"One critical advance was the increasing statistical rigor that we have tried to bring to these enterprises," Niedner says. "Window dressings are no longer enough to say 'we made things better.' You want outcome and process measures that are collected in a way that you can attack the signals amidst the noise."
The QTN accelerated the learning curve at Mott, which -- like many hospitals -- had been working on infection controls on its own before it joined the collaborative. "We demonstrated a statistically significant reduction in central line infection within the institution just looking at our own data, but it took us two-and-a-half years of intervention and collecting data to detect that signal at our site," he said.
"We knew that we were doing the right thing within four months of joining the collaborative because of we were able to pool our data. The statistical power that a collaborative has that can determine whether or not you are barking up the right tree is much greater than when you are trying to do it alone."
In addition, Niedner explained, Mott's participation provided an outside perspective that they otherwise would not have had. "There is that old saying that fish don't see the water they are swimming in. You have that institutional blindness to your own set of processes that you just take for granted or you assume this is how it is or how it has to be," he said. "We had the opportunity to look at top performers and try to understand what is different in their system or approach. We can always build a better system ourselves, but it is much more pragmatic and efficient to look at other example systems."