# Measurement by yaosaigeng

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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)
So what are we going to cover

 Measurement for Improvement
 What is Statistical Process Control (SPC)
 Understanding Variation
 Benchmarking
 Build you own SPC charts
So what are we going to cover

 Measurement for Improvement
 What is Statistical Process Control (SPC)
 Understanding Variation
 Benchmarking
 Build you own SPC charts
Measurement for Improvement
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.
There are three kinds of lies:

lies, damned lies and
statistics

After Mark Twain

 Is the routine data you collect and distribute
100% accurate?
 Is it complete rubbish?
 So it must be somewhere in between
Issues

   Definitions
   Accuracy
   Consistency
   Timing
The information vicious circle
Information
is not used

Information is:
Inaccurate
Incomplete
Late
Inconsistent

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?
Issues

 Timing
 Definitions
 Accuracy
 Consistency
Data types
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
Which types are you collecting?
Types of measurement
Different types of measurements
 measurements for judgement
league tables
Performance Indicators

 Measure probability not certainty
 Are better in groups
 Are better at identifying poorer performance
 Should not be used for league tables
Different types of measurements
 measurements for diagnosis
to show where the problems are
lots of measures
comparative data useful
 measurements for improvement
to show if improvement are being made
linked to the project objectives and aims
a few specific measures
Measurement for Improvement

or how do we know
that a change is an improvement?
Model for improvement
What are we trying to
accomplish?
How will we know that a
change is an improvement?
What changes can we make that
will result in the improvements
that we seek ?

Act       Plan

Study        Do
Model for improvement
What are we trying to         project aims
accomplish?
How will we know that a
change is an improvement?
What changes can we make that
will result in the improvements
that we seek ?

Act       Plan

Study        Do
Model for improvement
What are we trying to         project aims
accomplish?
How will we know that a         global measurements
change is an improvement?
What changes can we make that
will result in the improvements
that we seek ?

Act       Plan

Study        Do
Model for improvement
What are we trying to         project aims
accomplish?
How will we know that a         global measurements
change is an improvement?
What changes can we make that
will result in the improvements   change principles
that we seek ?

Act       Plan

Study        Do
Building Improvement Knowledge

P      Changes that
A
D
result in
S          improvement
D
S
Improvement

P
S           A
D
A
P
A
P
S
D

Time
Measurement for improvement:

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
Change areas, aims and measures
should be related
 Area - Effective Delivery of Health Care
treatment
 Measure - Reduce the number of days
between referral and first definitive treatment

Example from
Action On
programme
Measuring quantitative outcomes
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
An example - hospital cancellations

Monitor progress to target

16
14
12
Percentage

10
8       Baseline = 15%
6
4                                   Target = 5%
2
0
0        1           2    3           4       5   6
Time
How are we doing?
Setting the baseline
 Baseline period must be representative
 Small numbers issue
 Baseline period can be greater than
monitoring frequency
Over what period to measure baseline?

DNA rate with large variation

25%
Average = 8.7%
20%
Percentage

15%

10%

5%

0%
1         2          3          4   5   6
Time
Over what period to measure baseline?

DNA rate with small variation

25.0%
Average = 8.7%
20.0%
Percentage

15.0%

10.0%

5.0%

0.0%
1          2         3          4   5   6
Time
How will we know?
Tips on measurement
 Measurement periods
Census 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 precisely
eg process time in hours for patients from triage to
How much will we improve? -
Expressing the measurement of change
 Be realistic in your expectations
Don’t think you can reduce error rate from 50% to 0%
 Mostly express values to one decimal place
DNA rate = 5.6% (not 6%)
 Express target as a value not as an improvement
If baseline is 5 patients/hour and you want to improve by 10%
then state target as 5.5 patients/hour
 Avoid confusion over percentages
Baseline is 10% and you want to improve (reduce) by 25%
then state target as 7.5%
Process Mapping

Understand the process before settling
Route A - Self-referral

Arrival
in A&E                        W3                 W4

W1

Triage          Seen by               Seen by         DTA
A&E                  o/c team

Indicative waits
W1 - 5 minutes
W2 - by category
W3 - 1 hour
W2       W4 - 1 hour                   W5   Leave
W5 - 4 hours
A&E
We want to improve the overall patient journey

Global measure: % patients seen within
recommended waiting times at three key identified
stages in care
But Changes are made at specific points

Global measure: % patients seen within
recommended waiting times at three key identified
stages in care
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 measures
can be temporary
to monitor change ideas
Global measures
 are permanent
to monitor overall improvement
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
Impact of changes on global
measures (hopefully!)
Average waiting times across the care
pathway in days
60
50              Change 1
40                         Change 3
30
20
Change 2
10
0
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
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
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?
Patient experience monitoring
Why?

