staff members are familiar with, but they leave out the context, rendering their stories unrelatable. If your goal is to provide people with information they instantly recognize as accurate and relevant, your models must be specified to the people and contexts they presume to report on, and only then should they be examined empirically.
The stories machines tell leave out the context, rendering their stories unrelatable.
You are about to meet a 16‐step process for how to tell a story, using data, that is not only interesting; it is actionable operationally. No two organizations are the same, and no organization stays the same over time. Thus, it is critical to evaluate whether data presented within an organization accurately captures the context and nuance of the organization at a point in time.
Admittedly, the idea of 16 steps may initially feel prohibitively complex. As you spend time looking at the process in terms of some practical examples, however, you will find that what I have provided is simply a template for examining and sorting data which you will find not only simple to use, but ultimately quite liberating.
As you read through the steps, you are likely to intuit what role you would play and what roles you would not play, in this process. Some of the work described in the steps will be done by staff members closest to the work being analyzed, and some will be done by mathematicians, statisticians, programmers, and/or data analysts. If some of the content is unfamiliar to you or seems beyond your reach, rest assured that someone on the team will know just what to do.
If some of the content is foreign to you or seems beyond your reach, rest assured that someone on the team will know just what to do.
Step 1: Identify the Variable of Interest
What problem or improvement opportunity is of concern and/or interest to your team? This could be an issue such as patient falls or the civility of employees, managers, or patients. This problem or improvement opportunity is referred to as the variable of interest; it will also serve as the focal point of the story your team will eventually be telling with data. In some settings, the variable of interest may be referred to as the outcome of interest.
Step 2: Identify the Things That Relate to the Variable of Interest: AKA, Predictor Variables
If your team was looking to improve an outcome related to falls, for example, you would want to examine anything that could predict, precede, or contribute to a fall. Assemble members of the care team and think together about what might lead to a fall, such as (a) a wet floor, (b) staff members with stature too small to be assisting patients with walking, (c) the patient taking a heart medication a little before the fall, and so on. As the discussion of everything that relates to your variable of interest continues, designate one person to write down all the things being mentioned, so the people brainstorming what relates to falls can focus solely on describing the experience and are not distracted by writing things down (Kahneman, 2011). Do not search far and wide for possible predictor variables or even think about the evidence from the literature at this point; just brainstorm and share. Variables from the literature can and should eventually supplement this list, but the focus in Step 2 is on the team's personal experience and subsequent hunches about variables that could affect the variable of interest.
Step 3: Organize the Predictor Variables by Similarity to Form a Structural Model
Have the team organize into groups all predictor variables that seem to be similar to one another. For now, you will simply separate them into columns or write them on separate sheets of paper. For example, one group of variables that may be found to predict falls may be “patient‐related,” such as the patient's age, level of mobility, the different diagnoses the patient is dealing with, and so on. These could all be listed in a construct under the heading “patient‐related variables.” You might also create a construct for “staff‐related variables,” such as “was walking with a staff member,” “staff member’s level of training in ambulating patients,” “the staff member was new to this type of unit,” and so on.
Step 4: Rank Predictor Variables Based on How Directly They Appear to Relate to the Variable of Interest
First, rank the individual variables within each construct, determining which of the variables within each construct appear to relate most directly to the variable of interest. These will be thought of as your most influential variables. Knowledge from the literature of what relates to the variable of interest is welcome at this point, but you should continue to give extra credence to the clinical experience of the team and what their personal hunches are regarding the relevance of each predictor variable. Once the predictor variables within each construct are ranked, then rank each overall construct based on how directly the entire grouping of predictor variables appears to relate to the variable of interest.
Step 5: Structure the Predictor Variables into a Model in Order to Visually Communicate Their Relationship to the Variable of Interest
It will eventually be important to get others on board who are interested in studying and improving the variable of interest. This can be done by noting the variable of interest in a circle in the middle of a blank page and then grouping all the predictor variables around the outcome variable so the visual looks a bit like the hub of a bike wheel with each predictor variable connected to the outcome variable by a spoke. Figure 1.1 is an example of a structural model that is ready to be converted to a measurement model.
For ease of understanding, items in the same construct would be the same color, and items in each color would then be arranged with those considered most influential positioned closest to the variable of interest. Selecting the most influential variables is important because you may decide you have only enough time or resources to address some of the variables. If this is the case, select those variables that are perceived to be the most influential.
A collection of three worksheets used by a neurosurgical nursing care unit for a study on workload, showing the progression from a full list of predictor variables to a workable model (Steps 2–5), can be found in Appendix A. These worksheets visually represent the conversion of a structural model to a measurement model. While Figure 1.1 offers a visual representation of a model with multiple constructs, showing the variables arranged by rank, Appendix A shows a slightly different visual representation, which is the representation typically used by the author of this chapter. Readers are encouraged to try both methods of representing the constructs to see which is more useful in understanding and visually communicating the information.
Figure 1.1 Variable of interest surrounded by constructs of predictor variables, arranged by rank.
Step 6: Evaluate if and/or Where Data on the Predictor Variables Is Already Being Collected (AKA, Data Discovery)
Investigate whether data on any of the predictor variables in your model is already being collected in current databases within your organization. Where you find that data is already being collected, you will use the existing data. You may find that data related to the variables of interest is being collected in more than one place, which will provide an opportunity for consolidation, making your data management process more efficient and standardized.
Step 7: Find Ways to Measure