Pierre Barker MD

Senior Vice President IHI

Improving Quality: Theories of Change

“Every system is perfectly

designed to achieve the

result it gets” Paul Batalden

Every System……

Basic

science

Proof of

concept

Large

scale

efficacy

Study

What are we trying to solve?

The “What”

Reliable

“real-life”

implement-

ation

Scale-up to

populations

The “How”

Guidelines, Training & Resources,

Adaptive Designs that are context

sensitive

Designs that can be scaled-

up with the resources at

hand

Quality Improvement: Bringing Together

Two Types of Knowledge – (Deming)

Implementation

Knowledge Motivation/Leadership

Efficient Systems

Accurate Reflective Data

Context-sensitive

learning

Evidence

Based Subject

Matter

Knowledge Protocols/Guidelines,

Physical resources,

Clinical Training

the “what”

the “how”

Implementation

Knowledge

Evidence-based Subject

Matter Knowledge

Improvement

Improvement: Bringing Together Two

Types of Knowledge

Dr. Joseph M. Juran’s “Trilogy” 7

QUALITY PLANNING

QUALITY IMPROVEMENT

QUALITY CONTROL

Juran Trilogy: All three elements are

needed

8

PERFORMANCE SHIFT

Chronic waste (opportunity for improvement)

Sporadic spike

Source: Juran J, Godfrey AB, eds. Juran’s Quality

Handbook: Fifth Edition. New York: McGraw-Hill, 1999.

Juran Trilogy: All three elements are

needed

9

PERFORMANCE SHIFT

Chronic waste (opportunity for improvement)

Sporadic spike

Source: Juran J, Godfrey AB, eds. Juran’s Quality

Handbook: Fifth Edition. New York: McGraw-Hill, 1999.

Juran Trilogy: All three elements are

needed

10

PERFORMANCE SHIFT

Chronic waste (opportunity for improvement)

Sporadic spike

Source: Juran J, Godfrey AB, eds. Juran’s Quality

Handbook: Fifth Edition. New York: McGraw-Hill, 1999.

Policy, resources, coordination, accountability, mandates, etc.

Quality Planning

Components of quality: structure,

quality control and quality improvement

11

Quality

Improvement Motivation/Leadership

Efficient Systems

Reflective Data

Context-sensitive

learning

Standards/ Guidelines/

protocols

Professional oversight

Accreditation

Checklists

Inspection &

reward/censure

Quality Control

IMPROVED

OUTCOMES

12

Aims

Measures

Changes

Model for

Improvement

The “Engine” of Improvement: the

Model for Improvement

1. What are we trying to accomplish?

2. How will we know that a change is an improvement?

3. What change can we make that will result in an improvement?

Quality Planning

13

Quality

Improvement Aims

Measures

Changes

Quality Control

IMPROVED

OUTCOMES

QA and “QI” confusion: both use Model

for Improvement and long/short PDSAs

Aims

Measures

Changes

QA and QI: Differences in approach QA QI

Performance goal

Perform to standards (controls) across multiple parts of the system

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

QA QI Accreditation

COPE

SBM-R

Training and Supportive Supervision

QA Approach

IMPLEMENT

PROBLEM

SOLUTION

Review (Succeed/Fail)

SYSTEM BARRIERS

QI Approach

GREAT IDEAS

SYSTEM ANALYSIS

DO

STUDY

ACT IMPLEMENT

SUCCEED/ FAIL

PROBLEM

PLAN

Theory of Change: Drivers of Maternal

and Newborn Survival

Decrease Maternal and

newborn mortality by

50%

Motivation for change

Knowledgeable health workers ready to use their

skills

Immediate access to essential commodities

Data systems in real time

A Learning System Cross-professional teams meet regularly, review data, test changes, report progress

Progress celebrated, challenges supported

Informed/activated patients & communities

Drills, mentoring

Decision support, checklists

Key suppliers part of QI team

Data for improvement vs data for reporting

Data “owned” at every level

Multi-level leaders promote change

Supportive Supervision

% eligible women received at least one

dose of corticosteroids

2

1

% eligible women received at least one

dose of corticosteroids

Zone of control QI Status Quo

Sustainability

Hospital 1

Hospital 2

Nkhoma

1 1

2

1

1 1

2

1 1

2

2

1 1

3

1 1

3

1 1 1

Sustainability

February 2014

June 2014

December 2014

Role of QI, QA in Scale-up 24

P25

Set-up (Status Quo)

Scalable Model for Improvement

(innovation)

Test Scale- Up (Context adaptations)

Go to Full-Scale (Spread)

Phases of Scale-up

QA QI QA

Role of QI, QA in 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?