Jo Evans, Jude Stone & Steve Harrison

7 December 2017

R Floor, Royal Hallamshire Hospital

An Introduction to Quality Improvement

Welcome

• Jo Evans

– Continuous Improvement Manager

• Jude Stone

– Continuous Improvement Manager

• Steve Harrison

- Head of Quality Improvement

Aims / Objectives

To teach some of the basics

of Quality Improvement…

To support your microsystem improvement work

Aims / Objectives

• What do you hope to get out of today?

• How much do you already know about QI &

microsystem improvement?

Agenda Time Topic Duration Who

9.30 Welcome & Introduction 20 mins Jo

9.50 Complexity 10 mins Jo

10.00 What is Quality Improvement? 20 mins Jude

10.20 Systems Thinking & Microsystem Basics 30 mins Steve

10.50 Coffee 10 mins

11.00 Assessing your Microsystem using the 5Ps 20 mins Jo

11.20 Process Mapping 50 mins Jo

12.30 Lunch 30 mins

13.15 Measures & Activities 30 mins Steve

13.45 Psychology 40 mins Jude

14.25 Time Series Data 10 mins Jo

14.35 Variation 15 mins Jude

14.50 Run Charts 20 mins Jude

15.15 Coffee 10 mins

15.25 Potato Head 35 mins Steve

16.00 Reflections – Your Microsystem 15 mins Jo

16.15 Close

Complexity

09.50 - 10.00

Jo

Key Elements Required for Improvement to Happen

Will

to do what it takes to change to a new system

Ideas

on which to base the design of the new system

Execution

of the ideas

HOW FAR HAVE YOU

TRAVELLED?

10.00 – 10.20

Jude

What is Quality Improvement?

How would you define quality?

High Quality care is care that is:

• Safe – no needless deaths

• Effective – no needless pain or suffering

• Patient-Centered – no helplessness in those served

or serving

• Timely – no unwanted waiting

• Efficient – no waste

• Equitable – for all

Quality: The IOM’s Six Aims

Improvement

“The combination of a change with a

method to attain a superior

outcome”

Model I: Bad Apples

The

Problem

Quality

Frequency

The Simple, Wrong Answer

Blame Somebody

The Cycle of Fear

Increase

Fear

Micromanage Kill the

Messenger

Filter the

Information

(Denial, shift

the blame)

(Game the

data)

Model 2: Positive deviance

Quality

Frequency

Model 2: Continuous Improvement “Every Defect is a Treasure”

Quality

F

req

ue

nc

y

Quality Improvement -

The structure

Assessment - 5Ps

Diagnosis - Change Ideas

Treatment

- PDSA

SDSA

‘Standardise’

PDSA - experimentation • Always start with a specific aim - What are we trying to accomplish?

• How will know if this is an improvement? – Data.

• Small tests of change over a short time

• Debrief frequently

• Communicate results

• Repeated Cycles

• When we meet our aim? –

SDSA = Standardise

SDSA

1

3

2

P

DS

A

P

DS

A

P

DS

A

P

DS

A

P

DS

A

P

DS

A

4

5

6

The Value of “Failed” Tests

“I did not fail one

thousand times; I

found one thousand

ways how not to make

a light bulb.”

Thomas Edison

10.20 – 10.50

Steve

System Thinking & Microsystem basics

• Step 1 – Everyone stand up

• Step 2 – Without speaking; pick two people but

don’t say who they are or point at them (Keep it a

secret)

• Step 3 - Move to be equidistant between both of the

people

Understanding Systems

What is a system?

System = a collection of processes

working together to produce a defined

output

“Every system is perfectly designed to

get the results it gets.”

Paul B. Batalden, MD

Co-Founder The Institute for Healthcare Improvement

Founding Director, Center for Leadership and Improvement,

The Dartmouth Institute for Health Policy and Clinical Practice

Founding Director, Healthcare Improvement Leadership Development

The Dartmouth Institute for Health Policy and Clinical Practice

Co-Founder Institute for Healthcare Improvement

Processes?

• How is a process different from a “system”?

