Known for leading large-scale healthcare improvement using data and analytics to drive positive change, Dr. Charles Macias speaks to creating greater value in the EMR through analytics. This approach has done more to increase value than many other cost-reduction efforts. In this webinar you will 1) Explore each component of the value equation, 2) learn how TCH has increased the value of its healthcare using data to drive quality an ever more important need of those facing capitated or value–based care reimbursements and 3) consider a new ROI equation for systems who have invested heavily in their EMRs

1. Adding
Value
to
the
EMR:
A
Clinical
Perspec9ve
Texas
Children’s
Hospital
Charles G. Macias M.D., M.P.H.

2. Poll
Ques9on
#1
What
is
your
primary
area
of
focus?
q Physician/clinical
care
provider
q Quality
q Informa9on
systems
q Finance
q Administra9ve
execu9ve
q Other
2

3. Objec9ves
• Describe
the
power
of
pairing
an
EDW
with
an
EMR
to
realize
care
improvement,
subsequent
waste
reduc9on
and
cost
savings.
• Understand
early
results
of
TCH’s
cultural
shi:
to
focus
on
value
and
the
link
between
quality
and
cost.
• Discuss
how
TCH’s
focus
on
linking
clinical
science
and
payment
models
and
opera9on
science
have
driven
financial
stewardship
and
early
successes
in
popula9on
health
management.

4. The
Healthcare
Value
Equa9on
Quality
Value
=
Cost
• In
an
environment
where
cost
is
marginally
increasing,
healthcare
must
markedly
improve
quality.
• Adop9on
of
EMRs
and
clinical
systems
should
help
push
the
quality
agenda
but
alone
may
not
be
enough
to
deliver
data
intelligence.

5. Quality?
Access
to
Care
and
Care
CoordinaBon

6. Best
Prac9ces
Do
Exist
Best
Care
at
Lower
Cost,
IOM
2013
Report
• The
best
examples
come
from
communiBes
not
policymakers,
and
they
inevitably
involve
pa9ents,
doctors,
nurses
and
other
providers
working
together.
– Donald
Berwick,
former
administrator
of
the
Centers
for
Medicare
and
Medicaid
Services
during
the
session
en9tled,
“Controlling
health
care
costs
while
improving
quality.”
– Healthcare
project
in
Alaska,
where
team-­‐based
care
has
resulted
in
50
percent
fewer
hospital
bed
days,
53
percent
fewer
emergency
department
admissions
and
65
percent
fewer
specialty
visits.
• By
one
es9mate,
roughly
75,000
deaths
might
have
been
averted
in
2005
if
every
state
had
delivered
care
at
the
quality
level
of
the
best
performing
state.
• While
some
hospitals
in
southwestern
Pennsylvania
were
paid
an
average
of
$18,000
to
perform
heart
bypass
surgery,
others
were
paid
as
much
as
$35,000
for
the
same
procedure.
Similarly,
payments
for
heart
valve
surgery
ranged
from
a
low
of
$24,000
to
a
high
of
$54,000.
– Moreover,
the
lowest
priced
hospitals
had
lower
mortality
and
readmission
rates
(i.e.,
beber
quality)
than
the
highest-­‐priced
hospitals

7. Poll
Ques9on
#2
• How
concerned
are
you
about
realizing
ROI
on
your
EMR
investment?
A
–
Very
concerned
B
–
Somewhat
concerned
C
–
Neutral
D
–
Slightly
concerned
E
–
Not
concerned

8. ROI
on
EHRs
Proves
Difficult
In
Second
Look,
Few
Savings
from
Digital
Health
Records
New
York
Times:
January
10,
2013
2005
RAND
report
forecasts
$81
billion
annual
U.S.
savings.
“Seven
years
later
the
empirical
data
on
the
technology’s
impact
on
health
care
efficiency
and
safety
are
mixed,
and
annual
health
care
expenditures
in
the
United
States
have
grown
by
$800
billion.”
In
our
view,
the
disappoin9ng
performance
of
health
IT
to
date
can
be
largely
abributed
to
several
factors:
• Sluggish
adopBon
of
health
IT
systems,
coupled
with
the
choice
of
systems
that
are
neither
interoperable
nor
easy
to
use;
• The
failure
of
health
care
providers
and
ins9tu9ons
to
reengineer
care
processes
to
reap
the
full
benefits
of
health
IT.
EHRs,
Red
Tape
Eroding
Physician
Job
SaBsfacBon
Most
physicians,
however,
expressed
deep
frustra9on
with
costly
and
overly
complicated
EHRs
that
have
fallen
far
short
of
their
promise
to
improve
prac9ce
efficiency.
Twenty
percent
want
to
return
to
paper.
-­‐A
tension
between
figh9ng
to
improve
the
EMR
and
spending
late
nights
catching
up
on
data
entry

