As the Chief Medical Officer of North Memorial Health Care, Dr. Kevin Croston’s ultimate objective is to improve healthcare by driving variation out and improving cost efficiencies at North Memorial Healthcare. Core to his success has been a fundamental culture shift with physicians who are now using data to drive care optimization. During this webinar, you’ll learn: 1) how to shift to a data-driven decision making culture, 2) how to make the data meaningful so providers can make better decisions, and 3) examples of successes and challenges, including how North Memorial has reduced unnecessary pre-39 week inductions, improved cardiovascular care and uncovered a substantial revenue cycle process issue.

1. Changing
Healthcare
Using
Data:
A
Case
Study
of
One
Small
Health
System's
Odyssey
To
Achieve
Material
Improvements
North
Memorial
Health
Care
J
Kevin
Croston,
MD
FACS
CMO, President
-­‐
Physician
OrganizaEon

2. Poll
QuesEon
#1
What
is
your
primary
area
of
focus?
q Physician/clinical
care
provider
q Quality
q InformaEon
system
q Finance
q AdministraEve
execuEve
q Other
2

3. ObjecEves
You
will
learn:
– How
to
shiQ
to
a
data-­‐driven
decision
making
culture
• KPA
– How
to
make
the
data
meaningful
so
providers
can
make
beTer
decisions
• Permanent
processes
and
teams
– Examples
of
successes
and
challenges
• Pregnancy
–
ReducEon
of
pre
39-­‐week
unnecessary
inducEons
• Cardiovascular
care
• Revenue
cycle
process
–
professional
billing
• Catheter
associated
urinary
tract
infecEons
(CAUTI)

4. About
North
Memorial
• Minneapolis-­‐based
two-­‐
hospital
health
system
• Provides
full
conEnuum
of
services
• Level
I
Trauma
Center
• CommiTed
to
developing
clinical
effecEveness
guidelines
to
deliver
the
highest
quality
care
at
a
lower
cost
StaEsEcs
(2012)
Number
of
Licensed
Beds
648
Annual
InpaEent
Admissions
33,718
(includes
nursery
4,852)
Emergency
Room
Visits
87,684
InpaEent
Surgeries
8,722
OutpaEent
Surgeries
19,181
Providers
in
MulE-­‐
Specialty
Clinics
300
Total
FTEs
4,281

5. North
Memorial
SituaEon
Challenges
• Tough
regional
compeEtors
• Declining
payment
stream
• Data
created
confusion
“data
rich
-­‐
informa/on
poor”
• Clinicians
and
execuEves
clamoring
for
answers
• Hospital-­‐centric
decisions
(not
enterprise
based)
Opportuni@es
• Strong
improvement
and
quality
culture
• Insighiul
and
supporEve
leadership
• Recognized
substanEal
changes
were
required
for
survival

6. Key
Process
Analysis
(KPA)

7. KPA
Results

8. North
Memorial
Resources
Consumed
Key
Findings:
50%
of
all
in-­‐pa@ent
resources
are
represented
by
7
Care
Process
Family
•
80%
of
all
in-­‐pa@ent
resources
are
represented
by
18
Care
Process
Family
•
80%
CumulaEve
%
50%
%
of
Total
Resources
Consumed
for
each
clinical
work
process
Number
of
Care
Process
Family
(e.g.,
ischemic
heart
disease,
pregnancy,
bowel
disorders,
spine,
heart
failure)

9. Poll
QuesEon
#2
What
percent
of
your
quality
improvement
efforts
are
priori@zed
using
a
similar
varia@on/
resources
analysis?
q 76-­‐100%
q 51-­‐75%
q 26-­‐50%
q 0-­‐25%
q Unsure
9

10. How
North
Made
Data
Meaningful
People
• Formed
permanent
teams
– Clinical
OperaEons
Leadership
Team
(COLT)
– Guidance
Teams
(ex.
Women
&
Newborn,
Primary
Care,
Cardiovascular,
OPPE,
InfecEous
Disease)
• Repurposed
resources
without
adding
FTEs
• Selected
medical
leadership
to
champion
the
vision
and
process
Processes
• Data
organizaEon
-­‐
EDW
• Data
governance
• OrganizaEonal
team
structure
to
support
outcomes
improvement
processes
• Ensured
hospitals
and
clinics
were
included
in
consistent
change
while
maintaining
autonomy
• ArEculated
the
vision

