Continuous Quality 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) techniques.[3] SPC is a strategy, a philosophy and a set of methods for on-going improvement of systems,

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]

The application of the Shewhart chart, Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) to the monitoring process which allows continuous real time assessment. The Shewhart chart is used to detect large chanfes, while CUSUM and EWMA are more suitable to recognition of small to moderate changes. When the charts are used together, the Shewhart chart and EWMA are more suitable for monitoring Bacteraemia and multiresistant organism rates. While the Shewhart and CUSUM charts are suitable for surgical infection

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 performing consistently and it is in statistical control. Although this result may not agree with the clinician’s views, but it is not necessarily means that the result are acceptable. A process that are in control can be predictably as bad process. There may be exist common cause