9.
About
Texas
Children’s
Hospital
So
how
does
the
paBent
relate
to
healthcare
expenditures?
•
•
•
Houston-­‐based
and
na9onally
renowned
for
providing
top-­‐notch
pediatric
and
women’s
care
Provides
a
full
con9nuum
of
services
Commibed
to
developing
clinical
effec9veness
guidelines
to
deliver
the
highest
quality
care
possible
Sta9s9cs
Number
of
Beds
469
Annual
Inpa9ent
Admissions
21,744
Annual
Outpa9ent
Visits
1.44
million
Emergency
Room
Visits
82,049
Inpa9ent
Surgeries
8,655
Outpa9ent
Surgeries
14,439

10. Pareto
80/20
Principle
in
Healthcare

11. Asthma
Affects ~7M
children in the US,
~80,000 in
Houston (mostcommon chronic
disease of
children)
Acute asthma
accounted for
approximately
~3,000 ED visits
and ~800 hospital
admissions in 2011
at TCH
National asthma
practice guidelines have
been available since
1991 (updated 2007),
yet hospitalizations and
ED visits have not
decreased

12. Severity
Adjusted
Varia9on

13. Correla9on
Between
Costs
and
High
Quality
Care
Is
Low
• Describing
varia9on
in
care
in
three
pediatric
diseases:
gastroenteri9s,
asthma,
simple
febrile
seizure
– Pediatric
Health
Informa9on
System
database
(for
data
from
21
member
hospitals)
– Two
quality-­‐of-­‐care
metrics
measured
for
each
disease
process
– Wide
varia9ons
in
prac9ce
– Increased
costs
were
NOT
associated
with
lower
admission
rates
or
3-­‐
day
ED
revisit
rates
• Implica9ons?
– Op9mal
care
may
be
delivered
at
a
lower
cost
than
today’s
care!
Kharbanda
AB,
Hall
M,
Shah
SS,
Freedman
SB,
Mistry
RD,
Macias
CG,
Bonsu
B,
Dayan
PS,
Alessandrini
EA,
Neuman
MI.
Varia9on
in
resource
u9liza9on
across
a
na9onal
sample
of
pediatric
emergency
departments.
J
Pediatr.
2013

14. Higher
Quality
Is
Ooen
Lower
Cost
• A
Modern
Healthcare
analysis
found
that
in
seven
of
12
ci9es
examined,
the
hospital
with
the
lower
average
cost
for
inpa9ent
and
outpa9ent
Percutaneous
Coronary
Interven9on
procedures
also
had
a
lower
readmission
rate
for
PCI
pa9ents.
hbp://www.modernhealthcare.com/ar9cle/20131026/MAGAZINE/310269941#

15. Consumer
Care/Cost
Uncertainty
• Consumers:
– Trust
their
physicians
– Hope
for
the
best
– Struggle
to
understand
cost
and
care
– Don’t
ooen
know
what
they
are
geqng
– Don’t
always
get
great
outcomes
• Value
is
what
they
want

16. Challenge
of
Healthcare
• Physicians
are:
– Driven
by
science
and
key
values
– Overwhelmed
with
medical
literature
– Not
well
trained
to
turn
that
experience
into
high
quality
pa9ent
outcomes
• Transparency
of
local
data
is
part
of
the
solu9on!
Image
Source:
hbp://www.hopkinschildrens.org/pediatric-­‐
residency.aspx

17. Poll
Ques9on
#3
• For
non-­‐clinical
abendees
or
non-­‐prac9cing
physicians
in
abendance,
during
what
percentage
of
pa9ent
visits
are
your
physicians
talking
about
cost
and
care
tradeoffs?
A
–
80-­‐100%
B
–
60-­‐79%
C
–
40-­‐59%
D
–
20-­‐39%
E
–
00-­‐19%

