Guest blog written by David Galin
Coming into this course, I was under the impression it was going to help me better understand the nuances of implementing policy from a roadmap that was created for every situation. I was a bit nervous we would be taught a rigid set of procedures on how to implement policy – something that was made for every situation but really only worked for maybe one out of every ten, if we were all lucky. Thankfully I was very wrong. I learned about a theory that works for those situations that aren’t rigid and need meaningful analytical evaluation. Situations where you need to think outside the box, need support from your authorizers, but also need to continually build a team. Situations where you are not only implementing policy but in fact problem solving. Read: messy, confusing, complex situations.
I learned there truly are a variety of issues – complex and complicated and everything in-between. I think this is a principle that sometimes you think about in the back of your head but start to say to yourself you’ve overcomplicating the issue and that can’t be. Turns out, it is. I learned that you need to see things for what they are but also be willing to look past the first layer of the issue. You need to unpack the problem. People are very quick to hear what they think the issue is, and immediately try to come up with solutions. Sometimes the issue isn’t complicated and that type of problem solving can work. But sometimes the issue is so complex that you need to spend a significant amount of time unpacking the problem. Taking the time to understand the hurdles in front of you and the hurdles that may be hidden beneath the surface, before developing a game plan.
The other thing I learned is that PDIA is as much about relationships as it is about process. Building relationships – before, during, and after iteration and implementation – is very important. Having established relationships can cut down on the time needed to build them when trying to solve a complex problem, and helps foster a sense of trust – not only with your authorizers, but with your peers.
The entire process is designed to create a constant feedback loop – helping you to review whether your potential solutions are working or not, but also to getting you working with other people, obtaining and re-affirming authorization from your superiors, and brainstorming additional methods to tackling an issue. When it comes to our problem, we were able to learn that data-driven decision-making is optimal to use as part of PDIA. Having data and being able to evaluate it before and after the feedback review helps to determine whether that iteration was successful or not. We made progress narrowing down some of the core issues behind the perceived sub-optimal performance of See Click Fix, including no consistent methodology of using “acknowledged” vs. “closed,” and have also seen a decline in days to acknowledge and days to close as part of the expanded use of ipads as part of our improvements.