11. Pregnancy
(OB)
Team
Structure
Care
Process
Model
(CPM)
Core
Work
Group
Physician Lead
Dr. Jon Nielsen
Knowledge Manager
Bethany Hjelle, R.N.
Knowledge Manager
Cathy
Anderson, R.N.
Nurse Expert
Tanya
Thomas, R.N.
Nurse Expert
Maureen
Ehlers, R.N.
Nurse Expert
Sally
Walstrom, R.N.
Clinical
Director
Lead
Linda
Engdahl
R.N.
Nurse Expert
Barb
Pavek , R.N.
Key:
Subject Matter Experts
Quality/
Work Flow Expert
Mike Choi
Data Provisioning
Outcomes Analyst
Ashley Nguyen
Data Architect
Joel
Zwinger
Data Analysis
11

12. Women
&
Children
AnalyEcs

13. Pre-­‐39
Week
ElecEve
InducEons

14. Women
and
Newborn
Pre-­‐39
Week
ElecEve
InducEons
“We
wouldn’t
have
had
a
chance
to
do
some
of
the
things
we’ve
done
in
last
18
months
to
enhance
care,
reduce
waste
and
lower
costs
without
Catalyst.
It’s
amazing
how
differently
and
effec/vely
we
can
gather
and
use
data
now.”
-­‐Jon
Nielsen,
MD,
Medical
Director
Women
and
Children’s
Services
at
North
Memorial
Health
Care
ObjecEve
•
•
•
•
Define
exisEng
workflows
and
idenEfy
improvement
opportuniEes
Establish
baseline
metrics
and
measures
Define
evidence
based
standards
for
elecEve
inducEons
Reduce
rates
of
pre-­‐39
week
deliveries
from
1.2%
to
0.6%
to
qualify
for
a
payer
partner
bonus
Health
Catalyst
SoluEon
•
•
Late-­‐BindingTM
Data
Warehouse
Plaiorm
Cohort
Finder
•
Early
inducEon
advanced
applicaEon
•
•
Key
Process
Analysis
applicaEon
(KPA)
•
Results
to
date
•
CollaboraEve
IT
and
clinical
care
workgroups
•
•
•
Adopted
evidence
based
guidelines
and
standardized
workflows
Established
elecEve
delivery
baseline
measurements
to
track
quality
improvement
gains
Established
a
permanent
collaboraEve
team
Reduced
early-­‐term
deliveries
from
1.2%
to
0.3%
$200K
payer
partner
bonus
payment
14

15. MAJOR
LEARNING:
FOLLOW
THE
PLAN!

16. .
Cardiovascular
Care
Challenges
Lessons
Learned
• Difficulty
replicaEng
first
clinical
program
success
• Department
vs
condiEon-­‐
based
issue
• Difficulty
understanding
importance
of
guidance
teams
• OrganizaEonal
readiness
• Physician
leaders
changed
weekly
• Inspire
knowledge
leadership
and
organizaEonal
readiness
– Include
the
right
people
in
the
development
of
the
care
model
– Know
when
you
should
and
shouldn’t
be
involved
– Require
buy-­‐in
for
the
methodology
– Focus
of
project
did
not
line
up
with
opportuniEes
based
on
KPA
analysis

