
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all the areas, you know, including administrative,
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key metrics and other things.
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So keeping that in mind, we are we
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are rejuvenating some of those,
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specific governance process across where we can bring
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in more,
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clinical clinical related,
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you know, the subject matter experts, including the
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leadership level.
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So we have one integrated, you know, set
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of processes across our organization as enterprise.
9:15
Right? As a clinical medical center, as you
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may already know, when we have the the
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combination of, the medical system, the hospital side,
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the research, and then the school side. We
9:25
always have different data needs for the organization.
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So we also need to carve out the
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appropriate data governance structure for that. So for
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example, in the hospital side, it can be
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a very specific
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set of data governance policy and procedure that
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we need to create versus in research, it
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may, you know, enable some of those policy
9:45
procedures for their data needs, but also it
9:47
has additional layer,
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for the privacy where we also need to
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go through the,
9:53
IRB and other, compliance,
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layers to make sure that, you know, we
9:57
have we are compliant with the privacy and
10:00
then consent of the patient before we deliver
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any data. So those layers are always, you
10:05
know, the additional,
10:07
activity that we need to do. So keeping
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that in mind, at this point in time,
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we want to have
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create, these policies, procedures for hospital in a
10:17
very broader sense, and then also trickle it
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down across our organization as appropriately.
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And also, Ruben,
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as academic medical center,
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we have several of our clinician also,
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you know,
10:31
do, research. They are also PI for some
10:33
of the research grants that they have. So
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we need to balance those activity and then
10:37
make sure that those
10:39
data is governed appropriately, not just in the
10:42
EMR, but also outside the EMR. How we
10:44
manage it? How do we secure it? How
10:46
do we make sure that,
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what are the minimally needed data only is
10:50
available for them at the same time,
10:52
without, you know, stopping their research.
10:54
What are the ways we can address their
10:56
data needs? And whether it's a data defined
10:58
dataset, we can give in a a broader
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sense. And also, we are also enabling some
11:03
of the real world data,
11:06
by partnership with our EMR vendor so that
11:09
the researchers have an opportunity to go and,
11:12
you know, get those access to a very
11:15
large set. In this particular one, we have
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access to 300,000,000
11:18
de identified data,
11:21
across the globe,
11:22
this data of patient across the globe,
11:25
through our EMR. But that
11:27
so this is this also enables that. Okay.
11:29
Now it's a great now we have so
11:31
much access to the data. Now, how do
11:33
we make sure that this is safe?
11:35
So that's where the data governance for. That
11:37
specific real world data come into play. How
11:39
do we make sure that, you know, the
11:41
right people having access? How how do we
11:42
make sure that they are trained,
11:44
to use the data in appropriate way? So
11:46
all these things are, you know, also driving
11:49
us, to
11:50
make the,
11:52
you know, layers of data governance specific activity.
11:56
But I, again,
11:57
data governance is a very broad subject.
12:00
It's very tough for us to, you know,
12:01
start all the components of the data management,
12:04
data governance,
12:05
you know, aspects of it. So we need
12:07
to focus on some of the critical ones,
12:09
what would benefit,
12:11
UCI Irvine,
12:12
for next to, you know, three months, six
12:14
months, one year, and then address all those
12:17
specific component in that and then create the
12:19
policy procedures and then processes for that and
12:21
then execute that so that, you know, we
12:23
don't boil the ocean. We address each one
12:25
of these pieces and then move to the
12:27
next one. So that's what currently I'm working
12:30
on to make sure that, you know, what's
12:31
our roadmap for data governance? What area we
12:34
need to focus on data governance
12:35
to make sure that we create a policy
12:37
and procedures. So the user community as well
12:40
as, including the technical, nontechnical, and then the
12:43
users,
12:44
they have a very
12:46
transparent way to understand,
12:48
you know, to get, to get the data,
12:50
what I need to do. Simple. It could
12:52
be right. How do I access data? What's
12:54
the policy if my role is this x
12:56
of z?
12:57
How do we how do what kind of
12:58
a data
12:59
are currently available? Do we need to, you
13:02
know, install the appropriate data
13:04
catalog technology
13:05
to make sure that they understand what is
13:07
available before they ask for the new one?
