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Charles G. Macias MD, MPH
Chief Clinical Systems Integration Officer, Texas
Children’s
Director, National EMS for Children Innovation and
Improvement Center
Director, Evidence Based Outcomes Center and
Center for Clinical Effectiveness
Transforming Health Systems
Through Quality Improvement
Respiratory challenge: case illustration
• 14 month old girl with a viral
prodrome but no history of
asthma/prior episode of
breathing difficulty, cough
for one day; eczematous
rash
• Coarse wheezing heard (R)
but crying loudly-responsive
to bronchodilators; RR of 66
What do we really know to inform action that
is highly meaningful for this family?
Contextualizing a definition for quality
IOM Domain
for Quality
Coordination
Safe
Timely
Efficient
Effective
Equitable
Patient
Centere
d
Access
Indicated care for outpatients
Acute medical problems 67.6%
Chronic medical conditions 53.4%
Preventative care 40.7%
Preventative services for adolescents 34.5%
<50%
98,000 deaths per year
“Medicine used to be simple, ineffective,
and relatively safe. Now it is complex,
effective, and potentially dangerous.”
•Sir Cyril Chantler
Chantler BMJ, 1998
Four components for effective improvement
• Appreciation of a system
• Understanding variation
• Psychology/ human involvement
• Theory of knowledgeDeming called the interplay of these
“Profound Knowledge”
Improvement science
Applying knowledge to improvement science
Improvement
Profound
knowledge
Subject matter
expertise
What are the drivers for health care transformation?
Focus on results
not processes
• Set robust outcome goals: cascading metrics
• Be aspirational: 0 Serious Safety Events by 2018
• Report results and progress regularly
• Own and be accountable for results
• Generate analytics, not data reports
Driving transformation: Effective measurement
Measurement:
A reflection of performance
Compliance/regulatory
Financial
Operational
A reflection of the desired status
achieved from clinical care
delivery
Maturity of measurement
STRUCTURE
PROCESS
OUTCOMES
FUNCTIONAL OUTCOMES
Organizational
evolution over time
Context for care delivery: staff, buildings, finance, equipment
Transactions between patients and providers through healthcare delivery
The effects of health care on patients: health, behavior, knowledge, satisfaction
Ability to achieve behaviors or skills that allow the child to achieve everyday goals
A better
health care
product;
greater value
Ex. A clinic is created or a
policy is put in place
Ex. A checklist demonstrates activities are undertaken
Ex. Clinical improvement
in asthma scores
PedsQL survey (eg Better
school experience);
Social integration
post reconstructive
surgery
QUALITY of LIFE
The degree of satisfaction a family has with segments of his/her life
Measurement in the context of health
care transformation to drive QI
STRUCTURE
PROCESS
OUTCOMESFUNCTIONAL OUTCOMES
Organizational
evolution over time
A better
health care
product;
greater value
QUALITY of LIFE
Quality Assurance and compliance to treat a patient
QI to improve outcomes for the patient and family
Integrated strategies to improve the health of the populations we serve through improved systems of care
Measurement in the context of health
care transformation in payment reform
STRUCTURE
PROCESS
OUTCOMES
PAYERS
Organizational
evolution over time
A better
health care
product;
greater value
GOVERNMENTAL
Quality Assurance and compliance to treat a patient
QI to improve outcomes for the patient and family
Integrated strategies to improve the health of the populations we serve through improved systems of care
Fee for service
APR DRG
DSRIP/1115PPEs
Shared Savings Models
CIN’sACO models
Public reporting & accreditation account for ~90% of our metrics
Appendectomy scorecard: selecting
meaningful metrics
KEY IP = Inpatient, PCP = Primary Care Provider, ED = Emergency Department, OR = Operating Room
Measure Site of
Care
Quality Domain
Length of stay following surgery IP Effective, efficient
30-day readmission rate (simple, complex) IP, PCP Effective, safe,
patient centered
Among children with perforated appendicitis
or abdominal painTime from presentation to diagnosis
Time from diagnosis to administration of
antibiotics
Time from diagnosis to OR
ED, IP,
OP, PCP
Timely, efficient
% of patients with a superficial or deep
incisional surgical site infection
