Abstract will indicate a better or worst performance. Traditional


Improvement of health care are needed
to become different in processes of service. The process performance is
complicated by the existence of natural variation if it determined these
changes are having the beneficial effects. Such as same measurements are
repeated will get different values and some of it will indicate a better or
worst performance. Traditional statistical analysis methods will delay the time
of decision making due to a lots of measurements over time but it also consider
method that account for natural variation. Statistical process control (SPC) as
a tool for research and healthcare improvement because it is a kind of
statistics that combines presentation of data with time series analysis method.
The tool make the data more quickly and in a understand way to decision makers.
There are 7 main tools that mentioned in the statistical process control (SPC).
In this paper, we mentioned the primary tool which is the control chart. This tool
make a better understanding and communicating data from the healthcare
improvement efforts to all the researchers and public.  This paper also provides overview of statistical
process control and few examples of the healthcare applications which applied
primary tool of SPC—the control chart.

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health care is quite important for everyone include younger, teenagers, and
also elder because they know body health are quite important and necessary to
them. Without health, they cannot do anything that they want like travel, enjoying
life and so on. Maybe they just stay at the hospital or lying on the bed only
without do anything. Last time, people only busy work for earn money without
care their health and pollution level of world is not so much serious compared
to now. In last few years, people noticed that health care is quite important
for them to maintain a good and healthy lifestyle. They not only busy their
work and also care about health care like eating a healthy meal, buy a health
care insurance, always make appointment for body checkup and so on. Not only
that, they also care about that due to pollution nowadays quite serious
compared to last time. But, still a lot of people don’t know what is the
healthcare and why is it important. And nowadays health care should make some
improvement to benefit for everyone.

care also called healthcare is a kind of action to maintain or make improvement
of health via the prevention, diagnosis, treatment and other physical and
mental impairments inside human body. Health professionals delivered healthcare
in some occupation like physicians, optometry, audiology, pharmacy, psychology,
physician associates, dentistry, midwifery, nursing, medicine, and other health

and social condition mostly always affect the health care when the healthcare
may vary across individuals, groups and individuals. Every country have different
policies and plans to related personal or grouped health care within their
social. Due to meet the health needs for populations, health care system should
be prepared organization.  Their exact
configuration will make a difference between national and subnational entities.
In some country, planning occurs more centralized among government and private
organization while in some country, the plan is distributed among market
participants. According to the World Health Organization (WHO), a
well-functioning healthcare system requires a robust financing mechanism; a
well-trained and adequately paid workforce; reliable information on which to
base decisions and policies; and well maintained health facilities and
logistics to deliver quality medicines and technologies.1

Healthcare can
contribute to a large part of a country’s economy. It is important to promote
physical and mental health to people around the world. Example, in 1980, the
first disease in human history to be completely eliminated by health care
interventions was declared by WHO. 4

Even thought,
all improvement needed change, but not all change will carry out in the
improvement. The major components of measurement are:

(1) Determine and define key

(2) Collect data

(3) Analyze and interpret data


The third component which is analysis
and interpretation of data by using statistical process control (SPC) was
focused by this paper. To determine whether changes in processes to make a real
difference in outcome, statistical process control (SPC) can do so. In this
paper, we discuss an overview of statistical process control (SPC) theory, explain
control charts which is a major tool of SPC, and provide examples of their
application to common issues in healthcare improvement.





The basic theory of statistical
process control was developed in the late 1920s by Dr Walter Shewhart,3 a
statistician at the AT&T Bell Laboratories in the USA, and was popularised
worldwide by Dr W Edwards Deming.4 Both observed that repeated measurements
from a process will exhibit variation—Shewhart originally worked with
manufacturing processes but he and Deming quickly realized that their
observation could be applied to any sort of process. If a process is stable,
its variation will be predictable and can be described by one of several
statistical distributions.


One such model of random variation is
the normal (or Gaussian) bell shaped distribution which is familiar to most
healthcare professionals. While repeated measurements from many processes
follow normal distributions, it is important to note that there are many other
types of distributions that describe the variation in other healthcare
measurements such as Poisson, binomial, or geometric distributions. For
example, the random variation in the number of wound infections after surgery
will follow a binomial distribution since there are only two possible
outcomes—each patient either did or did not have a postoperative infection with
about the same probability (assuming that the data are adjusted for patient
acuity, surgical techniques, and other such variables).


