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>> Welcome back to the Seaport in Boston.
You're watching theCUBE's coverage of Red Hat Summit 2022.
A lot of bummed out Bruins fans,
but a lot of happy Celtics fans.
We're optimistic for tonight, Boston's crazy sports town,
but we're talking tech, we're talking open source.
Dr. Amanda Purnell is here.
She's the director of data and analytics innovation
at the US Department of Veteran Affairs,
and Ben Cushing is the chief architect
for federal health and life sciences at Red Hat.
Folks, welcome to theCUBE, thank for coming on.
>> Thank you for having us.
>> So glad to be here.
>> So we heard your keynote this morning, project Arches.
Now you were telling us just briefly
about your previous life as a clinician.
>> That's right.
>> That's really interesting,
because you know what the outcome has to be.
So talk about that project in your perspective.
What the goals were and how you actually got it done.
>> I could tell the long view.
I'm a psychologist by training.
I spent the first 10 years of my VA career
providing care to veterans.
So engaging in healthcare behavior change,
providing training to providers
and really trying to understand
what is the care pathway for veterans,
what's the experience of veterans
along each of those touchpoints,
and it became clear to me over time
that there were opportunities for us
to improve the transitions of care
and provide better information at the right time
to improve those decisions
that are being made at the point of care.
Ben and I were just talking before we began today,
part of the core of the development of Arches
was beginning with human-centered design.
We wanted to interview and better understand
what was the experience across the VA
of many different stakeholders
and trying to access meaningful information,
understand in that moment what do I need
to make a decision with a veteran
or what do I need to make a decision with my care team
and how can I improve the quality of care for veterans?
And so, hundreds of interviews later,
it became clear to us that we wanted to help
those individuals already working for the VA
to continue to improve excellence of care
and one of those ways that we're trying to do that
is using technology to make life easier
for our veterans and for our clinicians.
>> I always like to say, they say, "Follow the money."
I like to follow the data.
And you said something in your keynote
about nurses have to have access to information
and it just gets to an architectural question,
because as a caregiver, you have to get insights and data
and you need it fast, 'cause you're saving lives,
but a lot of times, architectures are very centralized.
They're monolithic and you have to beg, borrow, steal,
break through blockers to get to the data that you need.
How do you square that circle in today's world?
Maybe you could talk about that,
and then specific to Arches, how you dealt with that.
>> I can dive into that a little bit.
I have to say, Amanda had touched on this
during the keynote,
VA was one of the first, if not the first,
healthcare organizations in the world
to actually adopt electronic health records
and because of that, they just have
this incredibly rich amount of historical data
and the challenge, as you pointed out,
is gaining access to it.
So there are a number of programs
within VA designed specifically for that.
And they are bringing data not just
from the data warehouses,
but also data from the electronic health records
that are running inside of VA right now,
and then also third party community data sets,
as well as applications that run inside the VA.
Now the value here really happens when you produce insights.
Data by itself is useless.
>> Lot of data out there. They're plentiful.
>> You need to create knowledge
and then you need that knowledge
to inform your process that comes next.
Those actions are really what matters.
All of healthcare is process and activity
and data is really just a historical record.
I mean, all data that we look at is happening in the past
and then as we're reading it, we're producing knowledge,
again, to inform our process.
Arches, the program itself, is right in that space
at the knowledge layer of actually taking that data
and turning it into actual insight
and something that is usable and insightful for clinicians
to affect the ability to deliver better care
and also to actually improve their own working experience.
A lot of the models that are getting built out
are specifically designed to help their workflow,
help them reach better outcomes for the veterans,
but also for themselves, because if we can care
for the providers, it'll certainly help them
care for the patients even more so.
>> So how does it work?
I mean, from the provider's perspective,
how was their life improved by Arches?
>> That's a great question.
We want to make it easier to access the information.
So as Ben noted, the average person providing care
in the field doesn't know how to code,
doesn't know how to pull a unique request
for an individual data point,
and what we're trying to do with Arches
is provide a user interface that allows
for both a non-technical person
and a very technical person to access information,
and then what gets provisioned in front of a provider
is something that is farther abstracted
from the underlying data layer
and more like here's a specific insight.
