Welcome to the Becker's podcast. I'm Naomi Diaz,
health IT reporter here at Becker's. And today,
we're joined by doctor Mirage Gaine, director of
responsible
AI in health at the Coalition for Health
AI, also known as CHI.
In this episode, we'll discuss what hospital leaders
need to know about AI today, how to
prepare for the next few years, and where
AI is really headed in health care. Doctor
Gain, thank you so much for joining us
today. I appreciate it. I wanna start off
with our first question here. Just a simple
one of just introducing yourself and tell us
a little bit about your background.
Great to be here. Thanks so much, Naomi.
I am.
I have a PhD in clinical psychology,
actually,
and I have a background in computational and
clinical neuroscience,
behavioral science, and I used a lot of
machine learning and AI
in my research back in the academic days.
But,
I'm
far from those, not super far, but pretty
far from the academic days.
Since
finishing my postdoc, I've been working
I I started working as a principal behavioral
designer at ideas forty two, which is a
mission driven nonprofit,
and I focus my work in,
AI and machine learning and health,
sort of things around, like, identifying bias and
health AI solutions,
for organizations and helping organizations do that in
a behaviorally informed way.
I actually started working with CHI then.
So before CHI was ever a nonprofit, it
was a coalition for the willing,
coalition of the willing.
And I led their fairness and bias work
group,
in developing sort of responsible AI guidance,
and then
I transitioned to this role. So here at
CHI, I work across many sectors. I bring
together
any
sector you can think of in health care.
So anybody on the developer side, the clinical
side,
sometimes policymakers.
Who else do we have? Patient advocacy groups,
community health centers,
all of them into one space, and we
talk about how to build shared frameworks,
tools,
processes,
best practice guidance to make AI,
safer, more transparent,
and responsible
in health care.
Amazing. Great to hear about your background. And,
again, just responsible,
safety of AI implementations is just really what
we need right now. Would love if you
could just dive in and just tell us
about your work with consensus driven frameworks for
AI in health care. What have you learned
over these past years, and what do you
feel like is most important today?
Great question. Consensus work is hard and traditionally,
very slow, but we don't actually have that
privilege,
really in the AI space of being slow
because things are moving so so fast. And
so,
we
at CHI use sort of modified Delphi methods
to build these consensus driven frameworks,
And we convene, like I said, many stakeholders
across,
large academic medical centers like the Mayo's, like
the Dukes, UT Health,
as well as non academic medical centers, small
community health centers,
and, like I said, the developers of these
solutions as well to kind of think about,
how can we take what it currently exists
in many different silos with many different incentive
structures,
and bring them together to find
the shared
motivations.
How can we help each other improve the
health care system,
without sacrificing innovation, without sacrificing patient safety and
privacy,
and keep things moving at a pace that
that this this whole field really needs.
I think one of the biggest things that
I've learned is that everyone is trying to
do the right thing,
with AI.
We
most people. We have,
I think different lenses depending on where we're
coming at this problem from.
But and there is no map that says
here is how all these different lenses should
connect, and that's where we come in,
to sort of co create
practical practical,
sort of flexible guardrails,
tools, resources
to help
everyone in this landscape make better safer decisions.
And I think it's more important than ever
now because the consequences
of AI misuse
in health care in particular,
is huge, and
we have a health care system that is
already struggling,
both as a business
and for patients.
And so how can we really take this
opportunity, and I see this as a big
opportunity
to do better,
without
hurting the most vulnerable people.
Mhmm.
Mhmm. I I love what you touched on
there too is not stifling that innovation, but
also ensuring that, you know, you have those
safety guardrails for a technology that's continue
continuously evolving. I think that's super important there.
I think for our audience,
obviously, and our listeners of health care execs,
I would love to ask you as as
a follow-up here as well. What do you
feel like hospital executive teams really need to
know about health care AI today in this
such rapidly changing landscape, and what do they
need to do to prepare their teams for
the next two to three years?
It's a great question.
And they're we're doing a lot of work
in this space, but I think the first
thing to realize is AI is already being
used.
And and,
you know,
sometimes it's chat GPT
on somebody's phone.
And and it's also being used
for real use cases and documentation
and triage and
diagnostics.
Radiology is a big area and and administrative
tasks. So people think about AI and health
care like, oh, it must be a clinical
use,
but there are so many operational and administrative
processes
in health care that are struggling,
and leading to workforce
issues,
and and cost of having to retrain people
because people are so burnt out.
So,
just recognizing
that it is already being used, and there
are lots of spaces that you can use
it within the health care
landscape.
I think the second thing is that folks
need a plan,
and that means establishing internal policies,
governance structures,
processes,
working together with your teams and and within
your network. So some organizations
are much larger and have very robust internal
teams.
