Australian Masterclass
description
Transcript of Australian Masterclass
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Australian Masterclass
Sally Batley Deputy Director of Analysis,
NHS Modernisation Agency (UK)
Working in partnership with the Patient Flow Collaborative (Victoria AU)
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So what are we going to cover
Measurement for Improvement What is Statistical Process Control (SPC) Understanding Variation Benchmarking Build you own SPC charts
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So what are we going to cover
Measurement for Improvement What is Statistical Process Control (SPC) Understanding Variation Benchmarking Build you own SPC charts
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Measurement for Improvement
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Sir Josiah Stamp
Public agencies are very keen on amassing statistics - they collect them, add them, raise
them to the Nth power, take the cube root and prepare wonderful diagrams. But ...
what you must never forget is that every one of those figures comes in the first instance from the village watchman (or admissions clerk?) -
who puts down what he damn pleases.
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There are three kinds of lies:
lies, damned lies and statistics
After Mark Twain
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Collecting your data
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How good is your data?
Is the routine data you collect and distribute 100% accurate?
Is it complete rubbish? So it must be somewhere in between
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Issues
Definitions Accuracy Consistency Timing
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The information vicious circle
Information is not used
Information is:
InaccurateIncomplete
LateInconsistent
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Task
In groups you have to describe the people in the room so answer these questions …
How many people are there in the room? How many are wearing something red? How many are tall? How many types of footwear are there? Find one word to describe the group?
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Issues
Timing Definitions Accuracy Consistency
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Data types
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Types of data
Routine v special collection Qualitative v Quantitative Soft v hard Descriptive v numeric Example of current performance:
“Patients are satisfied” v waiting time is 4 hours Example of change:
“Communication with patients has improved” v Average X-ray waits reduced by 20 minutes
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Which types are you collecting?
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Types of measurement
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Different types of measurements
measurements for judgement league tables
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Performance Indicators
Measure probability not certainty Are better in groups Are better at identifying poorer performance Should not be used for league tables
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Different types of measurements
measurements for diagnosis to show where the problems are lots of measurescomparative data useful
measurements for improvement to show if improvement are being made linked to the project objectives and aimsa few specific measures
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Measurement for Improvement
or how do we know
that a change is an improvement?
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What are we trying toaccomplish?
How will we know that achange is an improvement?What changes can we make
that will result in the improvements that we seek ?
Model for improvement
Act Plan
Study Do
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What are we trying toaccomplish?
How will we know that achange is an improvement?What changes can we make
that will result in the improvements that we seek ?
Model for improvement
Act Plan
Study Do
project aims
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What are we trying toaccomplish?
How will we know that achange is an improvement?What changes can we make
that will result in the improvements that we seek ?
Model for improvement
Act Plan
Study Do
project aims
global
measurements
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What are we trying toaccomplish?
How will we know that achange is an improvement?What changes can we make
that will result in the improvements that we seek ?
Model for improvement
Act Plan
Study Do
project aims
global
measurements
change principles
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A
S
P
D
D
PA
S
D
PA
S
AP
DS
Data
Changes thatresult inimprovement
Building Improvement KnowledgeIm
pro
vem
ent
Time
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Measurement for improvement:
Answers the question How do we know change is an improvement ?
Is linked to the project objectives or aims usually requires no more than five to seven
measures crosses the whole process of care measures change over time
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Change areas, aims and measures should be related
Area - Effective Delivery of Health Care Aim - To improve access to the appropriate
treatment Measure - Reduce the number of days
between referral and first definitive treatment
Example from Action On
programme
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Measuring quantitative outcomes
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Measuring quantitative outcomes
A descriptive goal eg reduce DNAs
But by how much? Quantify the starting point (baseline) Set an objective (improve by x%) How will you measure that? (methods) Monitor progress
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An example - hospital cancellations
Monitor progress to target
0
24
68
10
1214
16
0 1 2 3 4 5 6
Time
Pe
rce
nta
ge
Baseline = 15%
Target = 5%
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How are we doing?Setting the baseline
Baseline period must be representative Small numbers issue Baseline period can be greater than
monitoring frequency
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Over what period to measure baseline?
