In my last two posts, about data points that really matter, an implicit assumption is that the data in question (house sales and auto sales) actually drives decisions. Surprisingly, this hasn’t always been the case in many fields. Until fairly recently, many decisions have been largely based on expectations, plausibility, and bias.
Medicine is a great example. I don’t mean to pick on health care—after all, it’s the source of the current gold standard for effectiveness studies, the double-blind clinical trial—but think about the current debate on medical costs. One of the principal arguments for the availability of cost reductions is that no one really knows which treatments are most effective for many problems. Surgery rates for back pain, for example, vary widely among regions—which wouldn’t be the case, I hope, if there were one clearly superior solution. An entire industry, pharmaceuticals, is built around rolling out new treatments that, in many cases, offer little measurable benefit over existing treatments, at a much higher cost. Doctors hold onto their right to practice as they please, without regard for studies of industry best practices. The fact is, in many areas of medicine, we really don’t know what works best and why.


