Quality charts which also known as control charts, run

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) techniques.3
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 studying 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 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 detect large changes, while CUSUM and EWMA are more suitable to recognition of small to moderate changes. The Shewhart chart and EWMA are more suitable for monitoring Bacteraemia and multiresistant organism rates when the charts are used together. While the Shewhart and CUSUM charts are suitable for surgical infection surveillance when the charts are used together.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. In the control charts, it is consist of three horizontal lines, 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 x ? chart and R chart. If the subgroup size of the data if greater than 10, the suitable control charts is x ? chart and s chart.
For the x ? chart, let x ?_1,x ?_2,x ?_3,…,x ?_m be the average of each of the sample. Next the process average is calculated by usingx ?=(x ?_1+x ?_2+?…+x ??_m)/m. Then, the x ? will be used as the center line of the x ? 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= x_max-x_min. Then let R1, R2…Rm be the ranges of the sample and calculate the average of the range by R ?= (R_1+R_2+?+R_m )/m. Then the upper control limit (UCL) is calculated by using UCL=x ?+A_2 R ?, while lower control limit (LCL) is computed by LCL= x ?-A_2 R ?. The center line is equal to x ?. For R chart, the control limits are calculate by using UCL=D_4 R ? , LCL=D_3 R ? and center line = R ?.
For s chart, the control limits is calculated by using the following formula which UCL=x ?+A_3 s ? , while LCL= x ?+A_3 s ? and center line=x ?.
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. Once the result shows that the process is stable which there is no out of control points and shows a non-random pattern, the parameters of the statistical model is used and the control limits are used for further monitoring process.8
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 x ? chart and s chart are the suitable control charts for these data. 2
The mean , x 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 x ? chart, while the bottom chart is s chart. For x ? 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 aid 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.