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This casebook focuses on the use of statistical tools as implemented in JMP for healthcare quality improvement. The cases take a holistic approach to data analysis beginning with background and a specification of the task to be undertaken. The available data is presented and the processing needed to prepare the data for analysis is illustrated. Every analysis begins with descriptive analysis and may be followed by the application of statistical methods, the results of which are interpreted in the case context. Insight and implications from the analysis are then discussed and next actions are suggested. Each case concludes with a table that summarizes the key concepts of statistical analysis, data management, JMP features, and quality tools illustrated. This table is followed by exercises and discussion questions that may be used to master and extend the material presented in the case.

Figure 1.5 shows the correspondence between the case structure and the DMAIC and PDCA frameworks. The key concepts are presented at the beginning of each case and associated with the relevant DMAIC and PDCA steps.

Figure 1.5 Case structure and DMAIC and PDCA frameworks.

The 13 cases presented in this book cover the most commonly applied statistical tools in quality improvement and show the step‐by‐step instructions to execute the methods in JMP. The three cases on TJR comprise a complete quality improvement project. Pairs of cases related to a single topic or data set show the application of a sequence of methods, as in the diabetes patient hospitalization data where the first case shows the creation of data visualizations and the second case integrates the data and visualizations in a dashboard (Chapters 3 and 4).