Kathleen Wessel (Host): Discover new ways of enhancing patient care,
improving outcomes at lower costs, and accelerating the digitization
and utilization of healthcare data.
Welcome to a HA Associates Bringing Value, a podcast from
the American Hospital Association.
In a series of podcasts, we speak with a HA associate program, business
partners check in on their efforts and learn how they support a HA
hospital and health system members.
I'm Kathleen Wessel, vice President of Business Management and Operations with
the A HA and today I'm joined by Dr.
Monica Coley, healthcare Informaticist and Senior Business Development
Manager for Amazon Web Services and her colleague Steve Meredith, US
Healthcare Lead Academic Medicine, also with Amazon Web Services.
Together we'll discuss how AWS partners with hospitals and health systems
to drive their mission via cloud innovations and optimizations in areas
such as EHR systems, medical imaging, integrated healthcare applications,
patient, consumer experience, digital health, population health and health
data platforms, including such domains as artificial intelligence,
machine learning, and generative ai.
Monica and Steve, welcome to the podcast.
Monica Coley: Thank you.
Kathleen Wessel (Host): I am really excited to learn
more about the work you do.
So let's kick off today's conversation by learning more about both your
professional journeys And what brought you to Amazon Web Services.
Monica, why don't we start with you.
Monica Coley: Sure.
Great.
Thank you Kathleen.
So my route to Amazon Web Services was by no means a straight line.
I actually started out in healthcare about 30 years ago, really in the
health policy and administration space.
And my goal was really to actually develop universal healthcare
services for needy populations.
And somehow along the way that got morphed and I fell in love
with computers and it by a strange set of circumstances, I ended up.
Running electronic health records procurement process at
Morehouse School of Medicine.
Fell in love with that.
Started running a mini IT department within a department for our Department of
Family Medicine, and that's how I get got introduced to electronic health records.
So I.
From there.
Um, after implementing both the clinical and technical pieces of the EHR and
going through the whole procurement and selection process, I decided
that's what I wanted to do full-time.
And so that really is what led me over to Cerner Corporation because I
wanted to implement systems full-time and design systems full-time.
So I was there, um, nearly two decades.
Moved on from, from there for a brief stint and got into hospital at home,
still working with EHRs, but how to support hospital at home models.
And then I got wooed away to AWS.
Um, so I came over to AWS about two and a half years ago and, you know, uh,
got this opportunity to work with cloud within the same market of customers
I've been working with the rest of my career, but with a different twist.
So that's kind of how I ended up in the cloud space.
A long journey to, to get there.
I don't, I don't know why that wasn't apparent at 18.
Go figure.
But, uh, I figured it out now, so that's what brought me here.
Kathleen Wessel (Host): That's amazing.
Thank you.
Steve, what about yourself?
Steve Meredith: Well, my journey to AWS isn't quite as long as Monica's.
I've spent the last couple decades in healthcare technology, and it's been
really fascinating to witness and participate in the industry's evolution.
Since 2000, I've been deeply involved in developing and implementing solutions
that address pressing challenges in healthcare from operational
efficiency to patient care delivery.
I spent most of my career with industry leaders like McKesson and GE Healthcare,
but one of the most formative experiences was joining an early stage healthcare
technology startup where we were pioneering real-time location solutions.
This technology was revolutionary at the time.
We were helping hospitals track assets, improve staff productivity,
and optimize patient flow.
Throughout my career, I've always been drawn to organizations that
are pushing the boundaries of what's possible in healthcare.
This pursuit of innovation ultimately led me to AWS in 2020.
What attracted me to AWS was its unique position at the intersection of
cloud and healthcare transformation.
I saw an opportunity at AWS to be part of something truly revolutionary
using the cloud to solve healthcare's most complex challenges at scale.
Today at A WSI lead a national team of healthcare executive advisors who
work with health systems across the us.
We help those health systems leverage cloud to enhance patient experience,
accelerate the research that they're doing, advance precision medicine
and improve operational performance.
