Paul E Plsek

Footnotes Competing interests: None declared.

Improvement of health care requires making changes in processes of care and service delivery. Although process performance is measured to determine if these changes are having the desired beneficial effects, this analysis is complicated by the existence of natural variation—that is, repeated measurements naturally yield different values and, even if nothing was done, a subsequent measurement might seem to indicate a better or worse performance. Traditional statistical analysis methods account for natural variation but require aggregation of measurements over time, which can delay decision making. Statistical process control (SPC) is a branch of statistics that combines rigorous time series analysis methods with graphical presentation of data, often yielding insights into the data more quickly and in a way more understandable to lay decision makers. SPC and its primary tool—the control chart—provide researchers and practitioners with a method of better understanding and communicating data from healthcare improvement efforts. This paper provides an overview of SPC and several practical examples of the healthcare applications of control charts.

How can physician executives interested in promoting change be more effective at the task? This article explores how to create an attraction towards change, as opposed to viewing change as overcoming resistance. Learning to recognize naturally occurring change, identify attractors, explore the rationality of others' points of view, and reduce risk are clear and constructive insights from research and emerging systems science. Other ideas to consider are: Understand the issues of those you wish to change, create changes that are "exothermic," produce system changes, accept responsibility, learn from failed efforts, and focus on building relationships of trust.