18. Poll
Ques9on
#4
• For
prac9cing
physicians
in
abendance,
during
what
percentage
of
pa9ent
visits
are
physicians
in
your
organiza9on
talking
about
cost
and
care
tradeoffs?
A
–
80-­‐100%
B
–
60-­‐79%
C
–
40-­‐59%
D
–
20-­‐39%
E
–
00-­‐19%

19.
Evidence to expertise
Physicians
and
Care
Cost
Patient
values and
preferences
Evidence
Clinical
Clinical
Decision
Expertise
Resource
issues
Physician
preferences
Source: SAEM. Evidence Based Medicine Online Course 2005

20. The
New
Healthcare
Once
taboo,
physicians
should
take
cost
into
consideraBon:
Without
money
.
.
.
there
is
no
mission.
there
is
no
expansion.
there
is
no
innova9on.
there
is
no
healthcare.
And
so
providers
must
.
.
.
understand
what
creates
improvements
understand
the
story
that
their
data
tells.
Data
linked
to
systems
of
care
can
drive
quality
iniBaBves!

21. TCH’s
Clinical
Integra9on
Strategy
• Build
a
comprehensive,
integrated
and
evidence-­‐based
quality
and
safety
program
resul9ng
in
measurable
improvements
in
processes
and
quality
care.
• Collect
and
meaningfully
use
data
that
provides
informa9on
about
clinical
outcomes
and
opera9onal
processes.
• Implement
an
enterprise-­‐wide
data-­‐management
infrastructure
that
will
leverage
the
clinical
systems;
star9ng
with
Epic
and
financial
informa9on
in
order
to
provide
easy-­‐to-­‐access,
meaningful
and
relevant
data
to
assist
in
accelera9ng
improvements
in
clinical
and
opera9onal
processes.

22. TCH’s
EDW
Architecture
Metadata: EDW Atlas Security and Auditing
Common, Linkable
Vocabulary
FINANCIAL
SOURCES
(e.g.
EPSi,)
Financial
Source Marts
Administrative
Source Marts
ADMINISTRATIVE
SOURCES
(e.g.
API
Time
Tracking)
EMR
Source Marts
•
•
•
•
•
•
•
Copyright
©
HealthCatalyst
2013
DEPARTMENTAL
SOURCES
(e.g.
Sunquest
Labs)
Departmental
Source Marts
Clinical
Asthma
Appendectomy
Deliveries
Pneumonia
Diabetes
Surgery
+
others
Operations
• Labor
produc9vity
• Radiology
• Prac9ce
Mgmt
• Financials
• Pa9ent
Sa9sfac9on
• +
others
Patient
Source Marts
PATIENT
SATISFACTION
SOURCES
(e.g.
NRC
Picker,
HR
Source Mart
Human
Resources
(e.g.
PeopleSoo)
EMR
SOURCE
(e.g.
Epic)
More Transformation
Less Transformation

23. How
TCH
Defines
Quality
1. Ins9tute
of
Medicine
domains:
•
•
•
•
•
•
3.
Safe
Effec9ve
Efficient
Timely
Pa9ent
centered
Equitable
2. Importance
of
minimizing
unintended
varia9on
in
health
care
delivery
The
degree
to
which
health
services
for
individuals
and
popula9ons
increase
the
likelihood
of
desired
health
outcomes
and
are
consistent
with
current
professional
knowledge.
–
Lohr,
K.N.,
&
Schroeder,
S.A.
(1990).
A
strategy
for
quality
assurance
in
Medicare.
New
England
Journal
of
Medicine,
322
(10):707-­‐712.
4.
Systema9c
infusion
of
evidence
into
a
system
that
integrates
opera9onal
improvement
and
data
transforma9on

24. Approach
to
Improving
Processes
of
Care
• Organizing
permanent,
integrated
workgroup
teams
consis9ng
of
physicians,
nurses,
IT,
quality
and
pa9ent
safety,
quality
improvement,
clinicians,
and
business
analysts
that
are
responsible
for
a
clinical
program
or
clinical
services
over
the
long-­‐term.
• Integra9ng
cri9cal
elements
of
evidence-­‐based
pracBces
into
the
delivery
of
care.
• Establishing
baseline
measures,
AIM
statements
with
measurable
goals
and
on-­‐going
review
of
results
versus
targets.
Outcome
and
balance
metrics
are
included.