DNA rate with large variation
0%
5%
10%
15%
20%
25%
1 2 3 4 5 6
Time
Pe
rce
nta
ge
Average = 8.7%
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Over what period to measure baseline?
DNA rate with small variation
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
1 2 3 4 5 6
Time
Pe
rce
nta
ge
Average = 8.7%
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How will we know?Tips on measurement
Measurement periodsCensus point (particular time of day - eg 12pm)Period of time (eg 24 hour period)Don’t mix the two!
Use routine data where possible to allow cross-checking
Specify method preciselyeg process time in hours for patients from triage to
admission onto appropriate ward
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How much will we improve? - Expressing the measurement of change
Be realistic in your expectationsDon’t think you can reduce error rate from 50% to 0%
Mostly express values to one decimal placeDNA rate = 5.6% (not 6%)
Express target as a value not as an improvementIf baseline is 5 patients/hour and you want to improve by 10%
then state target as 5.5 patients/hour
Avoid confusion over percentagesBaseline is 10% and you want to improve (reduce) by 25%
then state target as 7.5%
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Process Mapping
Understand the process before settling on your measures
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Route A - Self-referral
Arrival Arrival in A&Ein A&E
Seen by Seen by o/c teamo/c team
Seen by Seen by A&EA&E
TriageTriage DTADTA
Leave Leave A&EA&E
W1
W4
W5W2
W3
Indicative waits
W1 - 5 minutesW2 - by categoryW3 - 1 hourW4 - 1 hourW5 - 4 hours
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Global measure: % patients seen within recommended waiting times at three key identified stages in care
We want to improve the overall patient journey
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Global measure: % patients seen within recommended waiting times at three key identified stages in care
But Changes are made at specific points
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The Measurement Paradox
We want to improve the whole patient experience/ journey but we make changes at specific points. How do we cope with
measuring the change?
Specific measurescan be temporary to monitor change ideas
Global measures are permanent to monitor overall improvement
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Measurement at specific points
In addition to reported global measures plotted, additional measures may be required during changes:
specific measures related to the change results for sub-groups of patients results by consultant groups results for patients experiencing a particular
clinical process
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Average waiting times across the care
pathway in days
0
1020
30
4050
60
Jan
Feb Mar Apr
May Ju
n Jul
AugSep
tOct
NovDec Ja
n
Change 1
Change 2
Change 3
Impact of changes on global measures (hopefully!)
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Setting the baseline Or how are we doing right now?
Baseline period must be representative Watch out for small numbers! Baseline period can be greater than
monitoring frequency
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Measurement guidelines
key measures plotted and reported each month should clarify your project team’s aim and make it tangible.
be careful about over-doing process measures.
consider sampling to obtain data. integrate measurement into the daily routine. plot data on the key measures each month
during the programme
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Task: Creating measures for your project aims
Your Project is Improving Patient Flow what is your measurement strategy? what are you aims what quantified measures could be used?
Data collection method what baseline are you going to use? what is the potential performance? frequency of measurement? How are you going to feed it back and to
whom?
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Patient experience monitoring
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Why?
To use patient feedback to improve services
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Agenda
evaluating patient experience quantitative versus qualitative rating versus reporting practical hints and tips
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Task
On your table, brainstorm ideas for measuring and monitoring patients’ experience with a service:
How can we measure what patients think of the service?
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Approaches to monitoring
quantitative•structured questionnaires•“tick box” surveys
qualitative•semi-structured interviews•questionnaires that combine “tick box” with comment spaces
•unstructured interviews•patient focus groups•critical incident technique
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Report experience don’t rate satisfaction
How satisfied were you with the consultation you received with the doctor?
Please answer the questions by ticking the response which most closely matches your experience.
