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This is Laura Dirdle with the Becker's Healthcare
podcast. I'm thrilled today to be joined by
Sameer Sethi,
senior vice president and chief AI and insights
officer at Hackensack Meridian Health. Sameer, it's a
pleasure to have you on the podcast today.
Yeah. Laura, thanks for having me. I'm really
excited about this conversation.
Absolutely. I'm looking forward to it as well.
I know you're doing some really unique things
at Hackensack Meridian, so it'll be great to
share with our broader audience as well as
get a sense of how you're thinking about
the future, some of the big opportunities that
you see ahead. But before we do that,
I'm wondering, can you tell us a little
bit more about Hackensack Meridian Health and what
makes it unique?
Absolutely.
So, Hackensack Meridian Health is a health system
based out in New Jersey with a total
of 18 hospitals,
500 plus care sites, all in New Jersey,
and very focused on on on the state
and providing,
providing care to the community and the patients
we serve.
I function as the AI and insights officer.
I I joined the organization,
almost three years ago now. I started off,
functioning as their data and analytics officer. And
then, late last year,
I was promoted to function as their AI
and insights officer. The reason that is, that
that that is unique is because, you know,
our CEO,
mister Garrett Bob Garrett has has put an
emphasis on
on AI and RPA enablement. I think the
the push that he has made is
so I is is, I think, very unique,
I I think in in in the in
various industries, but especially in health care. I
think everyone is looking at AI and RPA
enablement, but I think our focus is quite,
I think, driven around, you know, what it
does to our workforce, what it does to
our patients, and how fast we can we
can get there. So I think we're we
as an organization are in addition to doing
what other health care systems do, from an
AI enabled perspective,
we are doing more.
We're we're we're scouting the market consistently.
We're seeing what,
what we need right away, what our patients
need. So there's a very, very heavy push
through,
mister Garrett's sponsorship to get this done.
That's amazing to hear. You know? And and
really, truly
outstanding you've got the support of,
Bob Garrett and all those on the c
suite to move forward,
in really in a smart way, be able
to use AI, use data analytics, use those
insights
that, in a way that you can grow
and develop in the future. I'm curious,
given this unique position that you're in, can
you talk a little bit about an accomplishment
that you're most proud of from the last
year or so?
Absolutely. So, you know,
what's all what's a bit unique and also
what led to the accomplishment
is is a speed at which we have
gone at this. I think that's number one.
You know, we put
a generative AI,
use case in production
within within three months, if not sooner,
of of the, you know, the word that
that got out with ChachiPT and others. You
know, we we were looking at this technology
evolve.
It came at at us very quickly, but
we were able to make sense of it
and produce a use case, put that in
production. And then now we have close to
nine,
use cases in production here. So I think
I think much ahead of the market,
and also driving value already
within the health system.
This helps our patients,
it helps our our workforce, our focus has
been around around workforce. So I think that's
one piece of this, the speed at which
we came at this. The other is I
think automation.
You know, while we while AI is new
and everyone is, you know, running towards making,
making sure that, you know, they drive value
from it. You know, automation has been a
big focus for us last year,
and and the years before as well. But
we were we were we were a bit
slow at it. Generally, I think when I
came into into the health system, we had
20 automations in production.
Last year, we committed to doing quite a
bit more. And just between in the last
two and a half years or two to
do
two and a half years, we've gone from
having those 20 automations to having 80
automations in production. You know, this varies from
anything from, you know, using automation for,
enrolling patients in MyChart a lot quicker. We
have the highest and then fastest in the
industry today.
So what we're doing is we're taking the
human,
out of the the process, wherever it makes
makes more sense. Right? Obviously, human in the
loop is important that exists, but we are
trying to speed things up. We have put
use cases where we're using robotics process automation
to,
to enter lab results into into patient charts.
And, obviously, there's a human in the loop
there as well, but the person is not
sitting there and typing it. We use this
inform use technology
to put in our female claims, for example,
which were in in the numbers of thousands.
