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