Hello.
I'm
Renita
Washburn,
a
PhD
student
at
the
University
of
Central
Florida
in
the
Modeling and
Simulation
program.
Today,
I'
ll
be
presentingâ¦
Sorry.
I
know
we
have
to
do
it
in
one
session.
Dr.
Amon,
really
quick,
do
you
want
me
to
say
mine
and
then
you
say
yours
and
then
I
do
the
title?
You could restart.
You
could
restart
if
you'd
like
to.
We've
got
time.
Sorry.
You
can
just
introduce
me
toâ¦
or
whichever
works
for
you.
I
can
go
ahead-
I'll
just
stop
that
I'm
in
the
program
and
then
you
can
introduce
yourself
and
then
I'll
keep
going.
Okay,
sounds
good.
After
I
say
in
the
school
Modeling,
Simulation
& Training
at
UCF
then
you
can
pick
it
up,
okay?
Okay.
By
the
way,
this
will
be,
like
I
said,
post
process,
so
we
can
all
edit
all
of
that
stuff
out.
Okay,
sounds
good.
Okay,
I'm
going
to
go
on
mute
again,
and
once
I
do,
it's
all
you.
Hello.
I'm
Renita
Washburn,
a
PhD
student
in
the
Modeling and
Simulation
program
at
the
University
of
Central
Florida.
And
I'm
Dr.
Mary
Jean Amon.
I'm
an
assistant
professor
at
the
University
of
Central
Florida
in
the
School
of
Modeling,
Simulation,
&
Training.
Today,
we
will
co-
present
our
poster
on
identifying
patterns
in patient
experience
ratings
with
machine
learning
clustering
techniques.
In
this
poster
session,
we'll
summarize
the
objectives,
method,
and
results
from
the
exploration
of
these
patterns
in
patient
experience
ratings.
Patient
experience
ratings
were
obtained
from
the
2019
Consumer
Assessment
of
Healthcare
Providers
and
Systems,
from
here
referred
to
as
CAHPS,
response
data,
and
limit
it
to
patients
seen
by
primary
care
provider.
We
use
JMP's
machine
learning
clustering
and
data
preparation
tools
to
identify
four
patient
groups
based
on
their
survey
responses.
Cluster
analysis
is
a
machine
learning
technique
used
in
many
industries
for
customer
segmentation.
The
goal
is
placing
customers
into
groups
based
on
similarities
within
the
group
and
differences
between
the
groups.
In
healthcare,
exploration
of
customer
segments
provides
insights
on
possible
differences
in
care
journeys
and
experiences,
such
as
disparities
between
race,
gender,
culture,
or
health
status.
Identification
of
distinct
groups
can
inform
the
design
of
tailored
care
delivery
models.
The
project's
three
objectives
were
to,
first,
conduct
a
hierarchical
cluster
analysis
on
categorical
survey
response
data;
second,
identify
clusters
through
visual
inspection
of
dendrogram
and
color
map
partitions
based
on
their
journeys,
which
was
measured
by
survey
questions
related
to
link
the
relationship
with
the
provider,
utilization
of
services,
and
level
of
care
management;
and
lastly,
conduct
pos
t-hoc
analyses
to
explore
differences
among
clusters
in
their
ratings
of the
provider,
their
overall
health,
and
overall
mental
health.
Before
we
dive
into
details
of
the
methods
and
findings,
we'd
like
to
acknowledge
the
US
Agency
for
Healthcare
Research
and
Quality
and Westat
for
providing
the
identified
C
AHPS data
for
this
effort.
The
CAHPS
data
is
used
to
gain
insight
into
the
healthcare
experience
from
the
patient's
perspective.
The
12 selected
questions
are
intended
to
capture
a
patient's
journey
and
interaction
with
your
primary
care
provider
over
the
last
six
months.
The
questions
again
focus
on
length
of
relationship
with
the
physician,
how
the
patient
interacts
with
the
physician's
office
for
routine
and
urgent
care
needs,
and
the
level
of
care
coordination
for
ancillary
services
requested.
Prior
to
initiating
JMP's
clustering
tool,
data
preparation,
including
assigning
the
appropriate
data
modeling
type
for
the
survey
questions.
