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The use of data and measurement is key to the quality improvement philosophy. Therefore, data collection and analysis tools play an important role in improvement initiatives. The process of applying statistical tools to a quality improvement initiative begins with collecting data that will address the question posed. Generating pertinent and reliable data forms the basis for analysis that guides process changes. The application of formal methodologies in study, experiment, and survey design help assure that the data collected meets the needs of a quality initiative. Once data has been acquired, a variety of data cleaning techniques, such as subsetting, recoding, or formatting may be needed prior to analysis. An important part of data preparation is making sure variable definitions are clearly understood. Data dictionaries accompany many databases and should be consulted for such definitions. Once the data is ready for analysis, the next step is to become familiar with the data through the use of descriptive statistics and visualizations. These initial data summaries are invaluable to help the analyst identify data anomalies, missing data patterns, outliers, time trends, and patterns of variation. They also assist the analyst in identifying additional statistical analyses that may prove useful in better understanding process performance. Figure 1.3 shows the data analysis process in relation to the DMAIC and PDCA frameworks.

In each of these analysis steps, there are a number of statistical and data management tools that can be applied. For example, hypothesis testing may be needed to ascertain if there are significant differences between average wait times of two different urgent care facilities within the same healthcare network. Data visualization is an integral part of the statistical analysis process. The statistical tools presented in this casebook are those most commonly applied in quality improvement. Additional detail on these tools and other statistical analysis techniques can be found in Babbie (2015), Hoerl and Snee (2012), Polit (2010), and Rosner (2015).

Figure 1.3 Statistical analysis process in QI framework.