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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
Collecting your data
        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
Issues




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




         Information is:
           Inaccurate
           Incomplete
              Late
          Inconsistent
                    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?
                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:

 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
 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
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
    admission onto appropriate ward
     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
         on your measures
               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
     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 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
  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?
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
                    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?
             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
                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
            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?
                   Task

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
What about this?
      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
 Reduce data overload
        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
Why not traditional?
         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
             Control limits added

                   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
                    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.
    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
  load
     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
                   Task

      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
             SPC Spreadsheet Formulae

 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.
                    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
 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
  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.

				
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