ArticlePDF Available

Machine learning as a supportive tool to recognize cardiac arrest in emergency calls

Authors:
  • Copenhagen Emergency Medical Services & University of Copenhagen Denmark

Abstract and Figures

Background: Emergency medical dispatchers fail to identify approximately 25% of cases of out of hospital cardiac arrest, thus lose the opportunity to provide the caller instructions in cardiopulmonary resuscitation. We examined whether a machine learning framework could recognize out-of-hospital cardiac arrest from audio files of calls to the emergency medical dispatch center. Methods: For all incidents responded to by Emergency Medical Dispatch Center Copenhagen in 2014, the associated call was retrieved. A machine learning framework was trained to recognize cardiac arrest from the recorded calls. Sensitivity, specificity, and positive predictive value for recognizing out-of-hospital cardiac arrest were calculated. The performance of the machine learning framework was compared to the actual recognition and time-to-recognition of cardiac arrest by medical dispatchers. Results: We examined 108,607 emergency calls, of which 918 (0.8%) were out-of-hospital cardiac arrest calls eligible for analysis. Compared with medical dispatchers, the machine learning framework had a significantly higher sensitivity (72.5% vs. 84.1%, p < 0.001) with lower specificity (98.8% vs. 97.3%, p < 0.001). The machine learning framework had a lower positive predictive value than dispatchers (20.9% vs. 33.0%, p < 0.001). Time-to-recognition was significantly shorter for the machine learning framework compared to the dispatchers (median 44 seconds vs. 54 s, p < 0.001). Conclusions: A machine learning framework performed better than emergency medical dispatchers for identifying out-of-hospital cardiac arrest in emergency phone calls. Machine learning may play an important role as a decision support tool for emergency medical dispatchers.
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Clinical
paper
Machine
learning
as
a
supportive
tool
to
recognize
cardiac
arrest
in
emergency
calls
Stig
Nikolaj
Blomberg
a,b,
*,
Fredrik
Folke
a,b,c
,
Annette
Kjær
Ersbøll
d
,
Helle
Collatz
Christensen
a
,
Christian
Torp-Pedersen
e,f
,
Michael
R.
Sayre
g
,
Catherine
R.
Counts
g
,
Freddy
K.
Lippert
a,b
a
Emergency
Medical
Services
Copenhagen,
Denmark
b
Department
of
Clinical
Medicine,
University
of
Copenhagen,
Denmark
c
Department
of
Cardiology,
Gentofte
University
Hospital,
Denmark
d
National
Institute
of
Public
Health,
University
of
Southern
Denmark,
Denmark
e
Department
of
Clinical
Epidemiology,
Aalborg
University
Hospital,
Denmark
f
Department
of
Health
Science
and
Technology,
Aalborg
University,
Denmark
g
Department
of
Emergency
Medicine,
University
of
Washington,
United
States
Abstract
Background:
Emergency
medical
dispatchers
fail
to
identify
approximately
25%
of
cases
of
out
of
hospital
cardiac
arrest,
thus
lose
the
opportunity
to
provide
the
caller
instructions
in
cardiopulmonary
resuscitation.
We
examined
whether
a
machine
learning
framework
could
recognize
out-of-hospital
cardiac
arrest
from
audio
files
of
calls
to
the
emergency
medical
dispatch
center.
Methods:
For
all
incidents
responded
to
by
Emergency
Medical
Dispatch
Center
Copenhagen
in
2014,
the
associated
call
was
retrieved.
A
machine
learning
framework
was
trained
to
recognize
cardiac
arrest
from
the
recorded
calls.
Sensitivity,
specificity,
and
positive
predictive
value
for
recognizing
out-of-hospital
cardiac
arrest
were
calculated.
The
performance
of
the
machine
learning
framework
was
compared
to
the
actual
recognition
and
time-to-
recognition
of
cardiac
arrest
by
medical
dispatchers.
Results:
We
examined
108,607
emergency
calls,
of
which
918
(0.8%)
were
out-of-hospital
cardiac
arrest
calls
eligible
for
analysis.
Compared
with
medical
dispatchers,
the
machine
learning
framework
had
a
significantly
higher
sensitivity
(72.5%
vs.
84.1%,
p
<
0.001)
with
lower
specificity
(98.8%
vs.
97.3%,
p
<
0.001).
The
machine
learning
framework
had
a
lower
positive
predictive
value
than
dispatchers
(20.9%
vs.
33.0%,
p
<
0.001).
Time-to-
recognition
was
significantly
shorter
for
the
machine
learning
framework
compared
to
the
dispatchers
(median
44
seconds
vs.
54
s,
p
<
0.001).
Conclusions:
A
machine
learning
framework
performed
better
than
emergency
medical
dispatchers
for
identifying
out-of-hospital
cardiac
arrest
in
emergency
phone
calls.
Machine
learning
may
play
an
important
role
as
a
decision
support
tool
for
emergency
medical
dispatchers.
Keywords:
Artificial
intelligence,
Machine
learning,
Cardiopulmonary
resuscitation,
Emergency
medical
services,
Out-of-hospital
cardiac
arrest,
Detection
time,
Dispatch-assisted
cardiopulmonary
resuscitation
*
Corresponding
author
at:
Emergency
Medical
Services
Copenhagen
&
Department
of
Clinical
Medicine,
University
of
Copenhagen,
Telegrafvej
5,
2750,
Copenhagen,
Denmark.
E-mail
address:
Stig.Nikolaj.Fasmer.Blomberg@regionh.dk
(S.N.
Blomberg).
https://doi.org/10.1016/j.resuscitation.2019.01.015
Received
16
October
2018;
Received
in
revised
form
9
January
2019;
Accepted
11
January
2019
0300-9572/©
2019
The
Authors.
Published
by
Elsevier
B.V.
This
is
an
open
access
article
under
the
CC
BY-NC-ND
license
(http://creativecommons.org/
licenses/by-nc-nd/4.0/).
