Very surprising. When cardiologists are away from the hospital, deaths after heart failure or cardiac arrest declined. I’ll probably use this in my course this Spring. (Or perhaps in both courses: Big Data, and Operations Quality in Healthcare.)
I agree 100% with the following discussion of big data learning methods, which is excerpted from an interview. Big Data is still in the ascending phase of the hype cycle, and its abilities are being way over-promised. In addition, there is a great shortage of expertise. Even people who take my course on the subject are only learning “enough to be dangerous.” It will take them months more of applied work to begin to develop reasonable instincts, and appropriate skepticism.
As we are now realizing, standard econometrics/regression analysis has many of the same problems, such as publication biases and excess re-use of data. And one can argue that it’s effects e.g. in health care have also been overblown to the point of being dangerous. (In particular, the randomized controlled trials approach to evaluating pharmaceuticals is much too optimistic about evaluating side effects. I’ve posted messages about this before.) The important difference is that now the popular press has adopted Big Data as its miracle du jour.
One result is excess credulity. On the NPR Marketplace program recently, they had a breathless story about The Weather Channel, and its ability to forecast amazing things using big data. The specific example was that certain weather conditions in Miami in January predict raspberry sales. What nonsense. How many Januaries of raspberry sales can they be basing that relationship on? 3? 10?
Why Big Data Could Be a Big Fail [this is the headline that the interviewee objected to – see below]
Spectrum: If we could turn now to the subject of big data, a theme that runs through your remarks is that there is a certain fool’s gold element to our current obsession with it. For example, you’ve predicted that society is about to experience an epidemic of false positives coming out of big-data projects.
Michael Jordan: When you have large amounts of data, your appetite for hypotheses tends to get even larger. And if it’s growing faster than the statistical strength of the data, then many of your inferences are likely to be false. They are likely to be white noise.
Spectrum: How so?