Interpreting Run Charts and Shewhart Charts

Agenda

• Features of Run Charts

• Interpreting Run Charts

• A quick mention of variation

• Features of Shewhart Charts

• Interpreting Shewhart Charts

Displaying Key Measures over Time - Run Chart

-Data displayed in time order

-Time is along X axis

-Result along Y axis

-Centre line = median

-One “dot” = one sample of data0

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Perc

ent

Process: Cardiac Surgical Patients with Controlled Post-operative Serum Glucose

Process Improvement: Isolated Femur Fractures

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1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64Sequential Patients

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utes

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to

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Median 429

1. Determine if change is an improvement

Three Uses of Run Charts in Quality Work

The Data Guide, p 3-18

Holding the Gain: Isolated Femur Fractures

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1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64Sequential Patients

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nt

Median 429

Three Uses of Run Charts in Quality Work

The Data Guide, p 3-18

2. Determine if improvement is sustained

Median 429

3. Make process performance visible

Current Process Performance: Isolated Femur Fractures

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1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64Sequential Patients

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Three Uses of Run Charts in Quality Work

The Data Guide, p 3-18

How Do We Analyze a Run Chart?

• Visual analysis first• If pattern is not clear, then apply probability based rules

% Timely Reperfusion

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J-05

F M A M J J A S O N D J-06

F M A M J J A S O N D J-07

F M

Per

cent

Median 35.5

Figure 3.11: Run Chart with ED Team Uncertain About Improvement

Protocol V.3-Test Protocol V.1-Test

Protocol V.2-Test

The Data Guide, p 3-10

Non-Random Signals on Run Charts

A Shift: 6 or more

An astronomical data point

Too many or too few runs

A Trend5 or more

The Data Guide, p 3-11

Evidence of a non-random signal if one or more of the circumstances depicted by these four rules are on the run chart. The first three rules are violations of random patterns and are based on a probability of less than 5% chance of occurring just by chance with no change.

Source: Swed, Frieda S. and Eisenhart, C. (1943)

“Tables for Testing Randomness of Grouping in a Sequence of

Alternatives.” Annals of Mathematical Statistics.

Vol. XIV, pp. 66-87, Tables II and III.

The Data Guide, p 3-14

Trend? Note: 2 same values – only count one

Shift? note: values on median don’t make or break a shift

Shift?

Interpretation?

- There is a signal of a non-random pattern

- There is less than 5 % chance that we would see this pattern if something wasn’t going on, i.e. if there wasn’t a real change

- There is a signal of a non-random pattern

- There is less than 5 % chance that we would see this pattern if something wasn’t going on, i.e. if there wasn’t a real change

Plain Language Interpretation?

There is evidence of improvement – the chance we would see a “shift” like this in data if there wasn’t a real change in what we were doing is less than 5%

There is evidence of improvement – the chance we would see a “shift” like this in data if there wasn’t a real change in what we were doing is less than 5%

Two few or too many runs?-

1. bring out the table2. how many points do we have (not on median?)3. how many runs do we have (cross median +1)4. what is the upper and lower limit?

Two few or too many runs?- **new slide

1. bring out the table2. how many points do we have 203. how many runs do we have (cross median +1) 34. what is the upper and lower limit? 6 - 16

Two few runs?Plain language interpretation

There is evidence of improvement – our data only crosses the median line twice – three runs. If it was just random variation, we would expect to see more up and down.

There is evidence of improvement – our data only crosses the median line twice – three runs. If it was just random variation, we would expect to see more up and down.

What if we had too many runs?Plain language interpretation

There is evidence of a non-random pattern. There is a pattern to the way the data rises and falls above and below the median. Something systematically different. Should investigate and maybe plot on separate run charts.

There is evidence of a non-random pattern. There is a pattern to the way the data rises and falls above and below the median. Something systematically different. Should investigate and maybe plot on separate run charts.

Astronomical Data Point?

Who is using run charts?

Understanding Variation

Walter Shewhart

(1891 – 1967)

W. Edwards Deming

(1900 - 1993)

The Pioneers of Understanding Variation

Understanding Variation:Intended and Unintended Variation

• Intended variation is an important part of effective, patient-centered health care.

• Unintended variation is due to changes introduced into healthcare process that are not purposeful, planned or guided.

• Walter Shewhart focused his work on this unintended variation. He found that reducing unintended variation in a process usually resulted in improved outcomes and lower costs. (Berwick 1991)

Health Care Data Guide, p. 107

Shewhart’s Theory of Variation

• Common Causes—those causes inherent in the system over time, affect everyone working in the system, and affect all outcomes of the system

– Common cause of variation– Chance cause– Stable process– Process in statistical control

• Special Causes—those causes not part of the system all the time or do not affect everyone, but arise because of specific circumstances

– Special cause of variation– Assignable cause– Unstable process– Process not in statistical control

Health Care Data Guide, p. 108

Shewhart Charts

The Shewhart chart is a statistical tool used to distinguish between variation in a measure due to common causes and variation due to special causes

(Most common name is a control chart, more descriptive would be learning charts or system performance charts)

Health Care Data Guide, p. 113

Control Charts – what features are different than a run chart?

Control Charts/Shewhart Charts

upper and lower control limits

•to detect special cause variation

Extend limits to predict future performance

Not necessarily ordered by time

•advanced application of SPC – is there something different between systems

Revised Limits After Improvement

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M-04

M J S N J -05

M M J S N J -06

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Ave

rage

Day

s

CL = 88.16

UL = 97.86

LL = 78.47

CL = 62.1

UL = 71.8

LL = 88.2

Example of Shewhart Chart for Unequal Subgroup Size

Health Care Data Guide, p. 114

Who has been using control charts?

