any time a problem of this magnitude hasn't been solved AND you think the answer is 'blindingly obvious', consider that you don't fully grok the problem. I've been in healthcare analytics for 10+ years and it's incredibly difficult to measure outcomes. Take your example of 3 year outcomes, are you saying that the entire reason why someone is alive is because of the hospital they went to 3 years ago? There are so many confounding variables that even trying to measure outcomes 90 days out is difficult.
On top of that, the other commenter mentioned the unintended consequences of simplistic measurements like the one you describe. Look at measuring patient satisfaction - 'what could possibly be wrong with measuring patient satisfaction', people say! hold docs/hospitals/etc. accountable. but when you look at the data, patient satisfaction scores are weakly correlated with evidence based care and healthy outcomes but it is strongly correlated with things like how much time the patient sat in the waiting room or if the doctor prescribed them a medication.
> There are so many confounding variables that even trying to measure outcomes 90 days out is difficult.
Very much this. Two patients, same doctor, same proceedure, with somewhat similar medical backgrounds. Nearly the same progression in the hospital, and released to go home, and possibly to doctors that don't interact with that hospital.
Perhaps one is poor and sometimes skips medication. Only one starts eating healthily and starts exercise. One has a stressful home life. Maybe one, when the recovery isn't going as quickly as possible, starts mixing in alternative medicines but doesn't tell his GP. Maybe they were both women, in the hospital to give birth - and one got pregnant again in the first year while the other didn't. These sorts of things greatly affect the outcomes, but none of them are under the hospital's controls.
> any time a problem of this magnitude hasn't been solved AND you think the answer is 'blindingly obvious', consider that you don't fully grok the problem.
That is a good point - this thread has turned out to be more complex than I expected, and had I been a bit more open-minded I would have avoided the phrase 'blindingly obvious'.
Still, I'm not yet convinced this is an intractable problem. The New York Times articles says that patients are 3 times more likely to die at one hospital than another, even after controlling for patient sickness, income and age. This may be due to (a) a confounding variable; (b) a real difference between the quality of care provided by the hospitals. How much weight should we place on each possibility? Is it 50/50, or 60/40, or 10/90?
If you have to have a medical procedure and were told the survival rate of Hospital A is 98% and at Hospital B is 94% would you say, "there are so many possible confounding variables, this statistic will have no bearing on my choice of hospital"?
On top of that, the other commenter mentioned the unintended consequences of simplistic measurements like the one you describe. Look at measuring patient satisfaction - 'what could possibly be wrong with measuring patient satisfaction', people say! hold docs/hospitals/etc. accountable. but when you look at the data, patient satisfaction scores are weakly correlated with evidence based care and healthy outcomes but it is strongly correlated with things like how much time the patient sat in the waiting room or if the doctor prescribed them a medication.