boating beyond simple shewhart model 11. destinations purpose—to provide a quick long distance...
TRANSCRIPT
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 NurseryCUSUM Chart
Monitoring CO2 in a NurseryCUSUM Chart
Or, you can use an exponentially weighted moving average chart.
Surgeries DeathsSource: Benneyan, 2001
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
0
20
40
60
80
100
A-07 J -07 O-07 J -08 A-08 J -08 O-08 J -09
0
500
1000
1500
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IPC Site
Denominator in blue
Screening rate in red
Rat
e (P
erce
nt) Denom
inator
Month
Small Multiple GraphsThink-Pair-Share
• Why are they powerful?
• What are their limitations?
An Alternative:Percent of Patients Screened for Depression:
A Period-Cohort Analysis.
0102030405060708090
100
1 2 3 4 5 6 7 8 9
Quarter beginning Jan 2009
Percent Screened
1st QTR Cohort
3rd QTR Cohort
5th QTR Cohort
A cohort is a group of patients empanelled within a particular quarter.
Summarizing beginning and ending results
//
12
34
56
78
910
11IP
C S
ite
-80 -60 -40 -20 0 20 40 60 80 1,334 2,052 3,129Percent Improvement
95% Confidence Interval Percent Improvement
over 12 months in 2009 by IPC site.Figure 1. Percent improvement in the Health Risk Screening Bundle
Compare to change in percent screened
123456789
1011121314151617181920
All SitesIP
C S
ite
-20 -10 0 10 20 30 40 50 60 70 80 90 100Percent
95% Confidence Interval
Change in Percent Screened (5th Q-1st Q)
Scatter plot comparing beginning and ending of period of observation.
Control Charts vs. Smoothed, Descriptive Data
010
2030
40N
o. o
f Pre
ven
tabl
e H
osp
italiz
atio
ns
0 20 40 60Month beginning January 2004
Number of Preventable Hospitalizations Curve fit by Median Spline
Preventable hospitalizations due to any one of the 11 conditions defined by AHRQ asPrevention Quality Indicators
Number of preventable hospitalizations by month for site 1
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?