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Colorado 5M WebEx Variation, Run Charts, and Control Charts Beth A. Katzenberg, EdM, MBA, CPHQ Director, Corporate Quality...
Types of variation <ul><li>Common cause </li></ul><ul><li>Always present </li></ul><ul><li>Inherent in process </li></ul><...
You must understand the type of variation that is occurring as this will determine how you address the problem.
Variation <ul><li>Identify and study special cause </li></ul><ul><li>If negative, minimize or prevent </li></ul><ul><li>If...
Pitfalls <ul><li>If only  common cause variation  and treat as special cause (tampering), leads to greater variation, mist...
Tools to identify variation
Run charts
Run chart Graph of data over time Track performance Display & identify variation
Run chart analysis:  Common cause variation only Common cause variation around the median:   Only common cause variation p...
Run chart analysis:  Runs <ul><li>Run = one or more consecutive data points on the same side of the median </li></ul><ul><...
Expected number of runs
High probability  of special cause variation: Too few runs Too many runs = 0.05) (
Run chart analysis:  Run length Special cause—run length: <20 data points  (not on median):  A run of  7  data points on o...
Run chart analysis:  Trends Special cause—trends:   Consecutive points all going up or all going down.  May cross the medi...
Run chart analysis:  Freaks Freaks:   The presence of more than one or two dramatic spikes suggests the process is out of ...
Run chart analysis:  Cycling Cycling:   A zigzag or saw-tooth pattern with 14+ points in a row alternating up or down.
Run charts tips <ul><li>How many data points? </li></ul><ul><ul><li>15-20 minimum is preferable </li></ul></ul><ul><li>Med...
Control charts
Control chart Run chart with control limits Determines  type of variation Is process stable?  Predictable?
Dividing control chart into zones Each zone is 1 sigma wide UCL LCL X Zone A Zone B Zone C Zone C Zone B Zone A
Identifying special causes <ul><li>Apply independently to each side of the center line: </li></ul><ul><ul><li>1 point outs...
Identifying special causes, cont. <ul><li>Apply this test to entire chart: </li></ul><ul><ul><li><21 total data points:   ...
Deciding which control chart to use
Types of data <ul><li>Costs </li></ul><ul><li>Temperature </li></ul><ul><li>% c-sections </li></ul><ul><li>% incomplete ch...
Control chart example 1 Common cause variation only
Control chart example 2 new hire snowstorm
Control chart example 3 Common cause variation only;  can predict will stay within control limits, if no changes
Control chart example 4 Out of control, unpredictable
Just because a process is under control (common cause variation only), it does not mean that the process is meeting expect...
Control charts tips <ul><li>Control limits are not specifications limits (specification limits related to customer require...
Share the data <ul><li>Team meetings </li></ul><ul><li>Post in break-rooms </li></ul><ul><li>Newsletters </li></ul><ul><li...
Examples of Software <ul><li>QI Macros  www.qimacros.com </li></ul><ul><li>StatSoft  www.statsoft.com </li></ul><ul><li>Mi...
References <ul><li>Carey, R.G. & Lloyd, R.C.  Measuring Quality Improvement in Healthcare:  A Guide to Statistical Process...
Beth Katzenberg, EdM, MBA, CPHQ Director, Corporate quality & compliance Colorado Foundation for Medical Care [email_addre...
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Variation, Run Charts and Control Charts PowerPoint

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Transcript of "Variation, Run Charts and Control Charts PowerPoint"

  1. 1. Colorado 5M WebEx Variation, Run Charts, and Control Charts Beth A. Katzenberg, EdM, MBA, CPHQ Director, Corporate Quality & Compliance Colorado Foundation for Medical Care
  2. 2. Types of variation <ul><li>Common cause </li></ul><ul><li>Always present </li></ul><ul><li>Inherent in process </li></ul><ul><li>Can predict performance with a range of variation </li></ul><ul><li>Cannot tell what specifically causes variation </li></ul><ul><li>Special cause </li></ul><ul><li>Abnormal, unexpected </li></ul><ul><li>Due to causes not inherent in process </li></ul><ul><li>Can be identified (e.g., change in shift, weather, process) </li></ul>
  3. 3. You must understand the type of variation that is occurring as this will determine how you address the problem.
  4. 4. Variation <ul><li>Identify and study special cause </li></ul><ul><li>If negative, minimize or prevent </li></ul><ul><li>If positive, build into process </li></ul>Special cause (unpredictable, unstable, out of control) <ul><li>Change the process </li></ul><ul><li>Do not react to individual differences or try to explain differences between high and low numbers </li></ul>Common cause (predictable, stable, in control, inherent in process) Appropriate action to take Type of variation
  5. 5. Pitfalls <ul><li>If only common cause variation and treat as special cause (tampering), leads to greater variation, mistakes, defects </li></ul><ul><li>If common cause and special cause , and change the process, leads to wasted resources because the change won’t work </li></ul>
  6. 6. Tools to identify variation
  7. 7. Run charts
  8. 8. Run chart Graph of data over time Track performance Display & identify variation
  9. 9. Run chart analysis: Common cause variation only Common cause variation around the median: Only common cause variation present. Output may or may not meet customer/ patient requirements
  10. 10. Run chart analysis: Runs <ul><li>Run = one or more consecutive data points on the same side of the median </li></ul><ul><li>Excludes data points on the median </li></ul>11 runs
  11. 11. Expected number of runs
  12. 12. High probability of special cause variation: Too few runs Too many runs = 0.05) (
  13. 13. Run chart analysis: Run length Special cause—run length: <20 data points (not on median): A run of 7 data points on one side of the median (either above or below) 20+ data points (not on median): A run of 8 data points on one side of the median
  14. 14. Run chart analysis: Trends Special cause—trends: Consecutive points all going up or all going down. May cross the median. Ignore 2+ consecutive points that are the same. (Pyzdek, 2003) 4 5 to 8 7 101 or more 6 21 to 100 5 9 to 20 # Consecutive points all increasing or decreasing Total # data points on chart
  15. 15. Run chart analysis: Freaks Freaks: The presence of more than one or two dramatic spikes suggests the process is out of control. Run charts not as sensitive in identifying, thus may fail to detect.