R
E
S
U
S
C
I
T
A
T
I
O
N
1
3
8
(
2
0
1
9
)
3
2
2
3
2
9
Available
online
at
www.sciencedirect.com
Resuscitation
jou
r
n
al
ho
m
epag
e:
ww
w.els
evier.c
o
m/lo
c
ate/res
u
sc
itat
ion
Introduction
More
than
600,000
people
a
year
sustain
an
out
of
hospital
cardiac
arrest
(OHCA)
the
United
States
and
Europe
combined.
1,2
OHCA
is
a
life-threatening
condition
that
needs
to
be
recognized
rapidly
by
dispatchers
and
recognition
of
OHCA
by
either
a
bystander
or
a
dispatcher
in
the
emergency
medical
dispatch
center
is
a
prerequisite
for
initiation
of
cardiopulmonary
resuscitation
(CPR).
Delivery
of
CPR
before
the
arrival
of
emergency
medical
services
improves
survival,
making
medical
dispatchers
recognition
of
the
condition
during
emergency
calls
pivotal.
35
However,
because
patients
in
cardiac
arrest
constitute
a
small
minority
of
the
overall
call
volume,
recognition
of
cardiac
arrest
is
difficult
and
approximately
one
quarter
of
all
OHCAs
are
not
recognized
during
the
initial
emergency
conversation
with
the
medical
dispatcher.
3,6,7
This
missed
cardiac
arrest
recogni-
tion
and
subsequent
provision
of
life-saving
CPR
for
OHCAs
remains
a
major
challenge.
Previous
research
has
identified
barriers
to
recognition
of
OHCA.
3,6,8
Improving
early
recognition
is
a
goal
for
both
the
American
Heart
Association
and
the
Global
Resuscitation
Alliance.
912
This
challenge
may
benefit
from
a
novel
approach
using
machine
learning.
Machinelearning
frameworks
have
been
applied
to
non-emergency
conditions,
showing
that
a
computer
can
assist
with
clinical
decision-
making
or
screening
under
certain
circumstances.
1316
However,
machine
learning
technologies
have
not
been
used
to
support
clinical
decision-making
in
an
acute
medical
context.
17
If
machine
learning
could
improve
OHCA
recognition,
a
condition
typically
representing
approximately
1%
of
all
emergency
calls,
it
holds
the
potential
for
other
more
frequent
time
critical
incidents
such
as
stroke,
acute
myocardial
infarction
or
sepsis.
In
this
study,
a
machine
learning
framework
was
used
to
recognize
OHCA
from
unedited
recordings
of
emergency
calls
to
an
emergency
medical
dispatch
center,
and
the
performance
of
the
machine
learning
framework
was
subsequently
assessed.
The
study
aim
was
threefold:
to
test
if
a
unique
machine
learning
framework
could
improve
the
OHCA
recognition
rate
compared
with
trained
dispatchers,
to
examine
if
the
machine
learning
framework
could
recognize
OHCA
faster
than
the
medically
trained
dispatchers,
and
to
identify
possible
caller
or
patient
subgroups
that
were
more
prone
to
bias
from
the
medical
dispatchers
or
machine
learning
framework.
Methods
Machine
learning
framework
Emergency
telephone
calls
contain
a
vast
amount
of
information
that
the
medical
dispatcher
must
decipher
to
draw
a
conclusion
about
the
urgency
of
the
patients
condition
and
the
type
of
response
required.
Issues
such
as
background
noise
and
confusing
or
conflicting
information
further
complicate
this
process.
Machine
learning
is
most
commonly
described
as
an
approach
in
which
a
model
or
a
framework
of
models
analyses
data
and
adapts
by
learning
from
its
mistakes.
18
We
used
a
machine
learning
framework
created
by
the
company
Corti
(Corti.ai,
Denmark).
The
machine
learning
framework
is
a
network
of
several
machine
learning
models
performing
specific
tasks,
in
this
case
deciphering
a
conversation
in
a
similar
manner
to
a
medical
dispatcher.
When
an
emergency
call
is
analysed
in
real-time
by
the
machine
learning
framework,
the
audio
file
is
processed
without
any
prior
editing
or
transcription
and
transformed
to
a
textual
representa-
tion
of
the
call,
which
is
then
analysed
and
outputted
as
a
prediction
of
cardiac
arrest.
The
classification
of
OHCA
is
an
end-of-call
binary
verdict.
Time-
to-prediction
is
a
continuum
of
intervals
and
requires
other
means
of
analysis.
Analyses
were
based
on
processing
of
the
raw
audio
file
by
the
machine
learning
framework
and
were
not
based
on
manually
transcribed
data.
The
audio
files
were
not
prepared
or
edited
before
processing.
To
teach
the
machine
learning
framework
we
used
a
dataset
containing
the
actual
audio
files,
labelled
for
the
absence
or
presence
of
an
OHCA
at
the
time
of
the
call.
Part
of
the
dataset
was
used
for
training,
and
another
part
was
used
for
validation.
We
performed
k-fold
cross
validation
to
avoid
evaluation
on
a
biased
split,
which
means
the
framework
was
not
evaluated
on
the
same
files
it
had
been
trained
on.
19
Study
population
We
included
all
emergency
calls
to
the
Emergency
Medical
Dispatch
Center
Copenhagen
serving
the
Capital
Region
of
Denmark
received
between
January
1,
2014,
and
December
31,
2014.
The
Capital
Region
of
Denmark
covers
1.8
million
inhabitants,
and
the
Emergency
Medical
Dispatch
Center
Copenhagen
responds
to
110,000
incidents
annually,
of
which
approximately
1200
(1.0%)
are
OHCAs.
The
medical
dispatchers
receiving
the
calls
are
nurses
(70%)
or
paramedics
(30%)
with
6
weeks
of
focused
training
in
communication
and
prioritization
of
emergency
calls.
6
The
decision-making
process
is
supported
by
a
criteria-based
dispatch
protocol
for
assessing
the
calls,
guiding
decisions
about
the
emergency
level
and
determining
the
appropriate
responses.
This
is
a
validated
standardized
criteria-
based,
nationwide
Emergency
Medical
Dispatch
System.
20,21
For
all
incidents,
the
associated
dispatch
audio
recordings
were
retrieved
to
create
a
dataset
for
both
teaching
and
evaluating
the
machine
learning
framework.
