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Using Process Control SPC Charts

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**1. **Using Process Control (SPC) Charts Michael B. Mundorff, MBA, MHSA
Data Project Manager, System Improvement Department

**2. **9/18/2012 © Intermountain Healthcare 2 Random variation the classic example: W. Edwards Deming’s “Red Bead Game”the classic example: W. Edwards Deming’s “Red Bead Game”

**3. **9/18/2012 © Intermountain Healthcare 3 Managing random variation

**4. **9/18/2012 © Intermountain Healthcare 4 Random variation

**5. **9/18/2012 © Intermountain Healthcare 5 Assignable variation

**6. **9/18/2012 © Intermountain Healthcare 6 Managing assignable variation When to implement assignable variation:
deep post-op wound infection protocol.When to implement assignable variation:
deep post-op wound infection protocol.

**7. **9/18/2012 © Intermountain Healthcare 7 Managing assignable variation When to implement assignable variation:
deep post-op wound infection protocol.When to implement assignable variation:
deep post-op wound infection protocol.

**8. **9/18/2012 © Intermountain Healthcare 8 Assignable variation Whether this outlier is “good” or “bad” is a matter of the definition of the measure. If this measure is for adverse drug events, the outlier would be bad: i.e., an increase in ADEs. If this measure is for protocol compliance, the outlier would be good: i.e., an increase in compliance.
example: second-generation cephalosporin use at IHC Health Plans (now SelectHealth)Whether this outlier is “good” or “bad” is a matter of the definition of the measure. If this measure is for adverse drug events, the outlier would be bad: i.e., an increase in ADEs. If this measure is for protocol compliance, the outlier would be good: i.e., an increase in compliance.
example: second-generation cephalosporin use at IHC Health Plans (now SelectHealth)

**9. **9/18/2012 © Intermountain Healthcare 9 Process Control (SPC) charts

**10. **9/18/2012 © Intermountain Healthcare 10 Components of an SPC Chart Time series data
Aggregated by time period
(week, month, quarter, year)
Maintain temporal sequence of data
Measure of central tendency
Mean, Median
Control limits / action thresholds
Multiple of standard deviation / standard error
Goals
Alarms

**11. **Imported metafile chart Note the non-uniform (not flat) control limits (3 standard deviations in this instance). This is because the control limits are sensitive to the sample size (number of deliveries) for each month.Note the non-uniform (not flat) control limits (3 standard deviations in this instance). This is because the control limits are sensitive to the sample size (number of deliveries) for each month.

**12. **This is the same chart with another two years (2002 and 2003) of data. The data table would be too small to read on this chart, so it was omitted.
[Discussion of the 2002 ACOG practice guideline which states that in order to undergo a trial of labor for a VBAC, the practitioner has to be “immediately available”.]This is the same chart with another two years (2002 and 2003) of data. The data table would be too small to read on this chart, so it was omitted.
[Discussion of the 2002 ACOG practice guideline which states that in order to undergo a trial of labor for a VBAC, the practitioner has to be “immediately available”.]

**13. **When charted using phased (stratified) means and control limits pre- and post-guideline, there are no points outside the control limits.When charted using phased (stratified) means and control limits pre- and post-guideline, there are no points outside the control limits.

**15. **Discussion of S.B. 969, the Newborns and Mothers Health Protection Act of 1996.Discussion of S.B. 969, the Newborns and Mothers Health Protection Act of 1996.

**17. **The error bar chart is a way to compare a phenomenon across several sites using measures of statistical variation.The error bar chart is a way to compare a phenomenon across several sites using measures of statistical variation.

**18. **As can be seen, this chart is almost never within control limits. It could, therefore, be reasonably posited that this is a statistically unstable process. There seems to be, however, a substantive change which begins around the end of 2005, which is the first point outside the upper control limit.As can be seen, this chart is almost never within control limits. It could, therefore, be reasonably posited that this is a statistically unstable process. There seems to be, however, a substantive change which begins around the end of 2005, which is the first point outside the upper control limit.

**19. **Stratifying by year, there is a complete discontinuity between the three years, indicating overall significant difference between years. Note that although the 2007 mean is similar to that of 2005, the process displays much more inherent variation.
Later we will see a clinical example of stratification by time period.Stratifying by year, there is a complete discontinuity between the three years, indicating overall significant difference between years. Note that although the 2007 mean is similar to that of 2005, the process displays much more inherent variation.
Later we will see a clinical example of stratification by time period.

