CUSUM charts for monitoring clinical practice quality using primary care prescribing data: a case study of an initiative to encourage generic prescribing of proton pump inhibitors

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Abstract

What is known and objective:

Expectations on organizations to monitor quality of care are growing. Whilst relevant data are increasingly becoming available it is, in many cases, difficult to distinguish real effects from background variation. Here, the potential usefulness of cumulative sum (CUSUM) charts for monitoring the use of medicines is explored through a case study of an initiative to encourage the prescribing of lowest cost proton pump inhibitors (PPI) in the context of implementation of national guidelines for the management of dyspepsia.

Methods:

This was a longitudinal study involving analysis of routinely collected prescribing data, set in all 12 primary care trusts (PCT) in the North East Strategic Health Authority. In it, comparison (by subtraction) of the time-series of the percentage of generic PPI prescription items for Gateshead with the mean of the other 11 PCTs was used to reduce both variation and bias. This was followed by the construction of a CUSUM chart displaying the effect of the Gateshead initiative.

Results and Discussion:

The simple process of comparison was very successful both in removing extraneous trends and reducing background variation, and the CUSUM highly effective for displaying the evidence for the hypothesized step-change in prescribing behaviour consequent on the Gateshead initiative. The effectiveness of the CUSUM here is strongly linked to the success of the preliminary comparison step.

What is new and Conclusion:

CUSUM, a statistical process control technique, has already been tested as a tool for interpreting hospital and general practice mortality rates. Here, its potential for more general applications in quality monitoring is demonstrated using routinely collected prescribing data. Such data contain valuable information about changes in clinical practice and CUSUM charts, when coupled with the idea of removing time trends and extraneous variation by reference to average behaviour, can provide a simple but effective technique for extracting it.

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