And if a person were to do it,
it would take us,
I believe the estimate was four months to
do it. And we did it in two
days overnight. Robots just just kept working. So
I think the accomplishments have been many, but
I think it usually is focused on bringing
the the tech as fast as possible to
bear, to drive value. And then I think
also continuing on the fundamentals of things like
robotics process automation. I'm happy to go through,
you know, one or two use case that
I'm really proud of, but in general, that's
the principle.
That's amazing to hear. And I I love
that overall strategy of trying to find those
right use cases or ways that you can
use the robot effectively and and use automation
as well as AI to boost especially the
workforce as I know that's a huge, huge
challenge for so many hospitals and health systems
across the country. And I definitely am interested
in zeroing in on some of those, use
cases,
that you find most,
most helpful or or beneficial. I I think
it's really cool to have,
grown that automation from 20 to a 80
in production so quickly, and and, clearly, the
culture is, is able to do that. So
I'm curious if you're able to dial in
on some of the things you're most proud
of there. I think, would love to hear
more about that. Yeah. Absolutely. I I'll talk
about, you know, something that's
that I think is is evolving today, and
and and it stays on top of our
mind or and on our minds.
You know, almost a year ago,
if not a bit more, you know, we
launched our use case where we were using
generative AI to summarize a clinical note.
This is so so think about how, you
know, when you and I go into a
physician office, generally, a physician is obviously being
present at the same time,
you know, trying to read on the screen
information about you. Now this is the patient
chart. It has your lab values. It has
what, you know, what was discussed last time
you were there and discussed the conditions that
you had. And it's quite a bit, you
know, we have
a lot of information that that that physicians
and clinicians in general,
you know, gather about the about you as
a patient in there. What we did was
we made it easier for the clinicians to
consume that information by summarizing all that.
Our first version of that was just a
general summary.
We then when when we put it in
production,
we realized that that the that the clinicians
were looking for,
summarization by specialty. So then we selected nine
specialties,
and the intention there was that, what a
primary care physician wants to see in a
summary is different from somebody in oncology or
cardiology.
Right. So we created
we then start to tailor these summaries
by specialty and that
we got a lot more
attraction and value from it. We then went
into now summarizing
lab values
into this this clinical note because the clinician
said, I would like to see lab values
in there as well. So I don't have
to go and look for it. So this
is an example of a use case and
even our strategy around doing this. Obviously, there's
a lot of governance around putting in team
production, and we abide by those things.
I believe we have the best AI
governance safety framework in place.
But what we try very hard to do
and then we this is where we push
the business, the clinicians, and everyone in the
world to put something out there as fast
as possible, as long as it passes through
the safety checks. Right. But then we iterate
over time. That's been a pretty big accomplishment
for us.
As I mentioned, you know, we did this
right right after this technology started to become
available to us. We put something out there,
and it's evolved and grown bigger and bigger
and bigger, and we are really proud of
that.
That's really cool to hear. And, definitely, as
you mentioned, something to be extremely proud of
knowing all the work and effort that goes
into it. It can be extremely complex to
put together on the technology side, but on
the people side too and just make sure
that everyone, is comfortable with these changes and
then, being able to do them quickly, as
you mentioned, in order to get the best
results. One thing as well, when you look
into the future, where do you see some
of the big growth opportunities
over the next twelve months or so? Yeah.
I think,
the biggest opportunity that I am seeing is
is how do we start to glue these
different technologies together? Right? And, you know, that's
a bit of a challenge. But I think
to me, that's a growth opportunity today.
You know, while,
you know, the industry in general thinks of
AI as it's as a separate thing, when
in fact, that is not the case.
AI, if if it were to exist,
separately, would will not drive value. It has
to work with other technologies and people.
It has to be embedded into the right
workforce so that capability is delivered at the
right time to the right person in the
right way.
So in my mind, that is the opportunity
today is where we're taking AI
as as as a conversation
and but also but then embedding it embedding
it into what what our workforce
do day to day.
You know, that's a that's a growth opportunity
we have, and, you know, we are really
focused on that. We have, you know, folks
that that that abide by, you know, concepts
of human factor engineering. This is,
the the in in some words, industrial engineering,
where,
technology is looked at as a part of
the process versus a technology alone. Right? And
and embedding that into a process. So I
think that's the, to me, that's the growth
opportunity for everyone, including us. I think we
are doing really well, but we can do
better where we are embedding different techno embedding
AI into the into into,
into a process, but then also bringing different
technologies together.