13:10
And then it could be what's the intake
13:12
process and what's the priority for us to
13:14
deliver data. If you have, you know, 15
13:16
tickets when we have resource constrained and all
13:18
the 15 has to be addressed this week,
13:20
how do we prioritize that? So all these
13:22
structures have to be put in place, policy
13:24
and procedures, and and make it transparent, make
13:27
it available for our user committee so that
13:29
they know exactly where to go and look
13:31
for what, and they they are very clearly
13:33
understand,
13:34
the policy and processes to get there. So
13:37
that's in works for us
13:39
at this time.
13:40
Absolutely. Thank you so much for going through
13:42
all of that. I know it's a lot,
13:44
to to take on, but it's truly helpful
13:46
to hear kind of those processes and the
13:48
reasoning behind
13:50
everything you put into place. And so, you
13:51
know, as other health systems are trying to
13:53
do something similar, they can,
13:55
see that level of specificity. Now what are
13:58
you most focused on and excited about right
13:59
now?
14:01
Well,
14:03
so we are I'm very excited about,
14:07
how do we evolve through the processes
14:11
I mean, people and processes
14:13
after our big,
14:16
I would say,
14:17
big initiative that we have done.
14:20
So we acquired four new hospital,
14:24
couple of years ago, and then we brought
14:26
in all the hospital and integrated them from
14:28
technology perspective,
14:31
this December.
14:32
And also we built a brand new hospital,
14:36
investing 1,200,000,000,
14:38
and then we integrated that in the same
14:40
tools and technology. And then we also moved
14:43
from our shared EMR instance to our own
14:46
shared instance.
14:48
And then now we have our, you know,
14:50
feature race,
14:52
feature enriched or feature futuristic
14:55
EMR available for our clinicians and administrators.
14:58
So all the three things happen,
15:01
just six weeks back in December.
15:03
So it was, you know, very,
15:06
highly,
15:07
what do you want to call it? As
15:08
a,
15:10
time consuming,
15:11
complicated,
15:14
tools, technology, processes, people, chain management,
15:17
all of them put together in a very
15:19
tight,
15:20
you know, schedule.
15:22
Right? And, I'm very proud that as a
15:24
UCI, you know, coworker
15:27
to make everything happen. And I I I
15:29
saw people so much motivated in spite of,
15:32
you know, they have to do their regular
15:33
job of patient care,
15:35
our research,
15:36
our other administrative work. Along with that, you
15:38
know, they have to participate in all these
15:40
meetings to make sure that those things are,
15:42
they are done. So now now that we
15:45
are we have all these things in place,
15:46
you know, which is, again,
15:48
greatest achievement from my perspective. In my career,
15:51
I would say, I haven't seen any organization
15:53
taking all these three major initiative all at
15:55
the same time. Right?
15:57
So now my focus is, okay, now that
15:59
we have all the things,
16:02
as you may know, you know, obviously, we
16:03
have the thin margin and then how do
16:05
we, you know, make sure that with, available
16:08
resources,
16:09
how do we make sure that we have
16:10
all the appropriate
16:12
tools technology available for our users, you know,
16:14
without any specific lag or,
16:17
or delays. Right? The the minute when we,
16:20
have any delays in enabling them this feature
16:23
functionality,
16:24
people may lose interest and then they may
16:25
not even come back. So how do I,
16:27
you know, look at my team and then
16:29
reorganize my team within my team and then
16:32
motivate my team members to look at the
16:34
opportunity to enable some of the tools technology
16:37
so that, you know, we have,
16:39
opportunity for our user community to do the
16:41
self-service. And so that's another self-service analytics is
16:43
another important thing that, we are strongly creating
16:46
so that some of the,
16:48
regular reporting request comes in. All those things
16:51
will be, you know,
16:52
relatively
16:53
reduced. So my team can focus more mainly
16:55
on advanced analytics as well as how do
16:58
we partnership with the business
17:00
so that we become more of a business
17:02
analytics consultant to them than as a typical
17:05
report writer through the ticket. Right? How do
17:07
we sit with them to tell that, okay.
17:09
I understand all the data structure behind it.
17:11
I understand all the tools technology behind it.
17:13
And then I understand to a large extent,
17:15
you know,
17:16
what a specific type of request comes in.
17:19
So how about you and the business and,
17:22
the analytics team partner,
17:24
and be as a consultant and then build
17:26
the solution so that that improve their,
17:29
day to day processes? And how do we
17:31
optimize some of the processes that they they
17:33
have? And what is what are the analytics
17:35
needed for them to track, monitor, and improve
17:37
it? So that's where we want to go.
17:39
So that's my focus.