17. Professional
Billing
ApplicaEon

18.
Professional
Billing
ApplicaEon

19. Professional
Billing
Efforts
“The
Health
Catalyst
Professional
Billing
Applica/on
has
given
me
what
I
need
to
be
successful.
Now
I
can
finally
accomplish
what
I
was
hired
to
do!”
Nancy
Young,
Manager
Professional
Coding,
North
Memorial
Professional
Services
ObjecEve
•
•
•
•
Ensure
accurate
and
complete
charge
capture
of
professional
services
performed
in
the
hospital
Address
physician
concerns
that
charges
were
not
reflecEng
actual
services
rendered
Health
Catalyst
SoluEon
•
Late-­‐BindingTM
Data
Warehouse
Plaiorm
•
Professional
Billing
applicaEon
to
idenEfy
revenue
cycle
and
educaEonal
opportuniEes
Automated
data
capture
for
efficient
and
complete
revenue
cycle
analysis
•
Reduce
manual
data
pulls
by
professional
coders
to
determine
which
provider
notes
to
review
•
Deliver
provider
educaEon
to
improve
clinical
data
capture
•
Starter
set
value
stream
mapping
to
idenEfy
workflow
process
gaps
IntuiEve
applicaEon
for
professional
coders
to
opEmize
workflow
Results
to
date
•
6%
increase
in
billing
for
notes
that
had
sufficient
clinical
data
•
PotenEal
$5.7M
charges
over
3
years
from
unbilled
services
•
25%
improvement
in
professional
coder
efficiency,
allowing
Eme
for
provider
educaEon
•
Health
Catalyst
delivered
results
in
6
weeks
vs.
consulEng
firm
who
was
unable
to
deliver
data
capture
and
applicaEon
19

20. Catheter-­‐Associated
Urinary
Tract
Infec@ons
(CAUTI)
• According
to
the
CDC
urinary
tract
infecEons
(UTIs)
are
the
most
common
type
of
healthcare-­‐
associated
infecEon
• Cause
of
450,000
annual
infecEons
leading
to
13,000
deaths
• Increasing
lengths
of
stay
by
as
many
as
four
days,
and
increasing
healthcare
costs
by
as
much
as
$500
million
per
year
naEonally.
• CMS
has
proposed
expansion
of
CAUTI
measures
beyond
current
ICU
areas
to
include
medical
units,
surgical
unites
and
medical/surgical
units
20

21. CAUTI
ApplicaEon

22. CAUTI
Surveillance
“We’re
extremely
strapped
for
/me
in
the
infec/on
preven/on
world
and
CMS
is
coming
out
with
new
regula/ons
every
year.
The
more
we’re
out
there
preven/ng
–
rather
than
measuring
–
infec/ons,
the
bigger
a
difference
we
can
make,
educa/ng
clinicians
and,
as
a
result,
increasing
pa/ent
safety
and
quality.”
~
Terra
Menier,
R.N.,
Infec/on
Preven/on
Prac//oner
ObjecEve
Health
Catalyst
SoluEon
• Scalable
CAUTI
soluEon
to
meet
proposed
CMS
regulatory
measures
• Leverage
NaEonal
Healthcare
Safety
Network
(NHSN)
definiEons
and
calculaEon
algorithms
• Late-­‐Binding™
Data
Warehouse
• CAUTI
ApplicaEon
• Clinical
Improvement
Services
• Starter
set
to
idenEfy
workflow
process
gaps
• ShiQ
clinical
resources
from
surveillance
to
intervenEon
• Automated
data
capture
for
efficient
hospital
surveillance
Results
to
date
• 50
percent
esEmated
reducEon
in
CAUTI
surveillance
acEviEes
• PotenEal
to
convert
from
manual
to
electronic
tracking
for
NHSN
required
catheter
days
reporEng
• Rapid
Eme
to
value
with
10-­‐
week
implementaEon
• InfecEon
prevenEonists
can
now
focus
on
intervenEon
instead
of
data
provisioning

23. Conclusions
• Spend
a
lot
of
Eme
up
front
with
teams
before
they
start
down
this
quality
improvement
journey.
Working
on
the
fly
comes
with
major
problems.
• Don’t
ignore
the
warning
signs
(Cardiovascular).
• Commit
one
physician
to
the
team.
An
outside
champion
may
try
to
prop
up
a
team.
• SEck
to
the
plan
and
moEvate
people
to
work
together.
• Communicate
successes
and
explain
reasons
for
success.
Hold
on
to
those
principles
rather
than
jumping
to
the
next
“shiny
object.”
• Financial
improvements
do
follow
improvements
in
quality
of
care.

24. Thank
You!
Please
submit
your
QuesEons
and
Answers
24