At athenahealth,
we know your ambulatory practice wants healthier, a
healthier business, healthier care teams, and healthier patients.
But the complexities of modern health care tech
make it hard for you and your care
teams to focus on what matters
most? That's where athenahealth can help. Our AI
native all in one solutions reduce administrative burdens,
streamline billing and payments, and deliver critical insights
when clinicians need it most. That means fewer
clicks, more time for patients, and stronger bottom
lines.
Practicing medicine is complex, but running a practice
can be that much simpler with athenahealth.
See how simpler is healthier@athenahealth.com.
Hello, everyone. This is Scott King with the
Becker's Healthcare podcast.
Thrilled today to be joined by Brenna Lofek,
director AI regulatory and quality center for digital
health at Mayo Clinic. Brenna, how are you
doing today? I'm doing great. Happy to be
here. Thanks so much for joining us. I
I we have a lot of big, you
know, tech questions and and dealing with health
care to get health care to get to.
But, if you can please just share a
little bit about your background and your work,
that would be great.
Certainly.
I lead AI regulatory and quality
strategy at Mayo Clinic in the Center for
Digital Health,
where our mission is to safely translate innovation
into clinical practice.
My work centers on building the infrastructure that
allows AI technologies to to be deployed responsibly.
We develop governance frameworks, ensuring regulatory compliance, and
establishing evaluation process for digital health tools
is across the entire enterprise.
My background spans both the medical device industry
and health care delivery,
which gives me this really cool dual lens
for balancing innovation
with patient safety and ethical responsibility.
From an academic perspective, I received my master's
from Johns Hopkins in patient safety and health
care quality,
and I'm currently a doctoral student at that
same institution
focusing on the intersections
of health policy
and AI.
My research is related to
the translational
practices across health care organizations
in applying laws, regulations, and standards to AI
enabled digital health technologies.
Well, that's awesome. Thanks so much for sharing
your your great background info. I would we're
gonna get a lot for your
role. I wanna ask you about specifically everything
that's going on digitally and with tech and
in health care.
First, I wanna you know, our our research
has told us that nearly half of medical
practices reporting
using AI in some capacity
last year and, obviously, remains a key topic
for health IT leaders.
From your perspective, what are the use cases
that are making a difference right now, and
how are you leveraging them in your organization?
Yeah.
So in my opinion, use of AI can
really be broken down into two key areas
within health care organizations.
The first one
are technologies that are doing administrative support related
tasks, And then the other area
would be tools that are doing clinical decision
support.
Administrative support tools are gonna be technologies that
are helping with workflow tasks within a health
care organization,
but they're not directly doing any sort of
diagnosis, treatment, cure,
or mitigation
of any diseases or conditions within patients.
Whereas the other category of tools, clinical decision
support software,
those are doing those activities.
And there are many use cases that are
making a difference
for
the practice,
providers,
and patients
in both of these categories.
In the admin support space, we see technologies
that are aiding in scheduling and communication,
which automate
previously
manual and tedious
tasks.
These are immensely
impactful because
they help not only with time savings,
but also with burdensome
tasks that
are, health care professionals
find,
annoying or not really worthwhile
or beneficial
in the actual care delivery
actions, which are the core of all health
care institutions'
mission, visions, and values.
In the clinical decision support space, we see
technologies
that
are either
directly or indirectly
assisting health care professionals
with diagnosis
and treatment of their patients. And this these
really span everything from
tools that are
leveraging information
found within the electronic
health record
to do
detection
of patients that are at risk of different
diseases or conditions.
Maybe they would benefit from some additional screening,
or
interacting with information in that electronic health record
to give recommendations
around
treatments or diagnoses.
I believe that
around sixty percent of health care professionals are
using a tool called open evidence,
which is assisting with different differential diagnoses happening,
and that is just highly beneficial for health
care professionals.
We also see more advanced technologies
doing,
really brilliant things
where they're analyzing
medical images,
signals, or patterns
to do,
early detection and diagnosis
or to automate different workflow steps,
that take a lot of time by our
health care professionals or that are not always
done
consistently
and these are tools that are interacting with
images signals or patterns
to do things like
segment medical images
and do calculations
of distance or volumetric measurements.