Some organizations like safety net hospitals, FQHCs,
community health centers,
they don't have that
luxury, so to speak, but they do have
access to things like
their
health center control network, their primary care association
network. And so how can we get really
creative about what it means to establish internal
policies, governance structures,
and not, you know, burden those that don't
have resources and and be able to develop
that? So we're doing that,
in partnership
with
NAC, the National Association for Community Health Centers,
but also with just our member organizations that
have come up and said we really care
about this. Even the larger organizations will come
up and say, if we're proposing this as
a process,
how will the smaller
entities, the less resource entities, actually make this
happen? So we're really thinking about this carefully.
So as part of that plan, folks need
to understand the risks. They need to determine
what are some of the responsible use cases.
Where can they start,
in this AI journey for their particular organization?
And, again, context will matters a lot.
And then thinking about the processes that you're
developing, the governance structures, the internal policies should
address things like bias
or risk of bias, security and privacy issues,
the usability and usefulness of that solution. You're
paying so much money not just to procure
this, but to use it. And if it's
not helpful, if it's just gonna make your
staff angry, like,
shouldn't
shouldn't be the you know, even if it's
bright and shiny and new,
there's a lot of change management that needs
to go in
to to bringing new, processes in and making
sure that they're helpful. And then safety and
transparency. So those are, I think, areas that
folks really need to start developing plans around.
And then finally I think investing in capacity
and in education
for staff, so upskilling existing staff,
training new staff to sort of have that
come in with the mindset of, like, where
AI is used, when it's helpful, how it's
helpful,
and what is it, for for many folks.
That's the first question.
So investing in that capacity now and investing
in a road map for the future,
data infrastructures,
assets, all of that making sure that
you have a sense of, like, where your
data's at, how it could be used, how
it could be leveraged
to actually improve the business of your organization,
in this sort of
in these times where data data is king
and queen,
all of the above.
I I really loved your takeaways, doctor Gaine.
Really hit the nail on the head when
it comes to just things I'm even hearing
from our audience here on just the the
obstacles they're going through in their own organizations
there. Really appreciate you diving deep. I wanna
stay on that track of what you mentioned
of, you know, those governance, those guardrails, and
ask you, you know, how should hospitals and
really health systems just address regular regulatory concerns
with AI and health care? And I know
you touched on this a bit in your
answer as well, but how do you really
create ethical standards within the organization
to deploy this safety and ethically as well?
Yeah. I think
I think we're in this sort of regulatory
purgatory, I would call it. And and I
don't think I think at the end of
the day, we shouldn't have to wait. Nobody
should have to wait for full regulation to
act responsibly.
If we're gonna only do the right thing
when somebody tells us to do the right
thing, I think, you
know, maybe it loses its value a little.
So
for me, ethical standards has to be they
have to be built into the AI life
cycle.
So all the way from procurement
to development, if that's something you're doing internally
in your organization or co development with,
with a vendor or purchasing,
all the way to monitoring and oversight. So
when we think about
ethics in health care, how can we then
extend that to
ethics in AI?
And I think
there is this
sense that sometimes people try to reinvent the
wheel. They're like, oh, AI is so different.
Everything about it needs
to be different, but safety still matters. Security
privacy still matters, and these things existed. You
know, transparency,
consent, like, these things have existed in health
care
even pre AI. Now the question is, how
do we identify where AI uniquely,
needs ethics in these spaces, and how can
we just add that to the processes that
already exist without
fully reinventing the wheel?
And I think at CHI, we try to
align our work with some of the regulatory
momentum that's out there.
You know, FDA has action plans out there.
ONC has their HTI one rule,
but
I think what we hear from our community,
and that's we are a community led nonprofit,
is they want practical
playbooks. They want practical tools, resources
to make these things possible. So one of
the things we're doing,
in partnership with the joint commission is we're
developing a set of governance playbooks for organizations
of all sizes.
And hopefully down the line, I mean, in
the near future, accompanying tools, templates, resources that
folks can leverage.
And as part of that playbook, I think
one of the biggest things that we wanna
prioritize,
not only getting feedback from our community, which
we are gonna do. We have a series
of workshops coming up,
but also illustrating
how differently sized organizations are aligning with some
of these ethical
guidelines that we're putting out there or some
of these governance controls that we're putting out
there in the playbook. So if we say
everybody should have AI policy, what does that
look like? How are different organizations
doing this?
So that folks have kind of a road
map or template to look to. Say, oh,
that worked for this size organization, which is
similar to me, so maybe I could start
there.
