Recent   federal   legislation   and   regulations   have   resulted   in   an   incentive   program   for   clinics   to   implement   and   meaningfully   use   electronic   health   records   (EHR).   It   is   widely   believed   that   EHRs   can   improve   medical   care   by   providing   more   timely   access   to   a   patient’s   health   information,   facilitating   the   tracking   of   patients   over   time   to   ensure  they  are  receiving  recommended  care,  and  helping  to  support   better  health  care  decisions.  It  is  hoped  that  broader  implementation   of   EHRs   will   help   in   improving   health   care   quality,   safety,   and   efficiency.  

1.
Research
Brief
No
1.
August
2010
Degree
of
EHR
Use
and
Quality
of
Care
Across
MN-­Area
Clinics
By
Rebecca
M
Prenevost,
PhD,
MPH
Data
Recent
federal
legislation
and
regulations
have
resulted
in
an
Minnesota
HealthScores
(www.mnhealthscores.org)
incentive
program
for
clinics
to
implement
and
meaningfully
use
was
used
to
obtain
recent
(published
2010)
quality
metrics
for
vascular
and
diabetes
care
as
well
as
EHR
electronic
health
records
(EHR).
It
is
widely
believed
that
EHRs
can
use
metrics
obtained
from
a
2010
health
information
improve
medical
care
by
providing
more
timely
access
to
a
patient’s
technology
(HIT)
ambulatory
clinic
survey.
health
information,
facilitating
the
tracking
of
patients
over
time
to
Health
and
demographic
characteristics
were
ensure
they
are
receiving
recommended
care,
and
helping
to
support
obtained
from
County
Health
Rankings
(http://www.countyhealthrankings.org).
These
data
better
health
care
decisions.
It
is
hoped
that
broader
implementation
were
linked
to
clinics
using
the
primary
care
service
of
EHRs
will
help
in
improving
health
care
quality,
safety,
and
area
(PCSA)
for
the
clinic’s
zip
code
and
the
county
efficiency.
associated
with
that
PCSA.
Measures
To
date,
there
has
been
limited
study
of
the
relationship
between
EHR
6
diabetes
care
and
5
vascular
care
quality
measures
use
and
healthcare
quality,
and
in
studies
that
have
been
published,
were
assessed,
and
7
health/demographic
variables
the
results
have
been
mixed.1-­‐6
To
help
address
this
knowledge
gap,
were
included
in
the
analysis
to
control
for
this
research
brief
analyzes
two
important
publicly-­‐available
datasets
differences
in
patient
populations
(Appendix
A).
published
by
MN
Community
Measurement
along
with
available
EHR
use
was
grouped
into
3
categories.
The
highest
degree
of
use
indicated
the
EHR
was
being
used
1)
for
county
health
statistics
to
assess
the
relationship
between
healthcare
lab/test
results,
2)
to
track
patient
health
problems
quality
and
EHR
use
among
healthcare
clinics
in
the
MN
area.
and
doctor
orders,
and
3)
to
create
benchmarks.
The
lowest
degree
of
use
indicated
clinics
were
not
yet
Study
Findings
using
an
EHR.
Moderate
use
represented
anything
in
There
were
531
clinics
that
reported
EHR
utilization
information
that
between.
also
reported
on
quality
measures
for
either
diabetes
care
(N=514),
Analyses
Descriptive
analyses
were
used
to
examine
average
vascular
care
(N=424),
or
both.
These
clinics
were
spread
across
99
difference
in
quality
scores
by
the
degree
of
EHR
use.
counties
in
four
states
(IA,
MN,
WI,
and
ND),
but
the
vast
majority
of
Multivariate
regression
analyses
were
used
to
clinics
(N=475)
were
located
in
Minnesota.
confirm
whether
the
differences
were
significant
after
controlling
for
population
characteristics.
The
graphs
below
illustrate
the
differences
in
quality
scores
according
to
the
degree
of
EHR
use.
The
red
lines
represent
the
average
percent
of
patients
meeting
the
quality
measure
in
clinics
that
were
non-­‐users
of
EHR.