So I use the example in my keynote
of chronic kidney disease.
So what's provisioned to the provider in that moment
is this person is at higher risk for chronic kidney disease
based on this basic information.
So it's surfacing just the right amount of information
to allow for that care pathway to be improved,
but the physician doesn't need
to see all of the layers of code underneath.
They need to trust that it's worthwhile,
but they don't need to know all the background abstractions.
>> So it's a self-service, essentially,
infrastructure in that sense.
You're hiding the underlying complexities.
You gave an example in your keynote
of an individual who realized
that they were under counting the probability
of a potential disease for African Americans.
>> Yes.
>> I believe she just rewrote the algorithm.
>> She did.
>> Describe that process, because in a lot of organizations,
injecting that new algorithm may have required
new data sources, would take an act of the Pope to do.
How did it work in Arches?
>> This is what I get excited about with Arches
is that we have the opportunity
to empower enthusiastic people like Dr. Joshi
to discover an insight and she's a talented informaticist,
so she could do the technical work
and provision a container for her to work in,
for her to do the data analysis,
the underground stuff that we're not letting
the average provider have to cope with.
We were able to provision the tools
that she need, the environment that she needed
to be able to test and develop the new insight,
confirm that they're there and then begin
to validate that and test it in other facilities.
So our thinking is, how do we bring the resources
to the users rather than saying to the users,
"This is what's available. Good luck." (chuckles)
>> So we've been talking a lot about, I'm sorry,
go ahead. >> I want to add on to that.
What we're actually experiencing
inside of healthcare right now
is the emergence of of learning health
systems. >> Yes.
>> And this is a great example of that.
The terrifying number is,
it takes 17 years for new knowledge
that gets created with healthcare research,
whether it's NIH or VA or elsewhere,
it takes 17 years for those practices
to make their way into practice.
Generally the way that happens is
through the education of new staff.
And so the dissemination of that knowledge
is just so freaking slow that we cannot move
nimbly enough to take on that new knowledge
and actually implement it in clinical space.
What Amanda's describing is something
that now happens in months.
New knowledge getting produced
and then actually getting disseminated out,
both the insights, whether they are those probabilities,
predictions and recommendations and the actual processes,
which are getting automated, as well.
So if you think about healthcare as just a process,
you can automate a whole lot of that
and we can move that needle really fast
and actually take that 70-year number
down to a couple months.
>> In the early days when we were all talking
about AI and getting excited about digital,
I would often ask the question,
will machines be able to make better diagnoses
than doctors and to your point, Ben, that's not
the right question. >> Exactly.
>> It isn't the right question. >> The question is,
how can machines compress the time
to better patient outcomes- >> Yes.
>> in concert with humans and that's what we're seeing now.
>> That's right, it's surfacing
those insights to start a conversation.
>> We've been talking a lot about artificial
intelligence for the last two days.
As clinician or someone with a clinical background,
how do you see the clinical experience changing
as machines grow more intelligent?
>> I think that there's a learning curve for people
to feel confident in an artificial intelligence.
It makes sense.
So someone spent decades, perhaps,
of their life obtaining medical training,
doing fellowships, doing additional training
that they have trust in that deep training.
There are times, however,
where a technology is able to surface something
that we didn't know that we didn't know
and it's important, as we make use
of artificial intelligence, that we clearly validate it
with independent means and that we clearly
also bring in additional analysis to understand
what are the elements and then test
that new technology in an environment
before we scale it widely,
so that clinicians can see, yes, this was useful.
If it wasn't useful, how can we make it even better?
So it goes back to what we were talking about earlier
that we have to bring in human-centered design
to figuring out how do we make use of AI
or machine learning models and make sure
that there's trust in those models
and that we can clearly articulate value
for the clinicians and care teams on the ground.
>> Is that a natural evolution of Arches?
>> This is all built around it.
Arches is the technology platform,
but there's no magical technology
that's going to change how humans interact.
And so the way we think about each project
is we think about what are the technological components
and what are the human factors components?