What really excites me about this role is seeing how cloud is
actually transforming healthcare.
The challenges that I encountered throughout my career, including.
Operational inefficiency, data silos, limited scalability are exactly what
the cloud is helping to solve today.
Being at AWS allows me to combine my deep healthcare experience with
cutting-edge technology to help healthcare organizations drive meaningful change.
It's incredibly rewarding to be part of a company that's really helping to
shape the future of healthcare delivery.
Kathleen Wessel (Host): That's amazing.
Thank you.
I, I remember the, uh.
Kind of evolution or the launch of RTLS.
I mean, that seemed revolutionary at the time.
So, uh, where we are today just is a amazing, Steve,
let's go to you for this one.
Most people are familiar with Amazon as a retailer, so can you share with
listeners kind of more about the evolution of Amazon Web Services?
Steve Meredith: Yes, yes, absolutely.
AWS.
Actually has a fascinating origin that's deeply rooted in
Amazon dot com's own evolution.
In the early two thousands, when amazon.com
was growing rapidly and needed to scale its e-commerce operations
efficiently to do this, amazon.com
built a robust internal infrastructure of standardized automated computing services.
By 2003, Amazon's engineering teams began to think about and realized
that these internal capabilities could actually be valuable to other
companies facing similar challenges.
So in 2006, AWS was officially launched, starting with simple services like Amazon
S3 for storage and EC2 for compute power.
At the time the idea was revolutionary offering businesses the ability to
rent computing power and storage on demand, rather than investing in
expensive data centers and hardware.
The pay as you go model fundamentally changed how companies thought about it.
Infrastructure.
Today AWS has grown to become the world's leading cloud platform offering.
Over 200 fully featured services from data centers around the world.
What started as an internal solution for Amazon dot com's needs has transformed
into a platform that powers millions of customers from fast growing startups to
large enterprises and government agencies.
Organizations like Netflix, Airbnb, and even NASA now rely on AWS
for the cloud computing needs.
Kathleen Wessel (Host): Yeah, thank you for that.
I think that really helps kind of, um, give the full picture of.
That evolution.
Now, if we take that example or some of the examples that you provided
in there and draw a through line to healthcare, what are some good examples
of how hospitals and health systems can leverage AWS cloud services to meet
patient care and, and business needs?
Monica Coley: Awesome.
So I'll, I'll say I can speak to that from a overarching perspective, but
also to state and local government, hospitals and health systems.
So.
If you think about AWS in healthcare, I like to talk about what we,
I call, I've labeled the wheel.
So I had a, a colleague that built this wheel that really showed all of the
things that we focus on, modernizing and innovating with health systems.
The EHR is the center of that wheel.
Kathleen Wessel (Host): Mm-hmm.
Monica Coley: Right?
As the primary system, the core that drives the health system.
Right.
From that really out protracts, different lines to patient engagement,
population health, remote patient monitoring, revenue cycle, right?
Also medical imaging.
So when we think about.
Optimizing, modernizing, working with health systems.
We're really talking about, hey, let's not only help you move your EHR to
the cloud, right, for cost savings, for disaster recovery preparedness,
for resiliency, redundancy in your network to stay up for patient care.
But let's also look at how we move and modernize all of those other systems
that your EHR also speaks to, right?
To make a complete patient experience.
So, um, that's something that we, we really focus on is how we can really.
Move that will, if you will.
And we have several organizations that are starting to go down that
cloud journey path, if you will.
I've had several healthcare organizations as of late, somewhat trending in the
state and local government space to really say, I'm really starting to
look at how do I move my healthcare applications out of my data center, right?
And move it to the cloud.
Or how do I build a secondary data environment, right, so that I can take
that secondary data environment if I have a natural disaster, which we know
parts of the country suffer from, right?
If I have a ransomware attack, which is all too common now, if I have any
of those challenges or even just even a unplanned downtime, I can flip over
to the cloud version of our ecosystem and keep patient care going, right?
