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Cerner’s Lifesaving Sepsis Control
Solution Shows the Potential of
Bringing More AI-Enabled IoT
To the Healthcare Edge
A discussion on how near real-time analytics at the edge helps caregivers at hospitals
head off sepsis episodes and reduce serious illness and deaths.
Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Hewlett
Packard Enterprise.
Dana Gardner: Hello, and welcome to the next edition of the BriefingsDirect Voice of
the Customer podcast series. I’m Dana Gardner, Principal Analyst at Interarbor
Solutions, your host and moderator for this ongoing discussion on the latest insights into
the confluence of edge computing and artificial intelligence (AI).
Our next intelligent edge adoption benefits discussion focuses on how hospitals are
gaining proactive alerts on patients at risk for contracting serious sepsis infections. An
all-too-common affliction for patients around the world, sepsis can often be controlled
when confronted early.
Now, using edge sensors, Wi-Fi data networks and AI solutions that identify at-risk
situations, caregivers at hospitals are rapidly alerted to susceptible patients so they can
head-off sepsis episodes and reduce serious illness and deaths.
Stay with us now as we hear about a cutting-edge use case that puts near real-time AI to
good use by outsmarting a deadly infectious scourge.
To learn how, please join me now in welcoming our
guests, Missy Ostendorf, Global Sales and Business
Development Practice Manager at Cerner Corp.
Welcome to the show, Missy.
Missy Ostendorf: Thank you very much.
Gardner: We’re also here with Deirdre Stewart, Senior
Director and Nursing Executive at Cerner Europe.
Welcome, Deirdre.
Deirdre Stewart: Thank you very much.
Ostendorf

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Gardner: And we’re also here with Rich Bird, World Wide Industry Marketing Manager
for Healthcare and Life sciences at Hewlett Packard Enterprise (HPE). Welcome, Rich.
Rich Bird: Thank you, Dana, and hello everyone.
Gardner: Missy, what are the major trends driving the need to leverage more technology
and process improvements in healthcare? When we look at healthcare, what’s driving
the need to leverage better technology now?
Time is of the tech essence
Ostendorf: That’s an easy question to answer. Across all industries resources always
drive the need for technology to make things more efficient and cost-conservative -- and
healthcare is no different.
If we tend to lead more slowly with technology in
healthcare, it’s because we don’t have mission-
critical risk -- we have life-critical risk. And the
sepsis algorithm is a great example of that. If a
patient turns septic, they have four hours and
they can die. So, as you can imagine, that clock
ticking is a really big deal in healthcare.
Gardner: And what has changed, Rich, in the nature of the technology that makes it so
applicable now to things like this algorithm to intercept sepsis quickly?
Bird: The pace of the change in technology is quite
shocking to hospitals. That’s why they can really benefit
when two globally recognized organizations such as HPE
and Cerner can help them address problems.
When we look at the demand-spike across the
healthcare system, we see that people are living longer
with complex long-term conditions. When they come into
a hospital, there are points in time when they need the
most help.
What [HPE and Cerner] are doing together is
understanding how to use this connected technology at
the bedside. We can integrate the Internet of Things (IoT)
devices that the patients have on them at the bedside, medical devices traditionally not
connected automatically but through the humans. The caregivers are now able to use
the connected technology to take readings from all of the devices and analyze them at
the speed of computers.
If we tend to lead more slowly
with technology in healthcare,
it’s because we don’t have
mission-critical risk – we have
life-critical risk.
Bird

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So we’re certainly relying on the professionalism, expertise, and the care of the team on
the ground, but we’re also helping them with this new level of intelligence. It offers them
and the patients more confidence in the fact that their care is being looked at from the
people on the ground as well as the technology that’s reading all of their life science
indicators flowing into the Cerner applications.
Win against sepsis worldwide
Gardner: Deirdre, what is new and different about the technology and processes that
makes it easier to consume intelligence at the healthcare edge? How are nurses and
other caregivers reacting to these new opportunities, such as the algorithm for sepsis?
Stewart: I have seen this growing around the world,
having spent a number of years in the Middle East and
looking at the sepsis algorithm gain traction in countries
like Qatar, UAE, and Saudi Arabia. Now we’re seeing it
deployed across Europe, in Ireland, and the UK.
Once nurses and clinicians get over the initial feeling of,
“Hang on a second, why is the computer telling me my
business? I should know better.” Once they understand
how that all happens, they have benefited enormously.
But it’s not just the clinicians who benefit, Dana, it’s the
patients. We have documented evidence now. We want
to stop patients ever getting to the point of having sepsis.
This algorithm and other similar algorithms alert the front-line staff earlier, and that
allows us to prevent patients developing sepsis in the first place.
Some of the most impressive figures show the reduction in incidents of sepsis and the
increase in the identification of the early sepsis stages, the severe inflammatory
response part. When that data is fed back to the doctors and nurses, they understand
the importance of such real-time documentation.
I remember in the early days of the electronic medical records; the nurses might be
inclined to not do such real-time documentation. But when they understand how the
algorithms work within the system to identify anything that is out of place or kilter, it really
increases the adoption, and definitely the liking of the system and what it can provide for.
Gardner: Let’s dig into what this system does before we look at some of the
implications. Missy, what does the Cerner’s CareAware platform approach do?
Ostendorf: The St. John Sepsis Surveillance Agent looks for early warning signs so that
we can save lives. There are three pieces: monitoring, alerting, and then the prescribed
intervention.
Stewart