The
columns
indicate
the
percentage
points
above
this
non-­‐user
benchmark
for
clinics
that
had
implemented
EHRs.
The
darkest
column
represents
the
clinics
with
the
highest
degree
of
EHR
use,
and
the
lighter
column
represents
clinics
with
moderate
EHR
use.
Graph
1:
Average
Difference
in
Diabetes
Quality
for
EHR
Users
Compared
to
Non-­‐User
Benchmark
www.evidity.org
1

2.
Research
Brief
No
1.
August
2010
Graph
2:
Average
Difference
in
Vascular
Quality
for
EHR
Users
Compared
to
Non-­‐User
Benchmark
Even
after
controlling
for
Table
1:
Regression
Results
Controlling
for
Population
Characteristics
population
characteristics,
Optimal
Diabetes
Care
Optimal
Vascular
Care
most
quality
differences
Coefficient
p-­‐value
Coefficient
p-­‐value
between
EHR
users
and
Highest
Degree
EHR
0.0901
0.000
0.0674
0.000
Moderate
Degree
EHR
0.0637
0.000
0.0527
0.007
non-­‐users
are
statistically
%
Smoking
0.0009
0.644
0.0006
0.768
significant
(see
Appendix
B).
%
Obese
0.0122
0.011
0.0102
0.049
%
Binge
Drinkers
-­‐0.0015
0.456
-­‐0.0021
0.358
The
table
to
the
right
shows
%
Uninsured
-­‐0.0082
0.052
-­‐0.0058
0.219
that
compared
to
clinics
PCP
Rate
-­‐0.0002
0.011
-­‐0.0001
0.183
%
College
0.0044
0.000
0.0055
0.000
that
have
not
yet
%
Unemployed
-­‐0.0082
0.209
-­‐0.0073
0.320
implemented
an
EHR,
an
average
of
9.0%
more
patients
met
all
five
of
the
optimal
diabetes
care
measures
when
seen
at
clinics
that
have
the
highest
degree
of
EHR
use,
and
6.4%
more
met
the
measures
when
seen
at
clinics
that
have
moderate
EHR
use.
Similarly,
an
average
of
6.7%
more
patients
met
all
four
of
the
optimal
vascular
care
measures
when
seen
at
clinics
that
have
the
highest
degree
of
EHR
use,
and
5.3%
more
when
seen
at
clinics
that
have
moderate
EHR
use.
Limitations
There
are
several
limitations
to
consider
when
interpreting
these
results.
First,
the
quality
measures
available
at
the
clinic-­‐level
were
limited
to
2
conditions,
which
represent
only
a
tiny
piece
of
healthcare
quality.
In
addition,
the
control
variables
were
at
the
county-­‐level
and
may
not
accurately
represent
the
actual
patient
populations
obtaining
care
from
the
clinics.
The
analysis
also
does
not
account
for
clinic
characteristics,
such
as
size,
teaching
status,
or
provider
specialties
that
may
affect
quality
scores,
nor
does
it
control
for
selection
bias,
or
the
likelihood
of
a
clinic
with
a
greater
focus
on
quality
to
be
an
early
adopter
of
EHR.
Finally,
these
results
do
not
indicate
whether
the
differences
shown
are
clinically
meaningful.
Specifically,
it
is
unknown
how
differences
in
these
quality
metrics
translate
into
other
downstream
effects,
such
as
fewer
inpatient
stays,
lower
rates
of
complications,
and
reduced
ER
utilization.
Conclusion
Publicly
available
healthcare
quality
and
EHR
utilization
data
show
a
greater
degree
of
EHR
utilization
is
associated
with
higher
quality
scores
for
diabetes
and
vascular
care.
Further
research
should
be
conducted
to
discern
causality
and
determine
whether
other
areas
of
healthcare
quality
have
similar
relationships.
www.evidity.org
2

3.
Research
Brief
No
1.
August
2010
Appendix
A:
Measure
Definitions
Quality
Measure
Definitions
Blood
Pressure:
The
percentage
of
diabetes
patients,
ages
18-­‐
References
75,
who
maintain
blood
pressure
less
than
130/80.