Cases
of
OHCA
were
identified
through
the
Danish
Cardiac
Arrest
Registry
4,22
where
bystanders
initiated
CPR
or
EMS
professionals
attempted
CPR.
We
excluded
cases
where
the
audio
file
was
damaged,
or
the
call
was
disconnected
(unsuitable
for
analyses),
cases
with
EMS
witnessed
OHCA
(cardiac
arrest
occurred
after
the
call),
cases
in
which
CPR
was
initiated
prior
to
the
emergency
call
(cardiac
arrest
already
recognized),
and
cases
where
the
patient
showed
signs
of
obvious
death.
Calls
where
dispatchers
erroneously
suspected
cardiac
arrest
were
identified
to
obtain
predictive
values
for
dispatchers.
These
false-positive
cases
were
identified
by
a
standardised
free-text
search
in
the
dispatch
system,
where
the
logs
of
all
incidents
were
scanned
for
expressions
related
to
OHCA
(arrest,
automated
external
defibrillator,
CPR
and
lifeless
while
excluding
phrases
such
as
not
arrest
etc.).
All
emergency
call
recordings
were
identified
and
labelled
according
to
whether
the
calls
concerned
an
OHCA.
Calls
concerning
OHCA
were
comprehensively
examined
by
the
investigators
using
a
predefined
and
pilot-tested
case
report
form.
3
Time-to-recognition
of
OHCA
was
defined
as
cases
where
the
dispatcher
or
the
caller
expressed
the
presence
of
an
OHCA
or
the
need
to
initiate
CPR
or
use
an
automated
external
defibrillator.
Time-to-recognition
was
deter-
mined
as
the
interval
from
the
time
the
call
was
answered
until
the
time
when
the
definition
of
cardiac
arrest
recognition
was
achieved.
Calls
where
ambulance
personal
observed
signs
of
irreversible
death,
but
resuscitative
efforts
had
been
initiated
prior
to
ambulance
arrival
were
labelled
as
OHCA
for
training
the
machine
learning
framework
but
excluded
from
the
analysis.
R
E
S
U
S
C
I
T
A
T
I
O
N
1
3
8
(
2
0
1
9
)
3
2
2
3
2
9323
Statistical
analysis
The
performance
of
the
machine
learning
framework
was
compared
to
the
medical
dispatchers
as
a
baseline.
The
machine
learning
framework
generates
a
binary
prediction
of
either
0
or
1
for
OHCA
classification,
corresponding
to
the
probability
of
that
condition
being
present
in
each
emergency
call.
Sensitivity
and
specificity
of
the
machine
learning
framework
were
calculated
to
characterize
performance
in
respect
to
the
reference
standard,
which
was
defined
as
OHCA
arrest
validated
via
the
Danish
Cardiac
Arrest
Registry
excluding
EMS-witnessed
incidents
and
calls
with
CPR
initiated
prior
to
the
start
of
the
call.
We
calculated
median
time-to-recognition
for
all
calls
with
recognition
by
either
medical
dispatcher
or
machine
learning
framework.
Differences
in
time-to-recognition
between
the
machine
learning
framework
and
the
dispatcher
for
paired
observations
(i.e.
calls
where
both
the
dispatcher
and
the
machine
learning
framework
recognized
OHCA)
were
compared
using
students
t-test
and
signed
rank
test.
The
analysis
of
time-to-recognition
on
paired
observations
is
illustrated
as
a
BlandAltman
plot.
23
This
method
is
used
to
compare
a
new
measurement
technique
with
an
established
one.
In
the
Bland
Altman
plot,
the
average
time-to-recognition
of
the
paired
observa-
tions
is
plotted
on
the
X-axis,
whereas
the
difference
between
the
same
observations
is
plotted
on
the
Y-axis.
Results
for
time-to-recognition
are
presented
for
all
observations
and
for
paired
observations.
The
results
are
presented
with
corresponding
interquartile
ranges
(IQR).
Univariate
logistic
regression
analyses
were
performed
to
identify
patient-,
setting-,
and
dispatcher-related
predictors
of
OHCA
recognition
in
calls
where
the
machine
learning
framework
recognized
out-of-hospital
cardiac
arrest.
Results
are
reported
as
odds
ratios
[OR]
with
95%
confidence
intervals
[CI]
and
p-Values
when
appropriate.
p
Values
of
less
than
0.05
were
considered
significant
for
all
analyses.
Data
management
and
statistical
analyses
were
performed
using
Statistical
Analysis
System,
SAS
software,
version
9.4
(SAS
Institute).
Approvals
The
study
was
approved
by
the
Danish
Health
Authority
(3-3013-
1289/1),
the
Danish
Data
Protection
Agency
(Journal
nr.:
PVH-
2018-001,
I-Suite
nr.:
6172),
and
the
regional
ethics
committee
(18005504).
Results
In
2014,
the
Emergency
Medical
Dispatch
Center
Copenhagen
responded
to
110,518
emergency
incidents;
and
the
associated
call
for
each
was
retrieved.
Patients
with
obvious
signs
of
death
were
excluded
(n
=
958),
as
were
calls
that
were
either
damaged
or
disconnected
within
the
first
10
s
(n
=
724).
We
identified
OHCAs
within
the
Danish
Cardiac
Arrest
Registry,
(n
=
1,147).
OHCAs
witnessed
by
ambulance
personnel
(n
=
126),
and
cases
where
CPR
had
been
initiated
prior
to
the
start
of
the
call
(n
=
103)
were
excluded
leaving
918
OHCA
calls
and
107,689
non-OHCA
calls
eligible
for
analysis.
(Fig.
1).
The
characteristics
of
the
OHCA
calls
are
shown
in
Table
1.
Of
the
918
calls
for
patients
with
an
OHCA,
665
(72.4%)
were
recognized
by
the
medical
dispatcher,
whereas
772
(84.1%)
were
recognized
by
the
machine
learning
framework
(p
<
0.001).
Among
calls
recognized
by
the
machine
learning
framework,
117
calls
were
not
recognized
by
the
medical
dispatchers.