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It goes to what Deirdre was speaking to about the documentation is being done in real-
time instead of the previous practice, where a nurse in the intensive care unit (ICU)
might have had a piece of paper in her pocket and she would write down, for instance,
the patients’ vital signs.
And maybe four hours later she would sit at a computer and put in four hours of vitals
from every 15 minutes for that patient. Well, as you can imagine, a lot can happen in four
hours in the ICU. By having all of the information flow into the electronic medical record
we can now have the sepsis agent algorithm continually monitoring that data.
It surveys the patient’s temperature, heart rate, and glucose level -- and if those change
and fall outside of safe parameters, it automatically sends alerts to the care team so they
can take immediate action. And with that immediate action, they can now change how
they are treating that patient. They can give them intravenous antibiotics and fluids, and
there is 80 percent to 90 percent improvement in lives saved when you can take that
early intervention.
So, we’re changing the game by
leveraging the data that was already
there, we are just taking advantage of it,
and putting it into the hands of the
clinicians so that action can be taken
early. That’s the most important part. We
have been able to actionize the data.
Gardner: Rich, this sounds straightforward, but there is a lot going on to make this
happen, to make the edge of where the patient exists able to deliver data, capture data,
protect it and make it secure and in compliance. What has had to come together in order
to support what was just described by Missy in terms of the Cerner solution?
Healthcare tech progresses to next level
Bird: Focusing on the outcomes is very important. It delivers confidence to the clinical
team, always at the front of mind. But it provides that in a way that is secured, real-time,
and available, no matter where the care team are. That’s very, very important. And the
fact that all of the devices are connected poses great potential opportunities in terms of
the next evolution of healthcare technology.
Until now we have been digitizing the workflows that have always existed. Now, for me,
this represents the next evolution of that. It’s taking paper and turning it into digital
information. But then how do we get more value from that? Having Wi-Fi connectivity
across the whole of a site is not something that’s easy. It’s something that we pride
ourselves on making simple for our clients, but a key thing that you mentioned was
security around that.
We’re changing the game by
leveraging the data that was already
there, we are just taking advantage of it
and putting into the hands of the
clinicians so that action can be taken
early.

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When you have everything speaking to everything else, that also introduces the potential
of a bad actor. How do we protect against that, how do we ensure that all of the data is
collected, transported, and recorded in a safe way? If a bad actor were to become a part
of external network and internal network, how do we identify them and close it down?
Working together with our partners, that’s something that we take great pride in doing.
We spoke about mobility, and outside of healthcare, in other industries, mobility usually
means people have wide access to things.
But within hospitals, of course, that
mobility is about how clinicians can
collect and access the data
wherever they are. It’s not just one
workstation in a corner that the care
team uses every now and again.
The technology now for the care
team gives them the confidence to know the data they are taking action on is collected
correctly, protected correctly, and provided to them in a timely manner.
Gardner: Missy, another part of the foundational technology here is that algorithm. How
are machine learning (ML) and AI coming to bear? What is it that allowed you to create
that algorithm, and why is that a step further than simple reports or alerts?
Ostendorf: This is the most exciting part of what we’re doing today at Cerner and in
healthcare. While the St. John’s Sepsis Algorithm is saving lives in a large-scale way –
and it’s getting most of the attention -- there are many things we have been able to do
around the world.
Deirdre brought up Ireland, and even way back in 2009 one of our clients there, St.
James’s Hospital in Dublin, was in the news because they made the decision to take the
data and build decision-making questions into the front-end application that the clinicians
use to order a CT scan. Unlike other X-rays, CT scans actually provide radiation in a
way that’s really not great. So we don’t want to have a patient unnecessarily go through
a CT scan. The more they have, the higher their risks go up.
By implementing three questions, the computer looks at the trends and why the
clinicians thought they needed it based on previous patients’ experiences. Did that CT
scan make a difference and how they were diagnosed? And now with ML, it can tell the
clinician on the front end that, “This really isn’t necessary for what you are looking for to
treat this patient.”
Clinicians can always override that, they can always call the x-ray department and say,
“Look, here’s why I think this one is different.” But in Ireland they were able to lower the
number of CT scans that they had always automatically ordered. So with ML they are
changing behaviors and making their community healthier. That’s one example.
Mobility is about how clinicians can collect
and access the data wherever they are …
the data they are taking action on is
collected correctly, protected correctly, and
provided to them in a timely manner.

