The model asserting that better health outcomes, better care delivery and better professional development are inextricably linked (fig 1 1) ) recognises that mutual support and stimulation among these three domains invites both sustainability and unending creativity in their efforts.ĭrawing everyone actively into the process of testing change, all the time, presumes that everyone will develop a basic understanding of the standards of their work, as well as the skills they need to test changes in that work. For the universal practice of change testing to happen, all those involved in supervising the work of healthcare-from the front line to the front offices-might, for example, be expected to offer specific, expert support and guidance to those they supervise as they design and execute tests of change. It is one thing to expect a specially commissioned “QI team” to be actively engaged in designing and testing the many changes needed for better patient and population outcomes, better system performance and better professional development it is quite another to expect everyone involved in healthcare to do so, and do so all the time. Of course, better knowledge by itself does not guarantee improved performance if these five knowledge systems are going to be effective, we need to pay careful attention to the way in which we deploy them. Doing so will generate a kind of “metaknowledge” that will be essential over the long run in becoming progressively better at improvement. Reflection on the nature of these five knowledge systems, how they grow and change, and the ways in which they work together to move evidence into practice will be essential if we are going to learn about learning. It requires knowing where power resides and how it is asserted it requires knowledge of the strategic aims, the usual ways of conducting work in that setting, the ways in which people are recognised and rewarded, and the ways in which they are held accountable for their work.Īcquiring these five kinds of knowledge requires both scientific and experiential learning. The “→” symbol (element #5) represents the knowledge required for execution-what you need to know to “make things happen”, the drivers of change, in a particular place. The “+” symbol (element #4) represents knowledge about the many modalities, including standardisation, forcing functions, academic detailing, and so on, which are available for applying and adapting generalisable evidence to particular contexts. Knowledge on the effect of improvements on system performance (element #3) requires special types of measurement, techniques that include time in the analysis, as all improvement involves change over time gaining this knowledge also requires the use of balanced measures that accurately reflect the richness and complexity of the phenomena under scrutiny. A knowledge of particular contexts (element #2) is developed by enquiry into the identity of local care settings-their processes, habits and traditions. The generalisable scientific knowledge we need (element #1) is constructed from empirical studies that work to control context as a variable, thus minimising or eliminating its effect on what is being studied. In almost all cases you would be using one of these well characterized strains and so would not need to worry about whether there were unknown plasmids.Figure 2 Formula illustrating the way in which knowledge systems combine to produce improvement.Įach of the five elements in this equation is driven by a different knowledge system (table 2 2). coli) that have been studied for decades. In practice microbiologists have domesticated strains of bacteria (a favorite is Escherichia coli - often abbreviated to E. This is easy to test - we just try growing the bacteria in the presence of ampicillin, if they don't then we can use our plasmid. All we need to know is that the bacteria were are transforming are not already resistant to ampicillin. However that doesn't matter as much as you might think.įor example, assume we are using a plasmid that contains a marker (selectable gene) encoding resistance to ampicillin. We could sequence all the DNA inside the bacteria, but that is still a lot of work. It could be difficult to know if you were just using a random bacteria isolated from nature - especially since there are likely to be many thousands of different plasmids (1730 were present in a sequence database as of 2009).
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