25. Quality
&
Clinical
Evidence-­‐Based
Team
Clinical Program
MD Lead
RN Lead
#5 Care
Process
MD Lead
RN Lead
#4 Care
Process
Knowledge Data Architect
Manager
(Analysis)
MD Lead
RN Lead
#3 Care
Process
MD Lead
RN Lead
#2 Care
Process
MD Lead
RN Lead Operations
Director
Clinical
Director
Domain
MD Lead
#1 Care
Process
Data Architect
(Visualization and Infrastructure)
Application Service
Owner
= Subject Matter Expert
= Data Capture
= Data Provisioning
Copyright
©
HealthCatalyst
2013
= Data Analysis

26. DATA
DRIVES
WASTE
REDUCTION

27. Alterna9ve
Approaches
to
Waste
Reduc9on
1.96 std
Mean
# of
Cases
1 box = 100
cases in a year
Excellent Outcomes
# of
Cases
Poor Outcomes
Excellent Outcomes
Poor Outcomes
Option 1: Focus on Outliers – the prescriptive approach
Strategy Identify extreme cases with the potential for high
costs from bad outcomes and eliminate the unfavorable tail of
the curve (“executive dashboard” approach)
Result If the outlier trim point is set at 1.96 standard
deviations, only 2.5% of cases fall under the adverse outcome
tail, so the impact is minimal
27

28. Alterna9ve
Approaches
to
Waste
Reduc9on
Mean
# of
Cases
1 box = 100
cases in a year
Excellent Outcomes
# of
Cases
Poor Outcomes
Excellent Outcomes
Poor Outcomes
Option 2: Focus On Inliers – improving quality outcomes across the majority
Strategy Identify best practices through research and analytics and develop
guidelines and protocols to reduce inlier variation
Result Shifting the cases that lie above the mean toward the excellent end of the
spectrum produces a much more significant impact
28

29. Improving
Cost
Structure
Through
Waste
Reduc9on
Ordering Waste
Workflow Waste
Defect Waste
Ordering of tests that are
neither diagnostic nor
contributory
Variation in Emergency
Care wait time
ADEs, transfusion
reactions, pressure ulcers,
HAIs, VTE, falls, wrong
surgery
29

30. Use Cases and Business Drivers
Care Redesign
Care Redesign Methodology
CXR utilization in
patients with known
asthma, steroids in
bronchiolitis
Quicker steroid delivery for
status asthmaticus, goal
directed therapy for septic
shock
Evidence
Supports
Evidence equivocal
Hypertonic saline and
bronchodilators in select
patients with bronchiolitis
Evidence against
30