• A process is a series of work activities that together

transform inputs into outputs for the benefits of

someone

• Can we brainstorm a series of processes which

make up a “system” we might encounter in our

improvement work?

Elements of a Process

28

Suppliers Outcomes

Thing being passed along

Inputs Outputs

Sequence of steps

Steve

Microsystem Basics

What is a Clinical Microsystem?

• ‘The Place where Patients, Families and

Clinical Teams meet’

• The essential frontline building blocks of any

healthcare system. It is where the quality

is delivered.

It’s where everything happens with, for and

to the patient and family

Chest

Medicine

STH

Team Coaching

Improvement

Science

Microsystem

The elements of Microsystem improvement

QI

18

Team Coaching

Improvement

Science

Microsystem

Improving Microsystems – The Elements

QI

18

Microsystems

• 1992 – Quinn – ‘Intelligent Enterprise’

• Studied the ‘best of the best’

• They are organised around the frontline

interface with the customer

• ‘Smallest replicable unit’

Microsystem in health care

• Nelson, Batalden, Godfrey 2000 – 2007

• Formulated a curriculum to develop high

performing microsystem teams

• Focusing on the front line

• This became their coach the coach

programme

169 coaches

Ownership not Buy In

‘If you want to make true and lasting

change, ask the people who do the

work how to go about it’

Daren Anderson, MD

VP/Chief Quality Officer

Community Health Center, Inc.

Kings

http://www.kingsfund.org.uk/publications/reforming-nhs-within

Coaching

It is not telling people what to do.

It is giving them a chance to examine

what they are doing in the light of their

intentions.

Peter Senge,

MIT and Society for Organizational Learning

‘Improvement in health care is

20% technical and 80% human’

Marjorie Godfrey, MS, RN

The Dartmouth Institute For Health Policy and

Clinical Practice

People and Behaviours

The Team Coaching Model

Transition Phase Reflection,

Celebration & Renew

`

Pre Phase Getting Ready

Action Phase Art & Science of

Coaching

Godfrey, MM (2012) In Press

Team Coaching Over Time

People vs. System

“80% of the problem is the

system not the people”

W. Edwards Deming

Professor of statistics at New York University (1946–1993)

Author, lecturer, and consultant

Photo © 2014 The W. Edwards Deming Institute Blog

Founding Director, Healthcare Improvement Leadership Development

The Dartmouth Institute for Health Policy and Clinical

Practice

Co-Founder Institute for Healthcare Improvement

Quality Improvement -

The structure

Assessment - 5Ps

Diagnose – Change Ideas

Treat

PDSA

SDSA

‘Standardise’

Define Themes

47

CSS Worker

Weekly meetings

Discharge on admission

Changed care plans

5P Assessment

Theme

Global Aim

Change Ideas

Specific Aim

Measures

Flowchart

Cause & Effect

The Microsystem

Improvement Ramp

Global Aim

1

2

3

SDS

A

P

DS

A

P

DS

A

P

DS

A

PDSA

1

3

2

Global Aim

1

2

3

5 P Assessment

Theme

Global Aim

Change Ideas

Specific Aim

Measures

SDSA

P

DS

A

P

DS

A

P

DS

A

PDSA

1

3

2

Dartmouth Microsystem Improvement Curriculum

11.00 – 11.20

Jo

Assessing your Microsystem using the 5Ps

Quality Improvement -

The structure

Assessment - 5Ps

Diagnose – Change Ideas

Treat

PDSA

SDSA

‘Standardise’

Define Themes

“To do things differently, we must see

things differently. When we see things

we haven’t noticed before, we can ask

questions we didn't know to ask before.”

John Kelsch, Xerox

Assessment - • We need data to understand the system

Purpose

5 Ps

Purpose -

• Why does your microsystem exist?

• What is the purpose of your efforts and work?

‘To enable Spinal Cord Injured patients to lead

as normal life as possible and reach the

maximum level of function possible’

To provide high quality care in an

environment that promotes patient

and employee satisfaction.

Patients • What is the patient age distribution?

• Where do you patients come from?

• Where do they go after interacting with your microsystem?

• How satisfied are they?

• Do you notice patterns based on seasons in your patient volumes and acuity?