And hopefully, and most of the time from what we're finding at a lower cost, right?
So that's what we're seeing hospitals really focus on.
That's where we're seeing some trends as hospitals and health
systems get used to the cloud, right?
It's still, you know, still in newer veins, still, people
are still filling it out.
Is this, you know, is this safe?
This is where I wanna go, right?
And so that's what we're seeing trending now as to how to make that journey.
So that's something that we at AWS and our partners will help
health systems work towards so they can create that cloud journey.
Steve Meredith: I'll build on what Monica described.
In addition to really working with SLG, uh, state and local government,
publicly owned health systems, we also support academic medical centers.
They're positioned at the intersection of patient care,
medical research and education.
It's really interesting to see how these organizations are accelerating
breakthrough research simultaneously improving patient outcomes.
With AWS, medical researchers had to wait weeks or even months to
access the computing power needed to process complex genomic data.
Run sophisticated machine learning models with AWS.
These same researchers can now spin up thousands of processors in
minutes, analyze massive data sets, And then shut them down when finished
paying for only for what they use.
This not only accelerates the pace of research, but also makes previously
impossible projects feasible.
What's particularly exciting is how AWS enables collaboration
across these institutions as well.
Researchers can securely share data sets and computing resources
while maintaining strict compliance with regulations like hipaa.
For instance, we're seeing AMCs create secure data lakes that combine clinical
genomic and imaging data, allowing researchers to identify patterns and
insights that weren't visible when looking at each data source in isolation.
AMCs are also using AWS to power precision medicine initiatives.
By combining high performance computing with machine learning capabilities,
they're analyzing individual patient characteristics to develop
more personalized treatment plans.
This might involve processing complex genomic sequences, analyzing medical
imaging data, or running sophisticated population health analytics, all
again while maintaining the highest levels of security and compliance.
The key advantage here is scalability and flexibility.
Whether an institution needs to analyze data from a small pilot or process
information from millions of patient records, AWS can easily scale to meet
these needs without requiring massive upfront investments in infrastructure.
This democratizes access to advanced research capabilities, allowing
both large and small institutions to pursue innovative research projects.
Kathleen Wessel (Host): Wonderful examples.
Monica, I wonder if we could dive a little deeper in there.
What are some specific AWS cloud use cases of interest that you
might be able to share with us?
Monica Coley: Well, of course I would be remiss not to say the EHR, right?
Let's we start there.
So one of the things go figure that we really focus on is how we can
help you to migrate your electronic health record to the cloud.
Again, that's, you know, protection for, you know, having
redundancy in the networks if, if you should have a unplanned
downtime or other type of disaster.
Or just even to reduce your costs, right?
Mm-hmm.
For running an environment, typically cheaper to run an environment in the
cloud than it is on premises, as most people do today in their own data center.
So we have organizations like, for example, Tufts Medicine.
They were one of our first customer examples that actually took a turn at.
Combining not only six electronic patient records into one epic migration
instance, But when they did that, they migrated not only that new epic
instance directly to the AWS Cloud, but also 42 of their other applications.
They did this over a span of 15 months, and now they're on a
journey to move totally out of their data centers over to AWS.
So we work with customers in the epic space heavily, but Meditech also, which
I know is very prevalent, especially in the state, local government market
of hospitals and county health systems.
For disaster recovery, we have customers that are actually running
their disaster recovery backup instance of their Meditech, EHR on AWS.
So that's the premise.
Of course, we talked about that wheel and all the other systems that integrate
with with EHR that we also look to move.
But another use case that I'm seeing or that's trending.
One of my colleagues called the whole county approach, right?
He was like, let's look at a problem that we can solve across a county, right?
That the health system And also health and human services and
other agencies and resources in the county can work on together.
A lot of times that ends up in the population health And the data space.
Mm-hmm.
Right?
How we could exchange data, so.
I've seen a trend in some projects going on now where organizations are saying the
health system, health and human services, whatnot, let's aggregate our data.
Right?