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Another example of where we are using the data and ML is with the Cerner Opioid
Toolkit in the United States (US). We announced that in 2018 to help our healthcare
system partners combat the opioid crisis that we’re seeing across America.
Deirdre, you could probably speak to the study as a clinician.
Algorithm assists in opiod-addicition reduction
Stewart: Yes, indeed. It’s interesting work being done in the US on what they call
Opioid-Induced Respiratory Depression (OIRD). It looks like approximately 1 in 200
hospitalized surgical patients can end up with an opioid-induced ventilatory impairment.
This results in a large cost in healthcare. In the US alone, it’s estimated in 2011 that it
cost $2 billion. And the joint commission has made some recommendations on how the
assessment of patients should be personalized.
It’s not just one single standardized form with a score that is generated based on
questions that are answered. Instead it looks at the patients’ age, demographics,
previous conditions, and any other history with opioid intake in the previous 24 hours.
And according to the risks of the patient, it then recommends limiting the number of
opioids they are given. They also looked at the patients who ended up in respiratory
distress and they found that a drug agent to reverse that distress was being
administered too many times and at too high a cost in relation to patient safety.
Now with the algorithm, they have managed to reduce the number of patients who end
up in respiratory distress and limit the number of narcotics according to the specific
patients. It’s no longer a generalized rule. It looks at specific patients, alerts, and
intervenes. I like the way our clients worldwide work in the willingness to share this
information across the world. I have been on calls recently where they voiced interest in
using this in Europe or the Middle East. So it’s not just one hospital doing this and
improving their outcomes -- it’s now something that could be looked at and done
worldwide. That’s the same whenever our clients devise a particular outcome to
improve. We have seen many examples of those around the world.
Ostendorf: It’s not just collecting
data, it’s being able to actualize the
data. We see how that’s creating not
only great experiences for a partner
but healthier communities.
Gardner: This is a great example of where we get the best of what people can do with
their cognitive abilities and their ability to contextualize and the best of the machines to
where they can do automation and orchestration of vast data and analytics. Rich, how
do you view this balancing act between attaining the best of what people can do and
machines can do? How do these medical use cases demonstrate that potential?
It’s not just collecting data, it’s being able
to actualize the data. We see how that’s
creating not only great experiences for a
partner, but healthier communities.

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Machines plus, not instead of, people
Bird: When I think about AI, I grew up in the science fiction depiction where AI is a
threat. If it’s not any taking your life, it’s probably going to take your job.
But we want to be clear. We’re not replacing doctors or care teams with this technology.
We’re helping them make more informed and better decisions. As Missy said, they are
still in control. We are providing data to them in a way that helps them improve the
outcomes for their patients and reduce the cost of the care that they deliver.
It’s all about using technology to reduce the amount of time and the amount of money
care costs to increase patient outcomes – and also to enhance the clinicians’
professionalism.
Missy also talked about adding a few questions into the workflow. I used to work with a
chief technology officer (CTO) of a hospital who often talked about medicine as
eminence-based, which is based on the individuals that deliver it. There are numerous
and different healthcare systems based on the individuals delivering them. With this
digital technology, we can nudge that a little bit. In essence, it says, “Don’t just do what
you’ve always done. Let’s examine what you have done and see if we can do that a little
bit better.”
The general topic we’re talking about here is digitization. In this context we’re talking
about digitizing the analog human body’s vital signs. Any successful digitization of any
industry is driven by the users. So, we see that in the entertainment industry, driven by
people choosing Netflix over DVDs from the store, for example.
When we talk about delivering healthcare technology in this context, we know that
personal healthcare data cannot be shared. It is the most personal data in the world; we
cannot share that. But when we can show the value of data when shared in a safe way --
highly regulated but shared in a safe way -- the clinical teams can then see the value
generated from using the data. It changes the conversation to how much does the
technology cost. How much can we save by using this technology?
For me, the really exciting thing about
this is technology that helps people
provide better care and helps patients
be protected while they’re in hospital,
and in some cases avoid having to
come into the hospital in the first place.
Gardner: Getting back to the sepsis issue as a critical proof-point of life-enhancing and
life-saving benefits, Missy, tell us about the scale here. How is this paying huge
dividends in terms of saved lives?
[It’s] technology that helps people
provide better care and helps patients be
protected while they’re in hospital, and in
some cases avoid having to come into
the hospital in the first place.

