breadth of application, and versatility of control charts as a
data analysis tool.
Flash sterilization rate
The infection control (IC) committee at a 180 bed hospital
notices an increase in the infection rate for surgical patients.
A nurse on the committee suggests that a possible contributor
to this increase is the use of flash sterilisation (FS) in
the operating theatres. Traditionally, FS was used only in
emergency situations—for example, when an instrument was
dropped during surgery—but recently it seems to have
become a more routine procedure. Some committee members
express the opinion that a new group of orthopaedic
surgeons who recently joined the hospital staff might be a
contributing factor—that is, special cause variation. This
suggestion creates some defensiveness and unease within the
committee.
Rather than debating opinions, the committee decides to
take a closer look at this hypothesis by analysing some data
on the FS rate (number of FS per 100 surgeries) to see how it
has varied over time. The committee’s analyst prepares a u
chart (based on the Poisson distribution, fig 1) to determine
the hospital’s baseline rate and the rate after the arrival of the
new surgeons.
During the baseline period the mean FS rate was around 33
per 100 surgeries (the centre line on the baseline control
chart) and the process appeared to be in control. However,
arrival of the new surgeons indicated an increase (special
cause variation) to a mean FS rate of about 50 per 100
surgeries. For example, the third data point (week 13) is
beyond the baseline UCL, as are weeks 17, 18, 19, and 21.
Additionally, several clusters of two out of three points are
more than 2SD beyond the mean, several clusters of four out
five points are beyond 1SD, and all of the new points are
above the baseline period mean. All these signals are
statistical evidence of a significant and sustained shift in
process performance. The IC committee can now look further