Let's assess the needs of a patient, or in this case a county
citizen, if you will, their social determinants of health, et cetera.
So, it doesn't matter if I show up at the hospital or if I show up, you know,
for Medicaid or if I show up 'cause I'm homeless or, or I'm a vet, right?
At any of those entry points into the county, I can aggregate data and.
Refer to other services within the county, no matter if it's health or not.
And why that's really important is because of the, we know 80%
of what impacts somebody's care.
Healthcare outcomes doesn't happen in the healthcare system.
It doesn't relate to the encounter.
It's everything around it, right?
And the social determinants of health.
So I think with the cloud, what that's allowing us to do is really, as Steve
mentioned, pull a lot of data together.
Across various different sources with a lot of power, right?
To run that data at a cheaper cost, but also analytics to really understand what's
going on in my population and get them the best care And the best outcomes.
So that's something that I'm seeing, you know, at large, over and above and beyond.
Just like the EHR, which is the bread and butter.
And
Kathleen Wessel (Host): Steve, anything to.
Steve Meredith: Yes, I, I'd like to, uh, continue the theme of research
that I started in my previous answer, and I'd like to share a powerful
example of this in action through the work that we're doing with Children's
Brain Tumor Network at the Children's Hospital of Philadelphia, pediatric
brain tumors are the leading cause of disease related death and children.
Yet unfortunately, research has historically been hampered
by data silos and limited collaboration across institutions.
The challenge is significant researchers need to share and analyze
massive data sets, including genomic sequencing, imaging, and clinical
data across many institutions.
We're talking about petabytes of data that previously has taken up
to months to transfer and process.
A single pediatric brain tumor genomic sequence can generate up
to a hundred gigabytes of data.
And meaningful research requires analyzing thousands of these cases together.
Using A-W-S-C-B-T-N has created a cloud-based data coordination
platform that's transforming how researchers collaborate.
Instead of actually shipping hard drives or waiting months for data transfers,
researchers from over 35 institutions worldwide can now securely access
and analyze the data in real-time.
They're using AWS to run complex genomic workflows.
Apply machine learning to medical imaging and identify patterns across
vast data sets that could lead to earlier diagnosis and more effective treatments.
What's particularly impactful is how this accelerates the research timeline
analysis that formerly took weeks or months, can be completed in hours or days.
For example, when a child presents with a brain tumor at any of
these member institutions, doctors can quickly compare the tumors.
Characteristics against CPT N'S extensive database, potentially
leading to faster and more accurate diagnoses and more targeted treatment
options for those patient's.
This is exactly the kind of transformation we're aiming for in healthcare at AWS
using the cloud, not just to solve technical problems, but to fundamentally
accelerate the pace of discovery and to improve patient outcomes.
Kathleen Wessel (Host): Those are great.
Those are really powerful examples on, on what can be accomplished there.
And maybe we dig a little further.
If we're thinking about artificial intelligence, machine learning, generative
ai, um, these are not only hot topics, but capabilities that when applied
strategically within a healthcare setting can really extend and enhance services.
So, you know, Monica, let's start with you.
Can you share some examples about how hospitals and health
systems are really using these technologies within the AWS platform?
Monica Coley: Yeah, sure.
Absolutely.
So I think it's kind of along a paradigm, if you will.
I believe we have what we see trending today, but then also for the future
and where we know we're heading, right?
And how gen ai, AI, and ML are gonna play into that.
So I'd say currently what I'm seeing, especially in the state
and local county market, is really starting off with the call center.
Like how can I use gen AI chat bots and other technologies?
As a part of my call center so that I can use virtual agents instead of using
a human being in some cases, right?
Of course, there's times you have to talk to a human.
You won't ever replace a human being And that human touch, but it allows
you to actually spread your resources more effectively so I can take off
some of those more routine tasks that could be handled simply and easily with
direct patient interaction with gen AI and other capabilities, and save
my people for the more complex issues.
Right.