Regarding
patient
characteristics,
there
were
only
minor
differ-
ences
between
these
incidents
and
incidents
recognized
by
medical
dispatcher.
The
patients
appeared
to
be
slightly
older
Fig.
1
Data
collection
and
validation
of
calls
to
Emergency
Medical
Dispatch
Center
Copenhagen
in
2014.
*
Out-of-hospital
cardiac
arrest.
y
Cardiopulmonary
resuscitation.
324 R
E
S
U
S
C
I
T
A
T
I
O
N
1
3
8
(
2
0
1
9
)
3
2
2
3
2
9
(74
vs.
70
years)
and
a
slightly
less
likely
to
be
male
(60.7%
vs.
64.1%).
Comparing
this
group
with
calls
recognized
by
medical
dispatchers,
these
calls
were
less
likely
to
have
consciousness
addressed
by
the
medical
dispatcher
(86.8%
vs.
96.2%),
more
likely
to
be
situations
where
the
caller
could
not
access
the
patient
(8.5%
vs.
1.8%),
and
more
likely
to
be
bystander
witnessed
arrests
(61.4%
vs.
48.7%)
than
the
group
of
calls
recognized
by
medical
dispatchers.
Only
10
calls
(1.1%)
were
recognized
by
the
dispatcher
but
not
by
the
machine
learning
framework
(Table
1).
Among
all
calls,
the
machine
learning
framework
reached
a
sensitivity
of
84.1%
(95%
CI:
81.686.3)
and
a
specificity
of
97.3%
(95%
CI:
97.297.4)
on
recognizing
OHCA
(Table
2).
The
corresponding
sensitivity
and
specificity
of
the
dispatchers
were
72.5%
(95%
CI:
69.5
75.4)
and
98.8%
(95%
CI:98.798.8),
respectively.
The
machine
learning
framework
had
a
positive
predictive
value
of
21.0%
(95%
CI:
19.722.3)
compared
with
33.0%
(95%
CI:
30.135.1)
for
the
dispatchers.
On
all
calls
recognized
by
the
machine
learning
model
(n
=
772),
time-to-recognition
was
significantly
shorter
for
the
machine
learning
framework
(median
time-to-recognition
44
s,
IQR:
2467)
Table
1
Characteristics
of
emergency
calls
with
an
out-of-hospital
cardiac
arrest
given
by
proportion
and
number
of
missing
values.
CARDIAC
arrest
recognized
by
All
(N
=
918)
Dispatcher
(N
=
665)
Machine
learning
framework
(N
=
772)
Machine
learning
framework
and
not
dispatcher
(N
=
117)
Dispatcher
and
not
machine
learning
framework
(N
=
10)
Patient
age
Age,
median
(Q1Q3)
71
(6181)
70
(6181)
71
(6181)
74
(6784)
63
(5078)
Age
missing
a
29
19
21
4
2
Patient
gender
Male
555
(63.4%)
407
(64.1%)
473
(63.8%)
68
(60.7%)
2
(33.3%)
Female
321
(36.6%)
228
(35.9%)
268
(36.2%)
44
(39.3%)
4
(66.7%)
Missing
a
42
30
31
5
4
Bystander
gender
Male
349
(38.4%)
248
(37.5%)
281
(36.7%)
40
(34.8%)
7
(70.0%)
Female
561
(61.6%)
414
(62.5%)
486
(63.4%)
75
(65.2%)
3
(30.0%)
N/A
a
8
3
5
2
0
Bystander
(callers)
relation
to
patient
Caller
relative
to
patient
383
(45.1%)
288
(46.5%)
335
(46.5%)
51
(46.4%)
4
(50.0%)
Caller
healthcare
professional
238
(28.0%)
169
(27.3%)
198
(27.5%)
31
(28.2%)
2
(25.0%)
Caller
all
others
228
(26.9%)
162
(26.2%)
189
(26.1%)
28
(25.5%)
2
(25.0%)
N/A
b
69
46
51
7
2
Access
to
patient
Caller
by
patients
side
654
(78.8%)
508
(83.0%)
571
(80.6%)
71
(67.0%)
8
(88.9%)
Can
access
patient,
but
must
leave
phone
132
(15.9%)
93
(15.2%)
120
(16.8%)
26
(24.5%)
0
(0.0%)
Caller
cannot
access
patient
44
(5.3%)
11
(1.8%)
19
(2.7%)
9
(8.5%)
1
(11.1%)
N/A
b
88
53
64
11
1
Incident
witnessed
Witnessed
by
bystander
487
(54.0%)
319
(48.7%)
384
(50.3%)
70
(61.4%)
7
(70.0%)
Not
witnessed
by
bystander
415
(46.0%)
336
(51.3%)
377
(49.7%)
44
(38.6%)
3
(30.0%)
N/A
b
16
10
13
3
0
Call
interrupted
Call
continued
until
arrival
of
ambulance
178
(20.5%)
170
(27.8%)
173
(23.5%)
4
(3.6%)
1
(10.0%)
Call
ended
or
interrupted
before
arrival
of
ambulance
692
(79.5%)
465
(73.2%)
565
(76.5%)
108
(96.4%)
9
(90.0%)
N/A
b
48
30
36
5
0
Patient
consciousness
Consciousness
addressed
829
(92.3%)
634
(96.2%)
724
(94.8%)
99
(86.8%)
10
(100.0%)
Consciousness
not
addressed
68
(7.7%)
25
(3.8%)
40
(5.2%)
15
(13.2%)
0
N/A
b
21
6
10
3
0
Patient
breathing
Breathing
addressed
844
(93.2%)
642
(97.0%)
741
(96.4%)
108
(93.1%)
10
(100.0%)
Breathing
not
addressed
62
(6.8%)
20
(3.0%)
28
(3.7%)
8
(6.9%)
0
N/A
b
12
3
5
1
0
a
Missing
values
are
missing
civil
registration
numbers
(containing
age
and
sex)
with
insufficient
information
on
recorded
call
to
identify
patient.
b
N/A
is
assigned
when
calls
are
interrupted
or
untimely
ended.