OR, IP Safe, effective
Drs Rodriguez, Luerssen, Fallon, Lopez and Nuchtern, K Carberry, M Girotto, E
Donel, et al and the Surgical Outcomes Center
Driving transformation: data reporting to analytics
Transforming data in QI
• Utilizing charts and graphs rather than traditional test statistics
alone
• Allows evaluation of a process over time– Veering away from structure
– Demonstrating sustainability
The value of run charts
Current Process Performance: Isolated Femur Fractures
0
200
400
600
800
1000
1200
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64Sequential Patients
Min
ute
s E
D t
o O
R p
er
Patient
Process Improvement: Isolated Femur Fractures
0
200
400
600
800
1000
1200
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64Sequential Patients
Min
ute
s E
D t
o O
R p
er
Patient
Holding the Gain: Isolated Femur Fractures
0
200
400
600
800
1000
1200
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64Sequential Patients
Min
ute
s E
D t
o O
R p
er
Patient
HC Data Guide, p 86
1. Makes process improvement visible
2. Allows
determination of
whether a change
is an improvement
3. Signals sustainability
of the improvement
Components of the control chart: applying greater rigor
Using data for PI, Provost, 2008
Driving transformation: data versus analytics
Data: quantitative or qualitative values; facts or figures
Information: data made meaningful; provides context for data
Analytics: the discovery, interpretation and communication of
meaningful patterns in data
Maturity of informatics (data to analytics)
Data reporting
Data analytics
Predictive analytics
Prescriptive analytics
Organizational
evolution over time
-EMR clinical reports-Financial reports
-Shortening event to reporting time-Transforming data and translating to meaningful clinical relevance
--Linking likelihood of outcomes to care decisions for populations-Predicting financial outcomes -Linking strategies across former silos in infrastructures
--Integrating best evidence into delivery system infrastructures-EMR based recommendations and alerts-Integrated plans of care across continuums-Utilizing big data bi-directionally
Improved
outcomes
for our
patients
and our
enterprise
How many? Who and where?
Who’s at risk? How do I prevent it in my patient?
Implementation: maximizing EMRs with
evidence based clinical decision support
"Clinical Decision Support systems link health observations with health knowledge to influence health choices by clinicians for improved health care"
Dr. Robert Hayward of the Centre for Health Evidence
• Workflow: eases data entry documentation and
rapid resource location
• Alerting: alerts and reminders that fire to deliver
information and interrupt workflow
• Cognitive: provides a patient management and
planning overviewRichardson AMAI 2010 Proceedings 1427-1431
25
Integration,
alignment &
focus
•Improvement should be embedded in organizational structures
• Focus on systems thinking
Driving transformation: system think
System think?
Leukemia
Pt OON
Pt w F&N
pt of ESL
TCP pt
another
counselor
referral
I’m Melanie Pena
I was in remission from my ALL
I have a fever and I feel horribleWe are TCH the system
We recognize you are in septic
shock. We care for you and your
family and will care for you even
after you go home
Quality, Service and
Safety
Council
Patient and
Family
Experience
Executive
Steering Team
Quality, Service and
Safety Committee ofthe Board
Texas Children’s Health plan Quality
Texas Children’s Pediatrics Quality
Content and
Analytics Team
Quality and Operations
Council: Main
Quality and Operations
Council: West Campus
Quality and Operations
Council: PFW
Clinical Implementation
Leadership Team
Quality, Service and Safety
Governance Structure
Clinical EMR Operations
Council
AmbulatorySurgery Team
Inpatient Team
Medical Practice
Team
Outpatient Team
NursingQuality (NCC)
SurgeryQuality Council
CSI Executive
Leadership
Council
Healthcare Organizations as Systems
• Traditional view
• Deming’s view
What the organization is to deliver and to whom
2015 © Cincinnati Children’s Hospital Medical Center. All rights reserved
Transforming our model of care
29
Traditional Model System Model
Subspecialty
Clinics
Community
“the art and science of preventing disease, prolonging life, and promoting health through organized efforts and informed choices of society, organizations, public and private, communities and individuals.”
