This is Laura Dirdo with the Becker's Health
Care podcast. I'm thrilled today to be joined
by doctor Philip Payne, inaugural chief health AI
officer at Washington Medicine and BJC Health System,
as well as Deborah O'Dell, chief data and
analytics officer at BJC Health System. Doctor Payne,
Deborah, it's a pleasure to have you on
the podcast today.
Great. Thanks for having us.
I'm excited for our conversation today because, you
know, we're really gonna zero in on a
collaboration or partnership between you both as well
as the Washington University and BJC Health System,
which created a new center for health AI.
How do these strengths of each organization complement
each other in advancing AI in health care,
and how will this collaboration evolve over time?
I would love to hear from your perspectives
what's really important in the development process and
and how you plan to continue to grow,
the partnership.
So maybe I'll start.
You know, I think there's this unique opportunity
that exists at the intersection of both
the Washington University School of Medicine and then
our partners at BJC.
And when you think about it, really what
we're trying to do is advance the design
and adoption and use of AI
in a way that really drives excellence in
both operations and ultimately care delivery.
And when you think about the combination of
our 2 organizations, we have a wealth of
expertise in that medical school from a biomedical
research and computational science and AI perspective. And
that really provides us with this sort of
academic foundation for our work,
and that leads to activities like cutting edge
algorithm design or training new models or validating
systems.
And then at the same time, BJC
Health System is
and then at the same time, BJC Health
System is a leading not for profit delivery
system and really provides the ideal real world
environment in which we can implement and scale
these AI solutions.
If you think about it, it's really sort
of, as I just mentioned, a living laboratory
in which we can think about how these
technologies scale, how they have impact,
and really how they provide value in real
world complex workflows.
And that means the innovations that we'll be
developing won't just work in the laboratory, but
they'll really have a direct and practical impact
on daily patient care. And this new center
is really all about bridging that gap and
connecting sort of those 2 sets of capabilities
so we can really,
navigate that
journey from identifying opportunities where AI can provide
value to deploying technologies, to evaluating them and
then scaling them.
And I also think this is emblematic of
just the collaborative nature of the relationship between
our 2 organizations,
especially as we work, even more closely together
to drive, care delivery for a substantial population
across
not only Missouri, but a number of surrounding
states.
That's amazing to hear. You know? It really,
very much, it penalizes
how these collaborations
between academic side and health system side can
really make a big difference. Deborah, is there
anything you wanted to add here?
I just echo what Philip said. I think
this is,
for us, the opportunity for us to bring
together both the benefits of the what the
university brings in terms
of their,
advanced research and what they bring from biomedical
research and AI development. But BJC brings the
place where we can operationalize that and scale
that. So it's a really effective partnership that
we have developed and, looking forward to continuing
to advance AI capabilities through that.
That makes a lot of sense. Now I
know AI has the potential to significantly enhance
patient care.
Deborah, starting with you, can you share some
of the specific ways that technology is being
applied right now to improve patient outcomes and
overall patient experience?
So,
you know, AI is already transforming health care
operations and clinical outcomes both, through our university,
Washington University School of Medicine and BJC Health
Care. So we have, for example, predictive algorithms
are optimizing patient flow,
looking at real time analysis,
historical data to help us identify
patient trajectories and optimize transitions in care.
AI tools are are enhancing early detection and
diagnosis through medical imaging data and identifying conditions
like cancer or cardiovascular
degree,
disease. So we are, really helping
to support clinicians in making timely and precise
decisions.
AI is also being used
to personalize treatment plans by analyzing electronic health
records and genomic data,
to really recommend
therapies that are tailored and particular in fields
like oncology.
So predictive analytics are driving,
population health, identifying high risk patients. So there's
so many opportunities where we're seeing,
the demonstration of how AI can,
enhance both the efficacy and the the operations
so that we can provide superior quality of
care.
Philip, what would you add?