And also being very clear about what the
challenges
are that people are facing in aligning with
some of these ethical guidelines,
and,
you know, recommended governance controls
and being able to say, like, okay. Are
there tools, are there templates, resources that we
can provide to help reduce some of that
barrier
and making sure that the ones who are
resourced, who have it, and I can develop
those things, great. For the ones who can't,
how can we help them get a step
up
in that process and not widen that digital
divide?
And I think ethical AI
isn't shouldn't just be within the health care
system. I think a big part of
what is now happening if we really want
to create person centered or human centered
solutions,
it is a lot about
the developers also understanding what ethics means in
health care and being able to
align their products with that so that when
they go to their customers,
the health care systems, and they say,
I have this really cool thing that they're
already speaking the same language. And I think
that's a lot of what we're trying to
do is get folks to hear each other's
different languages and at least understand, like, do
a little bit of translation there.
And and it's about the relationships.
So it's not it's also about how are
we supporting each other, how are developers supporting
their customers
in in aligning, and how
are the
the health systems
supporting the developers and improving the tools to
better fit their needs.
And so I think that's another big piece
of ethical AI is that relationship
between
the
the developer and the implementer.
Wow. I you just mentioned so many important
topics here. I think one of just an
incredible quote you just, you know, said in
the beginning as well, just nobody should wait
for regulations to do the right thing. I
think in essence, that was just amazingly said,
doctor Gaines. So appreciate that.
I wanna I I wanna look as as
well to the future a little bit here
for for our last question and and ask
you just where do you really see AI
headed in health care over the next two
years, and what do you expect will be
a little bit different because of AI?
Yeah.
So there's a lot
happening in the space.
I think we'll see definitely some expanded
AI use in areas like ambient documentation,
prior authorization,
both on the provider and the payer side,
and patient risk stratification.
I think that's gonna continue,
sort of predictive
prescriptive analytics types pieces aren't it's not gonna
go away even though GenAI has has come
up. I think GenAI will help improve some
of those processes.
I think
we have started
focusing on a lot of back office tools
as
a first pass for AI and health care.
Like, that's been the cautious approach.
I think this will expand to frontline clinical
decision support,
and I think it will also
move towards
patients taking agency over their health care differently
and interacting differently with their providers. Right? So
with
tools like ChatGPT,
people are inevitably going to ask their health
questions,
bring
a different kind of knowledge to their appointments,
and so I think that will really shift
the landscape quite a bit.
I think there will be greater demand for
transparency
and evaluation
over time, and I think we at CHI
are
developing sort of recertification
programs for
assurance resource providers,
which might be folks that are validated, like
third party validation of AI solutions,
individuals that enable sort of local validation or
training in a secure,
privacy enhanced way,
really making sure that people can actually access
these tools in a way that is is
safe.
And then I think we'll see a lot
more
involvement
from nursing staff, from community health centers, from
under resourced providers,
but we need to equip them with the
tools and the training to really participate in
this space.
So one of the things that we are
doing, I think there is quite a bit
of curricula out there for,
for physicians, and we are working on sort
of expanding that into specialty society type work,
but we also are partnering with Florida State
University to bring a responsible AI education to
nurses.
And I think
that will
be pretty important.
We have cross functional teams, and if only
some parts of the team understand what's happening,
and other parts don't, it can continue to
increase the tensions, the silos in a space
where we want actually
better care continuity and better collaboration.
So I think that's that's some of it.
And then agentic
things, hopefully,
maybe will come out in health care. Maybe
not in the next two years,
but definitely down the line, especially for some
of that back office stuff. I think for
the administrative
and operational work,
it it makes a little bit more sense.
But
there are no solutions that I know of
currently that are being deployed,
in the health care system there, but it
will
come up,
down the line for sure.
Lots to look forward to. Lots evolving so
quickly. I think, doctor Gain, you just had
such great light for our audience on what
this means for them and just the great
work Chai is doing as well. So it
doesn't become a place of AI have and
have nots as I like to describe it
where, you know, smaller system versus larger systems
can't get that
access to really, deploying this technology, like you
said, ethically and safety. So I wanna just,
give you a a a huge thank you
for shedding light on there, sharing your insights
on this, and as well as to our
listeners,
for taking the time out, listening to the
Becker's podcast today. That's it for our episode
today. Doctor Gaine, any last words or anything
you would like to share with our audience
before, we end today's episode?
No. I think,
join our newsletter list.
Keep up to date even if you're not
a member. If you want to be a
member, feel free to reach out to us.
We would love to chat and see how
we can be helpful.
And,
yeah, I'm just great I'm grateful for the
community. Without the community, like, the the things
we put out, the understanding we have of
the field just, like, really would not be
possible.
And so
I'm grateful for that. We we,
can only do this together.
Actualizing that learning health system and learning community.
Well, thank you again, doctor Gaines, and I
appreciate your time today. Appreciate you. Thanks so
much, Naomi.