All the treatment options were fully explained to me. I was given as much as much information as I wanted to know Treatment options were very briefly discussed with me The doctor did mention different treatments, but I did not really understand I did not feel that I was given a choice about treatment
very satisfiedquite satisfiedsatisfiedquite dissatisfiedvery dissatisfied
X
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Designing a questionnaire or survey
goal of the research research methodquestionnaire designpatient sample frequency of data collectiondata collection methodssystems for analysis reporting systems
What do you want to know?
How will you find out?
What sort of questions?
How many will you ask?
How often will you ask them?
How will you ask them?
How will you analyse the data?
How will you report the results and
to whom?
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Designing a questionnaire or survey
keep it simple plain English small patient sample and track changes over
time, little and often (run chart) combine quantitative and qualitative pilot first involve patient / user representatives in
questionnaire design, data collection and analysis of results
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Leave room for comments
How satisfied were you with the consultation you received with the doctor?
Please answer the questions by ticking the response which most closely matches your experience.
All the treatment options were fully explained to me. I was given as much as much information as I wanted to know Treatment options were very briefly discussed with me The doctor did mention different treatments, but I did not really understand I did not feel that I was given a choice about treatment
Add any other comments you wish to make in the box below
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The power of a good quote
“The best thing was getting the date for the operation, I was given a date that suited me and was given the letter to show my boss .”
“Everything was completed in one morning, I saw the Consultant went to pre-assessment and got my surgery date, this meant that I did not
have to take further time off work”
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Back to you measurement strategy…
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So what are we going to cover
Measurement for Improvement What is Statistical Process Control (SPC) Understanding Variation Benchmarking Build you own SPC charts
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What do you know?
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Task
What do you know about the following: Mean Variation Special causes Standard deviation
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What is SPC?
P is for ProcessWe deliver our work through processes
S is for Statisticalbecause we use some statistical concepts to help
us understand our processes
C is for ControlAnd by this we mean predictable
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What is SPC for?
A way of thinking Measurement for improvement - a simple tool
for analysing data Better way for making decisions Evidence based management Easy, sustainable
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What Can It Do For Me? To identify if a process is sustainable
are your improvements sustaining over time
To identify when an implemented improvement has changed a process and it has not just occurred by chance
To understand that variation is normal and to help reduce it
To understand processes - This helps make better predictions and improves decision making
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What about this?
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Where have we come from?
Compare to some arbitrary fixed point in the past the average (median) waiting time of those on the
list, at 2.97 months, fell slightly over the month, and remains lower than at March 1997 (3.04 months).
Show percentage change this month and to some arbitrary fixed point in the past the number of over 12 month waiters fell this
month by 3,800 (7.4%) to 48,100, and are now 24,000 (33%) below the peak at June 1998
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Comparing this year to last year
Delayed Discharges (w eekly Sitreps)
0
1000
2000
3000
4000
5000
6000
7000
W eeks from October
No. o
f del
ayed
di
scha
rges
2000/012001/02
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Waiting time performance
2000Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec85 76 83 58 62 80 53 71 64 82 55 78
2001Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec39 19 31 22 25 51 40 11 31 54 28 16
What can you tell me about the following data?
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Is this better?
Average wait in days
0102030405060708090
Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov
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Or better still?
Average wait in days
0
20
40
60
80
100
120
Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov
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Common management reactions to data
take 3 different numbers6 possible (& random) sequences
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"Upward Trend"?
0
1
2
3
4
1 2 3
"Setback"?
0
1
2
3
4
1 2 3
"Downturn"?
0
1
2
3
4
1 2 3
"Turnaround"?
0
1
2
3
4
1 2 3
"Rebound"?
0
1
2
3
4
1 2 3
"Downward Trend"?
0
1
2
3
4
1 2 3
3 points can give 6 possible (& random) sequences
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Unacceptable decision-making
Develop polite impatience with guesswork - single figure decision making shooting from the hip anecdotal data debate “known” solutions ?arbitrary targets and standards
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What else can it do for me?
