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Julia Mackel December 2019 Intro to Statistical Process Control (SPC)

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Julia Mackel December 2019 Intro to Statistical Process Control (SPC) Intro to Statistical Process Control (SPC) charts SPC charts differ from run charts as in addition to the middle line there are two control lines above and below the average line that allows for more statistical interpretation. The middle line on an SPC chart is the mean. The control limits are calculated using statistics and determines the variation range show and can accurately predict future performance. SPC charts are constructed by: • plotting data in time order • calculating and displaying the mean as a line • calculating and displaying upper and lower control limits as lines Tools are available to create SPC charts (QI charts add-on in Excel) + Lothian Quality website + Life QI. Depending on the type of data being plotted - attributes (classification or count) or variable (continuous) – and the purpose of analysis, different type of control charts should be used. The most common are: Data type Common chart Used for Example data Classification data P-chart Percentages Percentage of elements of a bundle completed C-chart Count Counting rare events for example number of falls Count data U-chart Rates Rate of Cardiac arrests T-chart Time between rare events Days between cardiac arrests I-chart Individual measureable data Waiting time (sometimes called X-MR; points MR = moving range) [also activity data] Continuous data X-bar Subgroups of data at same time For time, money, metric, scale point (with range or standard deviation or through put chart alongside to show variation within e.g. workload subgroup) How much for a baseline? 20 or more data points. An absolute minimum of 10 data points for baseline. 1. If the baseline data shows random (common cause) variation the mean and upper control limit and lower control limit can be frozen and extended. 2. New data added will then not influence centre line or limits. Julia Mackel December 2019 SPC chart rules Rule 1: Astronomical point - 1 point outside the control limits - A point exactly on a control limit is not considered outside the limit. This rule quickly identifies sudden changes in a measure. Rule 2: Shift - 8 successive or more consecutive points above (or below) the centreline - A point exactly on the centreline does not cancel or count towards a shift. This rule identifies small, sustained changes. Rule 3: Trend - 6 or more consecutive points steadily increasing or decreasing - Two consecutive points of the same value do not cancel or add to a trend. This rule detects a small, consistent drift in a process. Rule 4: 2/3 rule - 2 out of 3 successive points near a control limit (outer one-third) - Two out of three consecutive data points located close to one of the control limits. This rule adds additional sensitivity to detect changes that have not yet triggered an astronomical point or shift. Rule 5: 15 points rule - 15 consecutive points close (inner one-third of chart) to centreline - This is known as “hugging the centreline”. This indicates reduced variation in the process. Recalculating the limits When improvements have been made and the improvements result in special cause, the centre lines and limits can be calculated when the new process has 12 or more data points. References NHS Elect (2015) Guide to Measurement for Improvement Provost, LP & Murray, SK (2011) The Health Care Data Guide – Learning from data for improvement. Jossey-Bass Publishing Julia Mackel December 2019 .
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