Posts Tagged ‘Innovation’

Africa is a fascinating place.  According to a documentary that I saw last year we are ALL descended from a small tribe who escaped from North East Africa about 90,000 years ago. Our DNA carries clues to the story of our journey and it shows that modern man (Africans, Europeans, Asians, Chinese, Japanese, Australians, Americans, Russians etc) – all come from a common stock. It is salutory to reflect how short this time scale is, how successful this tribe has been in replacing all the other branches of the human evolutionary tree, and how the genetic differences between colours and creeds are almost insignificant.  All the evolution that has happened in the last 90,000 years that has transformed the world and the way we live is learned behaviour. This means that, unlike our genes, it is possible to turn the clock backwards 90,000 years in just one generation. To avoid this we need to observe how the descendents of the original tribe learned to do many new things – forced by their new surroundings to adapt or perish.  This is essence of Improvement Science – changing context continuously creates new challenges – from which we can learn, adapt and flourish.

To someone born in rural England a mobile phone appears to be a relatively small step on a relentless technological evolution – to someone born in rural Africa it is a radical and world-changing paradigm shift – one that has already changed their lives.  In some parts of Africa money is now managed using mobile phones and this holds the promise of bypassing the endemic bureaucratic and corrupt practices that so often strangle the greens shoots of innovation and improvement. Information and communication is the lifeblood of improvement and to introduce a communication technology that is reliable, effective, and affordable into a vast potential for cultural innovation is rather like introducing a match to the touchpaper of a firework. Once the fuse has started to fizz there is no going back. The name given to this destabilising phenomenon is “disruptive innovation” and fortunately it can work for the good of all – so long as we steer it in a win-win-win direction. And that is a big challenge because our history suggests that we find exploitation easier than evolution and exploitation always leads to lose-lose-lose outcomes.

So while our global tribe may have learned enough to create a global phone system we still have much to learn about how to create a global social system.

I love words – they are a window into the workings of our caveman wetware. Spoken and written language is the remarkably recent innovation that opened the door to the development of civilisations because it allowed individual knowledge to accumulate, to be shared, to become collective and to span generations (the picture is 4000 year old Minoan script) .

We are social animals and we have discovered that our lives are more comfortable and more predictable if we arrange ourselves into collaborative groups – families, tribes and communities; and through our collaboration we have learned to tame our enironment enough to allow us to settle in one place and to concentrate more time and effort on new learning.  The benefits of this strategy comes at a price – because as the size of our communities grow we are forced to find new ways to make decisions that are in the best interests of everyone.  And we need to find new ways to help ourselves abide by those decisions as individuals without incurring the cost of enforcement.  The word “civis” means a person who shares the privileges and the duties of the community in which they live.  And size matters – hamlets, villages and towns developed along with our ability to behave in a “civilised” way. Eventually cities appeared around 6000 years ago – and the Greek word for a city is “polis”.  The bigger the city the greater the capacity to support learning and he specialistion of individual knowledge, skills and experience. This in turn fuels the growth of the group and the development of specialised groups – tribes within tribes. A positive feedback loop is created that drives bigger-and-bigger settlements and more and more knowledge. Until … we forget what it is that underpins the whole design – civilised behaviour.  While our knowkedge has evolved at an accelerating pace our caveman brains have not kept up – and this is where the three “Poli” words come in – they all derive from the same root “polis” and they describe a process:

1. Politic  is the method by which the collective decisions are generated.
2. Policy is the method by which the Political decisions are communicated.
3. Police is the method by which the System of Policies are implemented.

The problem arises when the growth of knowledge and the inevitable changes that result starts to challenge the current Politic+Policy+Police Paradigm that created the context for the change to happen.  The Polices are continulally evolving – as evidenced by the continuous process of legislation. The Paradigm can usually absorb a lot of change but there usually comes a point when it becomes increasingly apparent to the society the the Paradigm has to change radically to support further growth. The more rigid the Policy and the more power to enforce if present the greater the social pressure that builds before the paradigm fractures – and the greater the disruption that will ensue as the social pressure is released.  History is a long catalogue of political paradigm shifts of every size – from minor tremors to major quakes – shifts that are driven by our insatiable hunger for knowledge, understanding and meaning.