To use patient feedback to improve services
Agenda

 evaluating patient experience
 quantitative versus qualitative
 rating versus reporting
 practical hints and tips

On your table, brainstorm ideas for measuring
and monitoring patients’ experience with a
service:

How can we measure what patients think of the
service?
Approaches to monitoring

quantitative                                     qualitative
•structured       •semi-structured
•unstructured
questionnaires     interviews
interviews
•“tick box”       •questionnaires that
•patient focus groups
surveys          combine “tick box”
•critical incident
with comment spaces
technique
Report experience
don’t rate satisfaction
How satisfied were you with the consultation you received with the doctor?
very satisfied

X
quite satisfied
satisfied
quite dissatisfied
very dissatisfied

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
Designing a questionnaire or survey
 goal of the research           What do you want to know?
 research method                How will you find out?
 questionnaire design           What sort of questions?
 patient sample                 How many will you ask?
 frequency of data collection   How often will you ask them?

 data collection methods        How will you ask them?
 systems for analysis           How will you analyse the data?
 reporting systems              How will you report the results and
to whom?
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
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
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”
Back to you measurement
strategy…
So what are we going to cover

 Measurement for Improvement
 What is Statistical Process Control (SPC)
 Understanding Variation
 Benchmarking
 Build you own SPC charts
What do you know?

What do you know about the following:
 Mean
 Variation
 Special causes
 Standard deviation
What is SPC?

 P is for Process
We deliver our work through processes
 S is for Statistical
because we use some statistical concepts to help
us understand our processes
 C is for Control
And by this we mean predictable
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
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
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
Comparing this year to last year

Delayed Discharges (w eekly Sitreps)                          2000/01
7000                                                                                2001/02
No . o f d e laye d

6000
d is ch ar g e s

5000
4000
3000
2000
1000
0
2

5

8
42

45

48

51

11

14

17

20

23

26

29

32

35

38

41
W eeks from October
Waiting time performance

 What can you tell me about the following
data?

2000
Jan Feb Mar Apr May Jun   Jul   Aug Sep Oct Nov Dec
85 76 83 58 62 80         53    71 64 82 55 78

2001
Jan Feb Mar Apr May Jun   Jul   Aug Sep Oct Nov Dec
39 19 31 22 25 51         40    11 31 54 28 16
Is this better?

Average wait in days

90
80
70
60
50
40
30
20
10
0
Jan   Mar   May   Jul    Sep   Nov   Jan   Mar   May   Jul   Sep   Nov
Or better still?

Average wait in days

120
100
80
60
40
20
0
Jan   Mar   May    Jul    Sep   Nov   Jan   Mar   May   Jul   Sep   Nov
Common management reactions to
data
 take 3 different numbers
 6 possible (& random) sequences
3 points can give 6 possible (& random) sequences
"Upward Trend"?               "Setback"?                  "Downturn"?

4                               4                        4

3                               3                        3

2                               2                        2

1                               1                        1

0                               0                        0
1

2

3

1

2

3

1

2

3
"Turnaround"?                "Rebound"?               "Downward Trend"?

4                               4                        4

3                               3                        3

2                               2                        2

1                               1                        1

0                               0                        0
1

2

3

1

2

3

1

2

3
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
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
What is a control chart
80                                                    Upper
70                                                   process
limit
60
50                                                   Mean

40
Lower
30                                                   process
20                                                     limit

10
0
F M A M J J A S O N D J F M A M J J A S O N D
So what are we going to cover

 Measurement for Improvement
 What is Statistical Process Control (SPC)
 Understanding Variation
 Benchmarking
 Build you own SPC charts
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
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
Why is Benchmarking important?

 Benchmarking can be used to improve the
overall performance of organisations through
sharing and developing different practices
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
Comparing performance of
different people or services
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
Who’s doing well?
Gold stars
to Health
The % patients on CHD register who are being
Services A                     treated with aspirin
&B                             February 2002

100.0%

90.0%

80.0%
Average
70.0%
Hanging
60.0%
for Health
50.0%                                                     Services I,
A   B   C   D   E   F   G   H   I   J   K
J&K
Remember who’s doing well?
Gold stars
to Health             The % patients on CHD register who are being
Services A                         treated with aspirin
February 2002
&B
100.0%

90.0%

80.0%
Average
70.0%
Hanging
60.0%
for Health
50.0%                                                     Services I,
A   B   C   D   E   F   G   H   I   J   K
J&K
A different way of presenting it

The % patients on CHD register
who are being treated with aspirin
February 2002

100.0%

90.0%

80.0%

70.0%
Average

60.0%

50.0%
A   B   C   D    E   F   G    H    I   J     K

The % patients on CHD register
who are being treated with aspirin
February 2002

100.0%

90.0%

80.0%
Average
70.0%                                                      Lower
Upper

