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This is Laura Dierda with the Becker's Health
Care podcast. I'm thrilled today to be joined
by doctor Rebecca Mashouris, chief medical information officer
and vice president at Mass General Brigham. Doctor
Mashouris, it's a pleasure to have you on
the podcast today.
Thanks very much for having me. Now I'm
looking forward to our conversation and really digging
deep into some of the cool things that
you're doing at Mass General Brigham as well
as how you're thinking about the future. But
before we dive into my questions, can you
tell us a little bit more about Mass
General Brigham and what makes it unique?
Sure.
Mass General Brigham is a,
health system,
primarily located in Massachusetts, but also up in
New Hampshire and a little bit into Maine.
And we are made up of two anchor
academic medical centers, the Mass General Hospital and
Brigham Women's Hospital,
and then a number of community hospitals and
a very large number of ambulatory care sites.
We are,
the largest employer in Massachusetts,
health care or otherwise,
and,
you know, really aim to serve the communities
in which we exist.
I think in terms of uniqueness,
we bring together
the patient care,
the research,
and the training and education aspects of health
care. And that's really important and and really
critical to our mission of,
serving the communities in which,
we
we sit.
Absolutely. That's fantastic to hear. You know, what
a great opportunity
to make a difference for a large patient
population that you have there that are serving
in the Boston area. Now,
could you tell us a little bit about
an accomplishment from the last year that you're
most proud of?
Sure. I think, I'm probably most proud of
the way in which we have brought
a new technology
to bear against clinician burnout.
That new technology is ambient documentation,
the use of generative AI,
to help draft a clinical note,
from a recording of a of a visit
between a patient and their,
health care provider.
And not just having brought that, to our
our providers,
but the way in which we did it
in a very robust,
methodical way in terms of
understanding
the technology,
evaluating the technology,
piloting the technology, and then bringing it to
scale.
And part of that intentionality
was really around the fact that this is
generative AI,
and really has only been on the on
kind of the in the market, for the
last two plus years and and, you know,
really in the market even less than that.
And it's a technology that clearly has a
role moving forward in health care, not just
for ambient documentation, but for lots of different
purposes.
But it is a technology that we have
not seen the likes of before in health
care or otherwise
in that it is
built off of an immense amount of data,
in that the algorithms
are,
you know, built by people but then are
built to evolve over time as the data
evolves, as the understanding
evolves. Right? They're they're meant to learn.
And that is very different than a lot
of the static technology we've had in health
care to date.
And so we undertook an approach that was,
modeled after actually clinical trials. So it's a,
you know, zero to four clinical trials. We've
taken a clinical trial informed approach to the
evaluation and implementation of generative AI,
really kind of first applying that at scale,
to ambient documentation
where we sought first to understand the technology,
understand the safety of the technology, use a
very small cohort of about 20 physicians,
to evaluate the safety
and workflows with ambient documentation,
having seen that it was safe, right, no
rampant hallucination,
wasn't making things up, from the visit,
and and seeing sort of the enthusiasm of
those first twenty providers in terms of the
workflow and the impact it was having on
them, very quickly moved to a, what became
a fairly large pilot or initial implementation, if
you will, because it ended up being about
800 physicians and advanced practice providers across our
system, all care settings, all sites, all specialties,
to really try and understand the impact that
the technology was having on our providers.
You could very quickly see that this technology
was transforming the way in which we interact
with our patients
that ends up being a note.
But it it was about more than just
writing the note. It was actually a technology
that was putting us back in the room
with our patients and that's really unlike any
other technology in healthcare. Right? A lot of
times technology will put up barriers between people
rather than take those barriers down.
And so we,
evaluated about 20 different measures
of impact of ambient documentation and from that,
particularly because of the impact it was having
on clinician burnout with a 20% absolute reduction
in clinician burnout,
have now moved to scaling
the technology across our system.
There is nothing else in health care that
has been able to
reduce burnout to that extent.
I don't necessarily
expect,
everyone to to experience that degree of impact.
But even if we were to impact burnout
by 5%, that would still be a huge
amount at scale.
And so, you know, the technology
really you know, it it takes away the
documentation
aspect of things while you're with the patient.
And so providers report 80% of providers report
that they're spending
more time looking at their patient. They're spending
more time paying attention to their patients in
the course of a visit. And 60% of
our providers reported that they were more likely
to extend the length of their clinical career.
And then, as I mentioned, 20%
reduction in providers reporting burnout symptoms.
And so, you know, again, really kind of
impressive
impact of the technology, but I think that
the way that our team went about evaluating
and implementing and bringing the technology to scale
is actually,
the the most important thing to me. And
the fact that we've been able to impact
so many,
providers' lives and, quite honestly, patients' lives as
well. Patients notice that their doctors are no
longer flapping away at the keyboard during their
visit.
And when I hear stories from my providers,
like, I can go to my child's gymnastics
meet on Saturday morning instead of writing my
notes, or I can sit down to dinner
with my family now instead of finishing my
notes, that is huge, huge impact.
Well, that's amazing to hear. You know? And,
certainly, it's so inspiring to have that personal
touch to it, not only from how it
impacts patients and can really improve the patient
care and experience, but then for the clinicians
as well and the physicians who very much
need to have that ability to, be there
for their families
and to fill their buckets in that space
as well.
Just knowing you have that kind of support
from technology to,
you know, do that, I can imagine, is
really gratifying.
I'm curious. I I know a lot of
health systems are going down this path of
trying to use this type of technology,
and and figure out, you know, who the
best partners are for them and what the
best investments are for their organization.