  16. 16. Run chart analysis: Cycling Cycling: A zigzag or saw-tooth pattern with 14+ points in a row alternating up or down.
  17. 17. Run charts tips <ul><li>How many data points? </li></ul><ul><ul><li>15-20 minimum is preferable </li></ul></ul><ul><li>Median = 50%/50% split point </li></ul><ul><ul><li>Precisely half of the data set will be above the median and half below it </li></ul></ul>
  18. 18. Control charts
  19. 19. Control chart Run chart with control limits Determines type of variation Is process stable? Predictable?
  20. 20. Dividing control chart into zones Each zone is 1 sigma wide UCL LCL X Zone A Zone B Zone C Zone C Zone B Zone A
  21. 21. Identifying special causes <ul><li>Apply independently to each side of the center line: </li></ul><ul><ul><li>1 point outside the 3 sigma limit </li></ul></ul><ul><ul><li>2 out of 3 consecutive points in zone A or beyond </li></ul></ul><ul><ul><li>4 out of 5 consecutive points in zone B or beyond </li></ul></ul><ul><ul><li><20 total data points: 7 consecutive points in zone C or beyond on one side of center line </li></ul></ul><ul><ul><li>20+ total data points: 8 consecutive points in zone C or beyond on one side of center line </li></ul></ul><ul><ul><ul><li>(continued) </li></ul></ul></ul>
  22. 22. Identifying special causes, cont. <ul><li>Apply this test to entire chart: </li></ul><ul><ul><li><21 total data points: 6 or more points in a row steadily increasing or decreasing </li></ul></ul><ul><ul><li>21+ total data points: 7 or more points in a row steadily increasing or decreasing </li></ul></ul><ul><ul><li>14 consecutive points alternating up and down in saw-tooth pattern </li></ul></ul><ul><ul><li>15 consecutive points in zone C (above and below center line) </li></ul></ul>
  23. 23. Deciding which control chart to use
  24. 24. Types of data <ul><li>Costs </li></ul><ul><li>Temperature </li></ul><ul><li>% c-sections </li></ul><ul><li>% incomplete charts </li></ul><ul><li># pt falls </li></ul><ul><li># medication errors </li></ul><ul><li>Time in minutes or hours </li></ul><ul><li>Weight in grams </li></ul><ul><li>Length of stay </li></ul><ul><li>Blood sugar levels </li></ul><ul><li>Yes/no </li></ul><ul><li>Dead/alive </li></ul><ul><li>Infected/not infected </li></ul><ul><li>On time/late </li></ul>Take on values on a continuous scale Whole numbers and decimals Can be converted to count Count observations or incidents falling into categories Whole numbers only Cannot be converted to measurement Measurement/continuous Count/attribute
  25. 25. Control chart example 1 Common cause variation only
  26. 26. Control chart example 2 new hire snowstorm
  27. 27. Control chart example 3 Common cause variation only; can predict will stay within control limits, if no changes
  28. 28. Control chart example 4 Out of control, unpredictable
  29. 29. Just because a process is under control (common cause variation only), it does not mean that the process is meeting expectations. It just means that the process is predictable and you are getting consistent performance.
  30. 30. Control charts tips <ul><li>Control limits are not specifications limits (specification limits related to customer requirements) </li></ul><ul><li>After removing special causes and recalculating chart, continue to plot new data on this chart, without recalculating control limits. </li></ul><ul><ul><li>Recalculate control limits only when a permanent, desired change has occurred in the process and only using data after the change occurred </li></ul></ul>
  31. 31. Share the data <ul><li>Team meetings </li></ul><ul><li>Post in break-rooms </li></ul><ul><li>Newsletters </li></ul><ul><li>Intranet </li></ul>
  32. 32. Examples of Software <ul><li>QI Macros www.qimacros.com </li></ul><ul><li>StatSoft www.statsoft.com </li></ul><ul><li>Minitab www.minitab.com </li></ul>
  33. 33. References <ul><li>Carey, R.G. & Lloyd, R.C. Measuring Quality Improvement in Healthcare: A Guide to Statistical Process Control Applications, Quality Resources, 1995. </li></ul><ul><li>Pyzdek, R. The Six Sigma Handbook: A Complete Guide for Green Belts, Black Belts, and Managers at All Levels, 2003. </li></ul><ul><li>The Six Sigma Memory Jogger II, GOAL/QPC, 2002. </li></ul>
  34. 34. Beth Katzenberg, EdM, MBA, CPHQ Director, Corporate quality & compliance Colorado Foundation for Medical Care [email_address]
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