31. Cost
Per
Case
and
Case
Volumes
31

32. Asthma: Care Process Team Cohort, Percentage of Chest X-rays Ordered*
Asthma: Care Process Team Cohort,- Apr. 2013) Chest X-rays Ordered*
(Oct. 2010 Percentage of
80%
80%
70%
60%
51%
51%
50% 50%
Percentage
40% 40%
35% 35%
30% 30%
Feedback of rates to hospitalists
and Emergency Center clinicians
20% 20%
Feedback of rates to hospitalists
and Emergency Center clinicians
10% 10%
Order set set
Order
revisions
revisions
Month year
Apr. 13
Apr. 2013
Apr. 13
Mar. 2013
Mar.
Feb. 1313
Feb. 2013
Feb.
Jan. 1313
Jan. 12
Jan. 13
Dec. 2013
Nov. 2012
Dec. 12
Dec. 12
Oct. 1212
Nov. 2012
Nov.
Sep. 2012
Sep. 12
Sep. 2012
12
Oct. 12
Oct.
Aug. 2012
Aug. 12
Aug. 12
Jul. 2012
Jul. 12
Jul. 12
Jun. 2012
Jun. 12
Jun. 12
May. 2012
May. 12
May. 12
Apr. 2012
Apr. 12
Apr. 12
Mar. 2012
Mar. 12
Mar. 12
Feb. 12
Feb. 12
Feb. 2012
Jan. 12
Jan. 12
Jan. 2012
Dec. 11
Dec. 2011
11
Nov. 2011
Nov. 11
Nov. 11
Oct. 2011
Oct. 11
Oct. 11
Sep. 2011
11
Sep. 11
Sep.
Aug. 2011
Aug. 11
Jul. 2011
Jul. 11
Jul. 11
Jun. 2011
Jun. 11
Jun. 11
May. 2011
May. 11
Apr. 2011
Apr. 11
May. 11
Mar. 2011
Mar. 11
Apr. 11
Feb. 2011
Feb. 11
Mar. 11
Jan. 11
Jan. 11
Feb. 2011
Dec. 2010
Dec.
Jan. 1110
Nov. 2010
Nov.
Dec. 1010
Nov. 2010
Oct. 10
Oct. 10
0%
Oct. 10
0%
Aug. 11
Percentage
60%
(Oct. 2010 - Apr. 2013)
Mar. 13
70%
Month year
* Inpatient, Emergency Center (EC) and observation patients (Care Process Team cohort), P-Chart based upon EDW data extraction of 5/14/2013 (M& W).
* Inpatient, Emergency Center (EC) and observation patients (Care Process Team cohort), P-Chart based upon EDW data extraction of 5/14/2013 (M& W).

33. Improving
Cost
Structure
Through
Waste
Reduc9on
Ordering Waste
Ordering of tests that are
neither diagnostic nor
contributory
Workflow Waste
Defect Waste
Variation in Emergency
Variation in OR room
ADEs, transfusion
turnover (cycletime or
Care wait time)
reactions, pressure ulcers,
Emergency Care wait time
HAIs, VTE, falls, wrong
surgery
33

34. Flow chart of a patient with acute gastroenteritis through the TCH Emergency
Department: Existing process
BEGIN
Patient discharged
home1
4
Does patient
have vomiting &/
or diarrhea
Patient
presents to
Emergency
Dept (ED).
3
Patient transferred
to inpatient bed2
Evaluate per
clinical symptoms
Fellow/
Attending
does pretransfer check
PCA checks
vital signs
Patient
registers
Triage nurse does the following:
·∙
Vitals
What is the
patient’s level of
dehydration?
Patient
waiting
Key:
___ solid arrow indicates “yes”
_ _ broken arrow indicates “no”
1 Outcome: Time in ED
2 Outcome: Time to inpatient bed
3 Outcome: Length of stay (LOS)
4 Outcome: Revisit from ED discharge
4 Outcome: Revisit from inpatient discharge
Patient
evaluated by
triage nurse
Nurse-Nurse
checkout
occurs
Nurse
discharges
patient
Bed approved
PCA checks
vital signs
ED secretary
requests bed
Mild or
Moderate
dehydration
Severe
dehydration
MD does
discharge
orders
MD does
admission
orders
Is the patient
vomiting?
Put patient in
ED room
Decision to
discharge
patient
Triage nurse does the following:
·∙
Give Zofran
·∙
Provide gatorade/pedialyte
Decision to
admit patient
Triage nurse does the following:
·∙
Nothing or give patient gatorade/
pedialyte
Is the patient ok
for discharge?
Follow TCH AGE
clinical algorithm
Patient
waiting
Patient put in
ED room
Patient
evaluated by
nurse
Patient
evaluated by
Medical
student
Patient
evaluated by
ED resident
Patient
evaluated by
ED fellow
Patient
evaluated by
ED attending
Process map before EBG
Modified: 7/21/2009