Dartmouth-Hitchcock
Driving transformation: understanding population health
Business Model
Business vision and population definitions, contracts,
market analyses,
CINs
Key Pillars of
Population Health Management
Quality, Safety, and Effectiveness
Payment Models
Government value driven care, shared
savings
Clinical Integration
Evidence based
practice standards, QI, CPTs, Analytics
Technology
Enhanced EMR, EDW,
clinical decision support, emerging
technology
$
Transparency • Talk about the bad as well as the good
Driving transformation: transparency of information
across a system
Internal Transparency
External Transparency
Understanding
the Science
• Teach a common improvement method
• Understand and learn from variation
• Apply theory and use logical frameworks
• Embrace reliability science
•Be aware of context
Driving transformation: empowering the system with the
science of improvement
What are we trying toaccomplish?
How will we know that achange is an improvement?
What change can we make thatwill result in improvement?
Model for Improvement
Act Plan
Study Do
Adapted from: The Improvement Guide: A Practical Approach to Enhancing Organizational Performance, 2nd Ed.
Gerald J. Langley, Ronald D. Moen, Kevin M. Nolan, Thomas W. Nolan, Clifford L. Norman, and Lloyd P. Provost
Jossey-Bass 2009
Surgical safety checklist: improve
compliance by 10%
IdeasAdapted from: The Improvement Guide: A Practical Approach to
Enhancing Organizational Performance, 2nd Ed. Gerald J. Langley,
Ronald D. Moen, Kevin M. Nolan, Thomas W. Nolan, Clifford L.
Norman, and Lloyd P. Provost; Jossey-Bass 2009
Survey team members/ experts about barriers
Trial a surgical safety checklist with a small number of teams
Implement the SSCL in all Ambulatory ORs at TCH
SSCL project courtesy of Drs Arnold, Price, Ris and D Sybilik
Why does it matter?
A parallel example
• RCT of treatment of hypertension on the jobsite (a steel mill) versus referral to the PCP
• No difference in compliance between the groups• Exploration of factors relating to therapy revealed specific
determinants of the clinical decision to treat some, but not other, hypertensive patients:
1. The level of diastolic blood pressure.2. The patient’s age.3. ????4. The amount of target-organ damage.
A parallel example
• RCT of treatment of hypertension on the jobsite (a steel mill) versus referral to the PCP
• No difference in compliance between the groups• Exploration of factors relating to therapy revealed specific
determinants of the clinical decision to treat some, but not other, hypertensive patients:
1. The level of diastolic blood pressure.2. The patient’s age.3. The year the physician graduated from medical school4. The amount of target-organ damage.
Understanding
the Science
• Understand and learn from variation
Empowering the system with the science of improvement
Common cause variation
Special cause variation
Understanding the science: variation
Intended variation: part of effective, patient-centered health care
Unintended variation: due to changes introduced into healthcare
process that are not purposeful, planned or guided
Reducing unintended variation in a process usually results in improved
outcomes and lower costs
Berwick, Donald M, Controlling Variation in Health Care: A Consultation with Walter Shewhart, Medical Care, December,
1991, Vol 29 (12); 1212-1225
Understanding
the Science
• Apply theory and use logical frameworks
Empowering the system with the science of improvement
Applying program theory
Understanding
the Science
•Embrace reliability science
Driving transformation: understanding improvement
science
Applying Levels of Reliability (IHI)
LVL Fail Rate Features in Healthcare QI
? >20% Chaotic (most of healthcare*)
I 5-20% Team building, education,
“trying harder next time”
II 1-5% Checklists, standardization,
make right way easy, observation
III <1%
“H.R.O.”
Preoccupation with failure
Avoid simplifying interpretations
Sensitivity to operations
Commitment to resilience
Deference to expertise
Resar RK. Health Services Research 2006. 41:1677–89.