You know,
if you we keep hearing about, you know,
and I'm digressing here a bit, but we
keep hearing about agentic AI. You know? And
agentic AI is a lot of things. But
to me, the biggest value there is that
it is an orchestration
of
of robotics process automation and and three different
AIs and a few other capabilities, all that
put together. So it works at a command
line. It works at a command to say
when a patient says, I need to go
to the doctor, so make me an appointment
and make sure that I have car service,
available for me. And then when I get
there, you know, there's a wheelchair there. That
is a command. And that's a command line.
Right? And I'm using the word command line
as a as a nontechnical term here. And
then
and then agents take that command and do
five different things. And that's what I'm talking
about bringing three to five different technologies together,
AI, RPA, digital, whatever you have, whatever is
required to do it, maybe even a robot
making a phone call into a real person
having conversation and saying, you know, this person
this person is coming in for this appointment
at this time. Please make sure you have
a wheelchair available. All that has to come
together. So that's the biggest opportunity, which is
being able to tailor all that together.
I love that. It seems like such a
a simple thing, yet I know nothing is
ever simple,
when it comes to this type of transformation.
And and certainly, those little things make a
big difference when you're looking at the patient
care journey, the experience, but then to how
things are operating, at the hospital and in
clinics and really, you know, how teams are
working together most effectively.
A lot of opportunity, it seems like, with
AI, especially in the technologies,
as we've been talking about. But I'm curious,
what are the big challenges that you're anticipating
as well?
Yeah. You know, our challenge is, I think,
the our
which is a blessing in disguise, but,
it's it's the pipeline. You know, we have
a close to
300 use cases that are waiting to be
looked at, waiting to be worked on, waiting
to be developed, waiting to be deployed, waiting
to be evaluated.
So it's, you know, our our organization,
you know, is very excited about,
you know, what the potential of what these
technologies, you know, bring to them. And and
the I think the time and space
to actually do this right,
is really about this is no longer for
us about
running after or looking at the the shiny
toy. Right? In addition to, you know, this
thinking about, you know, these use cases as
being a good idea, it's also about ROI.
Right? How do we make sure we capture
that information? And, unfortunately, that's not how people
are thinking about today about about AI today.
People want to do AI. But
when we have those conversations about, you know,
what you know, how do you how will
you drive value from it? And how do
we make sure it it impacts the bottom
line? And bottom line could be financials, could
be patient experience, could be work for experience.
Those those take a lot of time. I'm
not saying people aren't ready to have that
conversation,
but exercising those, I think those steps as
a part of our governance process,
is is is a is a challenge for
us. There's a large demand,
and we have to cater to that demand.
And it's, you know, it it requires times.
It requires resources.
That's a big challenge. These are all great
ideas. We just have to pick the right
ideas to work on because we can't work
on all of them.
That's such a great point. And I know,
as you mentioned, having so many ideas coming
in and so much,
possibilities,
and and then truly understanding the benefits, the
different, you know, return that you can get
from these, what should be prior prioritize is
key. When looking at that governance structure, I'm
wondering if we can, dig just one bit
deeper there. I know a lot of, again,
hospital and health systems are trying to figure
out where to do this right and understand
what they need in order to develop
a strong and and meaningful governance
process for especially,
AI driven projects.
What did you do at Hackensack Meridian, and
what have you found really effective? If there's
anything that you've changed or tweaked, that you
found didn't work, I'd just love to hear
your perspective on that briefly before we wrap
up here. Yeah. I think the biggest piece
that, you know, just looking back now in
our journey for approximately three years or, you
know, my journey at three years at Hackensack,
you know, I think we've done a lot
of great and lot of right things.
Having said that, I think it's fair to
say that, you know, we could have done
a lot of things better as well. One
thing that
continuously comes to mind,
you know, is is education. Right. I think
educating people
on this technology
is something that I think us as an
industry have haven't thought about or acted on
as much as we should have. Right?
