Evidence-Based Everything

Subtitle: Everything is about prediction is about data mining is about everything.

Here's what I mean:

1. "Everything is about prediction"
All information (books, articles, lectures, even conversations) is basically intended to provide you with a model from which to make future predictions. In some cases, the predictive models are explicitly spelled out, as in "the moral of the story". Other predictive models are more subtle. When your coworker says she thinks your employer is short-sighted, she is predicting that in the near future, that employer will do short-sighted things. When a self-help book tells you that people ate half the number of candies when the candy jar was moved 6 feet away from their desk, that book is providing you with a predictive model that says that mindless snacking goes down as snack food is moved away.

2. "prediction is about data mining"
All predictions are measurable using data mining. What is data mining? It is basically the scientific method, with particular focus on finding statistical correlations from tables of data. Data mining and associated scientific methods can be used to make just about any prediction. The best route to developing predictions about the real world is to use scientific methods, armed with -- you guessed it -- data mining.

3. "data mining is about everything."
Whether the subject matter is interpersonal relationships, dietary habits, or social hierarchies among crack dealers, it can be measured with data. And it should be! Because hiding with data everywhere are shocking findings that often argue against the conventional wisdom. Thus, data mining can dispel our misconceptions and enable us to better predict and manage our future, in all walks of life.

My broader point is that we are very subjective creatures, with an amazing capacity to see the world through tinted glasses. In my field of internal medicine, we have only in the last few decades appreciated the critical need to practice evidence-based medicine. This basically means that we rely on scientific proof that an intervention is justified, and that in the absence of that proof we should proceed with caution.

Before the evidence-based medicine movement, doctors relied exclusively on intuition and basic research into the pathophysiology (the nuts and bolts) behind diseases. The trouble with this approach is that intuition and even the science behind studying pathophysiology of diseases both can and do lie, frequently. Medicine has done many famous about-faces when it finally got around to studying whether an intervention is helpful. Examples include medicine's historic practices such as useless low-protein diets for kidney health, recommending toxic vitamin E to prevent cancer, widespread use of toxic anti-arrhythmic drugs and estrogen replacement therapy.

Bottom line: everything, not just medicine, would benefit from increased use of scientific data mining, to provide us with new and improved predictive models about... everything.