The weight of health information

Lately I've been speaking with several folks who are in possession of large amounts of health information for one reason or another.  They all believe their data is valuable and are looking for ways to leverage it to make a business or to advance medical understanding, or both.  Noble ambitions indeed but a question struck me as I listened to the the most recent of them this last week.  Could it be that the weight of all that information is actually crushing innovation rather than stimulating it?  It seems to me that we have a plethora of data looking for a problem to solve.  If we recall the old proverb, 'Neccessity is the Mother of Invention', and then consider that some of our most recent enduring inventions were developed locally and ground-up with little intellectualization at the start (some of the more successful social media forums for eg) we might conclude tha Nike may have it right in their newer proverb, 'Just Do It'.  Serve a local, immediate and acknowledged need; if it works scale it.  This avoids the political nightmares of having to first make a business case to those who don't believe there is one.  The latter is a very difficult way to start a business but we do it all the time.

If we start from the ground up, we all want better health but what does that really mean?  Here are some possibilities:  Better ways to know if we are sick, or going to get sick; better treatments to prevent or cure at costs we can afford; more control over our general health; less hassle in managing health for ourselves and those we care for; a more peaceful existence.  To me, the latter is perhaps the most important of all and to some degree relies on success in the former four.

There are numerous elements to each of the outcomes but it seems like the last place to start in attempting solutions might be in collecting the data.  There's no harm in it per se, provide one doesn't expect the answers to then simply reveal themselves.  We must exert some energy up front if we are to make sense of the problem and move toward solutions in a short time frame and at reasonable cost. However, given that my colleagues have started with the data,  let's look at the process from the data-first perspective.  We are forced to ask questions in the following way: 1) what data do you have and where does the data comes from 2) what problems can you apply it to 3) who might have a vested interest in either the data or a solution.  Very time consuming and like put the cake ingredients into a bowl and then asking what we might make with it.  If we think nationally or globally then the problem is magnified and can be prone to costly red herrings.

If we look at the problem first, then understanding what to do with the data becomes that much easier because the need is already understood. To further simplify, solving a problem locally first allows one to talk directly with those that have the problem rather than having to resort to assertions or assumptions about the population as a whole.  Locally, trial and error can be conducted at low cost and low risk.  Even for those that already have large data sets, perhaps as a side effect of another part of their business (pharmacies or drug companies for instance), this approach is a sensible way to determine how the data might be leveraged more broadly.

Starting with a large amount of data and a set of assumptions means starting with a muddy board.  The danger of solving the wrong problem is great, provided you can get out from under the weight of all that data.   Starting with a white board and a diverse group of passionate individuals with first-hand knowledge of the problem is an innovation waiting to happen.

So.....

1. Identify the problems before you collect/look at the data
2. Become a local hero first.

Anyone have anything to add?

Knowledge versus information

I am somewhat of a temporalist (yes, spellchecker caught this invented word), meaning that I like to create timelines to see how thought has changed over the years on various subjects. In other words, I am interested in what is believed at a moment in time. I do believe in ‘truth’ but I’m not sure how to define it. On the other hand, something that is ‘true’ is easy to define; a so-called fact that is commonly understood to be true according to certain conventions mainly based on science. I say mainly, because there is often general agreement that something is so, even when there is no way to prove it. That a sunset is beautiful for example, is an observation that is unverifiable scientifically because there are no criteria against which to measure the assertion other than everybody thinks so. Few would deny the beauty inherent in a good sunset, and many would even agree that it’s a fact that sunsets are beautiful. But if we try to convey that ‘fact’ to others we quickly find that it is impossible to pass on the experience. You just had to be there.
Similar problems occur when sorting out the differences with information and knowledge. In my view, the difference between information and knowledge is mainly this:
Information occurs in discrete packets that can be transferred without loss of meaning by word, in image, or by numbers. It is slightly more sophisticated than data in that there is already some context applied in the way it is packaged (eg labeled and categorized). Information can be true or false, and we can still be informed when the information is false.
Knowledge is the sum of knowing and occurs after we have received information in one form or another (experiential, or objective) and have interpreted it based on our own experience as well as the context with which it came to us, and have added it to our store of accepted wisdom (even if it is only accepted wisdom in our own heads). When we know something we accept it as true.
An example: you can have information that eating too many cherries causes Alzheimer’s Disease or you can know eating too many cherries cause Alzheimer’s. In the former you can be informed but still suspend belief; in the latter you have made your choice to accept the information as true (with or without caveats).
When you look at it this way it becomes very easy to see why institutionalized knowledge management efforts often fail. Knowledge is accepted wisdom for a moment in time. Information exists forever. Knowledge cannot be easily transferred because it requires the receiver to believe the contexts and conditions that apply to the knowledge in question. Information is easily transferred because there is little judgment involved. It is what it is. One can argue about what category it goes in, or about whether it is true or not, but information is just information at the end of the day.
Knowledge management inside corporations has historically confused information and knowledge. Sophisticated IT systems have been set up for ‘knowledge transfer’ and extensive people networks built to ‘share knowledge’. Computing is well suited to sharing information, but not to sharing knowledge unless there is a way to de-contextualize the knowledge back to data and allow re-sorting in the light of new and different contexts. This can happen with ontologically based systems that allow complex questions to be asked of the de-contextualized knowledge such that new insights can be derived. Most knowledge management initiatives within large institutions end up buried in an IT group where some system is build to store and share ‘best practices’, or to access multiple databases from a single interface in the hope of improving accessibility to knowledge deep within the organization. By ignoring the human side of KM, (ie that knowledge includes a belief element and therefore an element of trust), these initiatives cause untold political horrors and at best produce yet another data access tool that skims the surface of real knowledge while continuing to build deep silos to hold yet more information.
People networks are often used to ‘transfer knowledge’ from person to person, group to group. These approaches have yielded some genuine successes but can stumble when there is a failure to realize that transferring knowledge requires transfer of belief that the knowledge is true, or at least a forum to challenge and evolve an understanding. An open acknowledgement of the assumptions that accompany knowledge as it is passed along can alleviate the pain and create a path to success. This is a relatively simple task and I’m not sure why it is not more often undertaken.
Knowledge management is considered an old fad nowadays. It’s a shame since this is a time when organizations that deal in novelty and innovation surely need it the most.