But in systems thinking, there are a number of theories.
We've talked before about general systems theory with Brook and
looking at sort of theory of Theories.
And actually, when I just display that model of complexity in the health system,
is actually shows the theory around the notions of diversity
connecting this into the pins and learning, leading to complexity.
But there are other ones, catastrophe theory,
chaos theory, both based in mathematics.
And different,
related to showing how sudden that large changes occur in chao theory.
It's because, as we've discussed,
it's about small changes in initiating conditions.
In catastrophe theory, it may be other parameters that change along the way.
Learning organizations comes out of management
theories about how do you change organizations, how members learn, and
how you, not only how individual members learn, but how they work together as
teams to develop team learning through different kinds of approaches.
So there's theories in learning organizations.
Lots of theories in, anything from economics and
other social sciences to physics to explain path dependency.
How you get different outcomes from similar starting points
based on not just the initial conditions, but the choices you make along the way,
which sometimes are irreversible.
And then other ones about punctuated equilibrium, we've seen that in,
particularly, social theory around policy, but it was taken from developmental or
evolution theory as well, and then, apply to a policy change.
So again, different theory that have been used.
They're also a whole range of research methods and tools, and
there's a continuum of methods and tools.
There's not always a fine line between what point a method becomes a tool, but
basically they're methodologies that involve a series of tools.
And one of them is agent-based modeling, you're going to do a little bit of
agent-based modeling in this course and again in the introduction.
And that's where you have a way of representing a complex system based on
basically showing individual agents and how they interact with each other and
the environment.
Trying to set a set of predefined rules, and
watching how they interact and emerge with self organization.
Another set of methods in social network analysis, systems dynamics modeling.
I think you're actually going to be doing a little bit of systems dynamic modeling
in the course as well.
And then a set of tools cause of loop diagrams.
We've already shown some examples and this is one that you'll get some practice on.
To show basically to prove, to show, taking a mental model and
to try to show how things are related usually in a causal type of approach.
These are often done in a participatory way to try and get the key people involved
in a particular program to try and describe what they think is going on.
There's a number of other participatory ways of getting people involved to
describe or understand a system,
such as innovation change or management history where you look at things,
reconstruct how things occurred over time or participatory impact pathways analysis.
PIPA is another structured workshop approach to try to show
a logical pathway of how things actually work in real life,
involving stakeholders who are actually involved in a process and an intervention
trying to reach consensus then on how you actually get the kind of outcome that
you'd actually like and dealing with some of those unintended consequences.
Process mapping is very similar but for
the very simple thing it's a flow chart about how things work together or process.
But you can make them as simple and
as flexible into different kinds of work to look at bottlenecks or inefficiencies.
And the last one stock and flow diagrams you'll be doing this in class as well,
at least in an introductory way.
Often they start off with causal loop diagrams, and
you take some of the variables, and you create stock and flow diagrams that show
how things change over time and look at feedback again to try and
capture nonlinear dynamics between multiple parts in a system.
So again, this is an exhaustive list, but it shows you a broad range of theories,
methods and tools, and you know that they come across many disciplines.
Many of them are quantitative.
Many of them are qualitative.
There's a participatory element, particularly at the beginning that,
in defining the system, that's quite common to them, and all of them are used
to try to guess create explicit models of how things relate to each other.
Which again is the core of what systems thinking is about,
how things relate to each other.
So as we wrap this up, we're trying to look at how system thinking
informs health systems intervention.
And what we've seen is a whole range of applications, the theories,
the methods and tools, and what they are helpful for is in many kinds of settings.
We've talked about them in terms of settings for trying to understand how to
improve implementation, how to improve scale-up and sustainability.
But underlying it,
what it does is it gives us a better understanding of dynamics.
Whether it be dynamics of disease transmission,
or other parts of change in a health system.
Showing how things are related to each other,
particularly as they relate to contextual factors.
And in the the health system,
we're particularly interested with how they relate to population health.
It can be helpful in identifying root causes of variations.
Variations that are with us all the time.
These are variations in behaviors.
They can be variations in services, variations in health outcomes, and
this gives us the tools.
Systems thinking gives us the tools to be able to embrace and
understand those kind of variations.
It helps us to identify many factors from different sectors
that promote the spread of an innovation or an intervention.
Again, intended and unintended consequences gives us a better
understanding and appreciation for it as well as where to look.
And basically gives us new tools and approaches to understand and
facilitate decision-making.