• What are the top diagnoses?

Patients - Who is Evie?

A fictional typical falls

patient who is •83 years old• Lives on her own• Widowed 5 years ago

• Broke her wrist in a fall 6 years ago

• This year has started to have dizzy episodes and

has fallen 5 times• Her GP has referred her to the Falls clinic

Professionals

• Who does what and when

in your microsystem?

• Is the right person doing

the right activity at the right

time?

• What do staff think could

be improved?

• What is the level of staff

satisfaction?

What would you want to change in Renal OPD?

0% 20% 40% 60% 80% 100%

Clinical Outcome

Customer Care

Hospital environment - Cleanliness

Hospital environment - Layout

Hospital environment - Furniture

Clinc appointment scheduling

Information available in outpatients

Waiting time for patients

Seeing the appropriate staff member

Time of day clinic held

Room use/allocation

Staff working patterns

Available equipment

% of replies

1 - No change required 2 3 4 5 - Large change required

Processes

• Review the current system using process mapping

• Identify the ‘Value’ & the ‘Waste’

“I’ve worked in the trust

for so many years but

have never been able to

see the whole process”

Patterns

• What patterns exist in your microsystem?

• What is the variation across the day, week,

• How often do you meet to discuss patient care,

safety and quality?

• What are your results and health outcomes?

Clinic VC147B Tuesday 1/11/11

8.3

0

8.4

5

9.0

0

9.1

5

9.3

0

9.4

5

10

.00

10

.15

10

.30

10

.45

11

.00

11

.15

11

.30

11

.45

12

.00

12

.15

12

.30

12

.45

13

.00

13

.15

13

.30

13

.45

14

.00

Patient 1

Patient 2

Patient 3

Patient 4

Patient 5

Patient 6

Patient 7

Patient 8

Patient 9

Patient 10

Patient 11

Patient 12

Patient 13

Patient 14

Patient 15

Patient 16

Patient 17

Patient 18

Patient 19

Patient 20

Patient 21

Patient 22

Patient 23

Patient 24

Patient 25

Patient 26

Patient 27

Patient 28

Patient 29

Patient 30

Patient 31

Patient 32

Patient 33

Patient 34

Patient 35

Patient 36

Patient 37

Patient 38

Patient 39

Patient 40

61

Example 5Ps - Pulmonary Vascular Disease

Unit - RHH

The 5Ps

MCA Website - ‘1 page book’

Themes For

Improvement

CHANGE Themes

Ward rounds and

MDT processes

Coding

Medicines Management

Q

11.20 – 12.30

Jo

Process Mapping

PROCESS MAPPING

PROCESS

Processes

“Every system is perfectly

designed to get

the results it gets”

Paul B. Batalden

Process Mapping (Flowcharts)

• A flowchart is a picture of the sequence of steps in

a process

• Different steps or actions are represented by boxes

or other symbols

• Process mapping can help team members

understand what is happening now in a process

• It is important to flowchart the CURRENT process,

not the desired process first

High Level Example – Renal OPD

Referral Grading Admin—New Appointment

Prep clinic, Notes

Reception New and Fol-

low Up

Specimen Room

Dr or SPR or MDT Review

Dietician Re-view (Some

Patients)

Bloods Reception, Book Follow

Up

Visit Phar-macy for

Meds

Iron Clinic

Referral Grading Admin—New Appointment

Prep clinic, Notes

Reception New and Fol-

low Up

Specimen Room

Dr or SPR or MDT Review

Dietician Re-view (Some

Patients)

Bloods Reception, Book Follow

Up

Visit Phar-macy for

Meds

Iron Clinic

Added ‘value’

Analyse the process

• Number of steps

• Order

• Transfer of ‘object’ from one person to

another (loss and probability of error)

• Delays

• Added Value

• Bottlenecks

500 grains/30 secs

270 grains/30 secs

170 grains/30 secs

270 grains /30 secs

Bottlenecks

500/30 secs

270/30 secs

170/30 secs

270/30 secs

13.15 – 13.45

Steve

Measures in Healthcare P7

6

Measurement for Improvement

Improvement

Research Assurance

Three Types of Measures for Improvement

• Outcome Measures

• Process Measures

• Balancing Measures

Outcome Measures

• Outcome Measures:

• What is the outcome or result?

