In one real‐life example, we were measuring the performance of charge nurses as the variable of interest, and we had proposed that our three predictor variables were (a) demographics of the charge nurse, (b) attending the charge nurse program, and (c) the preceptor who trained the charge nurse into the role. The originally specified model looked like Figure 1.4.
After Model 1 was used to examine this variable of interest, and after the data was presented to unit managers, charge nurses, and staff members from the unit, those who attended the presentation reported that Model 1 was missing two influential predictor variables: (a) mentoring of the unit manager and (b) resources available on the job for charge nurses to execute their required role. These influential variables were added to a respecified model (Figure 1.5), and the study was conducted again to see whether analysis of the respecified model could further explain what was influencing performance of charge nurses.
Figure 1.4 Model 1 to measure new charge nurse performance.
Figure 1.5 Model 2, respecified with new predictor variables to measure new charge nurse performance.
Note also that in structural models such as Figures 1.4 and 1.5, we have rectangles that look like they are representing one variable, when in many cases they represent multiple variables. For example, the rectangle labeled “Demographics” in both figures might be representing a dozen or so variables. These smaller, more compact models, which appear throughout this book, are called over‐aggregated structural models. Remember when you see them that what looks like a model testing three or four variables is actually testing dozens of variables at the same time.
Step 14: Repeat Steps 2–13 if Explained Variance Declines
In nearly every instance, the data analyst will, along with staff, be repeating Steps 2–13. When initiating use of predictive analytics, conventional wisdom says that at least 50% of the variance should be explained using regression analysis, but it is the experience of this author that explained variance of 70–75% for a variable of interest can be achieved with a good fitting model, using 10 predictor variables or fewer, in a regression analysis.
As practice changes are implemented based on the information that emerges, variables from the initial model will no longer predict the variable of interest because the problem (or part of the problem) will have been solved by the practice changes. Traditionally, the analyst would then have to start over and develop a new model, but in this case, much of the work has already been done when developing the initial full model that is graphically depicted in Figure 1.1. As you return to Step 2, you will review the existing full model and rerun all the analytics to identify existing predictor variables that have now become an issue due to the new practice changes and/or identify new variables that relate to the variable of interest.
Step 15: Interface and Automate
Collecting data from a variety of software can take a lot of time, and it costs a lot of money for staff members to collect the data. These are compelling reasons to examine how technology can be used to automate the specified models developed to study the variable of interest. To examine outcomes in as close to real time as possible, interface the software and applications to one repository of data so the data can be examined as it comes in. Programs can be written to make the mathematical formula run every time one of the new variables comes into the dataset. A program can even be written for automatic respecification of the model as operations of clinical care improve. Manual respecification of a measurement model takes a lot of time, but if the program is set up to detect a fall in the explained variance for any variable, a program can be written that automatically reruns the correlations of all the variables in the model and then automatically builds a new model. Coefficients can be used to identify some specific aspects of how the newly added predictor variable is affecting the outcome variable. For example, if the variable of interest was CLABSI incidence, and the predictor variable is “central line type is causing infection,” the coefficients can identify what type of central line is causing infections, what unit/department it is most likely to occur in, and/or other specifics from other predictor variables in the model.
A program can be written for automatic respecification of the model as operations of clinical care improve.
Step 16: Write Predictive Mathematical Formulas to Proactively Manage the Variable of Interest
Over time, the analyses from models used to study how specific variables affect specific variables of interest will reveal trends that help us identify which variables pose the greatest and/or most immediate risks. Coding of the “variables of risk” into groups will allow you to use logistic regression or other procedures using odds ratios to automatically inform you of the probability that any given variable of risk (or group of risks) is actually causing an undesirable outcome. Real time analytics, made possible by the work you did in Step 15, will help you manage these risks before the undesirable outcome occurs. You might need to use more contemporary analytics, such as machine learning and simulation modeling for smaller samples. Machine learning and simulation modeling can also be used for testing reconfiguration of operations based on real‐time risk. For example, with staff schedules, machine learning and/or simulation modeling can be used to test how staffing ratios of RNs to nursing assistants (and other skill mixes) affect safety.
Summary 1: The “Why”
These steps have been used by this author repeatedly to save millions of dollars related to healthcare outcomes such as reduction of patient falls, reducing central line associated blood stream infections, and decreasing length of stay. With the advancement of mathematics and technology, the widespread use of predictive modeling for proactive management of outcomes in healthcare environments is happening now and will only increase. However, the biggest caution this author has is to not let machines do the interpretation and validation of the data that must necessarily be done by the people who are actually carrying out the work. Big data and machine learning can now quickly scan large datasets for patterns in the data, but it is clear to this author that the data must always be examined by a trained analyst and interpreted and validated by the people closest to the work.
Summary 2: The Even Bigger “Why”
We know that organizations want to provide the highest possible quality, safety, patient experience, and financial performance, and, ultimately, that is why we do the hard (but surprisingly fun) work of predictive modeling for proactive management of these and other outcomes. However, this author must confess that the biggest satisfier of all is the level of engagement and sometimes pure delight that this work engenders in the people involved. What follows is a personal account illustrating how this work is consistently received.
A manager met me as I walked toward her unit to talk to her staff about their unit‐specific results. She had in her hand the unit‐specific report on their