Improvement Science operates at the Policy stage and is therefore forms the critical link between Politics and Police.  The purpose of Improvement Science is to design, test and implement Policies that deliver the collective Win-Win-Win outcomes.  Improvement Science is an embodiment of civilised behaviour and it embraces both the constraints that are decided by the People and the constraints that are defined by the Physics.

Most people are confused by statistics and because of this experts often regard them as ignorant, stupid or both.  However, those who claim to be experts in statistics need to proceed with caution – and here is why.

The people who are confused by statistics are confused for a reason – the statistics they see presented do not make sense to them in their world.  They are not stupid – many are graduates and have high IQ’s – so this means they must be ignorant and the obvious solution is to tell them to go and learn statistics. This is the strategy adopted in medicine: Trainees are expected to invest some time doing research and in the process they are expected to learn how to use statistics in order to develop their critical thinking and decision making.  So far so good, so what  is the outcome?

Well, we have been running this experiment for decades now – there are millions of peer reviewed papers published – each one having passed the scrutiny of a statistical expert – and yet we still have a health care system that is not delivering what we need at a cost we can afford.  So, there must be someone else at fault – maybe the managers! They are not expected to learn or use statistics so that statistically-ignorant rabble must be the problem -so the next plan is “Beat up the managers” and “Put statistically trained doctors in charge”.

Hang on a minute! Before we nail the managers and restructure the system let us step back and consider another more radical hypothesis. What if there is something not right about the statistics we are using? The medical statistics experts will rise immediately and state “Research statistics is a rigorous science derived from first principles and is mathematically robust!”  They are correct. It is. But all mathematical derivations are based on some initial fundamental assumptions so when the output does not seem to work in all cases then it is always worth re-examining the initial assumptions. That is the tried-and-tested path to new breakthroughs and new understanding.

The basic assumption that underlies research statistics is that all measurements are independent of each other which also implies that order and time can be ignored.  This is the reason that so much effort, time and money is invested in the design of a research trial – to ensure that the statistical analysis will be correct and the conclusions will be valid. In other words the research trial is designed around the statistical analysis method and its founding assumption. And that is OK when we are doing research.

However, when we come to apply the output of our research trials to the Real World we have a problem.

How do we demonstrate that implementing the research recommendation has resulted in an improvement? We are outside the controlled environment of research now and we cannot distort the Real World to suit our statistical paradigm.  Are the statistical tools we used for the research still OK? Is the founding assumption still valid? Can we still ignore time? Our answer is clearly “NO” because we are looking for a change over time! So can we assume the measurements are independent – again our answer is “NO” because for a process the measurement we make now is influenced by the system before, and the same system will also influence the next measurement. The measurements are NOT independent of each other.

Our statistical paradigm suddenly falls apart because the founding assumption on which it is built is no longer valid. We cannot use the statistics that we used in the research when we attempt to apply the output of the research to the Real World. We need a new and complementary statistical approach.

Fortunately for us it already exists and it is called improvement statistics and we use it all the time – unconsciously. No doctor would manage the blood pressure of a patient on Ward A  based on the average blood pressure of the patients on Ward B – it does not make sense and would not be safe.  This single flash of insight is enough to explain our confusion. There is more than one type of statistics!

New insights also offer new options and new actions. One action would be that the Academics learn improvement statistics so that they can understand better the world outside research; another action would be that the Pragmatists learn improvement statistics so that they can apply the output of well-conducted research in the Real World in a rational, robust and safe way. When both groups have a common language the opportunities for systemic improvment increase. 

BaseLine© is a tool designed specifically to offer the novice a path into the world of improvement statistics.