Elsevier

The Lancet

Volume 357, Issue 9254, 10 February 2001, Pages 463-467
The Lancet

Public Health
Bristol, Shipman, and clinical governance: Shewhart's forgotten lessons

https://doi.org/10.1016/S0140-6736(00)04019-8Get rights and content

Summary

During the past century, manufacturing industry has achieved great success in improving the quality of its products. An essential factor in this success has been the use of Walter A Shewhart's pioneering work in the economic control of variation, which culminated in the development of a simple yet powerful graphical method known as the control chart. This chart classifies variation as having a common cause or special cause and thus guides the user to the most appropriate action to effect improvement. Using six case studies, including the excess deaths after paediatric cardiac surgery seen in Bristol, UK, and the activities of general practitioner turned murderer Harold Shipman, we show a central role for Shewhart's approach in turning the rhetoric of clinical governance into a reality.

Section snippets

Common-cause and special-cause variation

Consider a process such as writing a signature. Five of MAM's signatures are shown in the left of figure 1. Although these signatures were produced under the same conditions and by the same process, they are not identical. However, although they show variation, the variation is controlled within limits. They are all recognisably the same signature. This kind of variation suggests that a stable process produced the signatures.

In the UK National Health Service, three basic approaches are used to

Variation cannot be eliminated

Shewhart illustrated his concepts by applying them to the best data available to him at the time. This was a data set obtained from an experiment in which almost everything possible was done to obtain perfect results (ie, no variation)—Millikan's Nobel-Prize-winning measurements of the charge of an electron.3 Despite Millikan's best efforts, there was substantial variation in his measurements of the charge of an electron. However, as the control chart (figure 2) of Millikan's data shows, all

Case study 1: Bristol cardiac surgery

A control chart based on data from the UK Cardiac Surgical Register of the mortality rates for children younger than 1 year old during three epochs9 is shown in figure 3. The chart for epoch 1 will be used to explain the interpretation of a control chart.

In epoch 1, the mortality rates for nine hospitals lie within the control limits: common-cause variation. Action to reduce this variation must focus on the underlying process of care common to these nine hospitals. However, two hospitals

Case study 2: Harold Shipman

A control chart (figure 4) of mortality rates for women aged 65 years and older in Thameside and Glossop, UK, during 1992–98 10 shows that in 1992 and 1994, Harold Shipman's mortality rates were within common-cause variation. However, during 1993 and 1995–98, his mortality rates indicated special-cause variation. To reduce special-cause variation, the special cause must be found and removed. Subsequent legal proceedings identified that special cause as being Shipman himself.

Commentators have

Case study 3: IVF treatment

Marshall and Spiegelhalter12 analysed the case-mixadjusted livebirth rate at 52 IVF clinics in the UK (n=24 739 treatment cycles, range of livebirth rate 5–24%). They concluded that league tables were unreliable. No action point emerged from their analysis. In contrast, a control chart (figure 5) with the upper and lower control limits divides the clinics into three groups with guidance for action:

  • Group A—performance above the upper control limit. Find out why their results are better than

Case study 4: neonatal deaths

Parry and colleagues13 compared mortality for nine neonatal units (n=2671 infants, mortality range 15–28%), concluding that league tables were unreliable indicators of performance. In contrast, a control chart of the neonatal data (figure 6) shows only common-cause variation, suggesting that future improvement is best sought from a fundamental change to the underlying process of care. There are no grounds for taking action on individual neonatal units.

Case study 5: prevalence of coronary heart disease in primary care

The point prevalence of coronary heart disease in a primary group consisting of 16 general practices in Birmingham, UK, was reported (private communication, Birmingham Health Authority, 1999) as 9·67% (2999/3102), with wide variation (1–38%) between practices. A control chart of these data (figure 7) identifies 12 practices within control limits indicating common-cause variation. These practices should be left alone. However, five practices are outside the control limits, indicating

Case Study 6: mortality after fractured hips

Todd and colleagues14 compared differences in mortality after fractured hip in eight hospitals in East Anglia, UK (n=560, mortality range 5–24%). A control chart (figure 8) shows seven hospitals within common-cause variation. Improvement at these seven hospitals can only come from changing the underlying process of care for patients with fractured hip. One hospital had a very low mortality outside the limits of common cause-variation: this mortality rate is therefore likely to have a special

Discussion

These case studies illustrate an important role for Shewhart's approach to understanding and reducing variation. They demonstrate the simplicity and power of control charts at guiding their users towards appropriate action for improvement.

Actions based on Shewhart's approach are subject to two types of mistake.14 Mistake 1 is to treat an outcome resulting from a common cause as if it were a special cause. Mistake 2 is to treat an outcome resulting from a special cause as if it were a common

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