Recognise variation Evaluate and improve underlying process
is it stable? can it meet “targets”?
Help drive improvement has the process really improved or is it just chance? is it sustainable?
Prove/disprove assumptions and (mis)conceptions Use data to make predictions and help planning Reduce data overload
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What is a control chart
Upper process limit
Mean
Lower process
limit
0
10
20
30
40
50
60
70
80
F M A M J J A S O N D J F M A M J J A S O N D
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So what are we going to cover
Measurement for Improvement What is Statistical Process Control (SPC) Understanding Variation Benchmarking Build you own SPC charts
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What is Benchmarking?
Benchmarking compares practice and performance across organisations in order to identify ways to improve
It is in essence, the identification, understanding, dissemination and implementation of best practice
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Benchmarking encompasses…
Regular comparison of aspects of performance (functions and processes) with different practitioners
Identifying gaps in performance Seeking fresh approaches to bring about
improvements in performance Following through with implementation of
improvements Monitoring progress and reviewing the benefits
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Why is Benchmarking important?
Benchmarking can be used to improve the overall performance of organisations through sharing and developing different practices
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What are the benefits of Benchmarking?
Improving quality and productivity Improving performance measurement Learning from others and greater confidence
in developing and applying new approaches Greater involvement and motivation of staff
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Comparing performance of different people or services
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Measuring for judgement
The minister has decided that prescribing aspirin for patients on the CHD register is a Good Thing
Non-compliance will henceforth be a hanging offence
But who to hang? He has been given the latest data on several
Health Services
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Who’s doing well?
The % patients on CHD register who are being treated with aspirin
February 2002
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
A B C D E F G H I J K
Average
Gold stars to Health Services A & B
Hanging for Health Services I,
J & K
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Why not traditional?
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Remember who’s doing well?
The % patients on CHD register who are being treated with aspirin
February 2002
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
A B C D E F G H I J K
Average
Gold stars to Health Services A & B
Hanging for Health Services I,
J & K
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A different way of presenting it
The % patients on CHD registerwho are being treated with aspirin
February 2002
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
A B C D E F G H I J K
Average
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Control limits added
The % patients on CHD registerwho are being treated with aspirin
February 2002
50.0%
60.0%
70.0%
80.0%
90.0%
100.0%
A B C D E F G H I J K
AverageLowerUpper
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So what are we going to cover
Measurement for Improvement What is Statistical Process Control (SPC) Understanding Variation Benchmarking Build you own SPC charts
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What is Variation?
Everything varies - no two things are alike
Recognising this is a start but not enough: must understand it’s effect on customers and then manage it as appropriate
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Task
In pairs think of reasons why your journey driving to work may be delayed on a morning. – Write on post its
You have a few mins and we’ll come back to this later.
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Different Types of Variation
Common Cause = Stable in time & therefore relatively predicatable
For example traffic lights which hold us up today would probably hold us up in the next week.
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Different Types of Variation
Special Cause = Irregular in time and therefore unpredictable.
For Example a police convoy escorting a wide load
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Practical interpretation of the Standard Deviation
Mean - 3s
Mean
Mean + 3s
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3s and the Control Chart
6s
3s
3s
UCL
LCL
Mean
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Reducing Variation
Walter Shewhart - Statistician 1920’s Bell Telephones: every failure led to an
alteration to the telephones. Good idea? Started to look at limits and Common &
Special Causes
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“A phenomenon will be said to be controlled when, through the use of past experience, we can predict, at least within limits, how the phenomenon may be expected to vary in the future”
Shewart - Economic Control of Quality of Manufactured Product, 1931
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Task
Back to the Task-Journey to work
Which are common causes of variation? And which are special causes?
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0
20
40
60
80
100
120
Consecutive trips
Min.