17:41
And how do I need to train my
17:43
team,
17:44
in those function? Right? Improving their communication
17:47
or, create some of the roles like the
17:49
product management or the business analyst type of
17:52
roles, as well as to make sure that
17:54
we have all the
17:56
training and other things available,
17:58
so that it's not like, you know, every
18:00
time when some certain things are needed to
18:02
be done in self-service analytics, every time people
18:05
ping us. Rather, you know, we have a
18:06
regular scheduled training across our tools and different,
18:09
technology.
18:10
So we enable the user community to use
18:13
self-service analytics.
18:14
So keeping those things in mind, you know,
18:16
all these transformation need to happen and need
18:19
to happen pretty quickly,
18:21
so that, you know, we don't lose interest
18:23
from the users,
18:25
of using our tools and technology or use
18:28
lose their interest,
18:29
in terms of, you know, waiting for our
18:31
reports analytics for a long time. So that's
18:33
our goal, you know, to make sure that
18:35
we enable the sales service analytics so that
18:37
they can do certain things themselves.
18:39
And then, be a business partner,
18:41
with them so that we can enable their,
18:43
or enable and then improve their process and,
18:46
you know, other operational improvement. And also,
18:50
leverage our team skills in advanced analytics, data
18:52
science. So we have one data scientist on
18:54
our team. You know, how do we leverage,
18:57
some of the data content and then improve
18:59
those things using those data science, attributes.
19:02
Right? So those are the things that I'm
19:04
currently focused on and, make sure that, you
19:06
know, these things are in place for next
19:08
I have we have a road map for
19:10
next six months to twelve months. And, also,
19:13
we are building the road map for the
19:14
following,
19:15
twelve month six months to make sure that
19:18
we have all the things queued up and
19:20
then validate ourselves. You know, we interact for
19:22
the,
19:24
the the aim the the things that we
19:26
are aiming for and also making sure that,
19:29
our users are always,
19:31
open for free feedback and then, communicating with
19:34
them on a regular basis, partnering with them
19:36
on a regular basis to make sure that
19:37
we are on track, and they are on
19:39
track on their expectation for the analytics.
19:44
Absolutely. I think that makes a ton of
19:46
sense. You know, it's it's really, helpful to
19:48
understand the whole process and everything that has
19:50
gone into,
19:51
creating, you know, these types of processes and
19:54
systems for the data and for the technology
19:56
as you've been implementing it in the new,
19:58
spaces, in the newly acquired
20:00
hospitals as well. Now I'm curious. You said
20:02
you have created that six month road map
20:04
as well as the twelve month road map.
20:06
You know, how do you I guess, what
20:08
if everything goes into plan, what do things
20:10
look like a year from now? And, you
20:12
know, how do you know that the investments
20:13
you're putting in are going to actually,
20:16
make sense and have a return on the
20:18
back end?
20:19
Yeah. I know. That that's a awesome question.
20:22
So
20:25
so fast forward six months. So from six
20:27
months from now, you know,
20:29
we expect
20:33
our our report analytics request through the tickets,
20:35
the traditional, you know, reporting needs, you know,
20:37
which is, you know, maybe a give me
20:39
a patient list, you know, for this with
20:41
with this, specific attributes. So those kind of
20:44
tickets, we are expecting it to reduce anything
20:46
between 20 to 30 percentage.
20:48
So what that also means is it is
20:50
releasing some of our,
20:52
team's availability,
20:54
for doing much more sophisticated more, sophisticated analytics,
20:58
report they may need. Right? That's number one.
21:01
Number two is I'm seeing
21:03
my team
21:04
transforming ourselves
21:06
as a business consultants, meaning that they have
21:08
rich knowledge of the data structure and then
21:10
the data behind it. And also,
21:13
the patents of analytic request comes from different
21:15
departments. So we have a broader understanding, not
21:18
just focus on one department,
21:19
but a broader focus on, you know, several
21:21
departments. As you may know in any of
21:23
the conversation, there is one department is slightly
21:26
better in certain operational
21:28
maturity than the other departments. So we can,
21:29
you know, compare and contrast and help our
21:32
businesses,
21:33
to do it better. We can say show
21:35
that as a benchmark to show, hey, here
21:37
are the things that the other team is
21:39
doing. You know, how about you trying this?
21:41
Right? So we have that visibility.