• How is the overall system performing? (Voice of the customer)

• What might some examples of outcome measures be?

Process Measures

• Process Measures:

• What is the system telling you about how well it is working?

• Are the parts/steps in the system performing as planned? (Voice of the system)

• What might some examples of process measures be?

Balance Measures

• Balance Measures:

• Unrelated Processes which might be affected by the changes we make

• What happened to the system as we improved the outcome and process measures?

• What might some examples of balance measures be?

Weight loss and developing

measures exercise Background: A friend has come to you and asked you to help develop measures for a group she is working with

Aim: The aim of the improvement project is for participants to lose weight. They need regular feedback to keep them on task

Develop a family of 4 to 6 measures that could be reported each week for the project:

• Outcome Measures – 1-2 measures

• Process Measures – 2 measures

• Balancing Measures – 1 or 2 measures

Where do measures come from?

• Data Elements – raw information already (or in

need of) being collected by clinics and hospitals

• Usually found in clinic registers, summary forms or

centralised health information systems

• Can you give some examples of raw data your

microsystem is currently collecting?

• But….sometimes you need manual data collection

13.45 – 14.25

Jude

Psychology

Key Elements Required for Improvement to Happen

Will

to do what it takes to change to a new system

Ideas

on which to base the design of the new system

Execution

of the ideas

Exercise

• What satisfies you in your job?

• What dissatisfies you in your job?

MOTIVATION

One More Time: How Do You Motivate Employees?

Harvard Business Review (reprint Jan, 2003)

Maslow’s Hierarchy of Needs

Allow autonomy

Enable Mastery

Create sense

of purpose

How to motivate

CHANGE

change is hard

the world’s biggest change…

The other side of

change………………

Change Curve

Time

Motivation,

Perf

orm

ance

Elizabeth Kubler-Ross, 1969

The Everett Rogers curve

‘Improvement in health care is

20% technical and 80% human’

Marjorie Godfrey, MS, RN

The Dartmouth Institute for Health Policy and Clinical Practice

14.25 – 14.35

Jo

Measurement

Time Series Data

Looking at Data

• Here are two numbers…what’s going on?

0

5

10

15

20

25

A B

Value

Hold on…

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Series2

A B

But…

0

5

10

15

20

25

30

35

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Series2

A B

Erm…

0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Series2

A

B

But then again….

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Series2

A

B

Here are two pie charts – we wanted to decrease

DNAs (no shows)

Hold on…

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Series2

A B

Weeks

No S

how

s

But…

0

5

10

15

20

25

30

35

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Series2

A B

Weeks

No S

how

s

Erm…

0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Series2

A

B

But then again….

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Series2

A

B

Weeks

No S

how

s

What’s going on with this data?

Test 1 Test 2 Test 3 Test 4

1 8 11 26 30

2 12 21 25 28

3 8 16 20 24

4 12 12 21 16

5 20 20 20 20

6 8 18 18 18

7 7 15 4 19

8 5 9 7 15

9 19 22 6 14

10 22 15 7 11

11 27 21 9 12

12 10 10 10 10

13 28 23 12 8

14 30 14 15 6

15 34 9 18 8

16 35 18 25 4

What’s going on with this data?

12

0

Service Improvement

0

5

10

15

20

25

30

35

40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Series2

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Series2

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Series2

0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Series2

Test 1 Test 2

Test 3 Test 4

Beware of averages too…

• Here are our two numbers (Monthly data)

0

5

10

15

20

25

A B

Value

Here’s what’s happening by week…

Weekly Data

0

5

10

15

20

25

30

35

40

1 2 3 4 5 6 7 8

Series2

A B

?

Or Even…

0

10

20

30

40

50

60

1 2 3 4 5 6 7 8

Series2

A B

Summary

• One number will always be different to another –

look at the trend

• Tables take time to understand

• Chart your data to see what’s happening

• Beware of averages they can be misleading

14.35 – 14.55

Jude

Variation

Walter Shewhart

(1891 – 1967)

W. Edwards

Deming

(1900 - 1993)

The Pioneers of Understanding Variation

Reacting to Variation

“If I had to reduce my message for management to just a few words, I’d say it all had to do with reducing variation.” W. Edwards Deming

What Time is it?

