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Statistical process control as a tool for research and healthcare improvement
  1. J C Benneyan1,
  2. R C Lloyd2,
  3. P E Plsek3
  1. 1Director, Quality & Productivity Laboratory, MIME Department, 334 Snell Engineering Center, 360 Huntington Avenue, Northeastern University, Boston, MA 02115, USA
  2. 2Director, Quality Resource Services, Advocate Health Center, 2025 Windsor Drive, Oak Brook, IL 60523 USA
  3. 3Consultant, Paul E Plsek & Associates Inc, 1005 Allenbrook Lane, Roswell, GA 30075, USA
  1. Correspondence to:
 Mr P E Plsek
 Paul E Plsek & Associates Inc, 1005 Allenbrook Lane, Roswell, GA 30075, USA;


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.

  • quality improvement research
  • statistical process control

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