Making Better Decisions Making Better Decisions – WHAIP Info for Action course 4 November 2010 Presenter: Mike Davidge

Making Better Decisions

Making Better Decisions – WHAIP Info for Action course

4 November 2010

Presenter: Mike Davidge


Why do we analyse and present data?

• To understand the variation that lives within our data

• To help us make good management decisions in the way we react to that variation

Source: Robert Lloyd IHI 2006


But often we have presented data in a different way ...


The change in Merthyr is remarkable


Eco-friendly

% Delayed transfers of Care by Type - source SITREPS 7/1/02-31/08/03

-20.00

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3

%Aw aitAss<7 days

%Aw aitAss >7days

%Aw ait Public Funding

%Aw ait Further NHS care

%Aw ait Residential

%Aw ait Domiciliary package

%Patient Family choice

%Other Reasons


Nomination for the Turner Prize% Delayed transfers of Care by Type - source SITREPS 7/1/02-31/08/03

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%Other Reasons

%Patient Family choice

%Await Domiciliary package

%Await Residential

%Await Further NHS care

%Await Public Funding

%AwaitAss >7days

%AwaitAss<7 days


Every picture tells a story … Does it?

Reasons for Delayed Transfer August 2003

%AwaitAss<7 days9%

%AwaitAss >7days9%

%Await Public Funding5%

%Await Further NHS care12%

%Await Residential25%

%Await Domiciliary package

12%

%Patient Family choice18%

%Other Reasons10%

Looks pretty – but cannot show

change over time


Why do our processes vary?

Mrs Jones runs a B&B establishment in a seaside town. She makes breakfast every morning for her guests before going off to do a cleaning job in the local factory.Recently she has been late and her supervisor has given her a verbal warning. What can she do to avoid being late in future?Her son has been measuring her finish times as part of a GCSE maths project. Let’s look at the results


Breakfast finishing times

07:12

08:24

09:36

10:48

12:00

13:12

Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 Day 7 Day 8 Day 9 Day10

Day11

Day12

Day13

Day14

Day15

Day16

Day17

Day18

Day19

Day20

Day21

Day22

Day23

Day24

Why does her finish

time vary?


Walter A. Shewhart(early 1920’s, Bell Laboratories)

• While every process displays variation:

• some processes display controlled variation (common cause)stable,consistent pattern of variationconstant causes/ “chance”

• while others display uncontrolled variationpattern changes over timespecial cause variation/“assignable” cause


Shewhart’s purpose

• Data contains both signal and noise. To be able to extract information, one must separate the signal from the noise within the data.


01020304050607080

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

A typical process control chart


The variation in Mrs Smith’s process• Common Cause = Consistent over time & part of the

process

• Special Cause = Irregular in time & indicates a changed process

• Group the causes into common & special

• Are there any that cannot be grouped?


So why run or control charts?

• One picture, one message

• Shows change over time

• Allows you to see variation

MRSA performanceCity Hospital

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Date of event

Gap

bet

wee

n s

ucc

essi

ve e

ven

ts

Gap betw een specimens Average LCL UCL

Hospital X


Example #1: 2 data pointsMRSA at Hospital X

Are we getting better?

Measuring rare events


Have things improved during 2005?

MRSA specimens at Hospital X

0

2

4

6

8

1012

14

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18

20

Q1 Q2 Q3 Q4

2005

Sp

ecim

en

s p

er

mo

nth

Hospital collecting the date of specimen (event) for each MRSA specimen from January 2005 to February 2006

Improvement this

quarter?


We have 2 quarterly data points - is this an improvement?

Executive Time Series

0

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40

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80

100

J F M A M J J A S O N D

Months

So

me

thin

g I

mp

ort

an

t

Higher is better


Are we assuming something like this?

Executive Time Series - linear trend

0

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100


Months

So

meth

ing

Im

po

rtan

t


But it could be like this ...

Executive Time Series - no trend

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So

meth

ing

Im

po

rtan

t


Or this ...

Executive Time Series - seasonal dip

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So

me

thin

g I

mp

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Or this!

Executive Time Series - one month blip

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100


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So

me

thin

g I

mp

ort

an

t


How have I analysed the data?