The
data
modeling
type
was
either
nominal,
yes-
no
or
not
applicable,
or
null,
a
[inaudible 00:03:43]
scale.
Another
data
preparation
task
was
reformatting
of
select
questions.
The
CAHPS
survey
use
this
scale logic.
For
example,
one
question
asked
in
the
last
six
months,
did
you
make
any
appointments
for
a
checkup
or
routine
care
with
this
provider?
If
no,
skip
to next
question.
It
was
determined
that
the
skip
questions
were
relevant
to
the
exploratory
analysis.
Therefore,
values
are
recorded
from
missing
to
zero,
which
JMP
refers
to
missing
not
at
random.
The
last
step
of
preparations
that
we
highlight
is
related
to
the
missing
values.
Instead
of
addressing
this
prior
to
modeling,
we
use
JMP's
built-in
missing
value
feature
to
impute,
to
replace
with
estimates
those
missing
values.
This
is
an
option
selected
from
the
clustering
menu.
Given
the
Likert
scale
questions,
we
hypothesize
that
the
data
was
hierarchical
with
likely
subgroups
between
the
data.
Hierarchical
clustering
with
the
ward
distance
method
was
applied,
and
the
output
was
limited
to
four
clusters
for
ease
of
interpretation.
The
ward
method
was
appropriate
for
the
categorical
data
as
it
did
not
require
pure
measure
of
distance.
Instead,
it
builds
clusters
based
on
an
analysis
of
variants
like
in
Innova.
A
color
map
was
added
to
the
dendrogram
output
to
aid
visual
comparison
on
response
differences
across
the
groups.
Unique
patterns
within
and
differences
between
clusters
were
summarized
based
on
low,
medium,
or
high
maintenance.
Meaning
how
much
access
to
care
was
used
by
the
patient
such
as
frequency
of
routine
and
urgent
office
visits
or
contacting
the
office
during
or
outside
of
regular
hours,
as
well
as
how
well
patients
believe
the
office
was
managing
their
care,
which
was
weak,
higher,
sufficient,
defined
by
ratings
and
follow-
up
for
lab
and
prescription
needs.
The
cluster
output
was
saved
and
assigned
to
each
response
for
the
ad
hoc
analysis.
Now,
the
primary
focus
of
the
project
was
comparing
clusters
on
three
key
ratings
related
to
the
provider,
their
overall
health,
and
overall
mental
health.
However,
with
JMP,
you
can
use
the
cluster
assignments
to
explore
the
distribution
of
demographic
data
as
well
as
other
question
responses
between
the
groups.
For
future
analysis,
we
recommend
exploring
differences
in
age
or
race
distributions
between
the
maintenance
management-
based
clusters.
We
were
interested
in
understanding
if
there
is
a
relationship
between
the
cluster
assignment
and
the
patient's
ratings
of
the
provider,
their
overall
health,
and
mental
health.
Visual
inspection
of
the
mean
scores
for
each
of
these
three
variables
suggested
that
there
may
be
significant
differences
based
on
cluster.
For
example,
high
health
maintenance
patients
who
utilize
more
healthcare
services
but
also
have
satisfactory
ratings
for
lab
and
prescription
management
also
appear
to
have
higher
ratings
of
overall
health
and
mental
health.
If
we
go
to
the
next
slide,
these
observations
were
further
examined
using
JMP's
contingency
analysis,
which
is
a
method
for
examining
the
relationship
between
two
categorical
variables.
We
identify
statistically
significant
differences
in
provider,
overall
health,
and
mental
health
ratings
based
on
the
patient
cluster,
which
further
highlights
the
utility
of
our
clustering
approach
in
identifying
meaningful
patient
groups.
Overall,
understanding
the
relationship
between
each
group's
care
journey
and
overall
experience
and
health
ratings
can
inform
the
design
of
health
care
practices
such
as
enhanced
communication
channels
during
non-
regular
office
hours
or
care
navigation
services
to
aid
with
follow-
up
of
lab
and
prescription
management.
Thank
you
for
viewing
today's
session.
We
welcome
your
questions
and
comments.
