Shewhart's Theory for Statistical Process Control (SPC) focuses on error prevention rather than detection and can provide several healthcare benefits including increased patient focus, quality awareness, data-driven decisions, predictable processes, lower costs, improved safety and outcomes. However, processes vary and SPC uses control charts to distinguish between common cause variation, which does not suggest a process is undesirable but stable, and special cause variation, which is adverse and requires investigating and eliminating the root cause before making process changes. Control charts reveal whether variations are common or special causes and guide improvement efforts.
Shewharts Theory for Statistical Process Control (SPC) requires a c.pdf
1. Shewhart's Theory for Statistical Process Control (SPC) requires a change in thinking from error
detection to error prevention and has a few healthcare benefits. Several of the benefits include
patient focus, increased quality awareness, decisions based on data, implementing predictable
healthcare processes, reduced costs, fewer errors resulting in increased patient safety, and
improved processes that result in improved healthcare outcomes and better quality care.
However, every process varies. In SPC terminology related to a control chart, a common cause
variation does not suggest that a process functions at a desirable or undesirable level, but if the
nature of the variation is stable or predictable within certain limits. An unusual cause variation is
an adverse finding, and any changes made in a healthcare organization should not be made until
it identifies and eliminates special causes. A control chart will tell a healthcare organization if a
variation is a common or special cause and how to approach an improvement process. If it is a
special cause, the healthcare organization should investigate it and eliminate it, not change the
process. If there is a common cause variation, the implementation of a process change will
address the variation. Control charts will reveal whether the change was effective (Nash, Joshi,
Ransom & Ransom, 2019).
Look at these statistical tools for quality improvement and describe the differences between
common cause variation and unique cause variation. Also explain any ethical, legal, or moral
obligations that would support your rationale.