1. Boating Beyond Simple Shewhart Model 11

2. 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

3. Analyzing Rare Events Using Time or Occurrences Between Them

4. One way to analyze rare events

5. Another way to analyze rare events

6. 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

7. CUSUM and EWMA Early detection of shifts

8. Anatomy of a CUSUM chart

9. Monitoring CO 2 in a Nursery CUSUM Chart

11. Or, you can use an exponentially weighted moving average chart. SurgeriesDeaths Source: Benneyan, 2001

12. CUSUM vs. EWMA CUSUMEWMA 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

13. Following a Panel of Patients

14. Small Multiple Graphs Denominator in blue Screening rate in red Rate (Percent) Denominator Month

16. Small Multiple Graphs Think-Pair-Share Why are they powerful? What are their limitations?

17. An Alternative: Percent of Patients Screened for Depression: A Period-Cohort Analysis. A cohort is a group of patients empanelled within a particular quarter.

18. Summarizing beginning and ending results

20. Compare to change in percent screened

21. Scatter plot comparing beginning and ending of period of observation.

22. Control Charts vs. Smoothed, Descriptive Data

24. Compare to corresponding c-Chart

25. 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?