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This is Laura Dierda with the Becker's health
care podcast.
I'm thrilled today to be joined by Divya
Patek,
chief data and AI officer at New York
City Health and Hospitals. Divya, it's a pleasure
to have you on the podcast today. So
I'm excited for our conversation because I I
know there's so many cool things that you're
doing at New York City Health and Hospitals
and certainly a a very important community that
you're serving there. So, I'm excited to dive
in. But before we do, I'm wondering if
you could tell me a little bit more
about
New York City Health and Hospitals and what
makes the system unique.
Thank you, Laura, for inviting me to this
podcast.
My name is Divya Pathak. As you mentioned,
I'm the chief data and AI officer for
the New York City health and hospitals,
the largest public health system in our country.
And I provide executive leadership and strategic direction
to the data and AI organization,
to support our network of hospitals
and its partners
with data, digital tools, and AI capabilities and
insights
that drives quality and performance improvements,
enterprise operations,
and enhances patient outcomes.
And to speak about our health system,
our network includes 11 acute care hospitals and
a network of over 30 community based clinics,
long term care facilities,
and trauma centers
in the five boroughs in New York City,
allowing us to offer a full spectrum of
health care services.
Beyond just medical care, we focus on improving
the overall well-being of our patients
by addressing the social determinants of health, such
as access to housing, food, and other critical
resources.
What makes us unique is our unwavering commitment
to providing comprehensive
care to all individuals
irrespective of their
socioeconomic background,
racial, ethnic status, and legal status.
And as a safety net provider, we see
a diverse and often
underserved population ensuring that essential health care is
available to everyone in New York City, including
the most vulnerable communities,
and I'm very proud to be part of
it.
Well, that's amazing to hear. You know? And
as you mentioned, just such a a great
mission that you have there in New York
City to serve so many,
different unique and diverse populations.
It really I know it makes a big
difference for the health and and safety of
the community.
Now I'm curious. What, could you tell us
about an accomplishment that you're most proud of
from the last year?
Sure. So there are several,
but to speak of it, I'm very proud
of
a collaboration with revenue cycle services
department at health and hospitals. And one of
the key initiatives
we focused on is in building an AI
driven one stop benefits application.
Given the diverse and underserved
population,
this project has been more
meaningful and deeply impactful
in our community.
And to speak to it, what we have
developed is a holistic financial counseling model
that proactively addresses patients' financial needs, aiming to
improve their health,
well-being, and equity.
And by integrating AI and advanced data and
analytics, our financial counselors can now conduct proactive
screenings for financial assistance and public benefits
prior to patient appointments,
and this reduces their waiting times. And this
approach not only streamlines the process,
but also maximizes the financial support a vulnerable
patient will receive,
ultimately helping them to alleviate some of the
burdens they face while navigating the health care
system.
And it's a truly rewarding project personally as
well, and that makes a direct impact
to the lives of our patients.
And, we're continue continuing
to to more such,
efforts in support of our population.
Well, it's amazing to hear. You know? What
a great idea of being able to proactively
identify and then approach those patients who may
need a little bit more financial support, and
have a plan in place. I can imagine
that it's such a relief for so many
who are are trying to seek needed care
not to have to worry as much about
the financial burden that it could present for
them and their families.
Absolutely, Laura. And in fact, it's not just
on the patient side too. Right? A lot
of these benefits that's provided by our city
government goes unnoticed
or not even made aware of.
So we we take incredible
effort to make sure that there is a
lot of proactive support to these patients
and the appointments and their
length of stay,
during their time in health and hospitals is
seamless,
and,
they provide the you know, we provide the
compassionate
service that they need the most.
That's great to hear. You know, and and
certainly, you know, wonderful to have that type
of support technology to support your teams and
then mission to go out there and do
that. Now I'm curious. Where do you see
the big growth opportunities for you and your
team in the next twelve months or so?
So,
one of the recent focus areas is the
establishment of our AI organization,
under the portfolio of data and AI.
And this organization is relatively new, and we
are slowly ramping up our internal AI capabilities
across our enterprise.
In doing that, we're embracing AI with a
very strategic blend of ambition and pragmatism,
recognizing its potential to improve the experience for
our patients and clinicians
and driving operational efficiency,
but also to make sure we enable equitable
care delivery for New York City patients, which
aligns with our mission.
And in the next twelve months, I see
significant
growth in scaling our AI driven solutions, again,
with guardrails across both clinical and nonclinical areas
and fostering a culture of innovation and continuous
learning
within our AI and health care teams. It's
it is the biggest growth opportunity,
and,
we're following the trend in health care.
That makes a lot of sense. You know,
certainly, a lot of possibilities and opportunities with
AI, as you mentioned, both on the clinical
side as well as the operational side. And
as you're building out your AI organization,
what does that look like,
if you're able to speak to just briefly,
you know, what skills are you bringing in
in house, how are you partnering,
with other organizations
as you're kind of thinking about, the AI
for New York City health plus hospitals.
You know, what what really
comes to mind for you in terms of,
like, how you're building up the team, the
skill sets, the roles, and what you really
need,
from your team
in a for AI in particular?
That's a great question as we are in
the midst of it. So, in supporting an
AI org, we are talking about,
organization
that supports strategic procurement
and strategic development of AI solutions and supporting
our workflows.
And in doing that, we also understand that
there are several other front runners in our
health care system, several provider organizations. So there's
a lot of learnings,
that we want to leverage,
including
consortiums both in industry and academia.
For example, Coalition for Health Care AI,
Health AI Partnership, Valid AI. These are key
consortiums that we are part of.
