Читать книгу Improving Health Care Quality - Cecilia Fernanda Martinez - Страница 23

Data visualization plays an important role in quality improvement, as can be seen in Figure 1.3. Once data has been collected, visualizations are useful in the data cleaning process, for assessing variation, in understanding relationships between variables, and for monitoring key process indicators.

Univariate graphs such as histograms and box plots aid in identifying data anomalies, such as transcription errors or misspellings in character fields. These graphs also familiarize the analyst with the distribution of the observations. Outliers are easily seen in histograms and box plots. Outliers may be legitimate, but unusual values of a process or they may be errors that require either correction or removal from the data set. Careful study of outliers may lead to insights that benefit the quality initiative. A control chart, which will be described in more detail later, is another type of univariate graph used to monitor process performance over time.

Bivariate plots, such as scatterplots and run charts allow analysts to detect patterns of variation and time trends. They are also helpful to the analyst in choosing an appropriate form for a statistical model to quantify the relationship between two variables. Multivariate graphs such as bubble plots and scatterplot matrices are effective for displaying three or more variables. Maps are another valuable way to visualize geographic data. JMP®'s Graph Builder offers many options for creating multivariate graphs and implements the data visualization technique of “small multiples” (Tufte 2001). This method displays multiple variables using similar graphs with the same axis scales sequenced over one or two other variables. Small multiples allow the observer to focus on changes in the data rather than changes in the graphical elements.

Data visualizations are easily understood by participants in quality improvement projects and facilitate evaluation of process performance. They are also powerful tools for communicating with management, stakeholders, and the general public. There are a number of principles and best practices to create effective visualizations. The reader is referred to the works of Cleveland (1994), Tufte (2001), Few (2012), and Knaflic (2015) for more guidance on creating compelling data visualizations. The cases presented here illustrate how visualizations are applied in various phases of the DMAIC process and provide step‐by‐step instructions for how to create a variety of different types of graphs.