SECTOR FOCUS AI IN HEALTHCARE
ICNH’s DrDoctor deploys AI to
obtain the greatest use from every
scheduled appointment within a
hospital. It ensures attendance
is as high as possible by using
past appointment attendance and
demographic data to predict those
less likely to attend in future and
customising communication with
these demographics accordingly.
The AAC is now working with these
companies to nalise which site they
will be selected for use at and match
them to appropriate NHS healthcare
services to support testing for each
product. It will also evaluate where
appropriate evidence is collected
to enable faster roll-out of these AI
innovations into the NHS.
Streamlining patient experience
Time is money. Ef ciently providing a
seamless patient experience allows
hospitals, clinics and physicians
to treat more patients on a daily
basis. AI is streamlining the patient
experience, helping hospital staff
process millions, if not billions
of data points, faster and more
ef ciently.
Netta Myrhinen, head of
communications at DrDoctor,
explained, “We took patient
demographics and used this data
to nd out why some people turn
up, and why some don’t. The data
we received helped us work out who
needs to be sent extra reminders,
or bespoke communication, or
who needs a closer
touch point with the
hospital so that they
stay engaged.
“We found that men in their
early 20s are the least likely to keep
an appointment. This could be, for
example, that they’ve moved away
from home for the rst time, perhaps
they’re in university and their parents
used to sort out their appointments.
“We can now send them a
reminder that says ‘let us know and
we can nd you a time that suits
you’, to give them that control, not
just tell them that they have to
come in.”
Guys & St Thomas hospital in
London achieved a 25% reduction
in DNA (did not attend) rates via
DrDoctor’s appointment management
solution – which equates to £2.6m in
savings per year.
At the Royal Orthopaedic Hospital
(a smaller, more specialist Trust
with an entirely different patient
demographic), DrDoctor achieved a
35% reduction in DNA equating to
£100,000 savings annually.
Myrhinen adds: “There is huge
potential to improve upon these
results once AI is employed in
decision making. By using machine
learning to predict who should be
sent additional communication
(such as an appointment reminder)
it is possible to ensure that clinic
utilisation is optimal and patients
that need care actually receive it.”
Further funding
Prof Nachev says that for AI to
work most effectively in healthcare
applications, three things are required:
safely accessible data of adequate
scale and inclusivity, complex
algorithms that can deal with the
immense heterogeneity of clinical data,
and as much compute capacity as
possible. The last is often neglected.
He said, “If you gave me a 400
peta op machine, such as the
Cambridge-1 AI Supercomputer that
Nvidia is developing with King’s College
London and other partners, I could
use all of it even on a single task such
as predicting outcomes in stroke.
The underlying biology is so complex
no model could ever be perfect: only
different shades of good. And compute
is increasingly the limit on achieved
model delity.”
He adds that funds being spent in
other industries should be matched
in healthcare: “The government is
planning to buy the Met Of ce a
£1.2billion supercomputer, yet the
NHS lacks even modest computing
capabilities. If the decision to pick
up an umbrella in the morning is so
expensively informed, shouldn’t the
rather more important decisions taken
in hospitals be at least comparably
assisted?
“I think the government should
think hard about investing more in
computation; their focus now appears
to be mostly data, and to some extent
algorithmics. Without supercomputers
we’ll be con ned to basic models, and
there will be a hard ceiling on what we
can achieve.”
Above: DrDoctor
deploys AI to schedule
appointments
and customise
communications
Below: UCLH in
London is using AI to
diagnose patients
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