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Nine
Areas Where Complexity Might Inform QI Efforts
The nine areas are described in capsule form here and
then in more detail in the sections that follow.
- The Complex Patient.
How should we think about clinical quality improvement for patients
whose conditions would graph into the mid-zone of the Stacey Matrix;
somewhat far from certainty and agreement? EBM, guidelines, paths,
variation reduction, and so on should work in lower left of Stacey
Matrix. Perhaps we need additional Aides to deal with these other
patients. Also need ways to measure progress for patients in the Stacey
mid-zone.
- Diffusion of Innovation
and Best Practice. Improvements in one place are often difficult
to replicate in others. Complexity suggests that context is key. Can
we use complexity principles and Aides (eg. tune to the edge, coupling,
fitness landscapes, shadow organizations, and so on) to build on and
supplement existing thinking about diffusion of improvement? Can these
additional complexity-inspired approaches help us even more as we
move toward consolidation and ever-larger organizations in healthcare?
- Enabling Natural, Adaptive
Improvement. Complexity sciences suggest that evolution and
adaptation are natural behaviors in CAS. 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, actually just get in the way of adaptive improvement?
Perhaps complexity thinking can help us avoid information overload
and restrictive structures, making improvement more natural in organizations.
Can we demonstrate innovative, complexity-inspired approaches to improvement?
- Neural Nets. Neural
nets are based on the CAS of the brain; how the human mind functions.
Can neural net technology improve health care decision making? There
is existing literature on this; mainly from researchers and equipment
vendors. Can neural net technology be used by normal clinicians and
managers to improve day-to-day decision making?
- Quantitative Methods.
Traditional improvement technology is heavily analytical.
Complexity and chaos sciences offer new analytical techniques for
teasing out more information about patterns in time series data. These
patterns in the data can lead to a deeper understanding of the dynamics
of the underlying complex adaptive system. These better understanding
might lead to better ideas for improvement. This could be a major
extension of analytical improvement techniques; potentially as powerful
and paradigm altering as Shewhart's control charts were in the 1930s.
Got any data?
- Role of Human Interaction
in Improvement. It was suggested that the human element is
often a weak link in many current QI approaches. Pareto analysis doesn't
often yield things like "relationships" as a root cause. CAS theory
suggests that interconnections are key in emergent behavior of a system.
How can we balance and honor both rational analysis and human interaction
to shorten the path to constructive emergence of improvement?
- Organizational Performance
Measurement. Do current organizational
performance measurement systems give an adequate picture of how our
Organization-As-CAS (OACAS) is performing? What new measurements,
and what new thinking about measurement, can we get from a complexity
perspective?
- Metaphor and Leadership.
Mental models and metaphors are important
aspects of human CAS. What metaphors from complexity can get people
excited about quality again? Can we demonstrate the use of "equivocal
metaphors" and "good enough vision" as powerful aides to the leadership
of improvement?
- Collaborative Learning.
Complexity science, especially in biology
and computer simulations, informs us about the natural evolution of
large systems: for example, ecosystems and "colonies." There are discernable,
large-scale patterns. Learning is collective and is passed on. How
do groups and peer networks of health care professionals learn together?
Can collaborative learning in healthcare be accelerated if we take
complexity-informed approaches?
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