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Life-saving game changer
Ostendorf: It really is. The World Health Organization (WHO) statistics from 2018
show that 30 million people worldwide experience a sepsis event. In their classification,
six million of those could lead to deaths. In 2018 in the UK, there were 150,000 annual
cases, with 44 of those ending in deaths.
You can see why this sepsis algorithm is a game-changer, not just for a specific client,
but for everyone around the world. It gives clinicians the information they need in a
timely manner so that they can take immediate action -- and they can save lives.
Rich talked about the resources that we save, the cost that’s driven out, all those things
are extremely important. When you are the patient or the patient’s family, that translates
into a person who actually gets to go home from the hospital. You can’t put a dollar
amount or an efficiency on that.
It’s truly saving lives and that’s just
amazing to think that. We’re doing
that by simply taking the data that
was already being collected, running
that through the St. John’s sepsis
algorithm and alerting the clinicians
so that they can take quick action.
Stewart: It was a profound moment for me after Hamad Medical Corp. in Qatar, where I
had run the sepsis algorithm across their hospitals for about 11 months, did the data and
they reckoned that they had potentially saved 64 lives.
And at the time when I was reading this, I was standing in a clinic there. I looked out at
the clinic, it was a busy clinic, and I reckoned there were 60 to 70 people sitting there.
And it just hit me like a bolt of lightning to think that what the sepsis algorithm had done
for them could have meant the equivalent of every single person in that room being
saved. Or, on the flipside, we could have lost every single person in that room.
Mothers, fathers, husbands, wives, sons, daughters, brothers, sisters -- and it just hit me
so forcefully and I thought, “Oh, my gosh, we have to keep doing this.” We have to do
more and find out all those different additional areas where we can help to make a
difference and save lives.
Gardner: We have such a compelling rationale for employing these technologies and
processes and getting people and AI to work together. In making that precedent we’re
also setting up the opportunity to gather more data on a historical basis. As we know, the
more data, the more opportunity for analysis. The more analysis, the more opportunity
for people to use it and leverage it. We get into a virtuous, positive adoption cycle.
When you are the patient or the patient’s
family … [and] a person actually gets to
go home from the hospital, you can’t put
a dollar amount or an efficiency on that.

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Rich, once we’ve established the ability to gather the data, we get a historical base of
that data. Where do we go next? What are some of the opportunities to further save
lives, improve patient outcomes, enhance patient experience, and reduce costs? What is
the potential roadmap for the future?
Personalization improves patients, policy
Bird: The exciting thing is, if we can take every piece of medical information about an
individual and provide that in a way that the clinical team can see it from one end of the
user’s life right up to the present day, we can provide medicine that’s more personalized.
So, treating people specifically for the conditions that they have.
Missy was talking about evaluating more precisely whether to send a patient for a certain
type of scan. There’s also another side of that. Do we give a patient a certain type of
medication?
When we’re in a situation where we have the patient’s whole data profile in front of us,
clinical teams can make better decisions. Are they on a certain medication already? Are
they allergic to a medication that you might prescribe to them? Will their DNA, the
combination of their physiology, the condition that they have, the multiple conditions that
they have – then we start to see that better clinical decisions can be made. We can treat
people uniquely for the specific conditions.
At Hewlett Packard Labs, I was recently talking with an individual about how big data will
revolutionize healthcare. You have certain types of patients with certain conditions in a
cohort of patients, but how can we make better decisions on that cohort of patients with
those co-conditions? You know, with at a specific time in their life, but then also how do
we do that from an individual level of individuals?
It all sounds very complicated, but my hope is, as we get closer, as the power of
computing improves, these insights are going to reveal themselves to the clinical team
more so than ever.
There’s also the population health side.
Rather than just thinking about patients
as individuals, or cohorts of patients,
how could policymakers and
governments around the world make
decisions based on impacts of
preventative care, such as incentivizing
populations to do more health maintenance? How can we give visibility into that data into
the future to make better decisions for populations over the longer period of time?
We want to bring all of this data together in a safe way that protects the security and the
anonymity of the patients. It could provide those making clinical decisions about the
people that are in front of them, as well as policymakers to look over the whole
There’s also the population health
side. … how can we give visibility into
that [healthcare] data into the future to
make better decisions for populations
over the longer period of time?

