Transcript Boating Beyond Simple Shewart

Boating Beyond
Simple Shewhart
Model 11
Destinations
• Purpose—To provide a quick long distance view.
• Content
– Time or observations between events (g, h, t charts).
– CUSUM and EWMA
– Following a panel of patients
• Small Multiples (Thanks to Jerry Langley)
• Problem of changing denominators.
– Comparing beginning and ending performance
• Prevalence difference vs. Percent Improvement
• Scatterplots
– Smoothed Curves vs. Control Charts
Analyzing Rare Events
Using Time or Occurrences
Between Them
One way to analyze rare events
Another way to analyze rare events
t and g chart summary
• t-chart measures the time between events
• g-chart measures the number of incidents
(procedures, admission) between events
• Both charts are useful when looking at
rare events
– Eliminates the need to wait for a long time
period to collect enough data points
CUSUM and EWMA
Early detection of shifts
Anatomy of a CUSUM chart
Monitoring CO2 in a Nursery
CUSUM Chart
Monitoring CO2 in a Nursery
CUSUM Chart
Or, you can use an exponentially
weighted moving average chart.
Source: Benneyan, 2001
Surgeries
Deaths
CUSUM vs. EWMA
CUSUM
EWMA
Y-axis
Cumulative sum of the
difference between the
observed mean and the
target or average.
Avg. of surrounding values,
weighting close values very
high and far away values
very low (exponential
weighting).
X-axis
Measurement number
(observation).
Time interval.
Advantage
Detects small shifts
More sensitive than
EWMA.
Partially immune to
autocorrelation.
Detects small shifts.
Partially Immune to
autocorrelation.
Easier to understand than
CUSUM
Following a Panel of Patients
Small Multiple Graphs
100
3500
IPC Site
3000
80
60
2000
Denominator in blue
1500
40
1000
20
500
Screening rate in red
Month
0
A-07
J-07
O-07
J-08
0
A-08
J-08
O-08
J-09
Denominator
Rate (Percent)
2500
Small Multiple Graphs
Think-Pair-Share
• Why are they powerful?
• What are their limitations?
An Alternative:
Percent of Patients Screened for Depression:
A Period-Cohort Analysis.
100
90
80
70
60
Percent
50
Screened
40
30
20
10
0
1st QTR Cohort
3rd QTR Cohort
5th QTR Cohort
1
2
3
4
5
6
7
8
9
Quarter beginning Jan 2009
A cohort is a group of patients empanelled within a particular quarter.
Summarizing beginning and
ending results
1
2
3
4
5
6
7
8
9
10 11
Figure 1. Percent improvement in the Health Risk Screening Bundle
over 12 months in 2009 by IPC site.
//
-80 -60 -40 -20 0 20 40 60 80
1,334
2,052
Percent Improvement
95% Confidence Interval
3,129
Percent Improvement
IPC Site
Compare to change in percent screened
All Sites
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19
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15
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10
9
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5
4
3
2
1
-20
-10
0
10
20
30
40 50
Percent
60
70
95% Confidence Interval
Change in Percent Screened (5th Q-1st Q)
80
90
100
Scatter plot comparing beginning and
ending of period of observation.
Control Charts vs. Smoothed,
Descriptive Data
0
10
20
30
40
Number of preventable hospitalizations by month for site 1
0
20
40
Month beginning January 2004
Number of Preventable Hospitalizations
60
Curve fit by Median Spline
Preventable hospitalizations due to any one of the 11 conditions defined by AHRQ as
Prevention Quality Indicators
Compare to corresponding c-Chart
Your Turn!
1. Think about your work and select a key
quality characteristic (KQC).
2. Develop an operational Definition for the
KQC.
3. Evaluate your definition with the criteria
from the NQF in module 2.
4. Answer: What kind of chart or analysis
would you use?