R
E
S
U
S
C
I
T
A
T
I
O
N
1
3
8
(
2
0
1
9
)
3
2
2
3
2
9325
compared
with
that
of
the
dispatchers
(n
=
665)
(median
time-to-
recognition
54
s,
IQR:
3099)
(p
<
0.001).
The
analysis
of
paired
observations
where
both
the
machine
learning
framework
and
the
dispatcher
had
recognized
the
OHCA
(n
=
655)
yielded
a
median
time-
to-recognition
for
the
machine
learning
framework
of
44
seconds
(IQR:
2467
s)
and
for
the
dispatcher
of
54
s
(IQR:
3097
s)
(p
<
0.001).
Time-to-recognition
is
illustrated
in
the
Bland-Altman
plot
for
paired
calls
where
both
the
dispatcher
and
the
machine
learning
framework
recognized
OHCA
(Fig.
2).
This
plot
shows
a
visual
representation
that
a
longer
time-to-recognition
by
the
medical
dispatcher
does
not
always
equate
to
a
longer
time-to-recognition
by
the
machine
learning
framework.
The
BlandAltman
plot
illustrates
that
the
dispatcher
used
more
time
to
recognize
OHCA
than
the
machine
learning
framework,
as
the
difference
in
time-to-recognition
(the
Y-axis)
for
the
vast
majority
of
calls
was
greater
than
zero,
which
is
where
there
is
no
difference
between
the
time-to-recognition
between
dispatcher
and
machine
learning
framework.
The
mean
difference
was
26
seconds
(dashed
line,
Fig.
2).
An
observation
with
a
longer
mean
recognition
time
on
the
x
axis
also
has
a
greater
difference
in
recognition
time
on
the
y
axis.
Results
of
univariate
logistic
regression
are
shown
in
Fig.
3
as
odds
ratios
for
differences
in
recognition
of
OHCA,
where
OHCAs
recognized
by
the
machine
learning
framework
are
used
as
reference.
Among
calls
where
the
machine
learning
framework
recognized
OHCA,
dispatchers
recognition
was
positively
associated
with
the
callers
access
to
the
patient.
Specifically,
when
the
caller
was
by
the
patients
side,
the
dispatcher
was
2.37
times
more
likely
to
identify
the
cardiac
arrest
than
when
the
caller
was
not
by
the
patients
side
(95%
CI
1.503.74).
If
the
dispatcher
addressed
consciousness
they
had
an
odds
ratio
of
3.80
(95%
CI
1.907.40);
and
if
they
addressed
breathing,
the
odds
ratio
was
2.30
(95%
CI
1.005.40).
Table
2
Characteristics
of
recognition
and
time-to-
recognition
of
out-of-hospital
cardiac
arrests
in
emergency
calls.
Raw
audio
data
for
2014
(n
=
108,607)
Machine
learning
framework
Dispatcher
Sensitivity
(95%
CI)
84.1
(81.6;86.4)
72.4
(69.4;
75.3)
Specificity
(95%
CI)
97.3
(97.2;97.4)
98.8
(98.7-98.8)
Negative
predictive
value
(95%
CI)
99.9
(99.8;99.9)
99.8
(99.7;
99.8)
Positive
predictive
value
(95%
CI)
20.9
(19.6;22.3)
33.0
(30.1;
35.1)
Sensitivity
(95%
CI),
calls
unrecognized
by
dispatchers
44.5
(38.4-50.7)
Time-to-recognition,
all
observations
Median
(95%
CI)
(seconds)
44
(41;
48)
54
(50;
59)
Lower
quartile
(seconds)
25
30
Upper
quartile
(seconds)
72
99
Time-to-recognition,
paired
observations
Median
(95%
CI)
(seconds)
41
(38;
44)
54
(50;
59)
Lower
quartile
(seconds)
24
30
Upper
quartile
(seconds)
67
97
Fig.
2
BlandAltman
plot
comparing
time-to-recognition
measurements
on
calls
recognized
by
dispatcher
and
machine
learning
framework.
Mean
time-to-recognition
between
medical
dispatcher
and
machine
learning
framework
for
each
paired
observation
is
plotted
on
the
x-axis
while
the
difference
in
time-to-recognition
for
same
observation
is
plotted
on
the
y-axis.
Observations
where
there
is
no
difference
in
time-to-recognition
the
observation
will
be
placed
along
the
zero
bias
line.
Observations
where
the
dispatcher
recognizes
the
OHCA
faster
than
the
machine
learning
framework
are
found
below
the
zero
bias
line.
326 R
E
S
U
S
C
I
T
A
T
I
O
N
1
3
8
(
2
0
1
9
)
3
2
2
3
2
9
When
the
cardiac
arrest was witnessed
by
a
bystander,dispatchers
were
less
likely
to
recognise
the
cardiac
arrest
compared
to
the
incidents
that
were
unwitnessed
(OR
0.52,
95%
CI
0.390.89)
(Fig.
3).
Discussion
The
machine
learning
framework
succeeded
in
recognizing
OHCA
on
raw
audio
files
with
a
significantly
higher
sensitivity
and
similar
specificity
to
the
medical
dispatchers.
It
was
significantly
faster
than
medical
dispatchers
in
recognizing
OHCA.
Regression
analysis
showed
the
machine
learning
frameworks
overcame
some
of
the
barriers
to
dispatcher
recognition
that
previous
studies
have
identified.
3,6
Applying
a
machine
learning
framework
for
OHCA
holds
the
potential
for
increased
and
faster
recognition
by
dispatchers,
increased
initiation
of
telephone
CPR
efforts,
and
potentially
improving
arrest
survival.
We
found
a
10
s
decrease
in
recognition
time
of
cardiac
arrest.
The
AHA
program
guidelines
recommend
that
a
high
performance
system
have
an
elapsed
time
from
call
reception
to
initial
dispatch
of
a
response
team
of
less
than
60
s,
suggesting
120
s
be
the
minimal
acceptable
standard.
In
such
a
perspective
10
s
is
clinically
relevant.
24
These
findings
open
new
possibilities
for
machine
learning
frameworks,
and
the
potential
role
they
may
play
as
a
decision
support
tool
for
emergency
medical
dispatchers
in
both
recognition
of
OHCA
and
other
time
critical
conditions.
