(Bloomberg View) — The Obama administration has announced plans to tie 90 percent of all Medicare fee-for-service payments to some sort of quality or value measure by 2018. Sounds exciting! Who wouldn’t like to ensure that their doctors are paid for delivering value, rather than just randomly sticking needles into us?
Unfortunately, as both the Official Blog Spouse and Aaron Carroll of the Incidental Economist have noted, there is less to this announcement than meets the eye. Saying you want to pay for quality instead of procedures is quite easy to say; indeed, many an administration has said so, because “paying for outcomes instead of treatment” is the holy grail of health-care economists everywhere.
But actually doing this, rather than just saying it, turns out to be really hard.
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I think it’s fair to say that the Official Blog Spouse is one of the few journalists in the nation who has extensively reported on the history of Medicare payment reforms, all of which were supposed to move the system toward paying for valuable health care rather than cardiologists’ greens fees. As he details, they mostly failed. Medicare payments turn out to be a lot like one of those gel stress balls: You can squeeze them very small in one place, but the spending just pops out somewhere else.
There are a lot of reasons for this. Health-care lobbies are powerful, and Congress is almost uniquely easy to lobby, so ideas like controlling the growth rate of physician payments fell by the wayside once those payments actually had to be cut. The larger problem, however, is finding what to measure — and making sure that your measurement doesn’t introduce perverse incentives into the system. The fundamental problem is that while we want to pay for “health” or “outcomes,” we can’t really measure those very well.
Here’s a little exercise that will illustrate the problems of measurement that confound attempts to pay for “outcomes” or “health” instead of treatment: Tell me how healthy you are on a scale of 1 to 10.
Now before you blurt out an answer, stop and think. You’re probably already pondering some questions: What’s on the scale? What does a 1 look like, and what is a 10?
Let’s say that 1 is a terminal cancer patient in the ICU; 10 is an 18-year-old athlete in the prime of his physical powers. But you’re probably neither of these things. So where do you fall in between? Maybe you’re pretty healthy for a 47-year-old accountant, but your back gives you frequent trouble and you’ve got some acid reflux you need to watch, and, of course, there’s your blood pressure pills, or maybe in your case it’s a statin …
If you rate yourself compared to your neighbors, or other 47-year-old accountants, you might give yourself an 8 — 9 if you’re the cheery sort, 7 if you’re a perpetual grump. But if you compare yourself to that 18-year-old athlete, you’re probably more of a 5 or a 6.
And that’s only the stuff you know about. What about the stuff you don’t know about? How likely are you to die in the next five years? Or have a heart attack or a stroke or lose a limb?
The answer is “you have no idea.” If we had 50,000 of you, actuaries could predict these things pretty accurately: how many heart attacks, strokes, deaths, car accidents and so forth. But unless you are that terminal cancer patient in the ICU, no one can predict how likely you, personally, are to die in the next five years. We can say something about the expected life and health of large groups of people very like you. But not you personally.
Unfortunately, doctors don’t treat statistical universes; they treat individual patients. Those patients may unpredictably die, or just as unpredictably survive against incredible odds. Some of that is due to the skill of the doctor, some to the innate characteristics of the patient. How much of which? Hard to tell unless the doctor does something obviously completely wrong and stupid, like leaving an instrument inside the patient he’s operating on.
You can look at the whole pool of patients that the doctor treats, of course, but the more complicated and expensive the treatment, the fewer patients the doctor will be treating, which means that your data is prone to being swamped by a few outliers. Moreover, doctors do not treat identical patient pools. A good doctor who treats really sick patients may look worse than a bad doctor who confines their treatment to the relatively young and healthy.
See also: A Philadelphia hospital makes a bet on PPACA.
Of course, we can attempt to correct for this by adjusting the measurement for risk. The problem is that we don’t know all the risk factors; we know some risk factors that we can measure. There are a lot of risk factors we can’t, which means that this adjustment will be far from perfect.
If the adjustment is too imperfect, providers have recourse even beyond lobbying: They can stop taking patients covered by your program. That limits your ability to shrug your shoulders and say, “Gosh, well, the world’s imperfect, so I’m afraid that yes, some of you are going to get unfairly penalized under the new system. It’s the best we can do.”