Think about this as well, when you're thinking about like physician shortages
and burnout, nursing shortages, and having an agent, a virtual agent,
be able to take the place for doing patient triage or being a virtual
nursing assistant, right, that you can interact with before speaking to a human.
So I've had health system talk about like, you know, as we move along that paradigm
and, and get more complex, I want.
AI capabilities to be able to work with a patient, triage a patient,
do those tasks, those clinical tasks, up to a point, right.
Before handing off to a person, And that helps to relieve some of the burden.
It helps to what, help the health system be a little bit more efficient And also
deal with some of the shortages we have.
When you think about other things, I'm seeing, um, ambient listening, right?
And so for anyone that's not familiar with ambient listening, just think about
as having a device in an exam room or a hospital room where a clinician, whether
it be a physician or a nurse or whatnot.
Are having a natural conversation about the patient, right.
And their vision and their challenges.
And from that, the technology's able to sift out what the patient's saying versus
the provider and take that provider interaction to create a clinical note.
Mm-hmm.
Just by picking up a audio and translating it.
So we're starting to have people ask about that, pilot that a good
bit in the market because that also helps for more accurate notes.
Right.
But also takes some of the burden off providers when they're
trying to create that clinical note by using the intelligence.
And I think one final space is where we're going in the future.
So projections are by 2030, about 60% of healthcare can be handled at home.
It probably will be.
Right.
So I think, You know, preparing for technologies like AI and ML that actually
can assist with that patient care process.
Remote monitoring and other capabilities when you're at home is gonna be helpful.
But also for the hospitals that we still have, we have a lot of hospitals that
are actually undergoing construction and other types of projects, and as
they're doing that, they're like, I'm gonna make these hospitals smart.
I'm gonna have smart devices, I'm gonna have smart capabilities to be
able to talk to devices, take notes, open charts without even, you know,
having to have hands on keyboard.
We've got some hospitals using Alexa, right?
Training Alexa and running those properties to control the room.
So Those are things you're gonna see as we progress in the future.
And AI empowers all of that in the background.
And we have, um, organizations using those technologies on AWS.
So that's where I see where we are right now and probably where we're heading
next, if you will, in the future.
Steve Meredith: I'd like to share two examples of how healthcare organizations
that, that I'm working with are using AWS's generative AI capabilities.
To create a more personalized, improved experience for patient's.
So you heard Monica talk a little bit about how we're doing that, and I'll
share a couple specific examples.
Stanford Healthcare tackle the common challenge that patient's have And that's
helping them understand their lab results.
Traditionally, when patient's receive receive their test results, they
see complex medical jargon that can often be difficult to interpret,
leading to anxiety and confusion.
Stanford built an innovative solution using generative AI services from
AWS, including Amazon Bedrock and Anthropics Claude large language model,
to automatically generate patient friendly explanations of lab results.
Their physicians receive AI generated draft responses that translate
technical medical jargon into clear comprehensible explanations.
While the physicians can review and modify these drafts before
sending them to the patient's.
The tool has significantly streamlined the physician workflow while ensuring the
patient's receive timely, understandable explanations of their test results.
Another example is at Froedert Health, who's using AWS's generative
AI services to transform how they engage with patient's through
personalized recommendations?
Their challenge was making healthcare guidance more relevant and
actionable for individual patient's.
Using AWS, they've developed a solution that analyzes patient data
to generate personalized health recommendations and next steps.
For example, if a patient has diabetes, the system might provide
customized dietary suggestions, medication reminders, and preventive
care recommendations based on their specific health profile and history.
What's particularly exciting about both of these examples is
how they're improving the patient experience while simultaneously making
healthcare delivery more efficient.
Stanford's solution helps reduce patient anxiety and unnecessary follow-up
calls while saving physicians time.
Freight's approach helps patient's better manage their health
through personalized guidance.
While enabling more proactive care, these solutions demonstrate how thoughtfully
implemented AI can create wins, both for the patient's And the providers ultimately
leading to better health outcomes.