Write down the current time in

minutes past the hour

What do people call me?

Kevin Dad

Kev

DADDY!!

!! Mr Firth

‘Intended Variation’

Intended

and

Unintended Variation

Shewhart’s Theory of Variation

• Common Causes—those causes inherent in the system over time, affect everyone working in the system, and affect all outcomes of the system

•Chance cause

•Stable process

Common Cause Variation

Number of Emergency Admissions to NGH (Consecutive

Saturdays)

0

10

20

30

40

50

60

70

02

/05

/11

09

/05

/11

16

/05

/11

23

/05

/11

30

/05

/11

06

/06

/11

13

/06

/11

20

/06

/11

27

/06

/11

04

/07

/11

11

/07

/11

18

/07

/11

25

/07

/11

01

/08

/11

08

/08

/11

15

/08

/11

22

/08

/11

29

/08

/11

05

/09

/11

12

/09

/11

19

/09

/11

26

/09

/11

03

/10

/11

10

/10

/11

17

/10

/11

24

/10

/11

31

/10

/11

07

/11

/11

14

/11

/11

21

/11

/11

Consecutive Weeks (Week Commencing Date)

No

of

Ad

mis

sio

ns

Shewhart’s Theory of Variation

• Special Causes—those causes not part of the system all the time or do not affect everyone, but arise because of specific circumstances

• Assignable cause

• Unstable process

Special Cause - My trip to work

My trip to work in minutes

20

40

60

80

100

120

140

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Consecutive Trips to work

Min

ute

s

Monthly Theatre Incidents

0

2

4

6

8

10

12

May-0

8

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct-

08

Nov-0

8

Dec-0

8

Jan-0

9

Feb-0

9

Mar-

09

Apr-

09

May-0

9

Jun-0

9

Jul-09

Aug-0

9

Sep-0

9

Oct-

09

Nov-0

9

Dec-0

9

Jan-1

0

Feb-1

0

Mar-

10

Apr-

10

May-1

0

Jun-1

0

Jul-10

Aug-1

0

Sep-1

0

Oct-

10

Nov-1

0

Dec-1

0

Jan-1

1

Feb-1

1

Mar-

11

Months

Inc

ide

nts

Theatre Incidents May 2008 - March 2011

SSC implemented

Monthly Theatre Incidents

0

2

4

6

8

10

12

May-0

8

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct-

08

Nov-0

8

Dec-0

8

Jan-0

9

Feb-0

9

Mar-

09

Apr-

09

May-0

9

Jun-0

9

Jul-09

Aug-0

9

Sep-0

9

Oct-

09

Nov-0

9

Dec-0

9

Jan-1

0

Feb-1

0

Mar-

10

Apr-

10

May-1

0

Jun-1

0

Jul-10

Aug-1

0

Sep-1

0

Oct-

10

Nov-1

0

Dec-1

0

Jan-1

1

Feb-1

1

Mar-

11

Months

Inc

ide

nts

Regarding types of variation; what does this graph

show?

A. Common cause only

B. Special cause only

C. Both special and common cause

D. No variation

Responding to

Special Cause Variation

• Identify the cause:

• If positive then can it be replicated or standardised.

• If negative then cause needs to be eliminated

Responding to Common

Cause Variation

1. Reduce variation: make the process even

more predictable or reliable (and/or)