60.0%

50.0%
A    B   C   D    E   F   G    H    I   J     K
So what are we going to cover

 Measurement for Improvement
 What is Statistical Process Control (SPC)
 Understanding Variation
 Benchmarking
 Build you own SPC charts
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

 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.
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.
Different Types of Variation

 Special Cause = Irregular in time and
therefore unpredictable.
 For Example a police convoy escorting a wide
Practical interpretation of the
Standard Deviation

Mean - 3s         Mean          Mean + 3s
3s and the Control Chart
UCL
3s
Mean
3s
LCL

6s
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
“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

Back to the Task-Journey to work

 Which are common causes of variation?
 And which are special causes?
My trip to work
Accident on     tyre had      Stopped by police for
motorway        puncture      speeding

120

100

80
average
Min. 60
40      School holidays
20
Borrowed helicopter
0

COMMON CAUSE VARIATION -
Points within the yellow lines is
Consecutive trips
variation you would expect - normal
variation of the process (my trip to
work) E.G. traffic lights, pedestrians,
rush hour
CONTROLLED VARIATION

 stable,consistent pattern of variation

 “chance”/constant causes

80                                                  Upper
process
70                                                   limit
60
50                                                 Mean
40
30                                              Lower
process
20                                               limit
10
0
F MA M J J A S O N D J F MA M J J A S O N D
UNCONTROLLED VARIATION
•pattern changes over time

•“assignable”/special causes

100

80

60

40

20

0
F MA M J J A S O N D J F MA M J J A S O N D
2 Ways to improve a process

 If controlled variation
process is stable and predictable
variation is inherent to process
therefore, process must be changed
 If uncontrolled variation
process is unstable and unpredictable
variation caused by factor(s) outside process
cause should be identified and “sorted”
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”
Pause:

Think of some examples in your own area:
- Common cause variation
- Special cause variation
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
How to interpret the results
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 of
the region between the control limits differs
markedly from two-thirds of the total number
of points.
SPECIAL CAUSES - RULE 1
Point above UCL

X
UCL                                            UCL
X              X
X
X
X                                                      X
X           X
X
MEAN           X                               MEAN
X
X                                                      X       X
X                                                          X

X
X

LCL                                            LCL
X

Point below LCL
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 of
the region between the control limits differs
markedly from two-thirds of the total number
of points.
SPECIAL CAUSES - RULE 2
Seven points above
centre line

UCL                                            UCL

X
X
X                      X
X                              X
X                       X
X                          X
MEAN                                           MEAN
X                                                                              X
X                                                          X                   X
X                                                                      X
X
X                                                          X
X

LCL                                            LCL

Seven points below
centre line
SPECIAL CAUSES - RULE 2
Seven points in a
downward direction

UCL                                            UCL
X   X
X
X
X
X
X                           X
X                                          X
X                                                  X
MEAN                                           MEAN
X X
X
X                   X
X
X       X
X

LCL                                            LCL

Seven points in an
upward direction
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 of
the region between the control limits differs
markedly from two-thirds of the total number
of points.
SPECIAL CAUSES - RULE 3
Cyclic                                                                   Trend
pattern                                                                  pattern

UCL                                                               X
UCL
X                                         X
X
X                                                                                                                      XX
X                                                 X                                                                        X
X                                         X                                                                               XX
X
X                                                           X
X                                         X                                                   X
X                                                                                     X
X                                                                   X
X                                                                                                         X
X                                                                     X
X
XX
X                                X                  X
X X                                              X
XX
LCL                                                                   LCL
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 of
the region between the control limits differs
markedly from two-thirds of the total number
of points.
SPECIAL CAUSES - RULE 4

Considerably less than 2/3 of                                              Considerably more than 2/3 of
all the points fall in this zone                                           all the points fall in this zone

UCL                                                                                       UCL
X
X                                               X
X
X
X                           XX
X                                       X
X           X                                               X       X
X                                                                                                          X                           X               X       X
X
X       X               X                       X
X                                   X               X       X
X                                                                                      X
X
X                                           X
X       X           X                       X
X               X           X
X
X
LCL                                                                                       LCL
NOW FOR SOME MATHS!
 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.
Construction and
Interpretation of (X, Moving
R) Chart

Run chart, running record, time order sequence
Calculate the mean
Calculate upper and lower process limits
Interpret the chart for process control
Find the causes of real change & act to improve
Calculation of the mean

X1 X2 X3 X4 X5 X6 X7 X8              X19 X20
5.9 0.4 0.7 4.7 2 1.3 0.8 0.7         1.5 2

Mean = X

= X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + … + X19 + X20
2
= 5.9 + 0.4 + 0.7 + 4.7 + 2 +0 1.3 + 0.8 + … + 2
2
Mean = 2.545                 0

Σ means “ sum of ”                      ΣX       = 50.9
X= n
20
n = number of results