No. I mean, I think that was an
excellent summary. I mean, I think a lot
of this is anchored on, I think,
a driving philosophy here, which is that
our goal is to use AI to complement
the unique strengths of our providers and our
clinical workforce so that it's really this combination
of humans and computers working together
that is superior to either working alone. And
I think that's borne out in a lot
of the examples that Deborah gave,
which really come down to how do we
sort through and understand these massive quantities of
data and evidence and knowledge we have and
make better,
safer,
care related decisions for our patients and improve
the the value of that care along the
way.
I love that. It it seems like such
a meaningful way to apply technology into, the
health system and health system and health care
organizations
for patients as well as, the team members?
And and speaking of your team, how do
you envision AI tools improving health care employee
experience?
Are there any early signs of success or
feedback that you've received from those on the
front lines?
Yeah. I mean, I think you're sort of
touching on probably one of the most important
areas
that we have been looking at,
which is how do we remove sort of
these high friction, low value tasks,
that
are taking our providers away from focusing on
our patients?
And in a similar way, how do we
get sort of the computer out of the
middle of those interactions, again, between our
providers and patients so that we restore a
certain degree of humanism to how we practice
medicine.
So, of course, like many large delivery systems,
we've been
leveraging
ambient AI technology to help automate documentation during
patient visits and we've had some very promising
results not only in primary care settings but
now in procedural disciplines and we're looking at
other domains.
And this has been described by some of
our providers as transformative.
We have individuals who used to spend several
hours a day after normal business hours documenting
that are no longer doing that because they're
able to be more efficient and timely with
their documentation
during the course of their,
clinical work.
We're also
using those same types of technologies to look
at how can we do a better job
of summarizing the content of electronic health records
so that we can get the right information
to our providers more quickly, whether they're
getting ready to admit a patient
or they are seeing a new patient for
the first time,
or any number of other use cases where
prior workflows would involve them spending
potentially up to several hours trying to prepare
for these types of clinical encounters. And now
we've turned that summarization activity into sort of
a matter of minutes and mouse clicks. So
for us, it's all about how do we,
again, restore that humanism in medicine and really
let our providers focus on our patients by
taking those other tasks out of the mix
by automating them. So we're really excited and
I think our providers are really excited about
this.
That's amazing to hear. And, certainly, you know,
what what a great,
an interesting way to think about technology and
using that to then give the providers back
time to be more human with their patients
and have that really human to human connection.
Deborah, is there anything else you wanted to
speak to when you think about the employee
experience and those that you're working with, to
really truly elevate the way things are at
BJC Health Care?
The only other thing that I'll add is
that, when we think about what the new
center can can do and how we can
leverage technology, it's it's also our employees and
staff
that might not provide direct patient care. So
where can we look at opportunities to help
our,
our other capabilities, whether they be in our
finance team or supply chain to help with
tools that make day to day life more
efficient, more effective?
So I think the center is is here
primarily to help that clinical that clinician and
that patient experience, but we do see opportunities
that can help us as a health system
to also be
more effective, which really does,
ultimately have a positive impact on the patients
as well.
Absolutely. That's such a great point and and
such an important,
thing to remember when you're looking at the
system as a whole. Now I know the
Center For Health AI will offer opportunities for
medical students and residents to train on AI
driven care. How do you see this influencing
the next generation of health care leaders, and
what skills will they need in order to
succeed,
in an AI enhanced medical landscape? What are
your thoughts on this, doctor Payne?
Yeah. I mean, I think that's a really
important question because the fundamental
environment in which medicine is being practiced is
changing as a function of AI. And so
the question is, really, how do we prepare
our future practitioners,
researchers, and leaders to thrive in that
fortuitous timing for us that we've just gone
through a
refresh of our medical curriculum here at Washington
University.
And as part of that, we thought about
what are really the core areas of expertise
that the physician leaders of the future need
to have. And one of them was clearly,
informatics,
AI and data science.
And that means we're now integrating that into
our medical curriculum. We're teaching our medical students
to understand
how the use of technology impacts
their decision making and how they need to
sort of consider that whether that be for
diagnostic purposes or therapy planning or any number
of other scenarios.
