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I was eager to see the oft-reported sloppy assembly work, the poor-fitting doors, blotchy paint, and other manifestations of Tesla CEO Elon Musk's “production hell In fact, the Detroit Three, until about 15 years ago, routinely delivered cars and trucks with appalling gaps, often as wide as 8mm on one side, 2mm on the other. I don't think we'll hear healthcare success stories long the lines of, “I asked the hospital system CEO how they reduced patient harm by 90%: ‘I got all of the hospital directors in a room and told them they had six weeks to achieve consistent patient safety or they'd all be fired. I'm not sure that's the best approach, shaming healthcare leaders for patient safety problems, but here's what happened:

I'm going from memory, but I think this next section was about him and his attempt at uniform innovation in the late 19th century… look at the screenshots with closed captions that tell the story of Plan, Do, Study, and Adjust… One of the key principles, going back to Dr. Deming, is that we mitigate risk when attempt to make an improvement by doing a SMALL test of change. Modern-day Major League Baseball sometimes isn't great at problem solving, but they do generally test changes at the minor league level where they are, relatively, small tests of change. Other small tests of change include starting extra innings with a runner on second base as a way to shorten the length of games.

Speaking of the topic of “Process Behavior Charts” and metrics, I'm running a sale through the end of June where you can buy signed copies of my book for $24 including U.S. shipping. Dr. Low made a strong case for the use of control charts (Process Behavior Charts) as a way to better evaluate whether changes are really improvements. Maggie wrote three excellent blog posts that summarized a keynote talk by Dr. John Toussaint and two breakout sessions from Cleveland Clinic. Real-Time Lessons Learned from John Toussaint at the Lean Healthcare Transformation Summit Check out her posts, as she did a great job with those.

By ‘red, amber, green', we are referring to graphical data displays that use colour coding of individual data values based on whether this value is on the right (green) or wrong (red) side of a target value. The authors show, through an example, two different ways of visualizing data that beg leaders to react to the “red” data points that are worse than the target of 10. It looks like this: The authors correctly point out that all of those data points are below the “upper limit” so every data point is produced by the same system that has randomness or variation in its output. It is no more complicated to tell special from common cause variation than it is to tell red from amber and green.