Machine
learning
has
proven
clinically
relevant
when
applied
to
specific
non-urgent
medical
conditions
such
as
systemic
lupus
erythematosus
or
diabetic
retinopathy
1316
showing
that
machine
learning
frameworks,
though
not
superior
to
humans
in
predicting
certain
conditions,
can
support
clinicians
as
a
screening
tool.
However,
previous
studies
analysing
images
and
hospital
records
were
not
used
in
a
time-critical
setting.
In
this
study
the
machine
learning
framework
was
trained
directly
on
raw,
unedited
audio
files.
This
is
important
for
the
implementation
in
an
acute
clinical
setting
where
decision-making
has
to
be
efficient
and
precise.
Thus,
this
approach
would
make
the
transition
to
a
live
setting
easier.
Most
studies
have
published
positive
predictive
values
of
between
58.4%
and
97.9%
for
professional
dispatchers.
2531
However,
the
incidence
of
OHCA
in
these
studies
varied
greatly,
with
the
studies
that
reported
high
positive
predictive
values
also
reporting
a
low
incidence
of
OHCA.
7
Comparatively,
the
prevalence
of
OHCA
resulted
in
a
positive
predictive
value
of
21.0%
by
the
machine
learning
framework,
meaning
that
almost
four
of
five
machine
learning
recognized
OHCAs
would
be
a
false
positive.
While
the
positive
predictive
value
of
the
machine
learning
framework
was
lower
than
the
medical
dispatcher,
a
certain
amount
of
over-triage
is
generally
accepted
for
cardiac
arrest
and
other
time-critical
incidents.
As
such,
machine
learning
should
not
be
used
as
a
stand-alone
tool
that
can
independently
dispatch
ambulances
but
could
act
as
a
supplement
to
dispatchers
decision-making
processes
based
on
standard
operating
procedures,
algorithms
and
personal
experien-
ces.
Accordingly,
the
lower
positive
predictive
value
by
the
machine
learning
framework
should
not
be
a
critical
failure
because
it
could
simply
generate
cautions
about
suspected
OHCAs
and
function
as
an
awareness
flag
for
the
dispatchers.
This
could
then
prompt
the
Fig.
3
Associations
between
call
characteristics
and
dispatcher
recognition
of
OHCA
among
calls
recognized
by
machine
learning
model.
(Unadjusted).
The
results
of
regression
are
shown
as
odds
ratios
for
differences
in
recognition
of
out-of-hospital
cardiac
arrest
(OHCA),
where
OHCAs
recognized
by
the
machine
learning
framework
is
used
as
the
reference
(vertical
line).
This
means
that
all
observations
are
recognized
by
the
machine
learning
framework.
An
odds
ratio
greater
than
one
means
the
factor
was
positively
associated
with
dispatchers
recognition,
and
odds
ratios
less
than
one
mean
the
factor
was
associated
to
dispatcher
failing
to
recognize
the
OHCA.
R
E
S
U
S
C
I
T
A
T
I
O
N
1
3
8
(
2
0
1
9
)
3
2
2
3
2
9327
dispatchers
to
increase
their
focus
on
presence
of
breathing
and
level
of
consciousness
within
the
ongoing
call.
In
turn,
these
actions
could
potentially
lead
to
an
increase
in
the
initiation
of
CPR
by
bystanders,
shown
by
previous
research
to
improve
both
short-
and
long-term
survival
as
well
as
reducing
the
risk
of
anoxic
brain
damage
and
nursing-home
admission.
4
Regression
analyses
illustrated
that
in
the
OHCAs
recognized
by
the
machine
learning
framework
bystanders
access
to
the
patient
along
with
dispatchers
addressing
breathing
and
consciousness
were
associated
with
the
medical
dispatchers
recognition
of
OHCA
on
calls
also
recognized
by
the
machine
learning
framework.
The
analysis
also
illustrated
that
bystander
witnessed
OHCAs
were
negatively
associ-
ated
with
recognition
of
OHCA,
which
could
be
explained
by
the
presence
of
agonal
breathing
shortly
after
collapse,
which
is
present
in
55%
of
witnessed
OHCAs
and
may
delay
or
even
prevent
recognition.
32,33
These
findings
support
those
of
previous
studies.
3
Limitations
This
study
has
limitations.
Predictions
by
the
machine
learning
framework
are
made
at
the
termination
of
the
audio
recording.
In
a
live
setting,
the
end-of-call
prediction
is
less
useful
than
a
predication
made
while
the
dispatcher
is
still
on
the
phone
with
a
bystander.
The
machine
learning
framework
would
need
to
alert
the
dispatchers
in
the
case
of
a
suspected
OHCA
when
there
is
satisfactory
confidence
in
the
prediction
before
the
end
of
the
call.
The
results
from
this
study
need
to
be
tested
in
another
emergency
medical
setting
to
prove
transmissibility
to
other
languages
and
organizational
cultures.
Ideally
the
use
of
machine
learning
should
be
tested
in
a
randomized
controlled
trial
to
measure
its
impact
on
patient
survival
and
EMS
system
operations.
If
an
OHCA
can
be
recognized
from
a
short
conversation
over
the
phone,
using
machine
learning
to
identify
other
time
critical
incidents
as
stroke,
acute
myocardial
infarct
or
sepsis
holds
a
great
potential.
These
conditions
have
a
serious
health
as
well
as
economic
impact,
and
are
over
twice
as
prevalent
in
the
United
States
than
OHCAs.
1,34
Conclusion
Applying
a
machine
learning
framework
on
raw
audio-files
of
emergency
calls
to
identify
OHCA
showed
a
significantly
higher
sensitivity
and
similar
specificity
than
what
was
recognized
by
professional
medical
dispatchers.
Furthermore,
the
machine
learning
framework
was
significantly
faster
than
medical
dispatchers
in
recognizing
OHCA
albeit
with
a
lower
positive
predictive
value.
Machine
learning
may
also
play
an
important
role
as
a
decision
support
tool
for
emergency
medical
dispatchers
in
other
time
critical
conditions.
Conflict
of
interest
None.