• I have calculated the gap between successive specimen events in daysFor example if the first event was on 1 Jan 2005 and the

second was on 17 Jan 2005 then the gap is 16 days

• I have plotted the gaps on a run chart to see whether there is evidence of a change over time

• If MRSA is becoming less prevalent then the gaps between occurrences will get larger

• If MRSA is becoming more prevalent then the gaps between occurrences will get smaller



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Date of event

Gap

betw

een

su

ccessiv

e e

ven

ts


Hospital X

Setting the baselineUsing the first 25 gaps

Average is 5 days

But could have gaps of upto 18

days

Data plotted shows that

performance is predictable

within the limits


Performance over the year


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Date of event

Ga

p b

etw

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n s

uc

ce

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ive

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ts


Hospital X

Baseline period



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Date of event

Ga

p b

etw

ee

n s

uc

ce

ss

ive

ev

en

ts


Hospital X


A more unstable

period with a large gap typically

followed by a short one



0

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Date of event

Ga

p b

etw

ee

n s

uc

ce

ss

ive

ev

en

ts


Hospital X


In January 2006, a series of events one

after the other suggesting a worsening in performance


Questions

• What caused the unstable period in the second half of 2005?Was it simply that having found 1 event people were more

active in spotting another?Did one event cause the next?Was it a data collection issue?

• Did things really get worse in 2006?We don’t have quite enough points on the chart to be certain

but the question is worth asking as the pattern of events does seem to have changed


The MBFC Index

You will need a coin

And two free hands!

Just how competitive can you get?


Sustainable processes

• You need about 6 data points to confirm a change

• You need to continue measuring to assure yourself that the change is sticking

• “In God we trust, all others bring data” W Edwards Deming

Percent using SBAR


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


MEAN MEAN

Seven points above centre line


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


MEAN MEAN

Seven points in a downward direction


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



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


SPECIAL CAUSES - RULE 4Considerably less than 2/3 of all the points fall in this zone

X

XX X X

X

X

X

X

X

X X

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


Rules for special causes

• Rule 1

• Rule 2

• Rule 3

• Rule 4

Any point outside one of the control limits

A run of seven points all above or all below the centre line, or all increasing or all decreasing

Any unusual pattern or trends within the control limits

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


PROCESS 1

X

X

X

X X

XX

X X

X X

X X

X

X

X

X

X

X

X

X

PREDICTABLE? YES / NO / NOT SURE RULE 1 2 3 4


PROCESS 2

X X X

X

XX

X

X

X

X

X X X

X

X X X

X

X

X

X



PROCESS 3

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

XX X X X X

X

X X X

X

X X X

X X


PROCESS 4


PROCESS 5

X

X

X

X

XX

X

X

X

X

X

X

X

X X

X

X

X

X X

X



PROCESS 6

X

X X

X

X

XX

X

X X X

X

X

X

X X X

X

X X

X



SPC tools

• BP chartType BP Chart.xls into Google

• BaselineSee handout

• Winchart from Prism Europehttp://www.winchart.net/

• Chartrunner from PQ Systemshttp://www.pqsystems.com/products/SPC/CHARTrunner/

CHARTrunner.php


Useful references

• Donald Wheeler. Understanding Variation. Knoxville: SPC Press Inc, 1995

• Donald Wheeler. Making sense of data. SPC for the service sector. Knoxville: SPC Press Inc, 2003

• WE Deming. Out of the crisis. Massachusetts: MIT 1986

• American Society for Quality www.asq.org/about/history/shewhart.html

• Donald M Berwick. Controlling variation in health care: a consultation from Walter Shewhart. Med Care 1991; 29: 1212-25.

• Walter A Shewhart. Economic control of quality of manufactured product. New York: D Van Nostrand 1931.


Example #2: How often? Barnsley stroke pathway

time in hospital before getting onto stroke ward


Monthly data shows improvement

Average length of pre-ward stay on Barnsley Stroke Ward

from 01/2007 to 07/2007

0

0.5

1

1.5

2

2.5

3

3.5

1 2 3 4 5 6 7

Months


Weekly data tells a different story

Average length of pre-ward stay on Barnsley Stroke Ward

from 01/2007 to 07/2007

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

9.0

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31

Weeks


Example #3: Link to change SPI project

Medicines management


% medicines reconciledUHW MAU

010

2030

405060

7080

90100

Apr

-07

May

-07

Jun-

07

Jul-0

7

Aug

-07

Sep

-07

Oct

-07

Nov

-07

Dec

-07

Jan-

08

Feb

-08

Mar

-08

Apr

-08

May

-08

% r

eco

nci

led

Form piloted

Form printed

Letter from CDs

Pharmacy included

Documents

Making Better Decisions Making Better Decisions – WHAIP Info for Action course 4 November 2010 Presenter: Mike Davidge