This module introduces a variety of graphical tools—including frequency plots, Pareto charts, scatterplots, and radar charts—to deepen your understanding of variation in data. You will learn how to use these tools alongside Shewhart charts and apply stratification techniques to enhance data interpretation and process improvement.

This module guides learners through common challenges encountered when creating and interpreting Shewhart charts, including best practices for formatting, recalculating limits, and selecting appropriate chart types. Participants will also explore considerations for using SPC software, publishing charts, and distinguishing Shewhart's theory from statistical inference. By the end, learners will be equipped to troubleshoot and enhance the effectiveness of Shewhart charts in improvement projects.

This module introduces advanced Shewhart-type charts, including Xbar Range, Median, Negative Binomial, and multivariate charts, as well as adaptations like CUSUM and EWMA for enhanced detection of variation in health data. Learners will gain practical skills in selecting and interpreting specialized control charts to address complex data scenarios in healthcare quality improvement.

This module explores advanced adaptations of Shewhart charts for complex healthcare data scenarios, including handling seasonal effects, confounders, autocorrelation, and risk adjustment. Learners will discover how to modify standard control charts to address real-world challenges and improve data interpretation. The module also distinguishes Shewhart charts from comparison charts and discusses appropriate data transformations.

This module guides learners through the process of analyzing and improving healthcare outcomes using aggregate data. By applying the Drill Down Pathway and Shewhart charts, participants will learn to disaggregate data, identify improvement opportunities, and implement effective changes. Real-world case studies, such as adverse drug events, illustrate each step for practical understanding.