The best winning models are then used to predict the effect of possible interventions: what if demographic B3 was put on 2000 IU vit D? What if demographic Z2 stopped using coffee? What if demographic Y3 was put on drug ZB4? etc etc.

What about predictions of the form "highly expensive and rare treatment F2 has marginal benefit at treating the common cold" that can drive a side market in selling F2 just to produce data for the competition? Especially if there are advertisements saying "Look at all these important/rich people betting that F2 helps to cure your cold" in which case the placebo affect will tend to bear out the prediction. What if tiny demographic G given treatment H2 is shorted against life expectancy by the doctors/nurses who are secretly administering H2.cyanide instead? There is already market pressure to distort reporting of drug prescriptions/administration and nonfavorable outcomes, not to mention outright insurance fraud. Adding more money will reinforce that behavior.

And how is the null prediction problem handled? I can predict pretty accurately that cohort X given sugar pills will have results very similar to the placebo affect. I can repeat that for sugar pill cohort X2, X3, ..., XN and look like a really great predictor. It seems like judging the efficacy of tentative treatments is a prerequisite for judging the efficacy of predictors. Is there a theorem that shows it's possible to distinguish useful predictors from useless predictors in most scenarios? Especially when allowing predictions over subsets of the data? I suppose one could not reward predictors who make vacuous predictions ex post facto, but that might have a chilling effect on predictors who would otherwise bet on homeopathy looking like a placebo.

Basically any sort of self-fulfilling prophesy looks like a way to steal money away from solving the health care problem.