Measurement for Improvement

Why we look at data graphed over time

Change Made

Change Made

Change Made Change

Made

Change to process made in June

Improvement Science Consulting

O1

P1

Measure Types

O = Outcome Measure

P = Process Measure

B = Balance Measure

S = Process Step Measure

PDSA = Learning Cycle Measure

O2

P2

B B

P1 P2

S3S2S1

System of Feedback

PDSA1

PDSA2

PDSA3

PDSA4

S3S2S1

PDSA1

PDSA2

PDSA3

PDSA4

© Improvement Science Consulting

Break out – developing a useful project level dashboard

• Spend some time developing the measures you would like to include in a project level dashboard

• Which ones are outcome measures?• Which ones are process measures?• Do you have balance measures?• Process step measures?

• Note any gaps in your measures – where would you like to add measures?

Improvement Science Consulting

O1

P1

Measure Types

O = Outcome Measure

P = Process Measure

B = Balance Measure

S = Process Step Measure

PDSA = Learning Cycle Measure

O2

P2

B B

P1 P2

S3S2S1

System of Feedback

PDSA1

PDSA2

PDSA3

PDSA4

S3S2S1

PDSA1

PDSA2

PDSA3

PDSA4

© Improvement Science Consulting

As the scale of the test increases we move from qualitative to quantitative evidence

Improvement Science Consulting

Very small scale test of

a change idea

Large scale test of change idea or

Implementation of a change idea

Evidence primarily

Qualitative

Evidence primarily Quantitative with

noticeable impact on process measures

Sequence of learning and change

Run Charts

Making and Interpreting

So what is a run chart?

Murray and Provost, Pg 3-4

Defining elements of a run chart

• contains at least 10 data points

• must have a median

• tells the story through careful use of annotation

What time is it?

• Stop what you are doing

• Look at your watch

• Write down the time

How do we prevent this?

How do we interpret variation?

• Distinguishing between random variation and non-random variation

• Four rules for discovering non-random variation

• Shift• Trend• Too many or too few runs• Astronomical values

Shift Rule: Six or more consecutive data points either all above or all below the median (skip values on the median and continue counting data points. Values on the median DO NOT make or break a shift.)

Rule 1

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25Mea

sure

or C

hara

cter

istic

Murray and Provost, 3 (11-15)

Why do we need 6 data points?

What is the probability of a coin landing heads or tails?

.5.5 x .5 = .25.5 x .5 x .5 = .125.5 x .5 x .5 x .5 = .0625.5 x .5 x .5 x .5 x .5 = .03125.5 x .5 x .5 x .5 x .5 x .5 = .015625

Trend Rule: Five or more consecutive data points either all going up or all going down. (If the value of two or more consecutive points is the same, ignore one of the points when counting; like values do not make or break a trend.)

Murray and Provost, 3 (11-15)

Rule 2

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Mea

sure

or C

hara

cter

istic

Median=11

Run Rule: Too many or too few runs (A run is a series of points in a row on one side of the median. Some points fall right on the median, which makes it hard to decide which run these points belong to. So, an easy way to determine the number of runs is to count the number of times the data line crosses the median and add one. Statistically significant change signaled by too few or too many runs).

Murray and Provost, 3 (11-15)

Rule 3

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10

Measure

or C

hara

ceristic

Median 11.4

10 Data points not on medianData line crosses onceToo few runs: total 2 runs

Run Rule Reference TableTable for Checking for Too Many or Too Few Runs on a Run Chart

Total number of data

points on the run chart

that do not fall on the

median

Lower limit for the number of runs(< than this number of runs is “too few”)

Upper limit for the number of runs(> than this number of runs is “too many”)

10 3 9

11 3 10

12 3 11

13 4 11

14 4 12

15 5 12

16 5 13

17 5 13

18 6 14

19 6 15

20 6 16

21 7 16

22 7 17

23 7 17

24 8 18

25 8 18Table is based on about a 5% risk of failing the run test for random patterns of data.Adapted from Swed, Feda S. and Eisenhart, C. (1943). “Tables for Testing Randomness of Grouping in a Sequence

of Alternatives. Annals of Mathematical Statistics. Vol. XIV, pp.66 and 87, Tables II and III.

Murray and Provost, 3 (11-15)

Astronomical Data Point (For detecting unusually large or small numbers: Data that is a Blatantly Obvious different value. Everyone studying the chart agrees that it is unusual. Remember: Every data set will have a high and a low – this does not mean the high or low are astronomical).

Murray and Provost, 3 (11-15)

Rule 4

0

5

10

15

20

25

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Measure

ment or C

hara

cte

ristic

When should we transition from using a Run Chart to a Shewhart Control Chart?

Perla, Provost and Murray