What types of considerations did you make when
you were evaluating different opportunities, different partners, and
technologies, and that before making a decision on
how to move forward with, what you're currently
using today?
So, you know, first things first was, could
it write a good note? Right? Could it
draft a good note? And so we looked
at the actual functionality of the technology
at the current at that current time. Right?
And that that had a lot to do
with the,
robustness and sophistication of the algorithms that were
you know, the the large language models that
were underlying that. And,
so that was that was step one. Step
two was really paying attention to the workflow,
and looking for
opportunities
with, applications that would integrate into our existing
workflows, and that meant the ability to integrate
with our electronic health plan.
And then, you know, I think last but
certainly not least was really trying to get
an understanding of where that vendor was going
in the future because I don't think the
the end of this is writing or drafting
a a clinical note. Right? There are so
many things you can do on top of
that large language model and that recording of
a visit.
Everything from,
helping me place an order. Right? If I
mentioned during the visit that we're going to
start you on metformin a thousand milligrams once
a day, can it queue up that order
for me,
to helping me with my billing and coding
to some point in the future, maybe even
helping me with diagnostic,
and and clinical reasoning and decision making.
You know, if if I'm seeing a patient
for something and I go through kind of
my list of things that I wanna ask
them about to try and rule in or
rule out possible etiologies or or treatment options,
could that large language model prompt me if
I forgot to ask something, right, or,
alert me to something that I wasn't aware
of in the chart that might change my
thinking. We're not there yet,
but we were looking for vendors who were
who really kind of had that forethought and
foresight,
to to see what was coming and and
be able to build towards that.
That's amazing to hear. Thank you for going
a little bit deeper in there. Now I'm
curious. Looking ahead, what do you see as
some of the biggest opportunities for growth over
the next twelve months or so?
Oh, gosh. There's huge opportunities here. You know,
I think we have some of the biggest
challenges right now in health care from,
clinician burnout to the financial stress in health
care,
to just quality and safety of the care
that we're delivering and included in that the
equity of the care that we're delivering.
And so, you know, I think we continue
to see big opportunities in those spaces for
the technologies we have, but also really excited
about what we're seeing coming from,
the generative AI space,
and opportunities
that that provides that is significant again, significantly
different,
from what we've had available in the past.
Generative AI is clearly not going to be
the right tool for every problem that we
encounter in health care, But I think some
of our sickier problems,
right, it could have real impact,
moving forward.
That's great to hear. And, you know, really
cool to have that type of vision in
what's possible over the next few months and
and see, you know, where things are headed.
Now I know we've talked a lot about
the cool things and opportunities, especially with some
of the AI and other technologies, but I'm
curious as well. What are some of the
big challenges that you're anticipating?
So, you know, I think when it comes
to the technology, one of the biggest challenges
we're facing right now is,
how do we evaluate these technologies? Right? Do
we continue to use, and I think we
should,
our clinical trials informed approach and, how robust,
you know, should the robustness of that change
based off of the risk of the application?
Should it change based off of the risk
of the use case?
And then as part of that, we have
phase four, right, the last phase is actually
the monitoring phase.
And I think that is the biggest opportunity,
in the technology space actually is to ensure
that we have tools
that allow us to do the sufficient monitoring
of these applications moving forward.
Again, in somewhat of a risk based approach
where,
you know, some of the applications, some of
the use cases may be fairly low risk.
Think about a chatbot that is giving you
directions to get from one place to another.
Fairly low risk when it comes to health
care,
versus,
some of the tools I was describing that
don't quite exist yet, right, but may very
well in the in the kind of near
term future that are helping us with clinical
decision making. Those are really high risk when
it comes to to outcomes,
and and quality of care. And so we
need monitoring tools that are aligned with that
risk evaluation, that risk assessment
that allow us to monitor these technologies moving
forward. And in some spaces, actually in quite
a few spaces, the technology actually doesn't exist
yet, to do that monitoring. And so really,
looking forward to to seeing the evolution of
the applications themselves and also,
in our in the the applications that are
going to monitor those applications,
so that we can get in start to
get into some of these higher risk but
maybe higher reward spaces as well.
That's helpful for to understand. And, you know,
it seems like a really,
great way to look at things and make
sure that, you know, you're
seeing the challenges, but also developing solutions and
able to overcome them moving forward. Before we
wrap up here, I'm curious. What is the
number one thing that you can do right
now to set yourself up for success in
the future?
I think, you know, one thing we're doing
right now is actually pulling together the multidisciplinary
team that needs to move and drive this
forward.
And so, you know, a year and a
half, two years ago, we set up an
AI governance committee really to establish what our
rules of the road were, establish a responsible
use of AI,
framework for the organization.
And now it's about what,
groups of people do we need to bring
together in order to realize,
and bring to life these technologies. And so
that is much more about kind of the
the technologist that
with the data infrastructure, with data analytics,
with the rest of the organization that brings
the use cases,
for these for these, technologies. And so bringing
those people together into,
a multidisciplinary
team that actually will drive all of this
forward, I I think, is the biggest thing
we can do at this point, in addition
to working you know, continuing to work with
the vendors of these technologies to really understand
how they work, to evaluate them within the
context of our own health care system,
and as I mentioned, you know, develop the
tools to monitor them going forward.
That's amazing to hear. Doctor. Macheras, thank you
so much for joining us on the podcast
today. This has been a really fun conversation,
and I look forward to connecting with you
again soon. Thanks very much for having me.