And we have to think about the entire care pathway.
I'll go back to that example, the chronic kidney disease.
She identified that we were under identifying
African Americans for chronic kidney disease.
So she changed the algorithm.
Not only did she change the algorithm,
we also had to think about who would be informed
of those changes, how would that change,
who would be connected to the veteran in that point of care
and build out the care pathway in the care team
and that's really how you actually influence an outcome.
Surfacing an insight is important,
but it's one part of a much larger picture.
>> So what is Arches?
You said it's a technology platform built on open source.
At least, there's a lot of open source in there.
And it's got API connectors to all the legacy
technologies that you need it to.
Can you describe, paint a picture of what it actually is?
>> Arches is evolving as it should.
So it's designed to meet the unique needs
that aren't being met by other infrastructure in the VA.
So we started first by identifying the need
for cloud compute, so it's in the cloud,
it has open source technology so that we're not stuck
with any one provider and also has the ability
to use containers to be able to move insights
out of Arches to an enterprise solution.
We're also bringing in multi-cloud strategy,
which also something had been discussed
quite a bit at this conference,
to make sure that we're not saying only one cloud provider
can be the solution for veterans' needs.
Our mission is serving veterans
and so we want to have access to all the technology
and not just one and so we're looking at
how do we expand the scope to make sure
that we have the most variety possible
so we can meet the needs of veterans.
>> I can add a little bit to it, as well.
Think of Arches as a program.
It's an incubation space under the office of innovation.
So it's a place where the governance
allows for trying new ideas and really pushing
the envelope for VA in general.
There's not a lot of organizations, if any at VA,
that allow for that type of incubation
and so Arches is in a unique position
to create new technologies and new novel approaches
to solving big problems.
And then the next step to that is moving the work
from Arches out into the enterprise, as you called it out.
So for instance, the system of engagement
where the actual clinicians interact with patients,
the model needs to find its way there
and we can't do that in a way that disturbs
the current workflow that the clinicians have.
We need to be able to bring the model
to where the clinician is, have those recommendations,
probabilities and predictions surfaced
to the clinician in a way that is precise
to their existing workflow.
They need it at the time they need it.
Arches itself is not delivering that part of it.
It's more like the place where the innovation happens
and the incubation really occurs
and then it's about taking this container, really,
and moving out to other systems that are already deployed
out to the hospitals, the edge, and in the cloud.
>> And the federated governance
occurs in Arches or elsewhere?
>> It happens across the continuum.
It's starting in Arches.
the clinical validation that happens there
is wickedly important, because the clinicians need
to know that what they're working with is actually legit.
And so when they know that the researchers
and the clinicians who are involved
in that incubation period have done their work,
they can feel confident with the recommendations
they're getting from the machine learning models
that are getting deployed to one of them.
>> So many questions, so little time.
What's the business impact? How would you describe that?
>> For me, it's an emotional impact.
People have a sense of, "I have a place
to develop a solution and I can get in there quick,
and I can test out an idea.
I could potentially partner with an external partner
or if I have the talents and skills to do it myself."
It's empowering all of those innovators
who have great ideas to work together
to test and develop and validate solutions,
and they're not waiting years
to get the idea off the ground.
>> Amazing. >> Go ahead, bring it.
>> Is Arches open source?
>> Arches is a platform and it has open source component.
So that the underlying infrastructure
of technology is open source.
>> Why was it important to you that this be built
on an open source platform?
>> It's important for us that we not
marry ourself to any one technology
and that we allow for, as much as possible,
transparency and many different tools
and the right tools for the right solution.
So we didn't want to find ourselves connected
to only one way of doing things.
We want to have versatility to have the right tool
for the right problem at the right time.
>> I'm so sorry, we're out of time.
This is so interesting
and I really appreciate you here guys, coming on
and sharing your insights for theCUBE audience.
All right, keep it right there.
This is Dave Vellante for Paul Gillin.
We're in day two of Red Hat Summit 2022.
You're watching theCUBE.
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>> Due to the pandemic, the federal government
declared a public health emergency,
which created an urgency for healthcare coverage.
>> One of the biggest-