"A lot of the indicators I plotted were common cause. What should I DO?" [Data Sanity (Part 3 of 5)]

I hope you took my advice on what not to do from Part 1 and "plotted some dots" [Review Part 2]

One more thing not to do: another routine "How're we doin'?'" meeting

Just curious: Do you have monthly and/or quarterly and/or even weekly "How're we doin'?" meetings like the end-of-year review described in parts 1 and 2 – things like budgets, financials, never events, incidents, near misses, machine downtime, productivity, root cause analyses, returned shipments, rehospitalizations, complaints,customer satisfaction scores, employee satisfaction scores, etc.? 

Their vague agenda is usually to discuss: (1) only the past month's overall result (special cause strategy), "Were we red, yellow, or green? What do we need to do about it? Should we form a team to work on it?", (2) how overall performance seems to be "trending" (usually using only this month/last month/12-months ago – special cause strategy), (3) each incident that occured during the month (only) and how it could have been fixed (special cause strategy), or (4) which particular events need teams to do root cause analyses (special cause strategy)?

What is this costing you? "Unknown or unknowable" – but what difference does it make? It's a huge number!

The cancellation/no show process was stable at 10 percent. None of the four special cause strategies above will help improve the situation. So now what?

All common cause means is that you can neither consider either data points or consecutive differences individually as a special cause nor treat any individual difference from the goal as a special cause. A common cause strategy groups all of the cancellations/no shows that occurred for a stable period of choice – perhaps in this case the past 12 months. All the data is aggregated for the purpose of immediate disaggregation in various brainstormed ways.

 Common cause strategy #1: Stratification 

Stratification involves brainstorming various ways to "slice and dice" the cancellations/no shows by various process inputs (time of day, day of week, type of therapy, site, age, forgot, specific type of appointment, therapist, etc.)

Isn't this is a much better use of people's brainpower than the futility of using a tabulated report to ask: "Why did we go up? (Why did we go down?) Why weren't we green? (Why were we green?) What's the trend? What's our action plan for the upcoming month? What are we going to say at the upcoming operational review?". They will find reasons – such a waste of good people's time, energy, and talent.

A good initial question is to look at the IChart and ask whether the points that are all high (or all low) have the same reason, e.g. same month, day of the week, time of day, holiday, special product run? (not true for this scenario)

If additional more detailed data already exist, tally them into their appropriate brainstormed category, then perform a Pareto analysis of these reasons. 

In the current cancellation/no show example, it might be a good idea to at least start with "day of cancellation" and "time of cancelled appointment," which should be available. There could be a particular day, particular time, or specific day/time pattern that could be exposed as a more focused opportunity. 

To avoid a total redesign, you are hoping that Dr. Joseph Juran's beloved Pareto Principle applies: What are the 20 percent of the reasons accounting for 80 percent of the cancellations/no shows? Is the pattern different between the cancellations and no shows (sub-stratification)? If more data is needed in this case, she could now include any or all of the two previous years' data because they both show the same stable behavior as the current year (same process). 

 A very common issue. If you have multiple facilities, are these patterns similar? (probably not). A time plot comparison of individual overall performances (separately and on the same scale) could also be quite insightful.

 Focus...focus...Common cause strategy #2: Study the current process.

If any data aren't available beyond day/time, there is now a little more work to do: formal collection to record deeper reasons for cancellations/no shows (e.g., specific provider). It will need a little more effort and planning, but at least it won't become "permanently temporary." Its only objective is diagnostic – to isolate a possible "20 percent."

Using this strategy requires letting the process continue as is; but, there is temporary non-routine collection of data that are pretty much there for the taking. They just need some plan to be "grabbed" and recorded. Unlike using convenient in-house data, there is slightly more inconvenience, which usually proves to be worth it.

Keep it focused on your objective. Not "Wouldn't it also be neat to also know...?". No, it wouldn't – trust me, you're going to have enough problems with human variation collecting the data you need.

If you are able to focus some opportunities, now would be the time to brainstorm a productive cause-and-effect diagram – on your isolated 20 percent of the process.

Notice: Has there been any mention of any goal?

How does all this compare with an initial reaction of, "We're having a lot of trouble with the goal. Let's form a team and brainstorm possible reasons for the cancellations and no shows, then do some redesigning."

What happens to your reputation and credibility if you waste busy people's time like this? 

A mantra for you:Vague solutions to vague problems using vague (or no) data yield vague results.

At least "plot the dots" first.

Help your colleagues – and let them have all the credit if you get an eye-popping result

Approach this supervisor after the meeting and offer to help. And if there turns out to be no “20 percent,” your time has been well-spent to quickly confirm that the process may need some type of redesign.

Envision any data sanity process from initial run chart all the way through to a solution: What would it mean to culture and leadership if common cause strategies were appropriately applied? What if you could stop the dreaded routine, unproductive meetings, special reports, and quarterly- or year-end review meetings because issues are now appropriately addressed and many perhaps even solved? 

Think of how many of Dr.Deming's 14 Points this process could indirectly address – simultaneously.

Next time: "Unknown or Unknowable"? You don't know the half of it!

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For more information on stratification, studying the current process, and the two final common cause strategies, these are all covered in Chapter 5 of my book Data Sanity. Please see my LinkedIn profile for more information and selected downloads.