Pierre Barker

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Improving Quality:
Theories of Change
Pierre Barker MD
Senior Vice President IHI
“Every system is perfectly
designed to achieve the
result it gets” Paul Batalden
Every System……
What are we trying to solve?
The “How”
The “What”
Basic
science
Proof of
concept
Large
scale
efficacy
Study
Guidelines,
Training &
Resources,
Reliable
“real-life”
implementation
Scale-up to
populations
Adaptive
Designs that
are context
sensitive
Designs that
can be scaledup with the
resources at
hand
Quality Improvement: Bringing Together
Two Types of Knowledge – (Deming)
Evidence
Based Subject
Matter
Knowledge
Protocols/Guidelines,
Physical resources,
Clinical Training
the “what”
the “how”
Implementation
Knowledge
Motivation/Leadership
Efficient Systems
Accurate Reflective Data
Context-sensitive
learning
Improvement: Bringing Together Two
Types of Knowledge
Evidence-based Subject
Matter Knowledge
Improvement
Implementation
Knowledge
Dr. Joseph M. Juran’s “Trilogy”
QUALITY
PLANNING
QUALITY
IMPROVEMENT
QUALITY
CONTROL
7
Juran Trilogy: All three elements are
needed
Sporadic
spike
Chronic waste
(opportunity for
improvement)
PERFORMANCE
SHIFT
Source: Juran J, Godfrey AB, eds. Juran’s Quality
Handbook: Fifth Edition. New York: McGraw-Hill, 1999.
8
Juran Trilogy: All three elements are
needed
Sporadic
spike
Chronic waste
(opportunity for
improvement)
PERFORMANCE
SHIFT
Source: Juran J, Godfrey AB, eds. Juran’s Quality
Handbook: Fifth Edition. New York: McGraw-Hill, 1999.
9
Juran Trilogy: All three elements are
needed
Sporadic
spike
Chronic waste
(opportunity for
improvement)
PERFORMANCE
SHIFT
Source: Juran J, Godfrey AB, eds. Juran’s Quality
Handbook: Fifth Edition. New York: McGraw-Hill, 1999.
10
Components of quality: structure,
quality control and quality improvement
Quality Planning
Quality Control
Standards/ Guidelines/
protocols
Professional oversight
Accreditation
Checklists
Inspection &
reward/censure
Policy, resources,
coordination,
accountability,
mandates, etc.
Quality
Improvement
Motivation/Leadership
IMPROVED
OUTCOMES
Efficient Systems
Reflective Data
Context-sensitive
learning
11
The “Engine” of Improvement: the
Model for Improvement
12
Model for
Improvement
Aims
1. What are we trying to accomplish?
Measures
2. How will we know that a change is an improvement?
Changes
3. What change can we make that will result in an improvement?
QA and “QI” confusion: both use Model
for Improvement and long/short PDSAs
Quality Planning
Quality Control
Quality
Improvement
Aims
Measures
Changes
Aims
IMPROVED
OUTCOMES
Measures
Changes
13
QA and QI: Differences in approach
QA
Performance
goal
Perform to standards (controls) across
multiple parts of the system
QI
Aspire to a best performance goal for a
focused improvement area
Measurement Periodic inspection of past events (large
set of measures of inputs and/or
processes)
Continuous tracking of current activity
(few key processes linked to outcome)
Data Tracking
Before and after change
Continuous (e.g. run charts)
Data System
External: e.g. inspection tools
Internal: e.g. registers and tally sheets
Changes
Standards driven. Normative. Can be
linked to frontline system analysis
Theory driven. Adaptive. Always linked
to frontline system analysis
Motivation
for change
Management led, compliance,
incentives, competition
Shared governance, internal
motivation, “all teach all learn”
PDSA
Management planning with single
“slow” (months) intervention cycle.
Can use frontline rapid cycle to respond
to defects
Frontline planning
Rapid cycle (days/weeks) is core
activity
Putting it together on the QA/QI Spectrum
COPE
QA
QI
Accreditation
SBM-R
Training and Supportive Supervision
QA Approach
PROBLEM
SOLUTION
IMPLEMENT
SYSTEM BARRIERS
Review
(Succeed/Fail)
QI Approach
SYSTEM ANALYSIS
PROBLEM
GREAT IDEAS
PLAN
IMPLEMENT
SUCCEED/ FAIL
ACT
DO
STUDY
Theory of Change: Drivers of Maternal
and Newborn Survival
Decrease
Maternal and
newborn
mortality by
50%
Drills, mentoring
Knowledgeable health
workers ready to use their
skills
Decision support, checklists
Immediate access to essential
commodities
Key suppliers part of QI team
Supportive Supervision
Multi-level leaders promote change
Motivation for change
Progress celebrated, challenges supported
Informed/activated patients & communities
Data systems in real time
Data for improvement vs data for reporting
Data “owned” at every level
A Learning System
Cross-professional teams meet regularly,
review data, test changes, report progress
% eligible women received at least one
dose of corticosteroids
1
2
% eligible women received at least one
dose of corticosteroids
Status
Quo
QI
Zone of control
1
Sustainability
Hospital 1
2
2
1
1
1
Hospital 2
2
2
1
1
1
Nkhoma
1
Sustainability
June 2014
December 2014
February 2014
Role of QI, QA in Scale-up
24
P25
Role of QI, QA in Scale-up
QA
Set-up
(Status Quo)
QI
Scalable Model
for Improvement
(innovation)
QA
Test ScaleUp (Context
adaptations)
Go to Full-Scale
(Spread)
Phases of
Scale-up
Questions
Are some methods intrinsically more suitable for different
aspects/phases of implementation?
Should we ensure that large system improvement
designs include all elements of Juran trilogy (QP, QC,
QI)
How do we ensure that our improvement efforts are
sustained into the future?
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