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When Health-Care Reforms Don't Add Up
From Megan McArdle
"The Barack 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. 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.
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."
Medical systems are not the only systems that encounter these
problems. Just ask any organization that has tried to implement a new
sales compensation scheme to better align sales incentives with
"customer value." As one veteran of such attempts told me, suddenly
salesmen who majored in beer pong are "like Aristotle" -- they can
explain exactly why their sales territory is special and your new,
complicated system fundamentally mismeasures the value of their efforts.
Within six months, you'll have lost a few top performers who hate the
new system. Within a year, your burgeoning philosophers have probably
figured out how to game the new metrics.
Gaming -- "juking the stats," as it was called on "The Wire" -- is
the other major reason that these sorts of systems are hard to
implement. Let me illustrate with a little example. The town of Beachy
Head, England, had a big problem with suicide; people threw themselves
off its dramatic cliffs. In 1975, however, it managed to cut the rate of
suicide in half in a single year. An improvement in the national mood?
Or a dramatic triumph of public policy?
A new medical examiner.
The new chap decided to test the blood alcohol level of bodies found at
the base of the cliffs. Those with alcohol in their blood were ruled to
be accidents, rather than suicides.
You might argue that people bent on suicide could be taking a drink
to fortify their courage before attempting to take their own lives --
and you'd probably be right. Which is exactly the point. There is some
true rate of suicides at Beachy Head, but that's not information we
have. All we know is the suicide rate, which is dependent on things like
the assumptions of the medical examiner.
This is always a big problem, but it is particularly problematic when
you give the person taking the measurements strong incentives to see
things one way, rather than the other. On "The Wire," cops made their
crime rate look good by reclassifying serious crimes as less serious, or
as accidents, which did nothing about the underlying problem but made
the cops look much better. Unfortunately, we see the same behavior in
doctors and hospitals. It's called "upcoding": rating conditions as more
serious than they are in order to increase the reimbursement, or to
improve their performance on those risk-adjusted mortality measures.
This can go beyond just massaging a few figures and do active harm. For example, consider what happened when New York state started measuring cardiology outcomes.
The idea was that they were "ending years of private, clubby surgeon
culture." The public report cards "were intended to shine a light on
poor surgeons and encourage a kind of best-practices ethic across the
state. If the system worked, mortality rates would fall everywhere from
Oswego to NYU." And at first glance, the system worked beautifully:
Risk-adjusted mortality rates dropped by an astonishing two-thirds. But
as New York magazine reports, it rapidly became clear that one way
surgeons were achieving these advances was simply by refusing to treat
the sickest patients:
This isn’t just about high-risk patients. It’s about doctors playing
games with practically any patient to get better scores. Some surgeons
look for ways to make their easy cases seem harder. Others make their
hard cases appear so difficult that they place out of the state
reporting system. When it comes to the sickest patients, some surgeons
simply turn them away, asserting that they’re better off getting drug
treatments, or waiting in the ICU. “The cardiac surgeons refer their
patients to the cardiologists, and the cardiologists refer them to the
intensive-care unit,” says Joshua Burack, a SUNY–Downstate surgeon in
Brooklyn who in 1999 released a study revealing that nearly two-thirds
of all heart-bypass surgeons in the state anonymously admitted to
refusing at least one patient for fear of tainting their mortality
rates. “Everyone’s going to pass along the hot potato to the person
who’s not vulnerable to reporting.”
In the past five years, no fewer than five studies have been
published in reputable journals raising the possibility that New York
heart surgeons are not operating on certain cases for fear of spoiling
their mortality rates. The clincher came in January, when, in an
anonymous survey sent out to every doctor who does angioplasty in the
state, an astonishing 79 percent of the responders agreed that the
public mortality statistics have discouraged them from taking on a risky
patient. If you’re a hard case, in other words, four out of five
doctors would think twice before operating on you.
The Cleveland Clinic started getting a lot more referrals from New
York -- and their patients were sicker than the patients referred from
other states.
Now, you can make an argument that maybe this is all to the good --
that maybe the money we spent doing heart surgery on very sick people
was wasted, and it's better to concentrate our money on the relatively
healthy. But that's not the purpose of the report cards, which are
supposed to help patients make informed choices about their surgeons --
not to help surgeons better choose their patients.
The doctor profiled
in the article, who had New York's lowest cardiac mortality rate at the
time, told the reporter that he achieved that rate by not operating on
people who were "already dead." But what does that mean? Refusing to
operate on hopeless cases, or refusing to operate on people who have a
40 percent chance of living with surgery and no chance at all without
it? If that were me, I'd probably want to gamble -- and I'd probably be
pretty angry if surgeons were too afraid that a failure would show up on
their report card.
In some cases, surgeons code their patients as sicker than they used
to, even if doing so means doing additional, unnecessary treatment. This
can range from putting a patient on nitroglycerin to, the article
alleges, actually putting a little ring around someone's mitral valve,
which the surgeon who recalled the incident describes as "assault."
These measures either improve the risk adjustment or take the patient
out of the report card sample entirely, because they're deemed special
cases.
You get the point: A measure that was supposed to make patients
healthier and encourage the spread of best practices has instead kept
doctors from treating sick patients and encouraged unnecessary
treatments. Don't get me wrong: It may well have encouraged some better
treatment, too. But we always need to be mindful of the perverse
incentives by which even a simple, obvious solution like "more
transparency!" could actually make the system worse.
More broadly, when money is on the line, assume that people will act
against any system you come up with to preserve their income, even to
the detriment of patients -- like Medicare's plan to reduce hospital
readmission rates, which completely succeeded in reducing those numbers
and also apparently resulted in a lot more patients being put on
observation status rather than being admitted to the hospital. That
meant they didn't count as "readmissions" if they came back. It also
potentially left the patients on the hook for bigger bills.
I'm not saying that no payment reform program can ever work. I am
saying that most of the significant attempts to reform the way we pay
for health care haven't, and for similar reasons. Reformers have the
basic idea right: You'll get more of what you'll pay for, and less of
what you don't, so you should pay for what you want. Unfortunately, in
fields like health care and education, we can't pay for what we want; we
can only pay for what we can measure. And it's usually a lot easier for
people to play with the measurements than it is to change their
behavior or give up a big hunk of income."
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