SPC theory uses the phrase “common
cause variation”to refer to the natural variation inherent in a process on a
regular basis. This is the variation that is expected to occur according to the
underlying statistical distribution if its parameters remain constant over
time. For example, the random variation between body temperatures within a
population of healthy people is a result of basic human physiology, while the
random variation in week to week wound infection rates is a result of factors
such as training, sources of supplies, surgical and nursing care practices, and
cleanliness procedures. Processes that exhibit only common cause variation are
said to be stable, predictable, and in “statistical control”, hence the major
tool of SPC is called the “statistical control chart”.


Conversely, the phrase “special cause
variation”refers to unnatural variation due to events, changes, or
circumstances that have not previously been typical or inherent in the regular
process. This is similar to the concept in traditional hypothesis tests of data
exhibiting statistically significant differences, a key distinction being that
we now test for changes graphically and over time using small samples. For
example, heavy demand for A&E services brought on by an influenza epidemic
may create special cause variation (statistically significant differences) in
the form of increases in A&E waiting times. As another example, suppose
that the daily mean turn around time (TAT) for a particular laboratory test is
64 minutes with a minimum of 45 minutes and a maximum of 83 minutes; this mean
has been observed for several months. One day the mean jumps to 97 minutes
because a major power outage caused the computers to go down, the lights to go
out, and the pneumatic tube system to become inoperative. On this particular
day the process is said to be “out of control”and incapable of performing as it
had in the past due to the “special cause”of the power outage.


Note that special cause variation can
be the result of either a deliberate intervention or an external event over
which we have little control. Special causes of variation can also be transient
(being short staffed in A&E one day due to illness of a key person) or can
become part of the permanent common cause system (eliminating a staff position
through a budget cut).


Interventions in a research study or
change ideas in a quality improvement project are deliberate attempts to
introduce special causes of variation. Statistical tools are therefore needed
to help distinguish whether patterns in a set of measurements exhibit common or
special cause variation. While statistical process control charts and
hypothesis tests are both designed to achieve this goal, an important
difference is that SPC provides a graphical, simpler, and often faster way to
answer this question. The basic principles of SPC are summarised in box 1.


Box 1 Basic principles of SPC

Individual measurements from any
process will exhibit variation.


If the data come from a stable common
cause process, their variability is predictable within a knowable range that
can be computed from a statistical model such as the Gaussian, binomial, or
Poisson distribution.


If processes produce data with
special causes, measured values will deviate in some observable way from these
random distribution models.


Assuming the data are in control, we
can establish statistical limits and test for data that deviate from
predictions, providing statistical evidence of a change.


These observations lead to two
general approaches for improving processes. Because processes that exhibit
special cause variation are unstable and unpredictable, they should be improved
by first eliminating the special causes in order to bring the process “into
control”. In contrast, processes that exhibit only common cause variation will
continue to produce the same results, within statistical limits, unless the
process is fundamentally changed or redesigned.


Moreover, if a process remains in
control, future measurements will continue to follow the same probability
distribution as previously—that is, if a stable process produces data that
follow a normal distribution and it is not further disturbed by special causes,
we can expect about 95% of future measurements to fall within ±2 standard
deviations (SD) around the mean. We can make similar statements about
prediction ranges associated with any other statistical distribution. In
general, regardless of the underlying distribution, almost all data will fall
within ±3SD of the mean if the underlying distribution is stable—that is, if
the process is in statistical control.


The control chart therefore defines
what the process is capable of producing given its current design and
operation. If a different level of performance is wanted in the future, we must
intervene and introduce a change in the process—that is, a special cause. If we
simply want to sustain the current level of performance, special causes of
variation must be prevented or eliminated. Control charts can often help to
detect special cause variation more easily and faster than traditional
statistical methods, and therefore are valuable tools for evaluating the
effectiveness of a process and ensuring the sustainability of improvements over


Shewhart developed a relatively
simple statistical tool—the control chart—to aid in distinguishing between
common and special cause variation. A control chart consists of two parts: (1)
a series of measurements plotted in time order, and (2) the control chart
“template”which consists of three horizontal lines called the centre line
(typically, the mean), the upper control limit (UCL), and the lower control
limit (LCL).


