9 Change Models

It's often joked there are as many models for change as there are consultants selling them! I've curated a list of some of these, focusing mainly on the graphic that explains the model. If I've got an opinion on the model I say so. Each of the models has a link where you can get further information about it.

1. From Six Seconds, focusing on the emotional aspects underpinning any change

2. From ichnage.biz. I like the "and then you start all over again"

3. From Smartcompany; nice blend of known frameworks

4. The Innovation Change model is nothing new, but a good reminder

5. Peopleandprocess have an interesting model which made me think. I like it.

6. The Driving change model from CMA is simple but I like the language reframing

7. Not so much a model as a comment on models (but I liked the pic)

8. I like the input / output aspect of this model from Metavolution

9. This model is based on Prochaska & DiClemente's. I like this edit.

New systems model to simulate spread and adoption of good practice

I've worked with Ken Thompson of Bioteams to develop a systems simulation of the spread and adoption of good practice.  Our aim has been to provide a method for individuals and teams to play about with different strategies and to model the impacts of those strategies. It's not a prediction tool, but rather one which helps you gain an insight into the complexities  It's been important to us to produce a simulation which provides an adoption curve - having an idea of the speed (or not) of spread is crucial to your planning.

The simulation is ready for testing. It's not perfect, and we'd love to demo it and take your feedback on how to make it even better. Feel free to tweet Sarah @sarahfraser or Ken @kenthompson, leave a reply to this blog, or email Sarah, if you'd like to have a go.

A screenshot of the main screen is below. You can choose your strategies and then simulate, quarter by quarter, the rate of adoption. There are other input screens where you can assess your readiness for change and where you can enter details about the strategies you'd like to use.

Is Activity Theory useful for large scale change?

There's no shortage of theories and models for how good practice can be "spread". I'm part fo the problem by generating some of them.... I've recently become curious about the use of Activity Theory as a means of large scale change in healthcare settings.   It's a bit of an eclectic theory from the social sciences - but then, PDSA cycles were once an eclectic Japanese theory.

Wikipedia has a good intro.  Basically, the benefits of Activity Theory is it combines all system levels into one model of change - from individual through to policy. And I like that.

Greg, Entwistle and Beech have a new paper which considers how AT can be applied usefully in healthcare.

Soc Sci Med. 2012 Feb;74(3):305-12. Epub 2011 Mar 1.
Addressing complex healthcare problems in diverse settings: Insights from activity theory. 
This is their abstract: "In the UK, approaches to policy implementation, service improvement and quality assurance treat policy, management and clinical care as separate, hierarchical domains. They are often based on the central knowledge transfer (KT) theory idea that best practice solutions to complex problems can be identified and 'rolled out' across organisations. When the designated 'best practice' is not implemented, this is interpreted as local - particularly management - failure. Remedial actions include reiterating policy aims and tightening performance management of solution implementation, frequently to no avail. We propose activity theory (AT) as an alternative approach to identifying and understanding the challenges of addressing complex healthcare problems across diverse settings. AT challenges the KT conceptual separations between levels of policy, management and clinical care. It does not regard knowledge and practice as separable, and does not understand them in the commodified way that has typified some versions of KT theory. Instead, AT focuses on "objects of activity" which can be contested. It sees new practice as emerging from contradiction and understands knowledge and practice as fundamentally entwined, not separate. From an AT perspective, there can be no single best practice. The contributions of AT are that it enables us to understand the dynamics of knowledge-practice in activities rather than between levels. It shows how efforts to reduce variation from best practice may paradoxically remove a key source of practice improvement. After explaining the principles of AT we illustrate its explanatory potential through an ethnographic study of primary healthcare teams responding to a policy aim of reducing inappropriate hospital admissions of older people by the 'best practice' of rapid response teams."

Three Collaborative Models for Scaling Up Evidence-Based Practices

A new paper is out is Adm Policy Mental Health (See abstract below). Two of the models are those I've presented on and published about - the rolling cohort and the cascading dissemination model. There are also subsets of these methods - see my book 101 ways to improve your collaborative. 


Three Collaborative Models for Scaling Up Evidence-Based Practices

Chamberlain P, Roberts R, Jones H, Marsenich L, Sosna T, Price JM.

Source

Abstract

The current paper describes three models of research-practice collaboration to scale-up evidence-based practices (EBP): (1) the Rolling Cohort model in England, (2) the Cascading Dissemination model in San Diego County, and (3) the Community Development Team model in 53 California and Ohio counties. Multidimensional Treatment Foster Care (MTFC) and KEEP are the focal evidence-based practices that are designed to improve outcomes for children and families in the child welfare, juvenile justice, and mental health systems. The three scale-up models each originated from collaboration between community partners and researchers with the shared goal of wide-spread implementation and sustainability of MTFC/KEEP. The three models were implemented in a variety of contexts; Rolling Cohort was implemented nationally, Cascading Dissemination was implemented within one county, and Community Development Team was targeted at the state level. The current paper presents an overview of the development of each model, the policy frameworks in which they are embedded, system challenges encountered during scale-up, and lessons learned. Common elements of successful scale-up efforts, barriers to success, factors relating to enduring practice relationships, and future research directions are discussed.

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:
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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.