This
1.
Friedberg
MW,
Coltin
KL,
Safran
DG,
Dresser
M,
measure
is
used
for
diabetes
and
vascular
care.
Zaslavsky
AM,
Schneider
EC.
Associations
between
structural
capabilities
of
primary
care
practices
and
LDL:
The
percentage
of
diabetes
patients,
ages
18-­‐75,
who
performance
on
selected
quality
measures.
Ann
Intern
Med.
2009
Oct
6;151(7):456-­‐63.
lower
LDL
or
"bad"
cholesterol
to
less
than
100
mg/dl.
This
2.
Garrido
T,
Jamieson
L,
Zhou
Y,
Wiesenthal
A,
Liang
L.
measure
is
used
for
diabetes
and
vascular
care.
Effect
of
electronic
health
records
in
ambulatory
care:
retrospective,
serial,
cross
sectional
study.
BMJ.
2005
Non-­‐Smoking:
The
percentage
of
diabetes
patients,
ages
18-­‐ Mar
12;330(7491):581.
75,
who
don’t
smoke.
This
measure
is
used
for
diabetes
and
3.
Linder
JA,
Ma
J,
Bates
DW,
Middleton
B,
Stafford
RS.
vascular
care.
Electronic
health
record
use
and
the
quality
of
ambulatory
care
in
the
United
States.
Arch
Intern
Med.
Aspirin:
The
percentage
of
diabetes
patients,
ages
40-­‐75,
who
2007
Jul
9;167(13):1400-­‐5.
4.
Poon
EG,
Wright
A,
Simon
SR,
Jenter
CA,
Kaushal
R,
Volk
take
an
aspirin
daily.
This
measure
is
used
for
diabetes
and
LA,
Cleary
PD,
Singer
JA,
Tumolo
AZ,
Bates
DW.
vascular
care.
Relationship
between
use
of
electronic
health
record
features
and
health
care
quality:
results
of
a
statewide
HbA1c:
The
percentage
of
diabetes
patients,
ages
40-­‐75,
who
survey.
Med
Care.
2010
Mar;48(3):203-­‐9.
control
blood
sugar
so
that
A1c
level
is
less
than
8%.
This
5.
Welch
WP,
Bazarko
D,
Ritten
K,
Burgess
Y,
Harmon
R,
Sandy
LG.
Electronic
health
records
in
four
community
measure
is
used
for
only
diabetes
care.
physician
practices:
impact
on
quality
and
cost
of
care.
J
Am
Med
Inform
Assoc.
2007
May-­‐Jun;14(3):320-­‐8.
Optimal
Diabetes
Care:
This
measure
shows
the
“D5”,
or
6.
Zhou
L,
Soran
CS,
Jenter
CA,
Volk
LA,
Orav
EJ,
Bates
DW,
percentage
of
diabetes
patients,
ages
18-­‐75,
who
met
all
5
Simon
SR.
The
relationship
between
electronic
health
individual
diabetes
quality
measures:
blood
pressure,
LDL,
record
use
and
quality
of
care
over
time.
J
Am
Med
Inform
Assoc.
2009
Jul-­‐Aug;16(4):457-­‐64.
HbA1c,
non-­‐smoking,
and
aspirin.
Optimal
Vascular
Care:
This
measure
shows
the
percentage
of
diabetes
patients,
ages
18-­‐75,
who
met
all
4
individual
vascular
care
quality
measures:
blood
pressure,
LDL,
non-­‐smoking,
and
aspirin.
Community
Health
Measure
Definitions
%
Smoking:
Percent
of
adults
that
report
smoking
at
least
100
cigarettes
and
that
they
currently
smoke
as
obtained
by
the
Behavioral
Risk
Factor
Surveillance
Survey
(BRFSS).
%
Obese:
Percent
of
adults
that
report
a
BMI
≥
30
as
obtained
by
BRFSS.
%
Binge
Drinkers:
Percent
of
adults
that
report
binge
drinking
in
the
past
30
days
as
obtained
by
BRFSS.