We're also using AI powered simulation tools now
to create really realistic scenarios so students can
practice clinical decision making in sort of a
safe space before they end up in the
clinical environment. And that provides instant feedback and
insights into their performance that are highly tailored.
So much like we talk about precision medicine
for patients, this is really precision curricula.
And then I think much like it has
historically been the case that we teach sort
of future,
again, practitioners
and researchers and leaders to critically evaluate,
the literature. We're now teaching them how to
critically evaluate the output of,
AI tools like predictive algorithms
so that they understand sort of both the
strengths and weaknesses of these types
of predictive tools and how they can and
should influence clinical decision making in equal measure
where
sort of appropriate caution may be necessary.
And the last area that I would emphasize
is
we're also giving our students the opportunity to
really analyze extremely large datasets and gain insights
around
phenomena that might be of interest around
policy
or population health or other sort of
interventions that happen beyond the individual patient in
the exam room or hospital room. And we're
doing that by really democratizing how people access
data and analyze data, again, using AI tools
that sort of guide the trainee through that
process.
So really, when you put that all together,
we're not only
improving the quality and sort of the completeness
of the education we provide, but we're also
ensuring that those students are ready to go
out and practice in a world where
AI is going to be ubiquitous and, you
know, really a
sort of, in some ways,
sort of key dimension of the modern practice
of medicine.
Absolutely. Well, that's fascinating to hear, you know,
and certainly, as you think about those next
generations of medical students,
what a a great tool to have in
their back pocket with AI and understanding its
limitations, but also opportunities.
Deborah, is there anything you wanted to add
in there?
I'll just say I think, BJC wants to
continue to be,
a world class institution that brings world class
providers here, and so we need to stay,
at the forefront of this technology
that,
our providers are learning to use in school,
and we want them to have access to
that,
at a scale of a system our size
and breadth. So, this is why the partnership
between BJC and WashU is so important that
we can continue to do this in a
very unique way, to the medical school and
then the size and scale of the BJC
system.
That's amazing to hear, you know, and really
great that on the health system level, you
can have that foresight
and and support what the medical school is
doing. Now I have a few questions for
you, doctor Payne, and then we'll follow you
back to you, Deborah. But, doctor Payne, one
example that you've shared, is using AI to
predict excessive
blood loss during surgery, which helps improve resource
management. Can you expand how AI is improving
surgical procedures, diagnostic accuracy, and precision medicine overall?
Yeah. I mean, it's a it's a great
question and it's a project I'm particularly excited
about.
There's actually a research team here that spans
not only our Institute For Informatics and our,
Department of Anesthesiology
and Department of Surgery, but also,
key partners at BJC.
And that group has really sort of demonstrated
in a very compelling way how you can
use machine learning models to predict
blood
loss during surgical procedures. And if you're familiar
with this domain, sort of the current state
of the art is to use sort of
historical and largely,
I would say out of date data concerning
predictive blood loss as a function of how
the average patient may
sort of
experience blood loss during surgery. And the result
was a lot of blood products being used
in the OR
that were necessary but an equal measure of
blood products being sent to the OR that
were never used and therefore wasted. And so
this team actually used
over 4,000,000 surgical records from the American College
of Surgeons National Surgical Quality Improvement Program database
and actually built a highly accurate model that
recommended type and screen orders,
for patients during the sort of perioperative management
sort of period.
And actually,
this resulted in, if you sort of compare
the traditional model versus current model, a difference
of 96%
sensitivity in anticipating
perioperative blood loss using the machine learning model
as compared to sort of around 50%
sort
of sensitivity in a baseline model. And that
results in real savings of blood products. So,
in a lot of ways, this is about
how we can use personalized
data driven interventions in order to enhance that
perioperative care period and at the same time,
really be much more judicious in how we
manage
a very finite resource that's critical to surgical
safety. So, that's I think, a a great
example of how all these pieces come together.
That it really colors the work that you're
doing,
well and and spotlights the outcomes and impact
it can have not only on patients but
the organization too. You mentioned AI can help
identify the most effective treatments for individual patients
as well. How is this process being applied
in practice at WashU and BJC,
especially for the complex or rare diseases?