There are also technologies that are interacting with
patterns or signals like in cardiology
where at Mayo Clinic, we have tools that
analyze
ECGs
to flag patients
that may be at risk of different diseases
or conditions.
And so these technologies, the clinical decision support
tools,
really help move the needle on patient care
delivery, and we're really excited about these tools
at Mayo Clinic.
Yeah. A lot of those uses you just
mentioned were are incredibly interested. I I feel
like we're hearing about
new tools
and new uses every day now. Just with
your with your role and, I guess, what
you've seen and maybe even what you've heard.
Do you do you remember ever health care
having
so much cutting edge technology involved and and
how important
tech is in in this moment in time
to health care? Like, you ever heard that
before in the industry?
No. I think that the paradigm has really
changed in the last five years.
Whereas we saw
a lot of
the med tech innovation
coming out of startup companies
or established organizations
like Medtronic or Johnson and Johnson where they
were the ones really developing
the technology
often working in collaboration
with health care institutions and providers,
but they were the legal manufacturers of these
tools. They were the ones identifying the opportunity,
and being the ones legally responsible
for developing,
implementing, commercializing
these different products. But as I said, the
paradigm has really shifted now where AI enabled
technologies
are in the hands
of healthcare
professionals themselves.
They are able to identify
local problems
that could be solved or local opportunities
that could be addressed
with AI enabled digital health tools,
And they, in collaboration often with with data
scientists,
can develop these tools
and implement them into their practice.
So we have this space of booming innovation,
but also highly localized
development of technologies
that are solving problems that the health care
professionals
themselves are seeing. And this is just going
to continue
to grow with generative
AI and the accessibility
of using established large language models
to apply it to different activities
within your organization.
So in many ways, this is a massive
boom, incredible expansion,
but it's also a real shift in the
types of individuals
that are
identifying opportunities,
building tools,
testing them, and deploying them where historically we
saw a real shift,
a real separation
of those that were doing those activities and
then those that were actually
using the technology.
So this is a really exciting time to
be in health care and, helping innovators,
healthcare professionals
take tools from discovery or ideation to actual
actual implementation into clinical practice. But it also
opens up the door for a number of
ethical considerations
that we haven't thought of before, and it
also puts health care institutions
in the position of having to apply different
governance activities that they historically have not been
responsible for.
Yeah. Yeah. I'm sure you just touched on
it a little bit. But, yeah, obviously, new
challenges come with these tools and new tech
being introduced. If you if you had to,
like, focus on your top piece of advice
for leaders as they introduce this this new
technology or or, you you know, kind of
work it out, what would that be?
Yeah. So
your governance within your health care organization
should be seen as a mechanism
that accelerates
and prioritizes
innovation.
It should not be perceived as a roadblock.
At Mayo Clinic, we use a risk based
assessment framework that allows us to evaluate these,
internally developed solutions, but also vended solutions,
quickly while maintaining
rigor
for that development and testing process that is
proportionate to the risk of the tool. I
previously mentioned that in my opinion, there's really
kind of two main categories
of AI enabled technologies.
There's those that are in the admin support
space, and then there are those that are
directly or indirectly
doing clinical decision making or aiding.
We don't wanna apply the same level of
rigor around requirements,
within the software development life cycle,
specifically with how we demonstrate quantitative or qualitative
measure of safety,
efficacy, and ethical use to tools that are
being used for admin support that are, you
know, doing basic scheduling
compared with tools that are analyzing
medical images and doing early detection of patients
with pancreatic cancer. Those are two really polar
opposite examples, but you need to make sure
that the governance infrastructure that you develop
accounts for that range of diversity of technologies
that you certainly will be implementing into clinical
practice,
but you shouldn't apply the same level of
rigor for those two.
Under our chief AI implementation
officer, Micky Tripathi here at Mayo Clinic, we
are currently developing top down policies
that standardize those evidence requirements.
So our teams know in advance
what is needed for safety and compliance.
That transparency
really shortens the path from concept
to clinical use and implementation in the practice.
Innovation must move at the speed of trust,
and this is the trust with the practice,
providers,
patients, and the public.
At Mayo Clinic, we encourage experimentation
through suggested
tiered deployment
that allows for evaluating
safety,
equity, and value before scaling occurs.