**20. **There are several points on this chart which are outside the control limits. And there seems to be a general upward trend.There are several points on this chart which are outside the control limits. And there seems to be a general upward trend.

**21. **A better way of representing this trend is to compute a linear regression and plot the regression line. The R-squared in this instance is reasonably strong.A better way of representing this trend is to compute a linear regression and plot the regression line. The R-squared in this instance is reasonably strong.

**22. **This measure is highly cyclical, so in this instance a traditional process control chart would be of little use. We may wish to compare the magnitude of the peaks and the breadth of the peak periods.This measure is highly cyclical, so in this instance a traditional process control chart would be of little use. We may wish to compare the magnitude of the peaks and the breadth of the peak periods.

**23. **This is a subset of the previous dataset, aggregated by week rather than by month. In the previous chart, the magnitude of the monthly peaks was very similar for the two seasons. In this chart, it can be seen that the first peak is higher but narrower than the second. Overall, the number of patients seen was very similar.
We had the good fortune to have the first peak occur during the Christmas holidays in 2005, and the second occur during an unannounced Joint Commission audit.This is a subset of the previous dataset, aggregated by week rather than by month. In the previous chart, the magnitude of the monthly peaks was very similar for the two seasons. In this chart, it can be seen that the first peak is higher but narrower than the second. Overall, the number of patients seen was very similar.
We had the good fortune to have the first peak occur during the Christmas holidays in 2005, and the second occur during an unannounced Joint Commission audit.

**24. **9/18/2012 © Intermountain Healthcare 24 I tried to update this chart to see whether the chart itself or the data behind it were available online, but that page on the Clinical Workstation website was broken.I tried to update this chart to see whether the chart itself or the data behind it were available online, but that page on the Clinical Workstation website was broken.

**25. **9/18/2012 © Intermountain Healthcare 25

**26. **9/18/2012 © Intermountain Healthcare 26 Not only is this chart less confusing than the line-chart version, now it can be readily seen that data for December are missing.Not only is this chart less confusing than the line-chart version, now it can be readily seen that data for December are missing.

**27. **9/18/2012 © Intermountain Healthcare 27 This is a nice representation of a bivariate line chart. It still manages to represent the relationship between the two variables despite the dramatic difference in volumes of the two measures. Note left-hand (Y1) axis with exponential scale; the scale could just as easily have read: 0, 10, 102,103 ,104 ,105, etc.
The symbols along the X-axis refer to a table containing significant events in the timeline of rubella control.
SOURCE: MMWR Supplement 21 March 2005This is a nice representation of a bivariate line chart. It still manages to represent the relationship between the two variables despite the dramatic difference in volumes of the two measures. Note left-hand (Y1) axis with exponential scale; the scale could just as easily have read: 0, 10, 102,103 ,104 ,105, etc.
The symbols along the X-axis refer to a table containing significant events in the timeline of rubella control.
SOURCE: MMWR Supplement 21 March 2005

**28. **A “box and whisker” diagram (sometimes referred to as a “box plot” is a handy way to compare phenomena when they are not in a time series. The box contains the middle two quartiles, while the whiskers contain the outer two quartiles. The line in the box is the median, not the mean. So these are based on ranges, not on computed distributions.
A “box and whisker” diagram (sometimes referred to as a “box plot” is a handy way to compare phenomena when they are not in a time series. The box contains the middle two quartiles, while the whiskers contain the outer two quartiles. The line in the box is the median, not the mean. So these are based on ranges, not on computed distributions.

**29. **2004 = 5.58%
2005 = 9.02% (p = ns)
2006 = 5.22% (p = 0.02)
Even though there are no points outside the control limits, a chi-square test reveals a significant year-over-year difference (for the better) in the percentage of those who rated this question “fair” or “poor”.
2004 = 5.58%
2005 = 9.02% (p = ns)
2006 = 5.22% (p = 0.02)
Even though there are no points outside the control limits, a chi-square test reveals a significant year-over-year difference (for the better) in the percentage of those who rated this question “fair” or “poor”.

**30. **9/18/2012 © Intermountain Healthcare 30 Contact Information:
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