Yeah. So,
I think one of the things that we
know is that especially in rare disease,
the availability of therapeutic options is very limited
because they are frankly not as appealing to
pharmaceutical
or bio technology companies. And so we often
are in a position where we have to
create tailored therapeutic
approaches for those individuals.
Great examples, we have a research
program here working with our neurofibromatosis
type 1 center or NF1 center. And this
is,
the most common rare genetic diseases in children.
It's relatively easily diagnosed.
But then there's a multitude of phenotypes that
patients will present with. Some patients will be
fine and will not experience any meaningful symptoms.
Some will develop
optic pathway gliomas or plexiform neurofibromas,
which are painful nerve sheet tumors. Some will
develop ADHD. Some will develop scoliosis. I could
keep on going down a lengthy list. And
the problem is we're not very good at
anticipating
which of those phenotypes patients will
exhibit it. So we tend to manage those
patients extremely aggressively trying to find early signs
of those various sort of deleterious outcomes, but
we're not always good at finding them.
And we've actually been able to, again, using
artificial intelligence methods and a combination of imaging
data, data from the EHR and then various
neurobehavioral
phenotyping instruments applied to patients directly,
we're able to generate highly accurate
predictive models that can be used to forecast
sort of the disease course for children with
NF1. And so what that means now is
instead of every child having to have a
large amount of imaging or other potentially challenging
diagnostic
procedures, we can focus on those individuals who
are most at risk and often detect the
outcomes earlier, which leads to better therapeutic outcomes.
And I often say to people, sometimes precision
medicine is about finding the right treatment, but
sometimes precision medicine is just about demystifying diseases.
So we know what to expect and we
can be smarter about how we manage those
symptoms. And I think this is a great
example where we're getting smarter using AI so
that we can be very precise in how
we manage
the clinical course of kids with a very
challenging genetic disease.
And we need to do more of that.
There's no doubt that we have a wealth
of data to build these models. It's just
that we need to start doing the hard
work of bringing those data together,
working with our clinical colleagues and frankly running
large studies to validate that the models provide
clinical value, which is another big part of
the project I just described to you.
Absolutely. Thank you so much for that example.
And real quick, what steps are you taking
to ensure that those AI technologies are used
responsibly and safely in patient care?
Yeah.
That is a huge and really important question.
Right?
How do we make sure that when we
deploy ai, it's safe, it's efficacious
that we are considering the ethical
and legal and social implications of these emerging
technologies. And
I would start by saying we're committed across
Washington BJC to making sure that everything we
do through this new Center For Health AI
emphasizes
delivering these tools in a secure, scalable and
reliable manner, but also in a way that
prioritizes patient safety and privacy
and confidentiality.
And that means we have to really sort
of use what is often referred to as
sort of a privacy by design approach, which
means that data governance and the application of
appropriate regulatory standards as well as more technical
controls,
and then engagement of
key stakeholder groups to define early and often
the ethical
frameworks in which these tools exist, all of
that has to happen at the beginning of
projects, not at the end of projects. And
I think one of our challenges often is
that we treat these, again, ethical, legal, and
social implications of technologies
as something
that is assessed after the fact.
But the likelihood of success is greatly diminished
when we do that. And I think the
cornerstone of all of this is transparency, right?
Making sure that we're empowering our patients,
our providers and all the other stakeholders involved
in our delivery system so they know when
and how we're using AI.
And I
think ultimately patient safety and these ethical considerations,
they're gonna be central to every decision we
make about AI deployment.
And that means that the teams that we
put together will have to be interdisciplinary
and include not only technologists and clinicians
but ethicists and patient advocates and community representatives.
We're gonna have to make sure that we
focus on and commit to really rigorous
validation of these tools that includes
an assessment of sort of the ethical and
social impact of their use. And then we
have to create these continuous monitoring and feedback
loops so that we understand unintended consequences or
outcomes
of the use of AI rather than treating
the process as somewhat unidirectional where we declare
success when the tool works, deploy it in
the healthcare delivery system, and then move on
to the next project.
