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Statistical Process Control as a tool for research and
healthcare improvement


Tan Song Xian

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of Applied Science and Technology, University Tun Hussein Onn Malaysia,


.Nowadays, health issues care
are getting more concern by people. The awareness of health care among people
had increased. The Statistical process control (SPC) tools are being used in
health care industry and the usage is increasing. The SPC tools are applied
into health care improvement such as hospitals. This paper provided an overview of common uses of the
SPC tools in health care industry and example of applying SPC tools in health
care industry and how it is used for quality improvement.

Statistical control process; tools; health care; improvement


Quality can be
achieved by evaluating and improving production processes or service delivery. 1
Changes are required in order to improve the process of health care and service
delivery, but not all the changes will result in improvement.1,2

Health care
industry is under increasing pressure to be more efficient and more effective.
Nowadays, hospitals are able to adopt the techniques and methods of Continuous Quality
Improvement (CQI) as a part of their trust requirements. One of the main
challenges of implementing CQI into health care industry is on how to manage,
control and improve the process by using Statistical process control (SPC)

 SPC is a strategy, a philosophy and a set of
methods for on-going improvement of systems and processes. The SPC approach is
based on data and has its foundation in the theory of variation which are common
causes and special causes. The primary tools commonly used in SPC are Shewhart
charts which also known as control charts, run charts, histograms, Pareto
diagrams, scatter diagrams, flow charts4. By monitoring the systems or
process, it must also be able to minimize the false positive or false negative
that may arise and which could lead to inappropriate clinical decision making.

Recently, the
statistical process chart which also known as control charts are used in
monitoring for health care5. In year 1920, Walter A Shewhart developed a
theory of variation which then forms the basis of SPC.6,7 The SPC charts are
very useful tools for investigating and identifying the important process
variables  and the quality improvement.8
The control charts was originally used as a tool for controlling and monitoring
manufacturing process. The control charts is a set of simple graphical tools.
Generally, control charts is consist of a central line which represent the mean
of the data, a lower and upper lines represent the lower and upper controls
limits respectively which are usually set at three-sigma from the mean. Any
points that fall outside the limits in the control charts, is considered as out
of control points.5

There are
several control charts that are applied into the monitoring processes which are
Shewhart chart, Cumulative Sum (CUSUM) and Exponentially Weighted Moving
Average (EWMA) which allows continuous real time assessment. The Shewhart chart
is used to discover large changes, while CUSUM and EWMA are more suitable to recognize
of small to moderate changes.9







The control charts are the most frequently used tools in the
statistical process. Control charts are used to monitoring processes in order
to achieve a better mean value of the process or to reduce the variability of
the processes to improve the quality. There are three horizontal lines in the
control charts, which are the center line which also known as mean, and lower
and upper limits. While the vertical axis consist of the values of the
appropriate sample characteristics.

The sample size of the data must be specified before
designing a control charts. When the subgroup size of the data is 1, the
suitable control charts is Moving Range Chart. While for the subgroup size
between 2 to 10, the recommended charts is  chart and R chart. If the subgroup size of the
data if greater than 10, the suitable control charts is  chart and s chart.

For the  chart, let  be the average of each of the sample. Next the
process average is calculated by using. Then, the  will be used as the center line of the control charts. To construct
the control limits, the estimated standard deviation ? is needed. The range of
the sample is the difference between the smallest and largest observations
which is R= . Then let R1, R2Rm be the ranges
of the sample and calculate the average of the range by  . Then the upper control
limit (UCL) is calculated by using UCL, while lower control limit (LCL)
is computed by LCL= . The center line is equal
to  For R chart, the control limits are calculate
by using UCL , LCL=and center line = .

For s chart, the control limits is calculated by using the
following formula which UCL= , while LCL=  and center line=.



Figure 1: Example of
a control chart 8


          The control
limits are used to determine whether the process is stable or not and identify
whether there is points out of control or not. If the result shows that the
process is a stable process which there is no out of control points and showed
a non-random pattern, the parameters of the statistical model is used and the
control limits are used for further monitoring process.8





Results and Discussion

The following examples illustrate the application of control
charts as a data analysis tool.


Example: Laboratory
turn around time (TAT)


Several clinician are complaining about the turn around time
(TAT) for complete blood counts has been out of control and the condition is
getting worse. Thus, the laboratory manager decided to investigate this
situation by collecting data. The data are stratified by shift firstly and the type
of request to ensure that the analysis is conducted by a reasonable processes.
Generally, the TAT data is always follow the normal distribution. The  chart and s chart are the suitable control
charts for these data. 2


Figure 2: The control
charts of turn around time (TAT) for day shift routine orders for complete blood


The mean , and standard deviation, s of TAT of each day
were calculated for three randomly selected order for complete blood counts.

From figure 2, the upper chart is  chart, while the bottom chart is s chart. For  chart, it shows the mean of TAT for the three
orders each day. While the s chart shows the standard deviation for the same
three orders. It can be seen that there is no points are out of control which
indicated that the turn around time for complete blood counts for each day are
in control.

However, from the statement above, it stated that the turn
around time for complete blood counts are out of control and are getting worse.
If the clinician’s complains are true, it will observed that there is points
out of control and an increasing trend will be observed from the control chart.
But, the result from the control chart shows that the process is having a good
performance and it is in a statistical control. Although this result may not
agree with the view of clinicians, but it is not necessarily meaning that the
result are acceptable. A process that are in control can be predictably as a bad
process. There may be exist common cause variation.

In this case, the process is stable and predictable but it
is not acceptable to the clinicians. It is appropriate to consider to lower the
mean of TAT and reduce the variation which to lower the center line and the aim
is to bring the control limits closer as an improvement strategies since the
process is exhibit common cause variation only. From the strategy, a new and
more acceptable control limits will be produced and hence the level of
performance will also increased. Then, the new process with new baseline
measurements is tested to decide whether the process is improved, remain the
same or getting worse.2


In general, the statistical
process control tools such as control charts could help the teams to make
decision on the correct improvement strategy whether to search for special
causes when the process is out of control or to work on more fundamental
process improvements when the process is in control. In the example above, the
control charts can be used as a simple monitoring tools to ensure the improvement
are remain over the time.2From figure 2, it can be seen that the process is
in control. However, the result is difference as what the clinician’s claim.
Thus, the department has to collect more data to get a more reasonable control
limits. But, it general, the example had helped to generate a simple overview
on how SPC had been applied into health care industry.

Process monitoring
by using SPC tools is an important process in evaluating and improving the
framework in health care industry. The control charts are using three sigma
control limits generally.7

In conclusion, the
result indicated how SPC had been applied to health care industry although
there is still having some barriers but in order to overcome the barriers some
changes are needed so that the application could improve patients’ health. The
SPC tools are very useful as a tools for evaluating the performance of health
care providers. The SPC tools like control charts are useful, user friendly and
easy to use and it is a statistically strict process analysis tools that could
be used by quality improvement teams. These SPC tools could help quality
improvement managers and also researchers to use the data and result to make
appropriate decisions for quality improvement.




I would
like to express a special thanks to my lecturer Dr. Shuhaida Binti Ismail who
had gave me a chance to do a topic on Statistical process control as a tools
for research and healthcare improvement. She had gave me guidance and
motivation when doing the paper. I would like to express a special to my parent
too as they had gave me motivation to do the paper.



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