technology
that ‘Mrs Smith is now in a high probability
zone of falling’.
This is very complex, and pretty hard to
synthesise into a few sound bites, but the
outcome is that, carers – the people who
have the greatest interface with residents –
will be alerted in order to prevent a fall.
Typically, there are a few limbs that
contribute to falls. Continence is one
of them. People will run to the toilet for
reasons of dignity, fall and break a hip.
By pulling continence data and validating
it through a blockchain model, we
were able to then say that, with a high
probability, Mrs Smith is about to get up
and run to the toilet. Let’s get a carer to her
door before that happens.
So, from the collected data, for example,
you’ll be able to crosscheck at 5pm on
Tuesdays that men are most likely to
break their hips?
It’s beyond that. There are all kinds of
patterns that we’ll be looking to analyse
– what it is that people do before they
get around to the toilet. Or, I’ll give you
a different example, because continence
is only one of them. You could have
certain kinds of agitation, certain kinds of
movement. This gets back to the internet
of things, where we’re collecting online,
real-time data and connecting that into
the blockchain model as well. Everything
about this is online and real-time.
Otherwise it won’t be any use to get to the
preventive stage.
Another one of the ‘limbs’ would be
pharmaceuticals. Unmonitored doses of
antipsychotics or antihypertensives is a
big contributor to falls. As you age, your
renal and hepatic systems deteriorate.
Unmonitored dosage means that on an
antihypertensive, one day you stand up
and you fall. We want to take all that data
into account. Pharmaceuticals are going
to be quite a disruptive area for blockchain
in a number of contexts, and this is one
of them. We can say the unmonitored,
unreviewed dose is extending or increasing
the probability of Mrs Smith standing up
soon and falling over.
Are there wider applications for
blockchain in healthcare?
Yes, some of the frameworks we’re
building we think ought to be used in
pharmaceuticals. For instance, the last
mile in pharmaceuticals, from the local
pharmacy to a residential facility or the
home, is not well managed. There are
various ageing legacy systems where
validating the right medication for the
right resident or citizen, the right dose,
the right time – all those questions about
prescriptions and their distribution and
administration – I think blockchain has the
capacity to disrupt that in a big way and
improve its integrity significantly.
What do you predict to be the next tech
trend in healthcare beyond blockchain?
I think it’s going to be the ubiquity of
managed data – the seamlessness of
having repositories accessible to all people.
For instance, the ability for citizens to
control their own health records, which
we’re seeing now with the government, but
we’re going to see the private sector enter
that space and fill the gaps.
We’re going to see more control of
data, and seamless sharing of that data
with consent. So I could say, “Look, I want
my GP to see this, but I don’t want my
chiropractor to see that.” This is where
blockchain will underpin some of the
integrity of the system.
We’re also going to see a seamlessness
between hospitals using old fashioned HL7
protocols and things like that. We’re going
to see a reduction in the need for people
to speak a particular kind of computer
language to create meaning from a vast
array of data that’s out there. This will really
improve medical positioning and situations
for various people – but the focus for us is
on the elderly. ■
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Understanding and addressing
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