And I would say,
much as people have talked about the need
to be vigilant about adverse outcomes with new
therapeutics as they come to market. I think
the same will be very much the case
with AI.
And I'll just wrap up by saying, in
my mind, it is our responsibility
at Washington Medicine and BJC
to set the benchmark
for responsible, safe, and ethical AI innovation in
health care. So I don't believe we're satisfied
just with, adopting best practices from others, but
we really wanna set the standard for what
those best practices are.
That's amazing to hear. A big job, but
one certainly that you're set up well-to-do
responsibly.
Now, Deborah, from your perspective, one of the
primary goals of the center is to improve
operational efficiency, including streamlining scheduling and reducing administrative
burdens. Can you walk us through some of
the AI tools currently being piloted and how
they're making a tangible difference for health care
providers and patients?
Yeah. We're actually,
really looking at what our current vendor partners
offer embedded in tools that we might already
have. And I think this is very common
across,
the health care environment today that we're in
any environment, actually, where they're seeing,
existing
software and tools that we're using and they're
adding in components. So whether that be our
EHR vendor or our ERP vendor,
we are we are looking at those opportunities
to see how we can implement them. I
think
as Philip and I,
are standing up this new center,
we really talk about how we can
help move these projects along, how we can
unstick,
where things get get stuck with new technology
and,
implementation and change of processes. So we're looking
at really how we can leverage what we
already have today as well as,
the experience and knowledge of all the examples
that Philip just gave that come from our
university research and provider side.
So we have many different pilots going on
in those spaces where it might feel,
like, we don't see it every day, because
we're
processing
payment or we're ordering supplies or we're working,
through vendor
agree legal agreement. But those are places where
AI
tools can really help us be more effective
and efficient and move things through faster.
That's great to hear. You mentioned that AI
could help predict demand for equipment and staff
resources.
What are some of the real world examples
of how AI is improving logistics and resource
management in health systems, especially in large and
complex organizations like BJC?
I think today in health care, a lot
of health systems are really struggling with similar
things around capacity,
and throughput. And so understanding how we expect
people to come through, the needs and equipment
to come through, whether it's shortages
that we have in certain supplies,
and being able to project and predict when
those things are going to happen and what
the impact is going to be.
So I think
most of the country is experiencing some of
those at any given time, whether that be,
we have backups in our emergency department or
we have,
IV shortages due to
unforeseen,
world situations,
we need to,
we need to be move quickly at helping
improve how we can answer those problems and
address those resource needs. And so, those are
things that we do on a daily basis,
and we're hoping the new center can really
help us to figure out how to continue
to drive those sorts of decision making.
I love that. What a great use case
and and really strong example. Before we wrap
up here, I have one more quick question.
How do you plan to scale successful AI
tools and solutions from pilot projects to widespread
adoption across the health system? What challenges do
you anticipate in this process, and how can
the center help overcome them?
I think, the the challenges are gonna be
challenges with any new technology, new change, new
processes,
learning what works, what doesn't, trying some things.
Process change is always,
getting some people will be on board. Some
people will need more,
support and coaching around how this can be
different and how this can change their,
their workflow and their day. So I think,
luckily, I think Philip and I are privileged
to work in an organization
that,
really wants to learn and grow, and we
are often just trying to keep up with
the needs and demand around how we can
do things more efficiently, more effectively, and provide
better care.
So I think the center will we will,
we're really here to help our leaders and
our providers
do the best that they can for our
patients. So however that,
however that manifests itself in what we can
do every day to support them, I think,
I'm excited about it. I'm excited to continue
to work with Philip in in how we
can scale this and really support what our
providers do every day for our community.
That's amazing to hear. Doctor Payne, Deborah, thank
you so much for joining us on the
podcast today. This has been such a full
and rich conversation, and I look forward to
connecting with you again soon and catching up
on some of the great things that you
continue to do with the center.
Well, thank you for the opportunity.