This approach allows us to be bold in
trying new technologies
while maintaining the discipline that patients expect from
a world class health care institution.
And sticking with it, I think that's that's
fantastic. And sticking with
advice, you know, the the a lot of
the the use cases you work out, you
you know what's coming in, you work with
it. But the the unknown, you know, seeing
all the advancements we have had recently, like
you said, the last five years has been
monumental. But, you know, just thinking of what
could be out there down the road, what's
your top piece of advice for leaders as
they prepare for further
advancements in technology
and kinda weighing those with the rising demands
for care?
Mhmm.
I think the future belongs to those organizations
that effectively
operationalize
governance. Establish multidisciplinary review structures early and
multidisciplinary
review structures early, and ensure decisions about AI
deployment are data driven,
transparent,
and clinically grounded.
It is essential that you embed your organization's
relative risk tolerance
into this governance
structure.
What we've done here at Mayo Clinic may
not be directly applicable to your healthcare organization
because maybe you have a different risk tolerance.
Technology can't transform care unless people trust and
understand it.
So leaders should prioritize
digital literacy,
cross functional collaboration,
and clinician engagement
just as much as the technical infrastructure.
I really think that culture is the true
enabler of responsible AI
or really in any innovative
technology.
Every AI or digital tool must serve a
clear clinical or operational purpose,
and we should measure for outcomes that support
that this tool is achieving that through post
deployment monitoring activities.
Lastly, my advice is to align
innovation with your institution's
mission,
whether that's advancing healing, equity, or access for
your patients,
and then hold technology to the same ethical
standards
as the practice of medicine itself.
Thank you so much. And the the last
thing I wanna ask you is,
obviously,
all this tech that we're working with and
and applying in health care,
you and your team are, it takes it
takes great leadership. Right? So I just wanna
ask you on a human level, how have
you evolved as a leader?
So I started at Mayo Clinic
as a manager of the same space in
regulatory
and quality
where I was coming from the med tech
industry
really with the primary goal of getting technology
into,
the commercialized space. So my experience was really
in
submitting products to FDA and understanding what that
pathway to
commercialization
would
be. My formal and formative regulatory years were
at Medtronic in their cardiac rhythm and heart
failure division.
And here at Mayo Clinic,
my job is not so much to pursue
the commercialization
of these different technologies,
but to see how we can build those
roadmaps
from research
to implementation
into clinical practice in a local way. So
as a leader, I have really grown and
expanded
my understanding
of what regulatory
and quality means
when building roadmaps or pathways to implementation,
where I've really shifted my mindset
from the angle
of getting FDA clearance or approval for these
technologies that are then going to be marketed
to building pathways more generically
for broader AI enabled digital health technologies,
to be implemented in the clinical practice, but
not necessarily
commercialized.
So I've had to root myself
in the clinician's perspective
of understanding
what it really takes to develop safe, effective,
and ethical products
while leveraging information from laws, regulations, and standards,
but really relying on their expertise to understand
how and when can we develop AI technologies
to solve these local problems.
So I have I have grown from what
I would say is a pure,
formal regulatory
expert to being someone who is more of
a generalist
in translation
science. And I think that this is where
we are going to see,
opportunity
and growth in leaders
with effective implementation
of AI,
where we
build leaders and, expert contributors
who understand
the regulatory ecosystem
and what is written in laws and standards,
but they take a generalist approach that really
leans on the clinician's expertise
to understand what it takes to pragmatically
translate these tools into clinical practice.
Prada, thank you so much for joining us
on the podcast. I think it was a
great conversation. I learned a lot personally. I
know everyone listening did, and I hope to
work with you again soon.
Thank you so much.
At athenahealth,
we know your ambulatory practice wants healthier,
a healthier business, healthier care teams, and healthier
patients.
But the complexities of modern health care tech
make it hard for you and your care
teams to focus on what matters
most. That's where athenahealth can help.
Our AI native all in one solutions reduce
administrative burdens,
streamline billing and payments, and deliver critical insights
when clinicians need it most.
That means fewer clicks, more time for patients,
and stronger bottom lines.
Practicing medicine is complex, but running a practice
can be that much simpler with athenahealth.
See how simpler is healthier at athenahealth.com.