There is considerable interest in the use of statistical process control (SPC) in healthcare. Although SPC is part of an overall philosophy of continual improvement, the implementation of SPC usually requires the production of control charts. However, as SPC is relatively new to healthcare practitioners and is not routinely featured in medical statistics texts/courses, there is a need to explain the issues involved in the selection and construction of control charts in practice. Following a brief overview of SPC in healthcare and preliminary issues, we use a tutorial-based approach to illustrate the selection and construction of four commonly used control charts (xmr-chart, p-chart, u-chart, c-chart) using examples from healthcare. For each control chart, the raw data, the relevant formulae and their use and interpretation of the final SPC chart are provided together with a notes section highlighting important issues for the SPC practitioner. Some more advanced topics are also mentioned with suggestions for further reading.

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.

23. Distribution‐free Tests. By H. R. Neave and P. L. Worthington. ISBN 0 04 519020 8. Unwin Hyman, London, 1988. 430 pp. £40.00 (hardbound), £14.95 (paperbound).

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