Nearly
all
leaders
in
the
healthcare
industry
have
a
shared
understanding
that
while
AI
will
never
replace
workers’
lived
clinical
judgment,
the
technology
can
provide
them
with
much-needed
respite
from
administrative
burden
by
handling
non-value-added
tasks,
noted
one
health
system
executive.
Terry
McDonnell,
chief
nurse
executive
at
Duke
University
Health
System,
pointed
out
that
by
handling
routine
tasks,
AI
can
free
up
clinicians
to
devote
more
time
and
attention
to
patient
care.
“I’ve
always
said
that
I
can
teach
a
nurse
a
skill,
but
I
can’t
teach
what
sick
sounds
like
on
the
phone.
That’s
lived
experience,
that’s
learned
experience,
and
that’s
what
the
clinician
brings.
And
I
think
those
are
the
things
that
we
need
to
focus
on
and
make
sure
that
people
have
the
time
and
the
bandwidth
to
really
engage,”
McDonnell
declared.
She
said
this
need
to
scale
AI-driven
task
automation
is
more
important
than
ever,
given
that
the
nation’s
rising
demand
for
care
is
colliding
with
a
shrinking
clinical
workforce.
One
major
but
oftentimes
overlooked
bottleneck
is
the
shortage
of
faculty
available
to
train
new
nurses,
even
as
applications
to
nursing
programs
remain
plentiful,
she
added.
These
workforce
challenges
are
one
reason
Duke
has
invested
heavily
in
AI
products
designed
to
streamline
tasks
and
improve
patient
outcomes.
The
health
system
builds
some
AI
tools
in-house
while
purchasing
others
from
vendors,
McDonnell
said.
For
instance,
Duke
uses
an
in-house
AI
model
that
monitors
patient
data
from
Epic
to
flag
early
signs
of
deterioration,
giving
care
teams
the
advance
warning
they
need
to
intervene,
she
noted.
“We’re
upstream
of
it
—
we’re
not
reacting
in
an
emergency.
We’re
proactively
intervening
when
we
see
that
the
clinical
condition
may
be
changing,
and
that’s
being
driven
by
AI
algorithms,”
McDonnell
explained.
Duke
uses
another
internally
developed
AI
tool
that
focuses
on
sepsis.
It
analyzes
patient
data
to
detect
who
could
be
at
risk
and
triggers
early
treatment
bundles
before
the
condition
progresses
to
a
severe
state.
The
health
system
is
also
working
with
Artisight
to
embed
computer
vision
in
its
hospital
rooms,
McDonnell
stated.
She
said
Duke
is
installing
in-room
cameras,
which
will
work
with
AI
algorithms
to
monitor
fall
risks
and
eventually
automate
documentation
—
such
as
recording
a
patient’s
fluid
output
without
a
nurse
ever
having
to
write
or
dictate
a
note.
She
also
noted
that
Duke
recently
conducted
an
AI
pilot
with
Microsoft’s
Nuance,
and
it
rolled
out
Abridge’s
AI-powered
clinical
documentation
platform
earlier
this
year.
While
such
tools
have
proven
effective
in
reducing
burnout
for
physicians
working
in
outpatient
and
ambulatory
settings,
they
are
not
yet
fully
optimized
for
the
complexities
of
inpatient
care,
McDonnell
said.
However,
she
noted
Duke
is
currently
working
on
an
inpatient
documentation
pilot
with
Abridge.
When
it
comes
to
the
question
of
whether
to
build
or
buy,
McDonnell
said
it
depends
on
the
problem
that
the
health
system
is
trying
to
solve.
All
AI
pilots
at
Duke
begin
with
a
problem
statement,
she
noted.
Then,
leaders
see
if
there
is
a
solution
on
the
market
that
addresses
this
issue,
or
perhaps
a
tool
that
one
of
their
partners
is
already
developing,
such
as
Epic
or
Microsoft.
If
there
are
no
well-vetted
solutions
on
the
market,
Duke
then
considers
co-developing
a
solution
with
an
external
tech
partner.
And
if
that’s
not
an
option,
Duke
sees
if
it
can
build
a
tool
on
its
own
leveraging
its
engineering
school
and
IT
capabilities,
McDonnell
said.
“We’ve
got
great
internal
strength
in
our
own
IT
and
development
teams.
We’re
really
lucky
in
that
regard
—
not
every
system
has
that
luxury,”
she
stated.
Looking
ahead,
McDonnell
encouraged
health
systems
to
balance
practicality
with
innovation
when
trying
out
new
AI
models.
“You
can’t
get
excited
about
every
bright,
shiny
toy
that
comes
through
the
front
door.
But
I
also
think
that
we’re
starting
to
learn
that
we
can
pilot
things,
try
things,
and
rapidly
learn
what’s
going
to
work
and
what’s
not
going
to
work,”
she
remarked.
To
McDonnell,
AI
success
depends
not
on
chasing
the
newest
technology,
but
on
choosing
and
refining
the
tools
that
truly
ease
clinicians’
workload
and
improve
patient
outcomes.
Photo:
Yuichiro
Chino,
Getty
Images