21:43
And also, the third thing is some of
21:45
the focus thing that we want to create,
21:48
that we strongly believe that will help. Say,
21:50
for example,
21:52
one of the key thing that I'm getting
21:53
consistently
21:54
across all the business leader is the translation,
21:58
layer type of role.
21:59
So the business talks plain English, you know,
22:02
you know, very simple English. Yeah. I want
22:04
to know about x y z.
22:05
And then my team talks, you know, pure
22:07
table and data structure. Right?
22:10
And there's a big gap between these two.
22:12
So how do we, you know, with this
22:13
particular transformation, how do
22:15
we, you know, enable ourselves, learn ourselves
22:19
the business related functions? And then how do
22:22
we make sure that we work with the
22:24
application team? And then various other, you know,
22:27
groups
22:28
to understand
22:29
what's the mapping between the business plain English
22:31
need and then the analytics,
22:34
or the technology? And how do you break
22:35
that particular gap with the specific roles like
22:37
the business analyst or the product manager type
22:39
of role, where this person can understand and
22:42
translate this information into a technical information, even
22:45
though it's a very traditional, you know, system
22:47
analyst or, you know, business analyst previously discussed.
22:49
But we want to make it as much
22:51
more than that. It's not just translation alone,
22:54
but also, you know, help them to give,
22:56
you know, kind of a translation plus the
22:58
consulting type of,
23:00
skill set. They they
23:02
will, you know, groom them into that level
23:03
of sophistication.
23:05
So it will help them to,
23:07
enable some of these data products, which will
23:09
have a lifecycle to say that, hey, here's
23:11
what we are going to do this particular,
23:13
set of dashboards or a set of metrics
23:15
that we want to follow through. And then
23:17
this will help you to improve your business
23:19
by seven percentage rate. So that's a level
23:21
of sophistication that we want to,
23:24
do. And then that's the that's a kind
23:26
of a one of the outcome I'm expecting,
23:29
not within the six months, maybe the following,
23:30
you know, six months and one year. That's
23:33
where we want to grow into. And then
23:35
we'll be able to,
23:37
you know, figure that out with within the
23:39
existing team itself. Right? We our team has
23:41
very,
23:43
kind of a very,
23:45
you know, deep knowledge about business,
23:48
but it's matter of connecting the point to
23:50
make sure that we expose that, you know,
23:53
skill sets outside
23:54
and making the collaboration between our business so
23:57
that and that will make and that will
23:59
happen. Yeah. So so the thing is,
24:02
one is through the metrics that we can
24:04
track and monitor the number of tickets that
24:06
we'll be able to, you know, see, the
24:08
immediate impact of that road map. The number
24:11
two is now how we are going to
24:12
transform it. And then by transforming you know,
24:14
creating this,
24:16
business translation layer, that will help us to,
24:19
you know, accelerate some of this business consulting
24:21
type of,
24:22
roles
24:23
much more better. That's good for our, you
24:25
know, businesses as well as it's good for
24:27
the organization,
24:28
so that we are, you know, much more
24:30
generative on organization.
24:34
Absolutely. No. That that's incredible to hear, and
24:37
I I think, it it's really cool to
24:39
understand everything that goes into bringing these products
24:42
to life. And,
24:43
I'm excited to to see how it goes
24:45
over the next year. So we'll have to
24:46
have another conversation,
24:48
kind of in debriefing and seeing where it's
24:50
all headed.
24:52
I'm curious. Yeah. Happy to share. Perfect.
24:55
Before we wrap up here, I wanted to
24:57
ask, you know, you've got a ton of
24:58
experience,
24:59
you know, throughout your career. And so what
25:01
advice would you give all evolving leaders, especially
25:03
in the health IT and technology space that
25:06
are looking to,
25:07
build their careers today and in the current
25:09
environment? What advice do you have for them?
25:12
Yeah. I I think,
25:14
over a period of time, what I learned
25:16
is,
25:17
as a technologist,
25:19
specifically data analytics
25:20
technologist,
25:24
the importance
25:26
for
25:27
any of the data analytics technologies to to
25:31
be successful is to have a very tight
25:34
partnership
25:35
with the business team. That's very, very important.
25:37
I mean, compared I mean, relatively
25:39
to any other IT or technology department. Again,
25:42
in some of the organization, data analytics, you
25:44
know, doesn't report to the IT, but, I'm
25:46
talking about, you know, the organization where, we
25:48
are part of the IT.
