There are many types of problems in the world, and even more methods that can be used to solve them. The key is using the right method or tool to solve the problem at hand.
Let’s start with an analogy. You want to hang a picture on the wall. You have multiple tools at your disposal. Your tools include a screwdriver, a 3D printer, and a hammer and a nail. Now, you could use the handle of the screwdriver to punch the nail into the wall, which might be a bit slow and unsafe. You could use the 3D printer to make a hammer and then pound the nail into the wall, but making the hammer may take a long time and be very costly to produce. Or you could just use the hammer. All three methods would produce the same result, but some may not be as fast or efficient as others. I like to keep this in mind when looking at how to solve a problem.
Lean (or continuous improvement), Agile, Systems Thinking Practice, and Design Thinking are all methods that can be applied to a problem to solve it. Just like the hammer, some are more effective in certain situations. But how do you determine what method will work best?
Start by determining what type of problem you are dealing with, then you can determine which tool will best suit your needs. Based on the Cynefin framework – problems can fit into four areas.
According to Cynefin’s framework, in the obvious domain a relationship between cause and effect for the problem exists. It is predictable and repeatable. This relationship can be identified in advance by a reasonable person.
Problems that fit into the obvious domain are very common. For example, patients are leaving the Emergency Department without being seen. This is often the result of wait times being too long. By focusing on wait time reduction, the issue can be resolved.
Many lean tools, like 5S, can be applied to problems in the obvious domain and be very successful.
In the complicated domain a relationship between cause and effect exists, but the relationship is not self-evident, and therefore it requires some expertise to solve.
An example of this type of problem when a hospital is working on the overall patient experience through the emergency room, to inpatient, to discharge. In this scenario the relationships between flow stoppers within the system are not usually apparent without doing some digging into the entire flow process.
One tool that might be very effective in solving this type of problem is a value stream map.
The complex domain is a system without causality. Meaning the cause and effect relationships are only obvious in hindsight, with predictable, emergent outcomes. If an experiment succeeds we amplify it, if it does not we dampen it quickly.
An example of a problem in the complex domain would be an ED that is bogged down with many homeless people who are coming in for non-emergencies. This is potentially a population health issue. Are they coming in due to mental health issues, basic care problems, or loneliness? It would take multiple experiments to sense this problem, learn, and scale the actions that work.
According to Cynefin’s framework, a problem would fall into the chaotic domain if no cause and effect relationship can be determined. In this type of situation, action must be taken quickly to stabilize the system.
If the Emergency Department is overflooded with cases due to a nearby building collapsing, that would fall into the chaotic domain. In this situation, the hospital may be able to execute their disaster protocols, but there would be instances where caregivers, managers, or leaders need to improvise to stabilize the situation.
Some problem-solving methods that are often applied to issues in the chaotic domain are hastily-formed networks and Antifragile methods.
To be an effective problem solver is to be an adaptive problem solver. It is important to be aware of the type of problem that you are working on and use an appropriate method to solve the problem. I recommend staying curious, learning more, and keeping your mental models open.
Please share your comments or learnings in the comments section below.
Jason Schulist, Faculty
Adaptive Problem Solving workshop