Improvement of health care requires making changes in
processes of care and service delivery. Although process
performance is measured to determine if these changes are
having the desired beneficial effects, this analysis is
complicated by the existence of natural variation—that is,
repeated measurements naturally yield different values
and, even if nothing was done, a subsequent measurement
might seem to indicate a better or worse performance.
Traditional statistical analysis methods account for natural
variation but require aggregation of measurements over
time, which can delay decision making. Statistical process
control (SPC) is a branch of statistics that combines
rigorous time series analysis methods with graphical
presentation of data, often yielding insights into the data
more quickly and in a way more understandable to lay
decision makers. SPC and its primary tool—the control
chart—provide researchers and practitioners with a
method of better understanding and communicating data
from healthcare improvement efforts. This paper provides
an overview of SPC and several practical examples of the
healthcare applications of control charts.