The next BriefingsDirect 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 be controlled when confronted early using a combination of edge computing and artificial intelligence (AI). Edge sensors, Wi-Fi data networks, and AI solutions help identify at-risk situations so caregivers at hospitals are rapidly alerted to susceptible patients to head-off sepsis episodes and reduce serious illness and death.
Stay with us now as we hear about this cutting-edge use case that puts AI to good use by outsmarting a deadly infectious scourge with guests Missy Ostendorf, Global Sales and Business Development Practice Manager at Cerner Corp.; Deirdre Stewart, Senior Director and Nursing Executive at Cerner Europe, and Rich Bird, World Wide Industry Marketing Manager for Healthcare and Life sciences at Hewlett Packard Enterprise (HPE). The discussion is moderated by Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
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?
 |
| Ostendorf |
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.
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 |
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.
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.
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.
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 |
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.
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.
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.
They
take the data and build decision-making questions into the front-end of
the application the clinicians use to order a CT scan. We don't want to
have a patient unnecessarily go through a CT scan. Now with ML, it can
tell the clinician whether the CT scan is necessary for the treatment of
that patient.
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.
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 assisted opioid-addiction help
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.
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?
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.”
We
know that personal healthcare data cannot be shared. But when we can
show the value of the data when shared in a safe way, the clinical teams
can see the value generated . It changes the conversation. It helps
people provide better care.
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?
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.
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?
Rather
than just thinking about patients as cohorts, how could policymakers
and governments around the world make decisions based on impacts of
preventative care, such as more health maintenance? We can give
visibility into that data to make better decisions for populations over
long periods of time.
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
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, 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?
Listen
to the podcast. Find it on iTunes. Read a full transcript or download a copy. Sponsor: Hewlett Packard Enterprise.
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