SPC 33
Calculation of mean moving range

R1      R2 R3 R4 R5 R6 R7 R8                                       R18 R19
5.5 0.3        4       2.7 0.7 0.5 0.1             1.8             0.8 0.5

Moving Range MR

= R1 + R2 + R3 + R4 + R5 + R6 + R7 + R8 + … + R19
19
= 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
= 27.3

19
ΣR            = 27.3
R     = 1.437                                                             MR=       n              19
Σ means “ sum of ”
n = number of moving ranges
SPC 33
Calculation of σ = 1 standard deviation
Calculate   σ   From the formula
=
R
Never use
the standard
d2           deviation key
on a
calculator to
get this
= 1.437         figure

1.128

= 1.274

d2 is always 1.128 for a sample size of
2 (difference between 2 values)
SPC 33
Calculation of control limits

Calculate UCLX (Upper Control Limit) for X

=X      +       30

= 2.545         +    3.822
= 6.367                                    Plot on graph

Calculate LCLX (Lower Control Limit) for X

=X       -       30

= 2.545 -           3.822
= -1.277
can’t have negative so take to be 0   Plot on graph

SPC 33
And that’s how you get one of these!
- a Control Chart
80                                                    Upper
70                                                   process
limit
60
50                                                   Mean

40
Lower
30                                                   process
20                                                     limit

10
0
F M A M J J A S O N D J F M A M J J A S O N D
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
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”
CONTROLLED VARIATION

 stable,consistent pattern of variation

 “chance”/constant causes

80                                                  Upper
process
70                                                   limit
60
50                                                 Mean
40
30                                              Lower
process
20                                               limit
10
0
F MA M J J A S O N D J F MA M J J A S O N D
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”
UNCONTROLLED VARIATION
•pattern changes over time

•“assignable”/special causes

100

80

60

40

20

0
F MA M J J A S O N D J F MA M J J A S O N D
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
Variation

We live in a world filled with variation - and
yet there is very little recognition or
understanding of variation

WILLIAM SCHERKENBACH
So what are we going to cover

 Measurement for Improvement
 What is Statistical Process Control (SPC)
 Understanding Variation
 Benchmarking
 Build you own SPC charts

A       B          C                D                E                F               G
Date    Data    Average           Moving          Average           Lower           Upper
Field                             Range           Moving            Control         Control
Range             Limit           Limit
=AVERAGE(        =ABS(B3-B2)      =AVERAGE(        =MAX(0,C2       =C2+(3*(E2
B2:B10)                           D3:D10)             -           /1.128))
(3*(E2/1.12
8)))
Average of all   The difference   Average of the     Average       Average plus
the data list     between        moving range       minus 3        3 multiplied
consecutive          list        multiplied by    by Average
numbers                           Average       moving range
moving range      divided by
divided by         1.128
1.128
Example Data Set
AdmissionsInpatients Average MR           Average MRLower LimitUpper limit
01-Feb-02         20     21.90             13.44444         0 57.6565         Table 1. Shows what the data
02-Feb-02          6     21.90       14                     0 57.6565         should look like.
03-Feb-02         14     21.90        8                     0 57.6565
04-Feb-02         46     21.90       32                     0 57.6565
05-Feb-02         41     21.90        5                     0 57.6565
Table 2. Shows how the formula
06-Feb-02         32     21.90        9                     0 57.6565         should look.
07-Feb-02         40     21.90        8                     0 57.6565
08-Feb-02          9     21.90       31                     0 57.6565         Average, Lower limit and Upper
09-Feb-02          2     21.90        7                     0 57.6565
10-Feb-02          9     21.90        7                     0 57.6565         limit should only have the formula
in the first row and the value
pasted for the entire dataset.
Admissions   Inpatients   Average             MR                    Average MR       Lower Limit               Upper limit
20           =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                         57.6565011820331
14           21.9                =ABS(B3-B4)                            0                         57.6565011820331
46           21.9                =ABS(B4-B5)                            0                         57.6565011820331
41           21.9                =ABS(B5-B6)                            0                         57.6565011820331
32           21.9                =ABS(B6-B7)                            0                         57.6565011820331
40           21.9                =ABS(B7-B8)                            0                         57.6565011820331
9            21.9                =ABS(B8-B9)                            0                         57.6565011820331
2            21.9                =ABS(B9-B10)                           0                         57.6565011820331
9            21.9                =ABS(B10-B11)                          0                         57.6565011820331
Example SPC Chart
70
60                                                             Inpatients
50
40                                                             Average
30                                                             Lower Limit
20
10                                                             Upper limit
0
03

03

03

03

03
20

20

20

20

20
2/

2/

2/

2/

2/
/0

/0

/0

/0

/0
01

03

05

07

09
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.

 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
That’s all Folks !!!
Any Last Questions?
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