Reference no: EM131565684
Question: Shewhart's Theory for Statistical Process Control (SPC) requires a change in thinking from error detection to error prevention and has a number of benefits in health care. Several of the benefits include patient focus, increased quality awareness, decisions based on data, implementing predictable health care processes, reduced costs, fewer errors resulting in increased patient safety, and improved processes that result in improved health care outcomes and better quality care. However, every process varies. In SPC terminology as it relates to a control chart, a common cause variation does not suggest that a process functions at a desirable or undesirable level, but whether the nature of the variation is stable or predictable within certain limits.
A special cause variation is a negative finding, and any changes made in a health care organization should not be made until it identifies and eliminates special causes. A control chart will tell a health care organization if a variation is a common or special cause and how to approach an improvement process. If it is a special cause the health care organization should investigate it and eliminate the variation, not change the process. If there is a common cause variation, the implementation of a process change is what will address the variation. Control charts will reveal whether the change was effective (Joshi et.al, 2014).
In this Discussion, you will look at these statistical tools for quality improvement and describe the differences between common cause variation and special cause variation. You will also explain any ethical, legal, or moral obligations that would support your rationale.
To prepare: Review the Learning Resources for the week as they relate to Statistical Process Control, common cause, and special cause variation.
- Read the following situations and determine whether each situation is a common cause variation or a special cause variation:
- Dispensing the wrong medication to a patient
- Dispensing the correct medication several hours after it was supposed to be dispensed
- For both of these examples, apply data-collection and statistical tools to measure and explain your rationale for your determination.