What is the Vasa? It was a Swedish warship built in 1628. It was supposed to be the grandest, largest, and most powerful warship of its time. King Gustavus Adolphus himself took a keen personal interest and insisted on an entire extra deck above the waterline to add to the majesty and comfort of the ship, and to make room for the 64 guns he wanted it to carry.

This innovation went beyond the shipbuilder knowledge of the time... and would make it unstable. No one dared tell him. On its maiden voyage, the Vasa sailed less than a mile and sank to the bottom of Stockholm harbor in full view of a horrified public, assembled to see off its navy’s—and Europe’s—most ambitious warship to date.

What reminded me of the Vasa? The time has been ripe for visible motivational speakers to weigh in on Covid-19 and “inspire the troops.” From a speech using the Vasa as a backdrop:

“I want to see healthcare become world-class. I want us to promise things to our patients and their families that we have never before been able to promise them.... I am not satisfied with what we give them today.... And as much respect as I have for the stresses and demoralizing erosion of trust in our industry, I am getting tired of excuses....

During the late 1970s, quality began to evolve from its historically Neanderthal, passive inspection approach to its current Cro-Magnon state, where its more proactive, project-based approach is bolted on to the operational status quo. Joseph Juran was a pioneer in such efforts. Various subsequent adaptations such as Six Sigma and lean evolved it further, but over time, it has become comfortably stuck in a misguided focus on tactical improvements at the expense of strategic improvements—i.e., doing things right as opposed to doing the right things right.

In 2011 Jim Liker, a professor of industrial and operations engineering at the University of Michigan, wrote the following to leadership expert Jim Clemmer (emphasis mine):

As statistical methods become more embedded in everyday organizational quality improvement efforts, I find that a key concept is often woefully misunderstood, if it is even taught at all. W. Edwards Deming distinguished between two types of statistical study, which he called “enumerative” and “analytic.”

The key need in quality improvement is that statistics should relate to reality, which then lays the foundation for a theory of using statistics (analytic). Whether you realize it or not, the perspective from which virtually all college courses and many belt courses are taught is population-based (enumerative), its purpose is estimation. 

In a real-world environment, this becomes questionable at best because everyday processes are usually not static populations. Deming was emphatic that the purpose of statistics in improvement is prediction; the question becomes, “What other knowledge beyond probability theory is needed to form a basis for action in the real world?”

Think of population-based statistics as studying a static pond, and a designed study going even further to create a custom-made pond like a swimming pool—a sanitized version of a pond, much easier to study and sample because of reduction of “nuisance” (i.e., everyday) variation. 

In most healthcare settings, workers attend weekly, monthly, or quarterly meetings where performances are reported, analyzed, and compared to goals in an effort to identify trends. Reports often consist of month-to-month comparisons with “thumbs up” and “thumbs down” icons in the margins, as well as the alleged trend of the past three months or the current month, previous month, and 12 months ago.

The data below are typical of the types of performance data that leadership might discuss at a quarterly review, in this case, a year-end review. Suppose these are healthcare data on a key safety index indicator—for instance, some combination of complaints, patient falls, medication errors, pressure sores, and infections. The goal is to have fewer than 10 events monthly (less than 120 annually). In line with the craze of “traffic light” performance reporting, colors are assigned as follows:
• Less than 10 = green
• 10–14 = yellow
• 15 or higher = red

Year-end review performance data