How do you scientifically determine the effect of something on something else?
The answer (in whatever form it may take and regardless of the branch of science from which it originates) may often be answered through use of a well-established statistical technique referred to as regression analysis. It is through the application and appropriate use of such a method, along with many others, that we can derive value and make sense of mere data, answer questions, and—most importantly—gain new knowledge. Virtually all of the other learned disciplines make use of these tools. Why, then, has clinical engineering and the healthcare technology management (HTM) community yet to embrace and apply such powerful statistical tools to the masses of available data within our computerized maintenance management systems (CMMSs)? As a discipline, we have decades of accumulated data, but in essence still very little knowledge on the effects of one thing on another.
For example, we really do not know—beyond conjecture and the anecdotal—the effects of ventilator preventive maintenance labor hours (or inspection volume) on unscheduled ventilator corrective maintenance costs; or, the effects of device manufacturer on device cost of ownership; or, is there any meaningful relationship between hospital bed size and needed HTM full-time equivalents; or, is the apparent upward trend in total work order volume statistically significant? Such questions and more cannot be answered through the use of simple descriptive statistics (i.e., by just looking at sample averages and their associated standard deviations). Here, we need to apply and use the inferential statistics, such as t-tests, ANOVA (analysis of variance) regression, and/or chi-square analysis. Contrary to what we may initially think, these techniques are not at the level of “rocket science” in complexity. We already have the data. It’s now just a matter of properly formatting and importing them into (preferably) one of many available statistical software packages (even Excel would do). As such, what is the community and especially many of our great CMMS vendors waiting for?
Again, we have the data. So, let’s start to seriously and appropriately mine and interrogate this stuff and see what—if anything—emerges; even findings of no difference, no effect, and no significance, are meaningful. Such findings may not feel good, but they still represent and may provide new knowledge, which then often begets and prompts new and even more potentially revealing questions. This is the essence and goal of doing good science.
Even with the inherent “messiness” or variability associated with any given CMMS database, the beauty and elegance of these statistical techniques is that they will not only work in spite of it but also tell us the proportion of such variability that remains unexplained. That itself is a measure of just how messy the data is—again, new knowledge.
So, let’s start to move beyond merely reporting and describing the things we do and report the effects of what we did. Then and only then can we continue to evolve as a profession.
Larry Fennigkoh, PhD, is professor of biomedical engineering at the Milwaukee School of Engineering and a member of the BI&T Editorial Board for AAMI.