Acknowledgements
We
thank
Corti:
L.
Borgholt,
M.
Singh,
A.
Wahl-Rasmussen,
T.M.S.
Tax,
L.
Maaløe
who
created
the
machine
learning
framework.
Corti
analysed
audio-files
labelled
according
to
OHCA
for
training
and
testing.
Data
were
subsequently
returned
with
label
of
prediction
for
further
analysis
by
the
study-team.
Corti
had
no
influence
on
the
studys
design,
results
or
conclusions.
This
study
was
supported
by
an
unrestricted
grant
from
the
Danish
foundation
TrygFonden
and
The
Laerdal
Foundation.
All
authors
have
completed
the
ICMJE
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for
the
submitted
work;
no
financial
relationships
with
any
organizations
that
might
have
an
interest
in
the
submitted
work
in
the
previous
three
years;
no
other
relationships
or
activities
that
could
appear
to
have
influenced
the
submitted
work.
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... Previous research by Blomberg et al. [24] and Cleve et al. [25] has shown, that artificial intelligence (AI) in emergency medicine increases the accuracy as well as efficiency and reduces the time-to-treatment. Previously, an automatic speech recognition software (ASR) for the detection of Out-of-Hospital cardiac arrests (OHCA) by CORTI AI has proven to increase the sensitivity of OHCA from 72.5 to 84.1% and reduce the median timeto-recognition from 54 to 44 s at the EMS Copenhagen [24]. ...
... Previous research by Blomberg et al. [24] and Cleve et al. [25] has shown, that artificial intelligence (AI) in emergency medicine increases the accuracy as well as efficiency and reduces the time-to-treatment. Previously, an automatic speech recognition software (ASR) for the detection of Out-of-Hospital cardiac arrests (OHCA) by CORTI AI has proven to increase the sensitivity of OHCA from 72.5 to 84.1% and reduce the median timeto-recognition from 54 to 44 s at the EMS Copenhagen [24]. This software "listens" to the emergency call, processes the audio, transforms it into a textual representation, analyses it and outputs a prediction on the potential presence of cardiac arrest. ...
... Based on advanced speech analysis through AI, the technology structures and analyses all sounds and spoken information during a live EMS conversation and converts this data into a valid prediction [25]. The software continuously learns from previous patient consultations and published medical papers in the specific field of cardiac arrest [24,25]. ...
Article
Full-text available
Background and purpose Stroke recognition at the Emergency Medical Services (EMS) impacts the stroke treatment and thus the related health outcome. At the EMS Copenhagen 66.2% of strokes are detected by the Emergency Medical Dispatcher (EMD) and in Denmark approximately 50% of stroke patients arrive at the hospital within the time-to-treatment. An automatic speech recognition software (ASR) can increase the recognition of Out-of-Hospital cardiac arrest (OHCA) at the EMS by 16%. This research aims to analyse the potential impact an ASR could have on stroke recognition at the EMS Copenhagen and the related treatment. Methods Stroke patient data ( n = 9049) from the years 2016–2018 were analysed retrospectively, regarding correlations between stroke detection at the EMS and stroke specific, as well as personal characteristics such as stroke type, sex, age, weekday, time of day, year, EMS number contacted, and treatment. The possible increase in stroke detection through an ASR and the effect on stroke treatment was calculated based on the impact of an existing ASR to detect OHCA from CORTI AI. Results The Chi-Square test with the respective post-hoc test identified a negative correlation between stroke detection and females, the 1813-Medical Helpline, as well as weekends, and a positive correlation between stroke detection and treatment and thrombolysis. While the association analysis showed a moderate correlation between stroke detection and treatment the correlation to the other treatment options was weak or very weak. A potential increase in stroke detection to 61.19% with an ASR and hence an increase of thrombolysis by 5% in stroke patients calling within time-to-treatment was predicted. Conclusions An ASR can potentially improve stroke recognition by EMDs and subsequent stroke treatment at the EMS Copenhagen. Based on the analysis results improvement of stroke recognition is particularly relevant for females, younger stroke patients, calls received through the 1813-Medical Helpline, and on weekends. Trial registration This study was registered at the Danish Data Protection Agency (PVH-2014-002) and the Danish Patient Safety Authority (R-21013122).
... können . Die erzielte Detektionszeit des Modells wurde mit der von Disponent*innen verglichen . Der Vergleich zeigte, dass in Abhängigkeit von Datenqualität und -umfang der Einsatz des ML-basierten Entscheidungsunterstützungssystems dabei helfen kann, lebensbedrohliche Situationen schneller (ca . zehn Sekunden Zeitgewinn) und sicherer aufzudecken (Blomberg et al . 2019 ...
... fallrettung und Krankentransport und testen zeitreihen-und regressionsbasierte Ansätze für eine strategische Bedarfsvorhersage .In der Forschung gibt es viele Publikationen, die sich mit der Vorhersage von Herzstillständen außerhalb des Krankenhauses (engl .: Out of Hospital Cardiac Arrest, OHCA) befassen (z . B .Kao et al . 2017, Seki et al . 2019, Blomberg et al . 2019) . Während diese Vorhersagen auch einen Einfluss auf die Rettungsdienstplanung haben können, wenn zum Beispiel Überlebenswahrscheinlichkeiten in die Standortplanung von RTWs einbezogen werden, so werden sie doch vor allem dazu genutzt Ersthelfermaßnahmen zu organisieren, die vor Eintreffen des Rettungsdienstes stattfinden und nicht zum ...
