Interpreting Run Charts and Shewhart Charts. Agenda Features of Run Charts Interpreting Run Charts A...

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Transcript of Interpreting Run Charts and Shewhart Charts. Agenda Features of Run Charts Interpreting Run Charts A...

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Interpreting Run Charts and Shewhart Charts Slide 2 Agenda Features of Run Charts Interpreting Run Charts A quick mention of variation Features of Shewhart Charts Interpreting Shewhart Charts Slide 3 Displaying Key Measures over Time - Run Chart -Data displayed in time order -Time is along X axis -Result along Y axis -Centre line = median -One dot = one sample of data Slide 4 Median 429 1.Determine if change is an improvement Three Uses of Run Charts in Quality Work The Data Guide, p 3-18 Slide 5 Median 429 Three Uses of Run Charts in Quality Work The Data Guide, p 3-18 2. Determine if improvement is sustained Slide 6 Median 429 3. Make process performance visible Three Uses of Run Charts in Quality Work The Data Guide, p 3-18 Slide 7 How Do We Analyze a Run Chart? Visual analysis first If pattern is not clear, then apply probability based rules The Data Guide, p 3-10 Slide 8 Non-Random Signals on Run Charts A Shift: 6 or more An astronomical data point Too many or too few runs A Trend 5 or more The Data Guide, p 3-11 Evidence of a non-random signal if one or more of the circumstances depicted by these four rules are on the run chart. The first three rules are violations of random patterns and are based on a probability of less than 5% chance of occurring just by chance with no change. Slide 9 Source: Swed, Frieda S. and Eisenhart, C. (1943) Tables for Testing Randomness of Grouping in a Sequence of Alternatives. Annals of Mathematical Statistics. Vol. XIV, pp. 66-87, Tables II and III. The Data Guide, p 3-14 Slide 10 Trend? Note: 2 same values only count one Slide 11 Shift? note: values on median dont make or break a shift Slide 12 Shift? Slide 13 Interpretation? -There is a signal of a non-random pattern -There is less than 5 % chance that we would see this pattern if something wasnt going on, i.e. if there wasnt a real change -There is a signal of a non-random pattern -There is less than 5 % chance that we would see this pattern if something wasnt going on, i.e. if there wasnt a real change Slide 14 Plain Language Interpretation? There is evidence of improvement the chance we would see a shift like this in data if there wasnt a real change in what we were doing is less than 5% Slide 15 Two few or too many runs? - 1. bring out the table 2. how many points do we have (not on median?) 3. how many runs do we have (cross median +1) 4. what is the upper and lower limit? Slide 16 Two few or too many runs? - **new slide 1. bring out the table 2. how many points do we have 20 3. how many runs do we have (cross median +1) 3 4. what is the upper and lower limit? 6 - 16 Slide 17 Two few runs? Plain language interpretation There is evidence of improvement our data only crosses the median line twice three runs. If it was just random variation, we would expect to see more up and down. Slide 18 What if we had too many runs? Plain language interpretation There is evidence of a non-random pattern. There is a pattern to the way the data rises and falls above and below the median. Something systematically different. Should investigate and maybe plot on separate run charts. Slide 19 Astronomical Data Point? Slide 20 Who is using run charts? Slide 21 Understanding Variation Walter Shewhart (1891 1967) W. Edwards Deming (1900 - 1993) The Pioneers of Understanding Variation Slide 22 Understanding Variation: Intended and Unintended Variation Intended variation is an important part of effective, patient-centered health care. Unintended variation is due to changes introduced into healthcare process that are not purposeful, planned or guided. Walter Shewhart focused his work on this unintended variation. He found that reducing unintended variation in a process usually resulted in improved outcomes and lower costs. (Berwick 1991) Health Care Data Guide, p. 107 Slide 23 Shewharts Theory of Variation Common Causes those causes inherent in the system over time, affect everyone working in the system, and affect all outcomes of the system Common cause of variation Chance cause Stable process Process in statistical control Special Causes those causes not part of the system all the time or do not affect everyone, but arise because of specific circumstances Special cause of variation Assignable cause Unstable process Process not in statistical control Health Care Data Guide, p. 108 Slide 24 Shewhart Charts The Shewhart chart is a statistical tool used to distinguish between variation in a measure due to common causes and variation due to special causes (Most common name is a control chart, more descriptive would be learning charts or system performance charts) Health Care Data Guide, p. 113 Slide 25 Control Charts what features are different than a run chart? Slide 26 Control Charts/Shewhart Charts upper and lower control limits to detect special cause variation Extend limits to predict future performance Not necessarily ordered by time advanced application of SPC is there something different between systems Slide 27 Example of Shewhart Chart for Unequal Subgroup Size Health Care Data Guide, p. 114 Slide 28 Who has been using control charts? Slide 29 Adapted from Health Care Data Guide, p. 151 & QI Charts Software Slide 30 Slide 31 Note: Only for constant subgroup size Note: A point exactly on the centerline does not cancel or count towards a shift Health Care Data Guide, p. 116 Slide 32 Slide 33 Special cause: point outside the limits Slide 34 Slide 35 Special cause 2 out of 3 consecutive points in outer third of limits or beyond Slide 36 Slide 37 Slide 38 Slide 39 Common Cause Slide 40 Case Study #1a Slide 41 Case Study #1b Percent of cases with urinary tract infection Slide 42 Case Study #1c Percent of cases with urinary tract infection Slide 43 Case Study #1d Percent of cases with urinary tract infection Slide 44 Case Study #1e Percent of cases with urinary tract infection Slide 45 Case Study #1f Percent of cases with urinary tract infection Slide 46 Case Study #2a Percent of patients with Death or Serious Morbidity who are >= 65 years of age Slide 47 Case Study #2b Percent of patients with Death or Serious Morbidity who are >= 65 years of age Slide 48 Case Study #2c Percent of patients with Death or Serious Morbidity who are >= 65 years of age Slide 49 Case Study #2d Percent of patients with Death or Serious Morbidity who are >= 65 years of age Slide 50 References BCPSQC Measurement Report http://www.bcpsqc.ca/pdf/MeasurementStrategies.pdf Langley GJ, Moen R, Nolan KM, Nolan TW, Norman CL, Provost LP (2009) The Improvement Guide (2nd ed). Provost L, Murray S (2011) The Health Care Data Guide. Berwick, Donald M, Controlling Variation in Health Care: A Consultation with Walter Shewhart, Medical Care, December, 1991, Vol. 29, No 12, page 1212-1225. ****CHELSEA, can you add Lloyds run chart article reference from R2? Associates in Process Improvement website www.apiweb.orgwww.apiweb.org