QI 104 Lesson 2: How to Learn from Run Charts and Control Charts
When you analyze a run chart to understand the impact of PDSA (Plan-Do-Study-Act) test cycles, you’re looking for non-random patterns in the data — that is, evidence that performance has actually changed. But how can you tell if the variation you’re seeing is random or non-random? In this lesson, we'll explain the difference between common cause and special cause variation. We'll teach you four rules to distinguish between these two causes of variation, and we'll introduce you to another type of chart that can also help with this, called a Shewhart (or control) chart.
Estimated Time of Completion: 30 minutes
Learning Objectives
After completing this lesson, you will be able to:
1. Describe the difference between common cause and special cause variation.
2. Apply four rules to identify non-random variation in the data on a run chart.
3. Explain the purpose of a Shewhart (or control) chart.
Contributors
Author(s):
Kevin Little, Ph.D, Principal, Informing Ecological Design, LLC View Profile
Editor(s):
Laura Fink, Senior Managing Editor, Institute for Healthcare Improvement View Profile
Reviewer(s):
Matthew Eggebrecht, Senior Consultant, University of Minnesota View Profile
James Moses, MD, MPH, Medical Director of Quality Improvement, Associate CQO, Boston Medical Center View Profile
Lloyd Provost, MS, Statistician, Associates in Process Improvement View Profile
Richard Scoville, PhD, Improvement Advisor/Consultant, Institute for Healthcare Improvement View Profile
Requirements
You must be a registered IHI.org user to take this lesson.
You must achieve a minimum score of 75% to successfully complete this lesson.