Quality Improvement never Ends

I had an interesting discussion over dinner with an expert on quality measures earlier this week.  They spoke about how in quality measures, there appears to be a tendency to complicate the measure with exclusions.  There are many reasons to add exclusions to a measure.  I'll take an easy to understand example:

In treating patients with heart attacks, research has shown that amount of time between the event and being given aspirin is related to survival and future re-occurrences.  So, a quality measure could be developed to ensure that patients are given aspirin as early as possible in these cases when it is appropriate.

Now, there are a number of reasons why getting aspirin might not be a good idea for all patients in this case.  For some, they may have an adverse reaction;  for others, there may be other contraindications, such as concurrent use of other medications.  One could ask for exclusions for this reason or that reason because giving the patient aspirin might not be possible, or aspirin may not be in supply, et cetera.  So the measure can continue to be refined with more and more exclusions.

But what is the point?  The point of the measure is to improve the quality of care, not to enable organizations to get a perfect score.  Why do I say that?  Where do you go after you've reached 100%?  Surely those patients who are excluded from the measure are impacted by the issue.  Is it OK that we are able to provide quality care only to those patients for whom we have treatment for?  Surely not.  Quality improvement never ends, but the 100% score seems to indicate that there is the notion of "perfect quality".

As we get into the details, and start drilling into the last percentage points, do these additional exclusions really improve the measure?  I'm certain that there are other details about how care is provided in the institution that have a greater influence on the care that a patient receives than these small adjustments.  As you look at these measures and begin to compare them against those of other institutions, does a half a percentage point really matter, or are we into the statistical noise?

What about the complexity and cost?  Adding refinements to the measure requires capturing additional data that could make it even more costly to implement.  Which measure has more bang for the buck?  The one you can implement today, or the one you can implement next year?  That is another important consideration.

After all, perhaps the most important thing to think about in measuring quality is whether your quality is headed in the right direction.