Post on 29-Jul-2020
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
ple
ted
by N
oo
n
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?