A Tale of Two Industries: Contrasting Quality Assessment and Improvement Frameworks

Imagine the chaos that would result if industrial engineers each had their own tool sets calibrated in idiosyncratic metrics with unit sizes that changed depending on the size of what they measured, and they conducted quality improvement studies focusing on statistical significance tests of effect sizes. Furthermore, these engineers ignore the statistical power of their designs, and don’t know when they are finding statistically significant results by pure chance, and when they are not. And finally, they also ignore the substantive meaning of the numbers, so that they never consider the differences they’re studying in terms of varying probabilities of response to the questions they ask.

So when one engineer tries to generalize a result across applications, what happens is that it kind of works sometimes, doesn’t work at all other times, is often ignored, and does not command a compelling response from anyone because they are invested in their own metrics, samples, and results, which are different from everyone else’s. If there is any discussion of the relative merits of the research done, it is easy to fall into acrimonious and heated arguments that cannot be resolved because of the lack of consensus on what constitutes valid data, instrumentation, and theory.

Thus, the engineers put up the appearance of polite decorum. They smile and nod at each other’s local, sample-dependent, and irreproducible results, while they build mini-empires of funding, students, quoting circles, and professional associations on the basis of their personal authority and charisma. As they do so, costs in their industry go spiralling out of control, profits are almost nonexistent, fewer and fewer people can afford their products, smart people are going into other fields, and overall product quality is declining.

Of course, this is the state of affairs in education and health care, not in industrial engineering. In the latter field, the situation is much different. Here, everyone everywhere is very concerned to be sure they are always measuring the same thing as everyone else and in the same unit. Unexpected results of individual measures pop out instantly and are immediately redone. Innovations are more easily generated and disseminated because everyone is thinking together in the same language and seeing effects expressed in the same images. Someone else’s ideas and results can be easily fitted into anyone else’s experience, and the viability of a new way of doing things can be evaluated on the basis of one’s own experience and skills.

Arguments can be quite productive, as consensus on basic values drives the demand for evidence. Associations and successes are defined more in terms of merit earned from productivity and creativity demonstrated through the accumulation of generalized results. Costs in these industries are constantly dropping, profits are steady or increasing, more and more people can afford their products, smart people are coming into the field, and overall product quality is improving.

There is absolutely no reason why education and health care cannot thrive and grow like other industries. It is up to us to show how.

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LivingCapitalMetrics Blog by William P. Fisher, Jr., Ph.D. is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Based on a work at livingcapitalmetrics.wordpress.com.
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