Can Problems Lurk in Charts Without Control Violations?
Control charts are well-established tools for
characterizing process variability. Variability
is inherent in every process. These charts provide
a way to monitor this variability. Given the
available process data, control charts can identify
points that are outside the expected limits.
Control violations triggered by common rule
sets identify data trends that are expected
to occur by chance <.3% of the time. Identifying
data points that are expected to occur so infrequently
is a reasonable technique for triggering an
alarm that something may be occurring in the
process that is not expected from common cause
variation. When a process is completely out-of-control,
there may be multiple control violations since
the process itself may be unpredictable. Such
situations become readily apparent when examining
The information contained in a control chart
describes the process as it existed during the
period represented by the chart. This information
makes no inferences about how the process should
perform. It is possible that a chart appears
to be in control, but the process is not performing
in a desirable way. This is an insidious situation.
If the behavior of the process is not consistent
with internal goals or targets, it may be difficult
to convey that a problem, in fact, exists since
the control chart does not display control violations.
It is important to add as much information as
possible to a chart in order for the target
audience to be able to discern the ramifications
of the information being presented to them.
If there are internal goals or targets, for
example, add this information to the chart.
This will give the viewer an opportunity to
check the actual process performance against
In healthcare, there may be a level of performance
that is expected or required. For example, if
a facility strives to administer a treatment
to every eligible patient with a certain condition,
we can use a control chart to measure the goal
against the actual performance.
Here is an example of a P-chart with sample
data for the administration of ACEI or ARB to
eligible LVSD patients. Based on this data,
there are no control violations.
The calculated mean is 88.6%. Consider the
possibility that a healthcare facility has a
goal of 95% for this measure. The chart in its
current form offers no information pertinent
to the facility goal. If people viewing the
chart know the goal, they can see that there
is a discrepancy between the calculated average
and the 95% goal. This method, however, is not
a reliable way to convey the point that work
needs to be done to reach the goal. If the goal
is not common knowledge or if it has changed,
the audience has no way to compare the current
target to performance.
The next chart adds the target value to the
This technique makes it clear to the viewer
that the existing process of identifying and
treating eligible patients is not adequate to
achieve the facility’s goal for this measure.
The target audience can see from the chart that
only a few of the values meet or exceed the
Once a discrepancy is identified, there may
be many issues that need to be investigated
in order to effect a change. Here are a few
questions that can help to begin assessing a
- Does the data seem reasonable or is it what
- Is the data complete—have all the data
been collected and submitted?
- Is there additional information we could
get from the data if we grouped it differently
or had the ability to drill down in order
to see more detail?
- Are the subgroup sizes consistent with the
requirements for the type of data?
- Should the sigma limits be altered to detect
problems within a shorter time span?
in mind that it is possible for problems to lurk
under the surface of a control chart in the absence
of statistically defined control violations. It
is important to be aware of the data content and
compare that to what is expected behavior from
the process performance.