[MUSIC PLAYING]
DAVE WILLIAMS: Frequently, when you look at a problem,
you may have questions about various groupings within your data.
For example, is there a difference between new staff
members vs. experienced staff?
Do we see a difference between shifts or by clinical training?
Nurse vs. physician vs. paramedic?
This desire to look at the population as various subgroups is
called stratification.
By stratifying your data, you may be able to identify
opportunities for improvement that aren't obvious from the data
as a whole.
I worked with a group of pharmacists that
were trying to improve the process known as medication
reconciliation in hospital.
Medication reconciliation is the process of creating the most accurate list
possible of all medications a patient is taking and comparing
that list against the physician's orders.
The goal is to provide correct medications
to the patient at all transition points within the hospital.
As the pharmacists were discussing the process,
they quickly identified different scenarios
within the process worthy of stratifying,
including day versus night and weekday versus weekend.
It was predicted there were variations in the process in these areas
that would be worth understanding.
So let's take a look at an example of that data.
Let's imagine that their prediction came true.
So if we were going to draw ourselves a little bit of a run
chart, and we come down here.
And imagine we had our days of the week, right.
So we have Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday,
Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday.
So we got two weeks here.
And imagine we're coming along and we're plotting our data
and on Monday and Tuesday the data is kind of right here.
Natural variation.
But then all of a sudden we look up and on the weekend
we have a change in our data points that seem really different.
It's almost like there's a different system that's
occurring on the weekends.
These examples here are a different system than the ones down here below.
And so this is a very common example of where you might look at a place
where it makes sense to stratify out the two different parts of the week
because you're seeing that there are different systems that exist.
Another common example is to create a small multiples display of measures.
For example, imagine we're thinking about hand-washing.
So we may want to look at our data overall and have a run chart that
tells us about our overall data.
So overall.
But we may have a prediction that there is
some variation across our different sites,
and so we might have several different places that we're looking at.
And we'll just call 'em like A, B, C, D, and E.
And we'll stratify those individual units out--
or places-- so that we can look at their data
and see is there variation that exists across sites?
And the stratification helps us potentially
see where there's opportunity for improvement that
will help change our outcome.
So by doing this, it enables us to be able to see
where there's variation in the system.
When you think about your work, what are some of the obvious stratification
opportunities you see that might help you learn more about your process?