Kathleen Wessel (Host): Those are all great examples.
I think it's helpful to really explore each one of those, so I appreciate
you're spending the time on that.
I do have one more question for you both, and it relates to healthcare data.
You know, topics such as, such as security compliance.
Performance and reliability come to mind.
Um, can you delve into how AWS addresses these critical issues?
Monica Coley: Sure.
So, um, at AWS we, we kind of have a motto or slogan that says Security is Job zero.
You know, we refer to it as job one.
Now we refer to it as job zero, meaning that's the top thing that we actually do.
People may not recognize, I know Steve mentioned about some of our armed forces
and other agencies use AWS, right?
So they entrust us in with that security around their data and their
environments and they have to, right?
And so that, that puts a lot of trust in, in our technologies.
So when we talk about, I guess, security, resiliency, reliability, I'm gonna
take it from a healthcare standpoint.
From a healthcare standpoint, and when I worked in the hospital,
it was like, look, stay up.
Right?
Stay up because when you go down, you impact patient care.
When I have unplanned downtime or a planned downtime, it impacts patient care.
It causes chaos, right?
Mm-hmm.
So the goal is to stay up so I can take care of patient's, right?
So when we talk about that minimizing downtime is important.
So what we've seen with AWS And this cloud And this technology and
how we're architected across cloud providers, we actually have, um, the
least amount of service interruptions on our platform across the industry.
That should give people confidence that, you know, you can move to the cloud,
you can move to the AWS cloud, and I can stay up and reliable with security.
We really think about that in two lenses.
So one thing that we have is what we call the shared responsibility model.
A lot of times people say, if I go to the cloud, oh my gosh, AWS
is gonna have control of my data and security and I'm lose control.
You know?
And that's one of the fears and that's actually the opposite of what we do.
So we have a shared security model, that framework that we use.
So we at AWS, we control the physical infrastructure, you know, provide you with
the technologies And the tools to actually secure your environment and whatnot.
We take care of physical security around the building or the data centers
themselves, but with regard to the actual data and securing that, That is actually
the health system's responsibility And the, and their total control.
We can't touch your data.
We don't encrypt your data.
We don't secure your data.
We just give you the tools to do so.
So that's where that sharing splits, and that's how you can be confident
that, You know, AWS is not, is not touching your data and doesn't
have control of your environment.
You're just using that shared infrastructure, if you will.
Steve Meredith: Mm-hmm.
Monica Coley: The other thing with regard to healthcare we get
is like, oh, what about hipaa?
You know.
Security controls, am I gonna be compliant if I go to the cloud?
Right?
And so the answer is yes.
We do have HIPAA controls, NIST controls, and other healthcare related compliances,
not only from the US but globally that we can apply to your environment.
So when you begin your journey on AWS, what we would do is roll out what we call
a healthcare landing zone accelerator.
Basically, just think of it as we run some capabilities, as codes, these scripts.
They set up this landing zone around your environment, it pretty much sets
up all your security controls around your environment before you ever move
any of your PHI into the platform.
Right?
So that's when you set up your HIPAA controls, your NIST controls and security
and say, okay, my environment's set.
I'm comfortable with security around it.
So one customer was even gonna have a audit before they moved anything into the
environment to make sure security was set And then you start moving in your data.
So that's really how, um, security and compliance really works
at AWS at a very high level.
And, um, and why, you know, you can put your trust in the cloud and
to leverage that for healthcare.
Kathleen Wessel (Host): Yeah.
That's wonderful.
I thank you so much for kind of walking through those,
Those are all critical issues.
That I know, uh, people are much more confident knowing that those
kind of security measures exist.
With that, I've thoroughly enjoyed this conversation today, Monica.
Steve, I wanna thank you so much for joining me on the podcast today
and sharing your takeaways with a HA members For our listeners.
If you'd like to learn more about the a HA associate program,
please visit us@sponsor.aha.org.
This has been a, a Associates Bringing Value brought to you by
the American Hospital Association.
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