Chapter
Melanie Reuter-Oppermann, Luisa Pumplun, Helena Müller und Peter Buxmann untersuchen in ihrem Aufsatz „Künstliche Intelligenz im Rettungsdienst – Wege in die Zukunft“ zum einen die Möglichkeiten, die der Einsatz von KI im Rettungsdienst eröffnen könnte . Sie arbeiten aber ebenfalls sehr ausführlich heraus, dass dabei zahlreiche Anforderungen zu erfüllen sind, die sich sowohl aus der spezifischen Anwendungssituation ergeben als auch aus normativen Überlegungen . Damit zeigen die Autor*innen eine Verwendungsweise von KI auf, die in der einschlägigen Literatur nicht so oft in den Blick genommen wird, und lassen zudem erneut sichtbar werden, dass KI einem komplexen Geflecht aus Anforderungen gerecht werden muss
... We have considered AI models in different stages of their development and different requirements giving the opportunity to further improve and develop the initial Z-Inspection® process with real world complex examples. Our previous collaborations include evaluating: (1) a deployed ML pipeline estimating the risk of cardiovascular disease [11]; (2) a deployed ML model that supported a tool for detection of cardiac arrest in emergency calls [15], [24]; (3) a co-design of a Deep Learning-based tool to help dermatologists detect malignancy in skin lesions [25], [26]; and (4) a Deep Learning-based system for screening pulmonary findings in COVID-19 patients (in revision) ...
... The problem: Health-related emergency calls (112) are part of the Emergency Medical Dispatch Center (EMS) of the City of Copenhagen, triaged by medical dispatchers (i.e., medically-trained dispatchers, who answer the call, e.g., nurses and paramedics) and medical control by a physician on-site. In recent years, the Emergency Medical Dispatch Center of the City of Copenhagen has been unable to identify approximately 25% of cases of out-of-hospital cardiac arrest (OHCA) [24], [29]. These cases have only been recognized once the paramedics/ambulance arrived at the scene. ...
Preprint
Full-text available
This report is a methodological reflection on Z-Inspection. Z-Inspection is a holistic process used to evaluate the trustworthiness of AI-based technologies at different stages of the AI lifecycle. It focuses, in particular, on the identification and discussion of ethical issues and tensions through the elaboration of socio-technical scenarios. It uses the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI. This report illustrates for both AI researchers and AI practitioners how the EU HLEG guidelines for trustworthy AI can be applied in practice. We share the lessons learned from conducting a series of independent assessments to evaluate the trustworthiness of AI systems in healthcare. We also share key recommendations and practical suggestions on how to ensure a rigorous trustworthy AI assessment throughout the life-cycle of an AI system.
... During 2015-2020 over 200 medical devices using machine learning (a type of AI) received regulatory approval in Europe and the US (Muehlematter et al., 2021). While most healthcare AI applications have been developed in diagnostics (e.g., breast cancer screening (McKinney et al., 2020), it is likely that all areas of healthcare will see the introduction of AI tools, e.g., in ambulance service triage (Blomberg et al., 2019), sepsis management (Komorowski et al., 2018), palliative care (Avati et al., 2018) and mental health (Fitzpatrick et al., 2017). ...
Article
Introduction There is an increasing number of healthcare AI applications in development or already in use. However, the safety impact of using AI in healthcare is largely unknown. In this paper we explore how different stakeholders (patients, hospital staff, technology developers, regulators) think about safety and safety assurance of healthcare AI. Methods 26 interviews were undertaken with patients, hospital staff, technology developers and regulators to explore their perceptions on the safety and the safety assurance of AI in healthcare using the example of an AI-based infusion pump in the intensive care unit. Data were analysed using thematic analysis. Results Participant perceptions related to: the potential impact of healthcare AI, requirements for human-AI interaction, safety assurance practices and regulatory frameworks for AI and the gaps that exist, and how incidents involving AI should be managed. Conclusion The description of a diversity of views can support responsible innovation and adoption of such technologies in healthcare. Safety and assurance of healthcare AI need to be based on a systems approach that expands the current technology-centric focus. Lessons can be learned from the experiences with highly automated systems across safety-critical industries, but issues such as the impact of AI on the relationship between patients and their clinicians require greater consideration. Existing standards and best practices for the design and assurance of systems should be followed, but there is a need for greater awareness of these among technology developers. In addition, wider ethical, legal, and societal implications of the use of AI in healthcare need to be addressed.
... However, patient safety is the priority in prehospital emergency care [1,7]. Machine learning has been seen as a promising method to improve the practice of health care [10], as previous studies have shown that artificial intelligence can be used to identify high risk patients [11][12][13][14]. ...
Article
Full-text available
Background Predictors of subsequent events after Emergency Medical Services (EMS) non-conveyance decisions are still unclear, though patient safety is the priority in prehospital emergency care. The aim of this study was to find out whether machine learning can be used in this context and to identify the predictors of subsequent events based on narrative texts of electronic patient care records (ePCR). Methods This was a prospective cohort study of EMS patients in Finland. The data was collected from three different regions between June 1 and November 30, 2018. Machine learning, in form of text classification, and manual evaluation were used to predict subsequent events from the clinical notes after a non-conveyance mission. Results FastText-model (AUC 0.654) performed best in prediction of subsequent events after EMS non-conveyance missions (n = 11,846). The model and manual analyses showed that many of the subsequent events were planned before, EMS guided the patients to visit primary health care facilities or ED next or following days after non-conveyance. The most frequent signs and symptoms as subsequent event predictors were musculoskeletal-, infection-related and non-specific complaints. 1 in 5 the EMS documentation was inadequate and many of these led to a subsequent event. Conclusion Machine learning can be used to predict subsequent events after EMS non-conveyance missions. From the patient safety perspective, it is notable that subsequent event does not necessarily mean that patient safety is compromised. There were a number of subsequent visits to primary health care or EDs, which were planned before by EMS. This demonstrates the appropriate use of limited resources to avoid unnecessary conveyance to the ED. However, further studies are needed without planned subsequent events to find out the harmful subsequent events, where EMS non-conveyance puts patient safety at risk.
... These results provides perspectives for short-term applications of machine learning models that integrate semantic and acoustic parameters. Indeed, in 2019, Blomberg et al. developed a model based only on semantic elements that could reduce OHCA detection time 8 . The integration of the acoustic parameters described in the present study should increase the performance of such models. ...
... The flexibility grants great opportunities for MLe-POMDP to be implemented in various applications. For example, in healthcare applications, patient data can be collected in real time to predict symptoms such as cardiac arrest (Blomberg et al. 2019), diabetes (Nibareke and Laassiri 2020), and many coronary diseases (Sultan Bin Habib et al. 2019). Currently, ML models have been developed to make such predictions from real-time data. ...