People
have a sense of what AI is.
People don't know how
best to
use it
correctly.
You know, as a result of that, this
technology ends up, you know, getting a lot
of bad rap at times
when, in fact, in my opinion,
it's not used correctly.
To explain this, I'll use example of, you
know, the the likes of,
you know, chat GBT or the generative AI
capability. Right? I consistently have folks,
looking
for answers from such technologies,
that are that I think this technology cannot
answer. So they think of this as
one place for everything that solves, you know,
world hunger. Right? When that's not the case,
you know, I the the analogy that I
have used is, you know, the likes of
chat could be Gemini or it could be,
you know,
chat.
So it's it's like the high fructose corn
syrup or sweeteners.
The reason I call it that is because
it does the job. It's really sweet, but
it may not be good for everyone or
all use cases. So when a clinician is
going in and typing a use case question
in there that is very clinically focused
and demanding a accurate response. And when the
clinician doesn't get it, you know, this technology
gets bad rap.
We
educate our our clinicians internally
to find ways to ask the question correctly.
We actually build a use we we built
this capability where we use this technology called
RAG, r a g,
which sits on top of these generative AI
capabilities.
So it provides a better answer, but I
think it all comes with education. The point
I'm trying to make is that I think
we have ran fast at especially this generative
AI technology,
and folks are,
are not educated. It just, you know, if
if I had to go back, which is
which no one can,
I would say, you know, let's in parallel
invest enough in educating
the public and our workforce on what this
is and what this isn't and how it
should be used?
As we work on building this technology, I
think we all ran at building these technologies.
And then we we we didn't invest as
much in educating our masses around what this
is and what this isn't and how this
is useful to you.
That's such an excellent point. I I really
appreciate your perspective on this. And, certainly, I
can imagine some efforts to to try to
do that now, but still, you know, having
it from the beginning, it sounds like it
is really beneficial or would have been beneficial.
Yeah. It's not late. I mean, we're, you
know, we're we're now charging at this at
Hackensack where, you know, we have more people
involved than ever. You know, I'll give you
an example of our governance team. When we
started AI governance, it was, you know, it
was,
we we we it we had a total
of 13 domains
that evaluated all these use cases, and it
was 13 people. Today, it's over 26.
Right? So we have doubled.
We have better engagement from our leadership,
which is our executive leadership team on which
use cases to work on,
in in in the past. I'm not saying
that wasn't there, but they are more present.
They're more talking about this. They're demanding and
saying, why should we do this? So I
think it all came from learning. So I
think it's not too late. We're, you know,
you know, Hackensack is is obviously very keen
in making sure we get this right. Other
organizations are doing this as well. So it's
I I say better late than never.
Absolutely. Absolutely. I I love that. Now, before
we wrap up, one more question. What is
the number one thing that you're doing right
now to set the organization up for long
term success?
I think,
it's it's about being very I'm asking the
organization to be open to ideas.
Right.
There is no bad idea,
but it requires,
you know, attention from people. So I think
what I am asking the organization is is
is is open your door, listen to, you
know, how this technology
can help you, you know, come to us
with ideas that you have come to us
with ideas that you are hearing.
And I think we should
problem solve around what is the best thing
to do for you,
that that I think sentiment existed. It's not
fair. It's not something that,
that didn't exist, but it's it needs to
be hyperscaled.
Right? This is new tech, and there's some
great ideas out there. There are companies that
are doing it really well.
There's it. So I I think what what
I am
telling them is is this AI is not
a similar problem,
or similar initiative. This is an organizational
initiative. So keep your ears open.
Speak to us about this. We will tell
you let's collaborate.
Right? And and and get to a place
where either we decide to do this level
of AI enablement in your in your in
your team and in your department, or we
decide not to do it at that point
and move on to the next use case.
But I think it's all about being open
to what AI is,
realizing where that where it it drives value
and where it doesn't.
Absolutely. I I really appreciate that. Sameer, thank
you so much for joining us on the
podcast today. This has been such a valuable
discussion, and I look forward to connecting with
you again soon. Yeah. Likewise, Zor. Thank you
so much for the opportunity. Really excited to
talk to you again.