Run Charts

Run Charts

A run chart (or time-series chart) is a graph that displays observed data over time. It is used to determine whether a process is performing well (or not) by tracking changes and observing patterns or trends. Run charts are one of the most important tools for assessing the effectiveness of change in improvement projects. Patterns in a run chart can start to determine whether a change is due to a common cause (natural or expected) or special cause (due to specific circumstances) of variation within a process.

A control chart, is a type of run chart. It includes an upper control limit (UCL) and a lower control limit (LCL) that provide more specificity when distinguishing between common causes and special causes of variation. Control charts help improvement teams pinpoint special-cause variation, identify early signs of success in an improvement project, and monitor a process to ensure it is sustaining the gains from a quality improvement effort.

When to use a run chart

  • When depicting how well (or poorly) an existing process is performing.
  • When determining whether changes to a process are actually improvements by displaying a pattern of data that can be observed as changes are made.
  • When helping teams working on improvements decide on the value of particular changes.

Use a control chart when you have more than 15 data points and want more insight into your data.

How to use a run chart

  1. Decide the process to be observed. Agree on the value being measured over a period of time, e.g. # clients waiting more than 3 weeks for first appointment, # referrals, etc.
  2. Record data points in their natural time sequence (aim for a minimum of 10 data points). 
  3. Draw the vertical and horizontal axes, leaving room to title and label the graph.
  4. Label the vertical (Y) axis with the name of the value being measured, e.g. # clients waiting longer than 3 weeks for first appointment, # referrals, etc.
  5. Label the horizontal (X) axis with the unit of time or sequence in which the numbers were collected, e.g., April, May, June, etc., or Quarter 1, Quarter 2, etc.
  6. Determine the scale of the vertical axis. The scale should extend from a number 20 percent larger than the largest value to a number 20 percent smaller than the smallest value. Label the axis in equal intervals between these two numbers.
  7. Plot the data values in the sequence in which they occurred and draw lines to connect the data points on the graph.
  8. Calculate the median (the data point half way between the highest and the lowest data point) of the plotted numbers and draw the line on the graph.
    For a control chart, follow these two steps:
    i) Instead of calculating the median, calculate the mean or control limit (the average) of the plotted numbers and draw the line on the graph.
    ii) Calculate and then draw upper and lower control limits that correspond to +/- 3 sigma limits from the mean (this can be done in Microsoft Excel)
  9. Title the chart, and note the goal line and the sample size.
  10. Annotate the chart, indicating when tests of change were initiated, so that it is easy to see the effect of changes on the measure. Also indicate any external events that may have affected the performance of the process.
  11. Apply the rules of a run chart to test whether the change is making a difference to the process.

Rules of a run chart

There are four rules that can be applied to a run chart to help determine whether variation within the dataset is due to common-cause variation, or special-cause variation attributable to change in the process.

Resources

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References:

http://www.ihi.org/resources/Pages/Tools/RunChart.aspx

http://www.qihub.scot.nhs.uk/media/529936/run%20chart%20rules.pdf