Benjamin Scirica: We think the only way we can
transform healthcare is really a combination of
two key factors. How do you redesign the workflow?
How do we really take what we can do and put it
into a very different, updated workflow? And then
that has to be able to be supported by technology.
And you can't do one without the other to actually
get what we think is the ultimate transformation
of a learning health system. And what I mean by
that is, that we can come up with new ways of
seeing patients, have new ways of treating
patients. But if it's not incorporated in a way
that can scale, then all we are left with is a
little small pilot projects at one institution or
the other. Conversely, you can't just throw novel
technology or great new shiny app at something, if
it's stuck within a workflow that won't allow that
to scale. It won't allow that to get its greatest,
greatest synergy of efforts and really transform
care. So what we've done is we've taken areas of
chronic care, predominantly in cardio metabolic
areas, so lipids, cholesterol, and high blood
pressure, and tried to identify what are those
very well accepted therapies that everybody would
agree should be done, but we know aren't being
done, even in some of the best institutions in the
country. And then how do you help the doctor do
better care for their patients. Part of the
problem is for the right thing to happen, for a
patient, it usually requires that the doctor and
the patient are in the same physical location. And
that everybody's got to remember something needs
to be done. They've got to know what to do,
they've got to have the ability to do it, and they
have to have the time to do it. And there are so
many problems with that, that we allow care not to
be done the way we would want it to be done. So
what we've created is a couple of key features.
First, we try to identify patients through the
existing electronic medical records. And because
there are so many deficiencies in that, because
there's so many areas of poor data collection, we
have to do different types of machine learning and
rule-based algorithms to try to identify those
patients. Secondly, we really do think that we
need to be able to build a system that the doctor
doesn't need to be the center of every
interaction, and that we can task-shift many of
the routine aspects of care to either physician
extenders like pharmacists or nurse practitioners,
but even more importantly to non-licensed
personnel, and we call them care navigators or
patient navigators, who are non-licensed people
who are the, the real front face of our program.
They're the ones calling the patient, helping to
organize the patient, relaying the treatment
recommendations, making sure they're getting their
labs, making sure that they're getting their
prescriptions filled. And the reason they're able
to do that That is because they're working within
a very constrained algorithm of care that is then
supported by technology. So that is taking data
from the electronic medical records, it's taking
data from the patient, it's taking data from
devices, it's integrating that data. And based on
that, our treatment algorithms, which are designed
by our experts, are then being implemented
automatically, with the patient not being required
to come to the hospital, not requiring them to
come to the office, and it can be done at home.
And so for example, when we treat people with high
cholesterol, we can help them get them on the
right medicines, most of which are generic, to get
their cholesterol under control. And all we need
to do is be able to talk to them and make sure
they get labs drawn and they can get those drawn
at the most convenient place for them. If we're
talking about controlling blood pressure, it is
having a blood pressure cuff that is integrated
into our system so that the data flows to us and
we can turn around their bood pressure titrations
in a weekly basis because we're getting their
blood pressures.
So things that used to take months, if ever, to
happen in regular clinic, we can get done in
weeks. And we've shown that we've had reductions
of cholesterol and about 40% LDL over a three
month time period. And we showed blood pressure
control within eight weeks in the majority of
patients just because we've been able to do these
rapid iterations. We see the value actually plays
out on everybody in the ecosystem. And that's
what's a little bit odd, is that we feel that that
everybody should be able to get real benefit from
this. From the patients, it's a couple things.
First, we're trying to improve their experience
with the medical field, that we think that we can
provide a higher touch model, even though they're
not coming in for visits as often. And we can push
education to them. We can be the sounding board
for any questions they have. And we can engage
them in a way that most patients can't get engaged
in a 15 minute office visit. They're getting
better care. They're getting guideline directed
care. And that will correspond to reduced clinical
events.
For the doc, we really do see ourselves as an
extension of their clinic, that we're offering
them the service that we will take care of these
very algorithmic, routine jobs that they don't
need to do now. They can focus on what they're
trained for, it's much more complicated patient
management when they need to actually spend time,
and they don't have to spend time on their emails
or their inboxes at the end of the day, titrating
and changing medications; we'll take care of that.
And they can focus on the important part of trying
to integrate that. For a provider network. We
think this makes primary care and even specialties
work much more efficiently. They're able to open
up more office space because we can take away of
many of those return visits, and even initial
consults, that might be taken up with this. And
they can fill that up with more patients and drive
more patients into their contracts and into their
provider panels so that they can expand the number
of patients they can touch.
We also think from a provider standpoint, this is
a great way to try to enhance the digital doorway
that every provider's been trying to figure out.
How do I connect better with my patients
digitally? And that's coming as people become more
digitally enabled, and more comfortable, but this
is a great way we think to try to, to really help
jumpstart that type of process. For payers, we
also think this is a valuable tool for them. And
we partner with one payer, Allways Health Partners
in managing their members with lipids and
hypertension. And for them, it's a way of offering
a service, a benefit to their members. Additional
to the other benefits they do that is at no extra
cost to that member, where we can make manage
their lipids and hypertension. And the payers and
the employers are actually some that can see the
greatest reduction. If you get these reductions
and lipids and hypertension, and other things,
they'll see reductions in total medical expense
that most typically go to either the payers or the
employers. And so we think there's a valuable
value proposition for them. And then for us in
academic medical centers, we see this as a
powerful research tool as well, to be able to
build next generation clinical trials and
discovery platforms. And to be able to actually
work with the data scientists, to be able to us
this data, which we think is unique in that it'
the longitudinal transactional data acros
thousands of patients, that we'll be able to prov
de pretty powerful areas for machine learning
nd artificial intelligence to try to tease out s
me of the different signals in terms of how patie
ts react to the different therapies and who does w
ll and who doesn't. That would be very hard to te
se out if we were just doing our normal da
abase se