25:51
The important
25:52
thing that we need to keep in mind
25:53
is, you know, we are much more closer
25:57
to the business team because we are kind
25:59
of in the gray area between the business
26:02
and then the pure technology.
26:03
So what that means is the closer we
26:06
get to the business
26:07
and, you know, leverage
26:09
all of our technology knowledge along with the
26:12
business challenges our business team is having and
26:15
then build that partnership on a regular basis,
26:19
and then deliver the value
26:21
through that particular process. And also it is
26:24
imperative for us to make sure that we
26:27
have, on a regular basis,
26:29
keep in touch with the leaders. Regular basis
26:31
in a sense in a really regular basis,
26:33
regular cadence,
26:35
one on one with, you know, whether it's
26:36
a c suite or the VPs or whatever
26:39
different,
26:40
you know, senior leadership role in your organization.
26:43
Be with them
26:44
every
26:45
month minimum,
26:47
to make sure that we understand
26:49
their challenges
26:51
today
26:52
and how do we what are the opportunities
26:54
we have to solve it through data. Right?
26:57
So in,
26:59
as as as we all know, health care
27:01
is a very data intense business. Right? Everything
27:04
is data.
27:05
So
27:06
and it's very, very rare that anybody's gonna
27:08
tell them it's not a data problem or
27:10
something. So it's kind of a,
27:12
you know, it's it's it's obvious that as
27:14
a data leader,
27:16
you know, work with the business leaders, we
27:18
have a tight
27:19
relationship with them, and then be there to
27:22
solve their challenges,
27:24
in operations or in a strategical level. That's
27:27
number one. Number two is within our team
27:30
also,
27:31
you know,
27:32
keep them as a
27:33
a very broader skill set.
27:36
We don't want a specific
27:39
tool operator type of skills, meaning we don't
27:42
need you know, I'm I'm a informatica developer
27:44
or I'm a, you know, x y z
27:46
developer type of role. Rather, we need to
27:48
look at it as a, you know, broader
27:50
data analyst.
27:52
This person should think through from the start,
27:55
you know, how this data is created
27:57
till the analytics is delivered. Right? We need
28:00
to have
28:01
team members that has the wide,
28:04
aspect of the data, and then how do
28:06
we, you know,
28:07
get that particular skill set
28:09
built, not as a, you know, train for
28:11
a specific tool set or a specific tool
28:13
alone. So have that kind of a broader
28:15
overview. This also will motivate our team members
28:18
to see
28:19
if it here is what the business they
28:20
had. And then with my data knowledge and
28:23
then their business knowledge combined,
28:25
we solve that particular business problem with data.
28:28
So that gives them, you know, a good
28:30
understanding of how the business stands as well
28:33
as a good motivation of
28:35
how they
28:36
report a analytic solution solving business problem. K?
28:39
So I I would encourage our leaders to,
28:43
do that for our team,
28:45
so that, you know, we have that kind
28:46
of a motivated team.
28:48
And then the last one I would say
28:50
is, you know,
28:52
the important thing for all of us is,
28:54
the patient care or the, you know, research
28:56
outcome and other things.
28:58
So
28:59
that should be our foundational thing. We are
29:02
here to help enable our organization
29:04
to do,
29:05
you know, amazing patient care
29:08
with high quality.
29:10
So that's the foundation.
29:13
So that should be the foundation for all
29:15
the activities or the things that we do
29:18
in the organization.
29:19
So that's the foundation. So I I would
29:21
also encourage,
29:23
our leaders to think through that particular, you
29:26
know, process before making any decision
29:28
how possibly this is going to impact our
29:30
patients,
29:32
and how we are going to enable them
29:34
in such a way that,
29:36
you know, it it it will simplify some
29:37
of their,
29:39
activity that we need to interact with,
29:42
whether it's, you know, our MyChart or any
29:44
of our application or using our analytics. So
29:47
how do we simplify in such a way
29:48
that, you know, that will enable them and
29:50
then keep them satisfied,
29:53
and then,
29:54
help that that'll help a lot from our,
29:57
health care industry perspective too.
30:01
Absolutely. That makes a ton of sense. Myel,
30:04
thank you so much for joining us on
30:05
the podcast today. This has been a really
30:07
fun conversation. I I've learned a lot, and
30:09
I look forward to connecting with you again
30:10
soon. And then, you know, really continuing to
30:13
to learn and have this conversation ongoing.
30:15
Yeah. Thanks very much, for your thoughtful questions,
30:18
Laura. Really appreciate that.