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Why traditional statistical process control charts for attribute data should be viewed alongside an xmr-chart
  1. Mohammed A Mohammed1,
  2. Peter Worthington2
  1. 1Primary Care Clinical Sciences, University of Birmingham, Birmingham, England
  2. 2PRISM Europe Consultancy Ltd, Broughton (Scotland), UK
  1. Correspondence to Dr Mohammed A Mohammed, Primary Care Clinical Sciences, University of Birmingham, Birmingham B15 2TT, England; m.a.mohammed{at}bham.ac.uk

Abstract

The use of statistical process control (SPC) charts in healthcare is increasing. The general advice when plotting SPC charts is to begin by selecting the right chart. This advice, in the case of attribute data, may be limiting our insights into the underlying process and consequently be potentially misleading. Given the general lack of awareness that additional insights may be obtained by using more than one SPC chart, there is a need to review this issue and make some recommendations. Under purely common cause variation the control limits on the xmr-chart and traditional attribute charts (eg, p-chart, c-chart, u-chart) will be in close agreement, indicating that the observed variation (xmr-chart) is consistent with the underlying Binomial model (p-chart) or Poisson model (c-chart, u-chart). However, when there is a material difference between the limits from the xmr-chart and the attribute chart then this also constitutes a signal of an underlying systematic special cause of variation. We use one simulation and two case studies to demonstrate these ideas and show the utility of plotting the SPC chart for attribute data alongside an xmr-chart. We conclude that the combined use of attribute charts and xmr-charts, which requires little additional effort, is a useful strategy because it is less likely to mislead us and more likely to give us the insight to do the right thing.

  • Statistical process control
  • Quality measurement
  • Control charts, run charts

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