2. Not satisfied with result: redesign process to get

a better result

Process with

common cause

variation

Reduce variation:

make the process even more reliable

Not satisfied with result:

redesign process to get a better

result

Process with

special cause

variation

Identify the cause:

if positive then can it be replicated or

standardised. If negative then cause

needs to be eliminated

Monthly Theatre Incidents

0

2

4

6

8

10

12

May-0

8

Jun-0

8

Jul-08

Aug-0

8

Sep-0

8

Oct-

08

Nov-0

8

Dec-0

8

Jan-0

9

Feb-0

9

Mar-

09

Apr-

09

May-0

9

Jun-0

9

Jul-09

Aug-0

9

Sep-0

9

Oct-

09

Nov-0

9

Dec-0

9

Jan-1

0

Feb-1

0

Mar-

10

Apr-

10

May-1

0

Jun-1

0

Jul-10

Aug-1

0

Sep-1

0

Oct-

10

Nov-1

0

Dec-1

0

Jan-1

1

Feb-1

1

Mar-

11

Months

Inc

ide

nts

Theatre Incidents May 2008 - March 2011

SSC implemented

‘One Page Book’

Jude

14.55 – 15.15

Run Charts

Measurement

‘All improvement involves change,

not all changes are improvements’

Batalden & Davidoff Qual. Saf. Health Care. (2007)

You need to measure to differentiate.

Anatomy of a Run Chart

A Sample Run Chart

Time (x)

A v

ariable

(y) Median

A ‘run’ is one or consecutive

data points on the same

side of the median

1.The presence of too much or too little

variability

A Sample Run Chart

Time (x)

A v

ariable

(y) Median

A ‘Run’

8 runs and 22 data points (ignore the 3 data points on median line)

# observations

(not on median)

Lower limit Upper limit

14 4 11

15 4 12

16 5 12

17 5 13

18 6 13

19 6 14

20 6 15

21 7 15

22 7 16

23 8 16

24 8 17

25 9 17

26 9 18

27 9 19

28 10 19

29 10 20

30 11 21

2. The presence of a shift in the process

A Sample Run Chart

Time (x)

A v

ariable

(y) Median

A ‘Shift’

3. The presence of a trend

Sample Run Chart 2

Time (x)

A v

ariable

(y) Median

A ‘Trend’

Application – Responding to variation

Process with

common cause

variation

Reduce variation:

make the process even more reliable

Not satisfied with result:

redesign process to get a better result

Process with

special cause

variation

Identify the cause:

if positive then can it be replicated or

standardised. If negative then cause

needs to be eliminated

Application - Improvement

PDSA Intervention

Sample Run Chart 3

Time (x)

A v

ariable

(y) Median

A ‘Shift’

Exercise 1

Begin

standard

orders

8 runs and 18 data points (ignore 3 data points on median line)

Exercise 2

0

10

20

30

40

50

60

70

80

4-A

pr

6-A

pr

8-A

pr

12

-Ap

r

14

-Ap

r

18

-Ap

r

20

-Ap

r

22

-Ap

r

3-M

ay

5-M

ay

9-M

ay

11

-May

13

-May

15

-May

% D

aily

TT

Os C

om

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Ward x – % of total TTOs completed by 12 noon April 4 - May 15, 2012

Median

12 runs and 26 data points (ignore data points on median line)

15.25 – 16.00

Steve

Mr Potato Head

Plan

•Objective

•Questions and

predictions (Why)

•The plan – who what

where when

Do

•Do the Plan

•Document problems,

observations

•Begin analysis

of the data

Study

•Complete analysis of

data

•Compare data to

predictions

•Summarise the

learning

Act

•What changes

are to made now?

•What is the next

cycle

PDSA

PDSA - experimentation

• Always start with a specific aim - What are we trying to accomplish?

• How will know if this is an improvement? – Data.

• Small tests of change over a short time

• Debrief frequently

• Communicate results

• Repeated Cycles

• When we meet our aim? –

SDSA = Standardise

SDSA

1

3

2

P

DS

A

P

DS

A

P

DS

A

P

DS

A

P

DS

A

P

DS

A

4

5

6

Summary

• Knowledge is gained through testing

• Small, sequential & rapid testing builds knowledge

• Prediction and review are essential in comparison

to the result

• Accelerate learning by understanding others

• Measurement can be easy & accelerate learning

• Collaboration brings results

16.00 – 16.15

Jo

Reflections & Close

Next steps

• What have been your key learnings from today?

• How are you going to share this with others?

• What actions will you do when you return to help

enable the improvement work to succeed?

• Will you be involved in the improvement meetings?

EVALUATION

What went well? What could be improved?