%
Uninsured:
Percent
of
population
<
age
65
without
health
insurance
as
reported
in
the
Area
Resource
File
(ARF).
PCP
Rate:
Primary
care
provider
rate
per
100Kas
reported
in
the
Area
Resource
File
(ARF).
%
College:
Percent
of
population
age
25+
with
4‑year
college
degree
or
higher
as
obtained
by
the
American
Community
Survey
(ACS).
%
Unemployed:
Percent
of
population
age
16+
unemployed
but
seeking
work
as
reported
by
the
Local
Area
Unemployment
Statistics,
Bureau
of
Labor
Statistics.
www.evidity.org
3

4.
Research
Brief
No
1.
August
2010
Appendix
B:
Regression
Results
Diabetes
Care
Vascular
Care
BP
Coefficient
p-­‐value*
Coefficient
p-­‐value*
Highest
Level
0.1120
0.000
0.0598
0.001
Moderate
Level
0.1004
0.000
0.0663
0.001
%
Smoking
0.0022
0.344
0.0014
0.513
%
Obese
0.0195
0.001
0.0166
0.001
%
Binge
Drinkers
-­‐0.0024
0.324
-­‐0.0035
0.113
%
Uninsured
-­‐0.0059
0.241
-­‐0.0028
0.541
PCP
Rate
-­‐0.0001
0.160
-­‐0.0001
0.171
%
College
0.0065
0.000
0.0058
0.000
%
Unemployed
-­‐0.0049
0.526
0.0006
0.931
LDL
Highest
Level
0.1000
0.000
0.0639
0.000
Moderate
Level
0.0548
0.002
0.0300
0.115
%
Smoking
0.0006
0.760
0.0016
0.455
%
Obese
0.0133
0.006
0.0015
0.772
%
Binge
Drinkers
0.0002
0.915
0.0001
0.971
%
Uninsured
-­‐0.0121
0.004
-­‐0.0049
0.288
PCP
Rate
-­‐0.0001
0.292
0.0001
0.528
%
College
0.0024
0.046
0.0033
0.008
%
Unemployed
-­‐0.0132
0.044
-­‐0.0077
0.285
Non-­‐Smoking
Highest
Level
0.0275
0.006
0.0021
0.860
Moderate
Level
0.0279
0.011
0.0094
0.480
%
Smoking
0.0017
0.181
0.0003
0.842
%
Obese
0.0022
0.462
0.0029
0.403
%
Binge
Drinkers
-­‐0.0015
0.248
-­‐0.0015
0.340
%
Uninsured
0.0019
0.469
0.0027
0.398
PCP
Rate
0.0000
0.793
0.0000
0.867
%
College
0.0013
0.088
0.0027
0.002
%
Unemployed
-­‐0.0217
0.000
-­‐0.0117
0.019
Aspirin
Highest
Level
0.1238
0.000
0.0312
0.006
Moderate
Level
0.0909
0.000
0.0231
0.067
%
Smoking
0.0017
0.391
0.0024
0.089
%
Obese
0.0130
0.008
0.0028
0.409
%
Binge
Drinkers
0.0056
0.008
-­‐0.0001
0.929
%
Uninsured
-­‐0.0124
0.004
-­‐0.0036
0.242
PCP
Rate
-­‐0.0001
0.107
0.0000
0.868
%
College
0.0028
0.020
-­‐0.0009
0.293
%
Unemployed
-­‐0.0076
0.254
-­‐0.0125
0.009
HbA1c
Highest
Level
0.0521
0.000
Moderate
Level
0.0309
0.025
%
Smoking
0.0005
0.736
%
Obese
0.0098
0.010
%
Binge
Drinkers
0.0003
0.838
%
Uninsured
-­‐0.0061
0.067
PCP
Rate
-­‐0.0002
0.019
%
College
0.0018
0.047
%
Unemployed
-­‐0.0007
0.891
*P-­‐Values
of
.05
or
lower
are
considered
statistically
significant,
bolded.
www.evidity.org
4