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population, the means to make more informed decisions. We see massive potential
around prevention. It could have an impact on how much healthcare costs before the
patient actually needs treatment.
It’s all very exciting. I don’t think it’s too far away. All of these data points we are
collecting are in their own silos right now. There is still work to do in terms of
interoperability, but soon everybody’s data could interact with everybody else’s data.
Cerner, for example, is making some great strides around the population health element.
Gardner: Missy, where do you see accelerating benefits happening when we combine
edge computing, healthcare requirements, and AI?
At the leading edge of disease prevention
Ostendorf: I honestly believe there are no limits. As we continue to take the data
in in places like in northern England, where the healthcare system is on a peninsula,
they’re treating the entire population.
Rich spoke to population health management. Well, they’re now able to look across the
data and see how something that affects the population, like diabetes, specifically affects
that community. Clinicians can work with their patients and treat them, and then work the
actual communities to reduce the amount of type 2 diabetes. It reduces the cost of
healthcare and reduces morbidity rate.
That’s the next place where AI is
going to make a massive impact. It
will no longer be just saving a life
with the sepsis algorithm running
against those patients who are in the
hospital. It will change entire
communities and how they approach
health as a community, as well as how they fund healthcare initiatives. We’ll be able to
see more proactive management of health community by community.
Gardner: Deirdre, what advice do you give to other practitioners to get them to
understand the potential and what it takes to act on that now? What should people in the
front lines of caregiving be thinking about on how to best utilize and exploit what can be
done now with edge computing and AI services?
Stewart: Everybody should have the most basic analytical questions in their heads at all
times. How can I make what I am doing better? How can I make what I am doing easier?
How can I leverage the wealth of information that is available from people who have
walked in my shoes and looked after patients in the same way as I’m looking after them,
The next place where AI is going to make
a massive impact … it will change entire
communities and how they approach
health as a community, as well as how
they fund healthcare initiatives.

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whether that’s in the hospital or at home in the community? How do I access that in an
easier fashion, and how do I make sure that I can help to make improvements in it?
Access to information at your fingertips means not having to remember everything. It’s
having it there, and having suggestions made to me. I’m always going back and
reviewing what those results and analytics are to help improve the next time, the next
time around.
From bedside to boardroom, everybody
should be asking themselves those
questions. Have I got access to the
information I need? And how can I make
things better? What more do I need?
Gardner: I’m afraid we’ll have to leave it there. We’ve been exploring how hospitals are
gaining proactive alerts on patients at risk for contracting life-threatening sepsis
infections. But we’ve also learned about a larger perspective of how edge computing and
AI are enabling caregivers around the world to respond to more types of issues and
become more intelligent about providing better care for people.
Please join me in thanking our guests, Missy Ostendorf, Global Sales and Business
Development Practice Manager at Cerner Corp. Thank you so much, Missy.
Ostendorf: Thank you. It was fun to be here.
Gardner: We’ve also been joined by Deirdre Stewart, Senior Director and Nursing
Executive at Cerner Europe. Thank you so much, Deirdre.
Stewart: It was an absolute pleasure. Thank you.
Gardner: And lastly, we’ve been here with Rich Bird, Worldwide Industry Marketing
Manager for Healthcare and Life Sciences at HPE. Thank you, Rich.
Bird: Thank you.
Gardner: And lastly a thank you to our audience for joining this BriefingsDirect Voice of
the Customer Internet of Things and AI strategies interview. I’m Dana Gardner, Principal
Analyst at Interarbor Solutions, your host for this ongoing series of HPE-sponsored
discussions.
Thanks again for listening. Please pass this on to your community, and do come back
next time.
Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Hewlett
Packard Enterprise.
From bedside to boardroom,
everybody should be asking
themselves questions. Have I got
access to the information I need?
How can I make things better?

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A discussion on how near real-time analytics at the edge helps caregivers at hospitals
head off sepsis episodes and reduce serious illness and deaths. Copyright Interarbor
Solutions, LLC, 2005-2019. All rights reserved.
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