Run Charts: How to create and interpret

Managing data in an improvement project often feels like it is some magical craft, with spells, secrets and strange ingredients. But this isn't true. A run chart is the most basic and the most important analysis tool in your improvement armoury.

BMJ Quality & Safety have a very useful article with instructions on how to create a run chart and how to interpret it: click here to go to the article

Most popular posts on the Spread Good Practice blog

Here is the list of the most popular posts on this blog for the last 6 months.  If I'd had to guess I'd not have guessed these - nothing like a good bit of measurement to separate fact from opinion!

Model 2: data, information, knowledge, wisdom 22 Dec 2010, 1 comment 640 Pageviews

The quickest way to spread scale up change 21 Jul 2010 484 Pageviews

Model 1: data, information, knowledge, wisdom 20 Dec 2010 339 Pageviews

Using Twitter in the Classroom or Organisation 2 Sep 2010 320 Pageviews

Productivity 1: Operational Efficiencies are not e... 24 Oct 2010, 1 comment 284 Pageviews

Model 7: data, information, knowledge, wisdom

I thought I would stop at 7 models on the data, information, knowledge and wisdom theme - afterall, the brain is supposed to be able to hold a maximum of seven thoughts at any one time.  I've left my favourite to last. I particularly like the examples given at each stage. You can read more about this model on this blogsite.

Model 6: data, information, knowledge, wisdom

The Liebowitz (1999) model of DIKW is helpful if you are looking for something with details to give you ideas on what you might do in your own work. I can see how this can be used to evaluate large scale programs. The value aspects are useful reminders of the underlying purpose in moving to each stage, and therefore gives an idea of what might need to be planned for in a large scale program to achieve each step.

Model 5: data, information, knowledge, wisdom

So let's start 2011 by continuing the data-information-knowledge-wisdom series of posts with something creative from Topicscape's Mindmap directory:
.

I like the zone of potential - zone of possibility continuum as well as half the "effort" being below the surface which can only really be explained by doing a drawing like this.

Model 4: data, information, knowledge, wisdom

The Model from Infovis below shows just what I always struggle in explaining to tohers as to why the results of one project cannot just be pushed onto other, adopting, groups.

The producers, the pilot projects, create the basic data and some information (patterns) about it. The consumers (the adopters) need to retest this in their own environment.  Any pilot project that can be written up in a way that helps the consumer to bridge the gap from information to knowledge will likely be more successful at spread than others. This can include things like: you can adapt this in the following way, we did the following and it didn't work but it may work in xyz circumstances etc.

Model 3: data, information, knowledge, wisdom

One of the QI refrains is "increase the capability and capacity of employees". While this is a great concept, easy to declare and impossible not to support, for me it lacks any concrete applicability. What exactly is meant by this? There is another one of our data-information-knowledge-wisdom models which may help pin down what might be meant. Next time you hear somebody say the capacity/capability thing then whip this model out and ask them to explain their intentions and expectations along the data to wisdom curve.

 The challenge here is to produce learning experiences that enable someone to move up the curve. In my experience, much of healthcare improvement work is focused on developing data based skills - how to measure change. Some people get to the information stage where they learn to look for patterns, say by using SPC charts. Can they port this knowledge to other projects in a predictable way? Can they make intelligent choices? To what extent do the participants on a QI project become "wise"?

The above curve comes from Designing Knowledge Eco-Systems for Communities of Practice.  The web resources are excellent - especially if you are developing CoP's as part of your QI strategy.

Model 2: data, information, knowledge, wisdom

There is an excellent post about wisdom on one of my favourite sites - Big Dog and Little Dog's Performance Juxtaposition (yes, really!).  It's a place I recommend you spend some time checking out.

I like the way this model gets me thinking about how we learn - there are connections here to the Honey & Mumford Learning styles.  The fact that there is a continuum for context is also thought-provoking.  This model has left me wondering whether in many of our quality improvement projects we focus too much on the bottom left hand corner and assume the progression to wisdom will be automatic. What would happen if we thought more and designed more of the journey to wisdom into our improvement interventions (and by extension, into our evaluations of projects)?

Model 1: data, information, knowledge, wisdom

The old adage goes along the lines that knowledge can be defined as knowing a tomato is a fruit, and that wisdom is therefore knowing that you don't add a tomato to a fruit salad...  There are a number of models and frameworks that investigate the data-information-knowledge-wisdom continuum and in the this series of posts I cover a few of these.

For the theorist a good place to start is with an online paper A Primer:, Enterprise Wisdom Management and the Flow of Understanding by ScottCarpenter@CognitiveCybernetics.com

I like the way environment and context have come into play as important factors in understanding that knowledge and wisdom have a contextual perspective.