My trip to work
average
Accident on motorway
tyre had puncture
Borrowed helicopter
Stopped by police for speeding
School holidays
COMMON CAUSE VARIATION - Points within the yellow lines is variation you would expect - normal variation of the process (my trip to work) E.G. traffic lights, pedestrians, rush hour
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CONTROLLED VARIATION
stable,consistent pattern of variation
“chance”/constant causes
0
10
20
30
40
50
60
70
80
F M A M J J A S O N D J F M A M J J A S O N D
Upper process
limit
Mean
Lower process
limit
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UNCONTROLLED VARIATION
•pattern changes over time
•“assignable”/special causes
0
20
40
60
80
100
F M A M J J A S O N D J F M A M J J A S O N D
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2 Ways to improve a process
If controlled variationprocess is stable and predictablevariation is inherent to process therefore, process must be changed
If uncontrolled variationprocess is unstable and unpredictablevariation caused by factor(s) outside processcause should be identified and “sorted”
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2 dangers to beware of
Reacting to special cause variation by changing the process
Ignoring special cause variation by assuming “its part of the process”
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Pause:
Think of some examples in your own area:
- Common cause variation- Special cause variation
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So what are we going to cover
Measurement for Improvement What is Statistical Process Control (SPC)-the
math Understanding Variation Benchmarking Build you own SPC charts
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How to interpret the results
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Rules for special causes
RULE 1 Any point outside one of the control limits
RULE 2 A run of seven points all above or all below the centre line, or all increasing or all decreasing. RULE 3 Any unusual pattern or trends within the control limits.
RULE 4 The number of points within the middle third ofthe region between the control limits differs markedly from two-thirds of the total number of points.
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XX
X
X
X
X
X
X
X
LCL
UCL
MEAN
X
X
X
X
XX
X
X
X
X
LCL
UCL
MEAN
X
Point above UCL
Point below LCL
SPECIAL CAUSES - RULE 1
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Rules for special causes
RULE 1 Any point outside one of the control limits
RULE 2 A run of seven points all above or all below the centre line, or all increasing or all decreasing. RULE 3 Any unusual pattern or trends within the control limits.
RULE 4 The number of points within the middle third ofthe region between the control limits differs markedly from two-thirds of the total number of points.
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MEAN MEAN
Seven points above centre line
SPECIAL CAUSES - RULE 2
LCL
UCL
LCL
UCL
XX
X
X
X X
X
XX
XX X
X
XX
X X
X
XX
X
Seven points below centre line
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MEAN MEAN
Seven points in a downward direction
SPECIAL CAUSES - RULE 2
LCL
UCL
LCL
UCL
XX
XX
X
XX
X
X X
X
XX X
XX
XX
X
X
X
Seven points in an upward direction
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Rules for special causes
RULE 1 Any point outside one of the control limits
RULE 2 A run of seven points all above or all below the centre line, or all increasing or all decreasing. RULE 3 Any unusual pattern or trends within the control limits.
RULE 4 The number of points within the middle third ofthe region between the control limits differs markedly from two-thirds of the total number of points.
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SPECIAL CAUSES - RULE 3
X
X
X
X
X
X
XX X
X
X
X
X
X
X
X
X
X
X
X
Cyclic pattern
X
X X
XX
XX
X
X
X
X
X
X
X
X X
X
X
XLCL
UCL
LCL
UCL
Trend pattern
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Rules for special causes
RULE 1 Any point outside one of the control limits
RULE 2 A run of seven points all above or all below the centre line, or all increasing or all decreasing. RULE 3 Any unusual pattern or trends within the control limits.
RULE 4 The number of points within the middle third ofthe region between the control limits differs markedly from two-thirds of the total number of points.
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SPECIAL CAUSES - RULE 4
Considerably less than 2/3 of all the points fall in this zone
X
XX X X
X
X
X
X
X
XX
XX
X
XX
LCL
UCL
X
X
X
X
X
X
XX
X
X
X
XX
X
XX
X
X
XX
X X
X
X
XX
LCL
UCL
Considerably more than 2/3 of all the points fall in this zone
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NOW FOR SOME MATHS!
