understanding a run chart 101 - upmc
TRANSCRIPT
Understanding a Run Chart 101
Run chart
Control chart
QI Tools for Looking at Data over Time
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Where have we been, where are we now, and
how far are we from our goal?
x and y axes
Label axes
Title chart
Add data and goal
Median will generate
Show start of change Include baseline and
post- change data
Displaying Data: The Run Chart
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Time/Sequence M
easu
re
Title
Change
Median
Goal
Run Chart
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One or more consecutive data points on the same side of the median
Called a run chart because the data line “runs”
back and forth across the median Don’t include points falling on the median Count # of clusters of data and circle them or count
# of times the line crosses the median and add 1
What is a Run?
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Counting Runs
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Median 11.5
Rules to Detect Non-Random Variation
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Shift Trend
Too many or too few runs
Astronomical data point
Rule 1: Shift
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6 or > consecutive data points all above or all below median
Median 11.5
Rule 2: Trend
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5 or > consecutive data points all going up or all down
Median 11.5
Falls Control Chart
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Run Chart (Process Measure)
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Panini Process Implemented
Rule 3a: Too few runs
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Data line crosses median too few times
Median 11.5
Run Chart (Process Measure)
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Rule 3b: Too many runs
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Median 7.5
Data line crosses median too many times
Rule 4: Astronomical data point
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Median 5
Run Chart Template: Tab 1
Static: Before/After Data Can mask behavior of
the process May lead us to make
incorrect decisions
Static versus Dynamic Views of Data
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Static View
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After Change Before Change
Cyc
le T
ime
(min
utes
)
Average Cycle Time
0
10
20
30
40
50
60
70
80
1 2
70 min
25 min
WOW! A “significant drop” from
70 min to 25 min (64% Decrease)
Initiative Start
Dynamic View: Run Chart 1
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Dynamic View: Run Chart 2
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Initiative Start
Dynamic View: Run Chart 3
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Initiative Start
Selected References
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1. Benneyan JC, Lloyd RC, Plsek PE. Statistical process control as a tool for research and healthcare improvement. Qual Saf Health Care. 2003; 12: 458-464.
2. Perla RJ, Provost LP, Murray SK. The run chart: A simple analytical tool for learning from variation in healthcare processes. BMJ Qual Saf ; 2011; 20: 46-51.
Contact information: Linda W. Higgins, PhD, RN 412-268-0813 [email protected]
Or Kate Brownlee, MPM 412-647-5737 [email protected]
Questions?
Quality education tools, resources, and PowerPoints can be found on the Infonet at Infonet.UPMC.com/QualityEducation.