The Quality of Ambulatory Care Delivered to Children in the US. Mangione-Smith et al. N Engl J Med 357:1515, October 11, 2007 Special Article
The Quality of Health Care Delivered to Adults in the US. McGlynn et al. N Engl J Med 348:2635, June 26, 2003 Special Article
Understanding
the Science
•Be aware of context
Driving transformation: understanding improvement
science
Ebbinghaus illusion
1. 10%
2. 15%
3. 20%
4. 25%
5. other
Model for Understanding Success in Quality (MUSIQ)
Heather C Kaplan et al. BMJ Qual Saf 2012;21:13-20
Copyright © BMJ Publishing Group Ltd and the Health Foundation. All rights reserved.
Value based
reform
• Recognize that a demand for value based care
transforms our model of care delivery
From patients
From payers
From government
Driving transformation: creating value in healthcare
Consumers and value
The healthcare value equation
•In an environment where cost is marginally
increasing, healthcare must markedly improve quality.
Value =
Quality
Cost
Option 1: Focus on Outliers – the prescriptive approach
Strategy eliminate the unfavorable tail of the curve (“quality
assurance”)
Result Ithe impact is minimal
# of
Cases
Excellent Outcomes Poor Outcomes
1.96 std
# of
Cases
Mean
Excellent Outcomes Poor Outcomes
1 box = 100
cases in a year
Data drives waste reduction: alternative approaches
53
Excellent Outcomes Poor Outcomes
# of
Cases
Mean
1 box = 100
cases in a year
Excellent Outcomes
# of
Cases
Poor Outcomes
Option 2: Focus On Inliers – improving quality outcomes across the majority
Strategy Evidence and analytics applied through EBP clinical standards targets inlier
variation
Result Shifting more cases towards excellent outcomes has much more significant
impact
Alternative approaches to waste reduction
54
Improving cost structure through waste reduction
55
Ordering Waste
Ordering of tests that are neither
diagnostic nor contributory
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
Ordering Waste
Ordering of tests that are neither
diagnostic nor contributory
Improving diagnostic accuracy and therapeutic effectiveness
56
Evidence against
CXR utilization in
patients with known
asthma, steroids in
bronchiolitis
Evidence equivocal
Hypertonic saline and
bronchodilators in select
patients with bronchiolitis
Evidence
Supports
Quicker steroid delivery for
status asthmaticus, goal
directed therapy for septic
shock
57
51%
35%
0%
10%
20%
30%
40%
50%
60%
70%
80%
Oct
. 10
Nov
. 10
Dec
. 10
Jan.
11
Feb.
11
Mar
. 11
Apr
. 11
May
. 11
Jun.
11
Jul.
11
Aug
. 11
Sep.
11
Oct
. 11
Nov
. 11
Dec
. 11
Jan.
12
Feb.
12
Mar
. 12
Apr
. 12
May
. 12
Jun.
12
Jul.
12
Aug
. 12
Sep.
12
Oct
. 12
Nov
. 12
Dec
. 12
Jan.
13
Feb.