To interpret a control chart, data
that fall outside the control limits or display abnormal patterns (see later)
are indications of special cause variation—that is, it is highly likely that
something inherently different in the process led to these data compared with
the other data. As long as all values on the graph fall randomly between the
upper and lower control limits, however, we assume that we are simply observing
common cause variation.


Where to draw the UCL and LCL is
important in control chart construction. Shewhart and other SPC experts
recommend control limits set at ±3SD for detecting meaningful changes in
process performance while achieving a rational balance between two types of risks.
If the limits are set too narrow there is a high risk of a “type I
error”—mistakenly inferring special cause variation exists when, in fact, a
predictable extreme value is being observed which is expected periodically from
common cause variation. This situation is analogous to a false positive
indication on a laboratory test. On the other hand, if the limits are set too
wide there is a high risk of a “type II error”analogous to a false negative
laboratory test.


For example, for the familiar normal
distribution, in the long run 99.73% of all plotted data are expected to fall
within 3SD of the mean if the process is stable and does not change, with only
the remaining 0.27% falling more than 3SD away from the mean. While points that
fall outside these limits will occur infrequently due to common cause
variation, the type I error probability is so small (0.0027) that we instead
conclude that special variation caused these data. Similar logic can be applied
to calculate the type I and type II errors for any other statistical


Although traditional statistical
techniques used in the medical literature typically use 2SD as the statistical
criteria for making decisions, there are several important reasons why control
charts use 3SD. For the normal distribution approximately 95% of the values lie
within 2SD of the mean so, even if the process was stable and in control, if
control limits are set at 2SD the type I error (false positive) rate for each
plotted value would be about 5% compared with 0.27% for a 3SD chart. Unlike one
time hypothesis tests, however, control charts consist of many points (20–25 is
common) with each point contributing to the overall false positive probability.
A control chart with 25 points using 3SD control limits has a reasonably
acceptable overall false positive probability of 1–(0.9973)25?=?6.5%, whereas
using 2SD limits would produce an unacceptably high overall false positive
probability of 1–(0.95)25?=?27.7%! The bottom line is that the UCL and LCL are
set at 3SD above and below the mean on most common control charts.5


In addition to points outside the
control limits, we can also look more rigorously at whether data appear
randomly distributed between the limits. Statisticians have developed
additional tests for this purpose; for example, a common set of tests for
special cause variation is:


one point outside the upper or lower
control limits;


two out of three successive points
more than 2SD from the mean on the same side of the centre line;


four out of five successive points
more than 1SD from the mean on the same side of the centre line;


eight successive points on the same
side of the centre line;


six successive points increasing or
decreasing (a trend); or


obvious cyclic behaviour.


In return for a minor increase in
false positives, these additional tests greatly increase the power of control
charts to detect process improvements and deteriorations. The statistical
“trick”here is that we are accumulating information and looking for special
cause patterns to form while waiting for the total sample size to increase.
This process of accumulating information before declaring statistical
significance is powerful, both statistically and psychologically.


A final important point about the
construction of control charts concerns the mechanics of calculating the SD. As
with traditional statistical methods, many different formulae can be used to
calculate the SD depending on the type of control chart used and the particular
statistical distribution associated with that chart. In particular, the formula
for the SD is not the one typically used to calculate the empirical SD as might
be found in a computer spreadsheet or taught in a basic statistics class. For
example, if we are monitoring the proportion of surgery patients who acquire an
infection, the appropriate formulae would use the SD of a binomial distribution
(much like that for a conventional hypothesis test of proportions); if
monitoring a medication error rate the appropriate formulae would use the SD of
a Poisson distribution; and when using normally distributed data the
appropriate formulae essentially block on the within sample SD in a manner
similar to that used in hypothesis tests of means and variances. Details of
calculations for each type of control chart, when to use each chart, and
appropriate sample sizes for each type of chart are beyond the scope of this
paper but can be found in many standard SPC publications