Post on 25-Jul-2020
Control Charts: an overviewWilliam Peters
Improvement Coach Professional Development Program
Wave 3, Workshop 2
March 13th, 2017Day 1
Question 2 of the Model for Improvement
What are we trying toaccomplish?
How will we know that achange is an improvement?
What change can we make thatwill result in improvement?
Model for Improvement
PlanAct
DoStudy
Source: Associates for Process Improvement
The most basic
understanding of the
2nd question is this:
MEASUREMENT
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How Do We Know That a Change
is an Improvement?• Improvement is about testing and implementing change
within a system, not measurement!
The purpose of measuring is
to answer critical questions
and to guide intelligent action.
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Distribution of Wait Times
0
10
20
30
40
50
60
5 15 25 35 45 55 65 75 85 95 105
Wait time (days) for Visit
nu
mb
er
of
vis
its
Clinic Wait Times > 30 days
0
2
4
6
8
10
12
14
16
C F G D A J H K B I L E
Clinic ID
# o
f w
ait
s >
30 d
ays
Relationship Between Long
Waits and Capacity
0
5
10
15
20
75 95Capacity Used
# w
ait
tim
es >
30 d
ays
Run Chart Shewhart Chart
Frequency Plot Pareto Chart Scatterplot
IH p. 8-34
There are several
tools we use in
improvement to
make sense of the
variation that is
inherent in EVERY
measure.How else would we do it? Make it up?!
Understanding variation in data
I know these! I DON’T know these!
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before after
1 Improvement is the result of a change in the process
and gains are held …3
result is positive, relevant, meaningful …
2
on a measure that matters to the organization4
Cycle
time
We need data over time to show improvement
REAL SUSTAINABLE IMPROVEMENT
The biggest difference between Run
and Control Charts:
If you are using the Run Chart, you use:
a Run Chart
If you are using the Control Chart, you use:
… with special software
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Differences between Run and Control Charts:
You need: Pencil and Paper or Excel Special Software ($1,200 -
$5,000)
Training: [you tell me, how hard is it?] Definitely more difficult
Who creates: Anyone, front-line safe ideally Specially trained software dude
Interpretation: [you tell me, how hard is it?] More difficult
Center Line: Median Mean
Sensitivity: Easily enough to get the job
done
More sensitive, more
sophisticated, more detail
Tests: 4, pretty easy to learn Dozens of sets, harder to learn
• Shift
• Trend
• Too many, too few runs
• Astronomical
Each one of these is a SET of rules
containing 4 to 8 different rules
How many:
Ideal use:
Data needed:
Cool Factor:
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Differences between Run and Control Charts:
You need: Pencil and Paper or Excel Special Software ($1,200 -
$5,000)
Training: [you tell me, how hard is it?] Definitely more difficult
Who creates: Anyone, front-line safe ideally Specially trained software dude
Interpretation: [you tell me, how hard is it?] More difficult
Center Line: Median Mean
Sensitivity: Easily enough to get the job
done
More sensitive, more
sophisticated, more detail
Tests: 4, pretty easy to learn Dozens of sets, harder to learn
How many: THE run chart 7, depending on the measure
Ideal use: All improvement activities System dashboards
Data needed: At least 10 points, >= better At least 15 points, >= better
Cool Factor: “Impressive!” “Improvement Ninja”
Do by hand: 10-20 minutes? See you in your next life!
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11
0
2
4
6
8
10
12
14
16
Number of Interruptions During Med Passes by Day
Oh look! A Run Chart!
Baseline median 11, extended over test
20 data points, 1 on median, 19 useful observations
10 Runs, at 19 points table shows btw 6 - 15
Begin Sterile
Cockpit test
almost a shift
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Oh look! A CONTROL CHART!
“API” Rules that I use in healthcare
• Trend of 6 descending/ascending
• Shift of 8 above/below mean
• 2 of 3 beyond the second sigma
• Above/below upper/lower control limit
• 15 within the 1st sigma
Sigma zones
1
1
2
3
3
2
(RARE!)
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Oh look! A CONTROL CHART!
“API” Rules that I use in healthcare
• Trend of 6 descending/ascending
• Shift of 8 above/below mean
• 2 of 3 beyond the second sigma
• Above/below upper/lower control limit
• 15 within the 1st sigma
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Remember the “Astro” in the Run Chart?
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
Me
as
ure
me
nt
or
Ch
ara
cte
ris
tic
Blatantly obvious that value is unusual. SMEs mostly agree
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A control chart with UCL crossed
There is no argument here … these data are different!Quality Improvement Fundamentals LLC
It can get hairy …
Remember, I have to teach interpretation to CEO AND the BoD!!!
1 res. had 3 ED trips resulting in 2 admits (HB), 1
res. admitted after fall, 1 res.
admitted d/t unresponsive
episode
1 res. related to repertory distress
and expired, 1 fall, 2 related to
family insistence, 2 admitted with
illness
NICE!
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The most complicated to grasp:
Individuals – Moving Range
SME interpretation: Stable since building addition. Op-Def: Total income from all sources of revenue all levels of
care.
New addition opened
with 13 nursing bedsFoundation Campaign
for the new bus started
The range chart: only interpret UCL/LCL violations!
If range chart is in control we hide it
In a control chart we can “ghost data points”
… IF reasonable and rational people can agree to our
reasoning!
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Notice the “staggered limits” in this P Chart
The larger the denominator, the tighter the limits
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The Control Chart Decision Tree
Variables Data Attributes Data
>1
observation
per
subgroup?
< 10
observations
per
subgroup?
X bar R X bar S XmR
Are the
subgroups of
equal size?
Is there an
equal area of
opportunity?
Occurrences
& Non-
occurrences?
np-chartp-chartu-chartc-chart
Decide on type
of data
Yes
YesYes
Yes
Yes No NoNo
No No
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15.9
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
Percent All-Cause, 30 Day Readmissions
20 data points
8 runs 6/16
6 shift
But never forget …
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point
I&P
or
I&E
&
LEARN
& TAKE
ACTION!