And,
additionally, we want to also,
as I mentioned, leverage the blueprint from existing
organizations, but also have an organizational
design with the multidisciplinary
skills needed to support AI adoption and enablement.
And in addition to that internal
capabilities, we also want to have an open
ecosystem of partnerships
that spans across
big to small
companies,
startups,
cloud providers,
because, you know, this is such an evolving
space, Laura, that we see that we have
to be truly multi cloud. We need to
have options to work with multi multiple vendors,
and more importantly,
also have a data strategy, support that open
data sharing,
with AI vendors. So there's a lot of
focus related to open ecosystem of partnerships.
So in terms of skills we are trying
to bring in, we want to support the
entirety of AI life cycle.
Right? All the way from ideation to development
to deployment
to performance and monitoring and also looking at
evaluation and outcomes. So we're in the process
of bringing a multidisciplinary
team,
some through our professional services, but slowly,
you know, develop the inbuilt in house talent,
across data science,
AI, engineering,
ML ops,
UI, UX development, and most importantly, right, integration,
developers in support of integration with our existing
systems.
That's really a helpful outline. Thank you so
much for going through that with us, Didier.
Now
I wanted to ask you as well, lots
of opportunities ahead, but also challenges. What are
some of the big challenges that you're anticipating
over the next year or two?
Oh, yeah. I mean, always challenges and opportunities
go in hand. Right? But I take challenges
as opportunities as well. So one thing, Laura,
that, you know, I've been really spending some
time on is to see how we integrate
AI into our health care systems
and how do we balance the return on
investment.
Yes. There is AI everywhere,
and there's proven,
efforts across other health systems.
But when we talk about return on investment,
the hard ROI such as cost savings or
potential revenue,
operational
efficiencies,
or direct improvements in patient out outcomes
will be more straightforward to measure and track
as we implement AI. And we need to
make sure that we have success metrics along
the
line.
But let's talk about areas where there is
soft ROI, which includes
more intangible benefits,
like improving clinician
satisfaction, reducing burnout, enhancing patient
experience, and fostering a culture of innovation.
These are much more difficult to quantify and
measure.
So achieving a balance is essential
for the long term success of AI initiatives.
So we need to be ensuring that while
we focus on hard metrics like cost savings
and efficiency, we also measure and prioritize softer
human centric aspects of AI that contribute to
better outcomes,
trust, and a sustainable
people first approach to AI integration.
Additionally, overcoming potential resistance to change,
ensuring data privacy and security, and upskilling our
workforce for workforce adoption and trust
alongside AI solutions will be, you know, hurdles
that, you know, I anticipate to address.
And that makes a lot of sense. You
know, I I really love that idea of
having those soft metrics as well as the
hard metrics to think about the return on
investment and keep the humanity,
within, you know, the health care system, which
is such a a human to human industry.
When you look at those, you know, soft
ROI,
how do you think about that? How do
you know that you've been successful?
Yeah. So, any initiatives that,
we we prioritize,
and I can give an example. Right? Ambient
listening is a great example,
that we are actually
looking to see how we can,
procure and roll it out because it's a
very proven solution in the market.
One of the things we are doing is
we're making sure we understand the metrics for
success early on
even prior to actually moving moving to proceed,
with procurement. So having those metrics, having baselines,
you know, again, baselines that cannot be quantified
but can be qualified. So having baselines,
making sure we we have inputs from the
multidisciplinary
team who's going to eventually use the solution.
And having a baseline is critical to a
project success because that's how we're able to
then see how we're actually improving care, how
are we
improving the efficiency, how are we actually improving
patient experience. So,
there's there's a very deliberate thought
put in place by design to have baseline
for every AI initiatives. And, software initiatives is
another reason. Right?
And which is very qualitative, but, certainly, we
we make a deliberate attempt to capture that
early on.
That's helpful to know. I I appreciate your
time and kind of going a a bit
deeper there as well. Now before we wrap
up with our last minute or two, I
wanted to ask, what is the number one
thing that you're doing right now to set
the health system up for long term success?
That's a great question, Laura. To set New
York City Health and hospitals up for long
term success,
What I'm focusing on right now is to
build strong AI governance framework
that ensures equity,
fairness,
and transparency in the use of AI technologies.
And given the diverse and vulnerable patient populations
populations we serve, it is essential that our
AI solutions are fair, ethical, and free from
bias.
So it's a number one priority for our
system. So by embedding governance practices, we aim
to prioritize patient safety, reduce the disparities
of care, and enhance the delivery while ensuring
AI aligns with our mission of providing equitable
care. So this approach,
we hope. Right? The plan is to actually
foster more trust
and improve patient outcomes and ensure our AI
solutions benefit all populations.
So governance is key.
That makes a lot of sense. I I
know how important it is to set up
that governance, have the right stakeholders at the
table, and, really truly make sure that AI
is doing,
so much good for the organization and and
not, getting into some of the risks involved
as well. That's right, Laura. I think having
a multidisciplinary
diverse stakeholder team is key to good governance
so that we have the diverse perspectives. And
in health and hospitals, this includes legal, compliance,
IT, clinical, and business leaders.
And,
it's been going great.
That's amazing to hear. Didya, thank you so
much for joining us on the podcast today.
This has been such a informative and inspiring
conversation, and I look forward to connecting with
you again soon. And, also, you know, seeing
you in person in Chicago at our health
IT digital health revenue cycle event, in October,
I am really looking forward to your session
and certainly will be excited to to see
how these initiatives are continuing to evolve and
grow.
Oh, it's been absolutely a pleasure talking to
you to today, Laura, and, looking forward to
meeting you in person as well.