Adapted from Health Care Data Guide, p. 151 & QI Charts Software

%Percent Trauma Patients D/C to Home

M626.7

231

A658.0

241

M597.0

220

J600.0

227

J570.0

260

A651.0

233

S588.0

238

O628.0

250

N626.0

270

D645.0

240

J594.0

227

F600.0

228

M723.0

264

A658.0

278

M598.0

261

J543.0

208

J627.0

268

A658.0

293

S582.0

264

MonthTrauma Volume

# D/C to Homep chart

M A M J J A S O N D J F M A M J J A S25

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50

55

UCL = 45.83

Mean = 39.93

LCL = 34.03

Note: Only for constant subgroup size

Note: A point exactly on the centerline does not cancel or count towards a shift

Health Care Data Guide, p. 116

Rat

e pe

r 100

ED

Pat

ient

sUnplanned Returns to Ed w/in 72 Hours

M41.78

17

A43.89

26

M39.86

13

J40.03

16

J38.01

24

A43.43

27

S39.21

19

O41.90

14

N41.78

33

D43.00

20

J39.66

17

F40.03

22

M48.21

29

A43.89

17

M39.86

36

J36.21

19

J41.78

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A43.89

24

S31.45

22

MonthED/100

Returnsu chart

0.0

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0.6

0.8

1.0

1.2

UCL = 0.88

Mean = 0.54

LCL = 0.19

Rat

e pe

r 100

ED

Pat

ient

sUnplanned Returns to Ed w/in 72 Hours

M41.78

17

A43.89

26

M39.86

13

J40.03

16

J38.01

24

A43.43

27

S39.21

19

O41.90

14

N41.78

33

D43.00

20

J39.66

17

F40.03

22

M48.21

29

A43.89

17

M39.86

36

J36.21

19

J41.78

22

A43.89

24

S31.45

22

MonthED/100

Returnsu chart

0.0

0.2

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0.8

1.0

1.2

UCL = 0.88

Mean = 0.54

LCL = 0.19

Special cause: point outside the limits

%Percent Trauma Patients D/C to Home

M626.7

231

A658.0

241

M597.0

220

J600.0

227

J570.0

260

A651.0

233

S588.0

238

O628.0

250

N626.0

270

D645.0

240

J594.0

227

F600.0

228

M723.0

264

A658.0

278

M598.0

261

J543.0

208

J627.0

268

A658.0

293

S582.0

264

MonthTrauma Volume

# D/C to Homep chart

M A M J J A S O N D J F M A M J J A S25

30

35

40

45

50

55

UCL = 45.83

Mean = 39.93

LCL = 34.03

%Percent Trauma Patients D/C to Home

M626.7

231

A658.0

241

M597.0

220

J600.0

227

J570.0

260

A651.0

233

S588.0

238

O628.0

250

N626.0

270

D645.0

240

J594.0

227

F600.0

228

M723.0

264

A658.0

278

M598.0

261

J543.0

208

J627.0

268

A658.0

293

S582.0

264

MonthTrauma Volume

# D/C to Homep chart

M A M J J A S O N D J F M A M J J A S25

30

35

40

45

50

55

UCL = 45.83

Mean = 39.93

LCL = 34.03

Special cause2 out of 3 consecutive points in outer third of limits or beyond

#

of N

eedl

estic

ksEmployee Needlesticks

c c ha r t

UCL = 12.60

Mean = 5.54

New Needles Test

1-05 3-05 5-05 7-05 9-05 11-05 1-06 3-06 5-06 7-06 9-06 11-06 1-07 2-070

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20

#

of N

eedl

estic

ksEmployee Needlesticks

c c ha r t

UCL = 12.60

Mean = 5.54

New Needles Test

1-05 3-05 5-05 7-05 9-05 11-05 1-06 3-06 5-06 7-06 9-06 11-06 1-07 2-070

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Con

tam

inat

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/100

0Blood Culture Contaminations Org 1: last 2 years

u chart

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45UCL

Mean

LCL

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0Blood Culture Contaminations Org 1: last 2 years

u chart

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45UCL

Mean

LCL

Common Cause

Case Study #1a

Case Study #1b

Percent of cases with urinary tract infection

Case Study #1c

Percent of cases with urinary tract infection Percent of cases with urinary tract infection

Case Study #1d

Percent of cases with urinary tract infection

Case Study #1e

Percent of cases with urinary tract infection

Case Study #1f

Percent of cases with urinary tract infection

Case Study #2a

Percent of patients with Death or Serious Morbidity who are >= 65 years of age

Case Study #2b

Percent of patients with Death or Serious Morbidity who are >= 65 years of age

Case Study #2c

Percent of patients with Death or Serious Morbidity who are >= 65 years of age

Case Study #2d

Percent of patients with Death or Serious Morbidity who are >= 65 years of age

References

BCPSQC Measurement Report http://www.bcpsqc.ca/pdf/MeasurementStrategies.pdf

Langley GJ, Moen R, Nolan KM, Nolan TW, Norman CL, Provost LP (2009) The Improvement Guide (2nd ed).

Provost L, Murray S (2011) The Health Care Data Guide.

Berwick, Donald M, Controlling Variation in Health Care: A Consultation with Walter Shewhart, Medical Care, December, 1991, Vol. 29, No 12, page 1212-1225.

****CHELSEA, can you add Lloyd’s run chart article reference from R2?

Associates in Process Improvement website www.apiweb.org