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Use individual values to calculate the Mean
Difference between 2 consecutive readings, always positive = Moving Range, MR
Calculate the Mean MR
One standard deviation/sigma = (Mean MR) ÷ d2 * s or σ
Upper Process Limit (UPL) = Mean + 3 s
Lower Process limit (LPL) = Mean - 3 s
* d2 is a constant for given subgroups of size n (n = 2, d2 = 1.128)
H.L. Harter, “Tables of Range and Studentized Range”, Annals of Mathematical Statistics, 1960.
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Construction and Interpretation of (X, Moving
R) Chart
Run chart, running record, time order sequenceCalculate the meanCalculate upper and lower process limitsInterpret the chart for process controlFind the causes of real change & act to improve
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Calculation of the mean
Σ means “ sum of ”
Mean =
= X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + … + X19 + X20 2
0
X
= 5.9 + 0.4 + 0.7 + 4.7 + 2 + 1.3 + 0.8 + … + 2
20Mean = 2.545
X nXΣ=
X19 X20
1.5 2
SPC 33
n = number of results
X1 X2 X3 X4 X5 X6 X7 X8
5.9 0.4 0.7 4.7 2 1.3 0.8 0.7
= 50.9
20
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Calculation of mean moving range
Σ means “ sum of ”
Moving Range
= R1 + R2 + R3 + R4 + R5 + R6 + R7 + R8 + … + R19
19
MR
= 5.5 + 0.3 + 4 +2.7 + 0.7+0.5 + 0.1 + 1.8 +0 + 0.8 +0.7 + 3.7 + 0.5 + 3.8 + 0.1 + 0.2 + 0.6 +0.8 + 0.5
19
= 1.437 MR nRΣ=
R18
0.8 0.5
SPC 33
n = number of moving ranges
R1 R2 R3 R4 R5 R6 R7 R8
5.5 0.3 4 2.7 0.7 0.5 0.1 1.8
= 27.3
19
R19
= 27.3
19
R
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Calculate
= 1.437
1.128
SPC 33
= 1.274
Calculation of σ = 1 standard deviation
From the formula R
d2
=
d2 is always 1.128 for a sample size of 2 (difference between 2 values)
Never use the standard
deviation key on a
calculator to get this figure
σ
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Calculation of control limits
Calculate UCLX (Upper Control Limit) for X
SPC 33
Calculate LCLX (Lower Control Limit) for X
= X + 3 0
= X - 3 0
= 2.545 + 3.822
= 6.367 Plot on graph
= 2.545 - 3.822 = -1.277
can’t have negative so take to be 0 Plot on graph
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And that’s how you get one of these!
- a Control ChartUpper process limit
Mean
Lower process
limit
0
10
20
30
40
50
60
70
80
F M A M J J A S O N D J F M A M J J A S O N D
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Things to remember
only need 20 data points to set up a control chart “standard deviation”
this is not the one used in formulae in Excel or on calculators.
