Issues and Open Questions… Complexity sciences
suggest that evolution and adaptation toward a generally improving
state are natural behaviors in CAS. In the context of typical quality
improvement efforts, this observation leads to several questions:
- What information do we need to make effective adaptability
decisions?
- What information do we need in order to learn
from these decisions?
- Would some improvements happen more naturally if
we decreased the amount of information available?
- Do the organizational structures that we have
put in place hoping to enable improvement (for example, quality
councils), actually just get in the way of adaptive improvement?
- Can we demonstrate innovative, complexity-inspired
approaches to improvement?
These questions have been posed, but there has not
yet been any substantial discussion of them. This goes to the heart
of the relationship between complexity and the Deming/Shewhart PDSA
cycle.
Notes About Where We Might Focus Under This Topic…
We really have done very little development on this thread. This thread
could be merged with the diffusion of innovation and best practice
thread. It is also important to note that VHA has just formed a new
group to address the topic of building more adaptive organizations,
so we could defer to that group for more development on this issue.
On the other hand, the use of structures like quality
councils and formal committees/teams is so pervasive in QI efforts
that if complexity has some new insight to offer, we should take advantage
of that to break new ground here. The discussion that we had on-line
based on the Tom Petzinger case about the Infection Control effort
might serve as a good base for further development on this line.
Complexity Concepts That Might Help… What information
do we need to make effective adaptability decisions and to learn from
those decisions? The information needed is not likely to be apparent
to those involved -- so, avoid converging too early. A big issue here
involves "tuning" the information flow. In many cases, we see that
there is too much information; or, better said, too much data and
too little information. How can CAS ideas help us separate out information
amid the data chatter.
John Holland's language in Hidden Order (tagging,
agents, internal models, aggregation, catalyzation, non-linearity,
flows, and so on) might be helpful here. Also, understanding coupling,
diversity, and Kaufmann's fitness landscapes.
Thoughts About Demonstration Projects… Need
to do more thinking about this.
Ultimate Goal of This Line of Thinking… More
rapid, naturally-occurring, efficient, and effective quality improvement
efforts in healthcare organizations. Getting back to the notion that
continuous improvement is really a fundamental professional ethic
in healthcare.