13
Mar
. 13
Apr
. 13
Perc
enta
ge
Month year
Asthma: Care Process Team Cohort, Percentage of Chest X-rays Ordered*
(Oct. 2010 - Apr. 2013)
Feedback of rates to hospitalists
and Emergency Center cliniciansOrder set
revisions
* 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: improving throughput
Evidence based approach to early medication weaning
12110997857361493725131
200
150
100
50
0
Observation
In
div
idu
al
Va
lue
_X=27.4
UC L=70.9
LC L=-16.1
Baseline Improv ement 1Improv ement 2
12110997857361493725131
200
150
100
50
0
ObservationM
ov
ing
Ra
ng
e
__MR=16.4
UC L=53.4
LC L=0
Baseline Improv ement 1Improv ement 2
5
1
11
I-MR Chart of CT - 1st q3h to d/c by Phase
• 35% reduction in LOS
• No change in 7 or 30 day readmission rate
• No change in days of school/days of work missed
• Direct variable cost ($60/hr)
Financial conversations
The future: Don Berwick’s 3 Era’s of Healthcare
ERA I:
Professional Dominance
Medicine is noble
Special knowledge
Beneficent
Can self-regulate
Challenges:
Why unwanted variation
Why harm
Why waste
ERA II:
Business Dominance
Focused on accountability
Incentives and markets
Challenges:
Health care providers
are self-protective
Payers become suspicious;
demand inspection/control
ERA III:
Moral Dominance
Focus on continued
recreation of care
Challenges:
Retaining positive
components of Eras I and II
requires dedicated effort
Improving experience and
care for good value
The future: Don Berwick’s 3 Era’s of Healthcare
ERA III:
Moral Dominance
Focus on continued
recreation of care
Challenges:
Retaining positive
components of Eras I and II
requires dedicated effort
Improving experience and
care for good value
9 steps to achieve Era III
1. Reduce mandatory measurement
2. Stop complex individual incentives
3. Shift business strategy: revenue to quality
4. Give up personal prerogative for the system
5. Use improvement science
6. Ensure complete transparency
7. Protect civility
8. Hear the voices of the people served
9. Reject greed
Transforming healthcare is your work
Transparency
Und
erst
andi
ng v
aria
tion
An
aly
tic
s
ContextMeasurement
Systems think
Data
Clinical Decision Support
Ele
ctr
on
ic
me
dic
al re
co
rd
High ReliabilityValue
Standardize
Collaborative
Evidence based
EC: Early administration of Dexamethasone
Expanding evidence based practice
-Provider and staff inservicing-Clinical decision support-Bridging a continuum for home care: second dose 10% decrease in TID
Care shifted to DCU delivered by CDNs
14.1%22.9%
29.4%
55.8%
81.4%
1.8% 5.1% 6.3%
38.7%
80.0%
22.2% 19.0% 19.1%25.7%
55.8%
97.6%
0%
20%
40%
60%
80%
100%
120%
2010 2011 2012 2013 2014 2015
Shift-Level CDN and DCU Bed Utilization
CDN Contact
Multidisciplinary work from the Progressive Care Unit around addressing alarm
fatigue. Multiple PDSA cycles showing a reduction in Alarms/Bed/Day (blue)
and Percent Time in Alarm (red)Led by E. Williams
Baseline (3 dayn=12; 30 day n= 30)
Q3 - 2012 (3 day n=7; 30 day n= 10 )
Q4 - 2012 (3 day n=1; 30 day n= 6))
Q1 - 2013 (3 day n=1; 30 day n= 5)
Q2 - 2013 (3 day n=1; 30 day n=3)
3 days 4% 9% 1% 1% 1%
30 days 11% 12% 7% 5% 3%
0%
5%
10%
15%P
erc
en
t D
ied
Quarter
Severe sepsis or septic shock mortality
Severe sepsis QI collaborative in 13 hospitals
Led by: C Macias
Transforming healthcare video
Theory in health care transformation: Juran Trilogy/ Berwick’s application
Quality control:
Evaluation of
performance and
comparison of
goals
Keeping things in
order, fixing the
flat
Quality planning:
The larger response of
the stakeholder and
organization/ structure
Innovation, abandoning
the car and making the
plane
Quality improvement:
Identification of
problem, causes,
remedies, and change
Keeping the car
maintained, making the
car better
All improvement is change…
but not all change is improvement.
Respiratory Challenge: Case Illustration
• 14 month old girl with a viral
prodrome but no history of
asthma/prior episode of
breathing difficulty, cough
for one day; eczematous
rash
• Coarse wheezing heard (R)
but crying loudly-responsive
to bronchodilators; RR of 66
What do we really know to inform action that
is highly meaningful for this family?
Respiratory Challenge, a Case Illustration
Uncertainty of the
system
▪ Best treatment, best
advice?
▪ Continuums of care?
Asthma?
▪ Treat it like a cold or a
life threatening
disease?
Accountability and stewardship
Transforming healthcare is your work…
don’t just change the world….
IMPROVE it.