d2 constant sample size of 2 refers to the sample size for moving range
(which is nearly always 2) - NOT the number of data points
20 data points produces 19 moving ranges
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Remember the 2 ways to improve a process
If controlled variation process is stable variation is inherent to process therefore, process must be changed
If uncontrolled variation process is unstable variation is extrinsic to process cause should be identified and “treated”
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CONTROLLED VARIATION
stable,consistent pattern of variation
“chance”/constant causes
0
10
20
30
40
50
60
70
80
F M A M J J A S O N D J F M A M J J A S O N D
Upper process
limit
Mean
Lower process
limit
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Remember the 2 ways to improve a process
If controlled variation process is stable variation is inherent to process therefore, process must be changed
If uncontrolled variation process is unstable variation is extrinsic to process cause should be identified and “treated”
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UNCONTROLLED VARIATION
•pattern changes over time
•“assignable”/special causes
0
20
40
60
80
100
F M A M J J A S O N D J F M A M J J A S O N D
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DEFINING LACK OF CONTROL
A single point falls outside the 3-sigma control limits
2 out of 3 successive values fall on the same side of, and more than 2-sigma units from the central line
4 out of 5 successive values fall on the same side of, and more than 1-sigma unit from the central line
8 (or 7??) successive values fall on the same side of the central line, or all increasing or all decreasing
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We live in a world filled with variation - and yet there is very little recognition or understanding of variation
WILLIAM SCHERKENBACH
Variation
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So what are we going to cover
Measurement for Improvement What is Statistical Process Control (SPC) Understanding Variation Benchmarking Build you own SPC charts
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SPC Spreadsheet Formulae
A B C D E F GDate Field
Data Average Moving Range
Average Moving Range
Lower Control Limit
Upper Control Limit
=AVERAGE(B2:B10)
=ABS(B3-B2) =AVERAGE(D3:D10)
=MAX(0,C2-
(3*(E2/1.128)))
=C2+(3*(E2/1.128))
Average of all the data list
The difference between
consecutive numbers
Average of the moving range
list
Average minus 3
multiplied by Average
moving range divided by
1.128
Average plus 3 multiplied by Average
moving range divided by
1.128
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Example Data SetAdmissionsInpatients Average MR Average MRLower LimitUpper limit01-Feb-02 20 21.90 13.44444 0 57.656502-Feb-02 6 21.90 14 0 57.656503-Feb-02 14 21.90 8 0 57.656504-Feb-02 46 21.90 32 0 57.656505-Feb-02 41 21.90 5 0 57.656506-Feb-02 32 21.90 9 0 57.656507-Feb-02 40 21.90 8 0 57.656508-Feb-02 9 21.90 31 0 57.656509-Feb-02 2 21.90 7 0 57.656510-Feb-02 9 21.90 7 0 57.6565
Admissions Inpatients Average MR Average MR Lower Limit Upper limit20 =AVERAGE(B2:B11) =AVERAGE(D3:D11) =MAX(0,C2-(3*E2/1.128)) =C2+(3*(E2/1.128))6 21.9 =ABS(B2-B3) 0 57.656501182033114 21.9 =ABS(B3-B4) 0 57.656501182033146 21.9 =ABS(B4-B5) 0 57.656501182033141 21.9 =ABS(B5-B6) 0 57.656501182033132 21.9 =ABS(B6-B7) 0 57.656501182033140 21.9 =ABS(B7-B8) 0 57.65650118203319 21.9 =ABS(B8-B9) 0 57.65650118203312 21.9 =ABS(B9-B10) 0 57.65650118203319 21.9 =ABS(B10-B11) 0 57.6565011820331
Table 1. Shows what the data should look like.
Table 2. Shows how the formula should look.
Average, Lower limit and Upper limit should only have the formula in the first row and the value pasted for the entire dataset.
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Example SPC Chart
010203040506070
I npatients
Average
Lower Limit
Upper limit
Within this process Trust x could expect to see between 0 and 58 admitted Inpatients per day, with and average of 22. Therefore, there needs to be 58 inpatient beds available everyday to match current demand.
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Task
Split into equal groups around each laptop At least one analyst in each Let someone use the computer who is not
use to working with excel Others can coach them on how to use it You have a data file on your computers
called example.xls Compose a SPC chart and feedback
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That’s all Folks !!!Any Last Questions?
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Useful references Donald Wheeler. Understanding Variation. Knoxville: SPC
Press Inc, 1995 Walter A Shewhart. Economic control of quality of
manufactured product. New York: D Van Nostrand 1931. American Society for Quality
www.asq.org/about/history/shewhart.html WE Deming. Out of the crisis. Massachusetts: MIT 1986 Donald Wheeler. Advanced topics in statistical process control.
The power of Shewhart's charts. Knoxville: SPC Press Inc, 1995 Donald M Berwick. Controlling variation in health care: a
consultation from Walter Shewhart. Med Care 1991; 29: 1212-25.