Post on 11-Dec-2015
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 data
0
20
40
60
80
100
Perc
ent
Process: Cardiac Surgical Patients with Controlled Post-operative Serum Glucose
Process Improvement: Isolated Femur Fractures
0
200
400
600
800
1000
1200
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64Sequential Patients
Min
utes
ED
to
OR
per
P
atie
nt
Median 429
1. Determine if change is an improvement
Three Uses of Run Charts in Quality Work
The Data Guide, p 3-18
Median 429
Three Uses of Run Charts in Quality Work
The Data Guide, p 3-18
2. Determine if improvement is sustained
Holding the Gain: Isolated Femur Fractures
0
200
400
600
800
1000
1200
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64Sequential Patients
Min
utes
ED
to
OR
per
P
atie
nt
Median 429
3. Make process performance visible
Three Uses of Run Charts in Quality Work
The Data Guide, p 3-18
Current Process Performance: Isolated Femur Fractures
0
200
400
600
800
1000
1200
1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64Sequential Patients
Min
utes
ED
to
OR
per
P
atie
nt
How Do We Analyze a Run Chart?
• Visual analysis first• If pattern is not clear, then apply probability based rules
The Data Guide, p 3-10
% Timely Reperfusion
0
10
20
30
40
50
60
70
80
90
100
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
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?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.
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.
Two many runs? Plain language interpretation
Astronomical Data Point?
Understanding Variation
Walter Shewhart
(1891 – 1967)W. Edwards Deming
(1900 - 1993)
The Pioneers of 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 differ from 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
40
50
60
70
80
90
100
M-04
M J S N J -05
M M J S N J -06
M M J S N J -07
M M
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
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
30
35
40
45
50
55
UCL = 45.83
Mean = 39.93
LCL = 34.03
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
22
A43.89
24
S31.45
22
MonthED/100
Returnsu chart
0.0
0.2
0.4
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
0.4
0.6
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
5
10
15
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
5
10
15
20
Con
tam
inat
ions
/100
0Blood Culture Contaminations Org 1: last 2 years
u chart
20
25
30
35
40
45UCL
Mean
LCL
Con
tam
inat
ions
/100
0Blood Culture Contaminations Org 1: last 2 years
u chart
20
25
30
35
40
45UCL
Mean
LCL
Common Cause
Note: A point exactly on the centerline does not cancel or count towards a shift
Health Care Data Guide, p. 116
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
Note: A point exactly on the centerline does not cancel or count towards a shift
Health Care Data Guide, p. 116
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.
Perla R, Provost L, Murray S (2010) The run chart: a simple analytical tool for learning from variation in healthcare processes, BMJ Qual Saf 2011 20: 46-51.
Associates in Process Improvement website www.apiweb.org