35. Flow chart of a patient with acute gastroenteritis through the TCH Emergency Deparment
BEGIN
4
Does patient
have vomiting &/
or diarrhea
Patient
presents to
Emergency
Dept (ED).
Patient discharged
home1
3
Patient transferred
to inpatient bed2
Evaluate per
clinical symptoms
Fellow/
Attending
does pretransfer check
PCA checks
vital signs
Patient
registers
Key:
___ solid arrow indicates “yes”
_ _ broken arrow indicates “no”
Triage nurse does the following:
·∙
Vitals
·∙
Assess dehydration (Gorelick score)**
** New process
1
Outcome: Time in ED
2
Outcome: Time to inpatient bed
3 Outcome: Length of stay (LOS)
4 Outcome: Revisit from ED
discharge
4 Outcome: Revisit from inpatient
discharge
What is the
patient’s level of
dehydration?
Patient
waiting
Patient
evaluated by
triage nurse
Nurse-Nurse
checkout
occurs
Nurse
discharges
patient
Collect ORT
tracking sheet
PCA checks
vital signs
ED secretary
requests bed
Mild or
Moderate
dehydration
Severe
dehydration
MD does
discharge
orders
MD does
admission
orders
Is the patient
vomiting?
Put patient in
ED room
Follow TCH AGE
clinical algorithm
Triage nurse does the following:
·∙
Give Zofran
·∙
Provide patient education on ORT
·∙
Initiate ORT
·∙
Give ORT tracking sheet**
Patient
waiting
Patient put in
ED room
Triage nurse does the following:
·∙
Provide patient education on ORT
·∙
Initiate ORT
·∙
Give ORT tracking sheet**
Patient
evaluated by
nurse
Bedside nurse does the following:
·∙
Assesses dehydration (Gorelick score)**
·∙
Monitors progress on ORT tracking sheet**
·∙
Reemphasizes patient education on ORT
Patient
evaluated by
Medical
student
Bed approved
Decision to
discharge
patient
Decision to
admit patient
Is the patient ok
for discharge?
Patient
evaluated by
ED resident
Patient
evaluated by
ED fellow
ED Fellow does the following:
·∙
Assesses dehydration (Gorelick score)**
·∙
Monitors progress on ORT tracking sheet**
·∙
Reemphasizes patient education on ORT
·∙
Determines patient disposition
Patient
evaluated by
ED attending
Process map after EBG
Modified: 5/9/2009

36. Improving
Cost
Structure
Through
Waste
Reduc9on
Ordering Waste
Workflow Waste
Defect Waste
Ordering of tests that are
neither diagnostic nor
contributory
Variation in Emergency
Care wait time
ADEs, transfusion
reactions, pressure ulcers,
HAIs, VTE, falls, wrong
surgery
36

37. CClinical Decision Support to
Minimize Errors
SStreamlining and Improving
Processes and Operations to
Minimize Errors
*used
by
permission
of
BMJ
Group
37

38. Shioing
Quality
Improvement
Culture
to
Effec9veness
and
Efficiency
• Stewardship
responsibility
• TCH
financial
APR-­‐DRG
calculator
– Capitated
model
of
care
– Cash
value
of
waste

41. Registry
Financial
Score
Card

42. Examples
Demonstra9ng
ROI
• Improved
clinical
care
– Decreases
in
LOS
– Decrease
in
readmission
rates
– Decreased
unnecessary
chest
x-­‐ray
u9liza9on
– Millions
in
savings
across
several
disease
processes
• Reducing
waste
by
systemi9zing
repor9ng
– EDW
reports
cost
70%
less
to
build
• Labor
produc9vity
tools
allow
global
views
for
increased
opera9onal
efficiency

43. Popula9on
Management
Goal: Drive
value
across
a
system
resul9ng
in
a
healthier
popula9on
Popula9on:
Women
and
Children
enterprise-­‐wide
data
management
infrastructure
Claims
data
Clinic
systems
Epic
Pharmacy/Lab
Health
Plan
Pediatric
Hospital/
Sub-­‐Specialty
Clinics
Texas
Children’s
Prac9ces
&
Clinics
Women’s
Pavilion

44. The
Healthcare
Value
Equa9on
Quality
Value
=
Cost
• Recognizing
the
investment
in
the
EMR
and
opportuni9es
for
linkages
to
decision
support
• Using
the
EDW
to
link
science,
opera9ons
and
data
management
to
drive/accelerate
rapid
cycle
process
improvement
• Understanding
and
driving
the
importance
of
financial
stewardship
• Driving
value
through
higher
quality
of
care
delivery

45. Ques9ons
and
Answers
Speaker
Contact
Info
Charles
G.
Macias
MD,
MPH
cgmacias@texaschildrens.org
832-­‐824-­‐5416
Next
Webinar:
Changing
Healthcare
Using
Data
North
Memorial
CMO
Nov.
13,
2013
1-­‐2
pm
ET
45