You are currently browsing articles tagged MRI.
“Our imaging and diagnostic tests are so good, we can see things we couldn’t see before. But our ability to understand what we’re seeing and to know if we should intervene hasn’t kept up.”
Tags: CT, Imaging, MRI, Overtreatment
Do MRIs increase the liklihood a patient receives back surgery?
“Orthopedists and primary care physicians who begin billing for the performance of MRI procedures, rather than referring patients outside of their practice for MRI, appear to change their practice patterns such that they use more MRI for their patients with low back pain. These increases in MRI use appear to lead to increases in low back surgery receipt and health care spending among patients of orthopedic surgeons, but not of primary care physicians.”
What is it about patients who see primary care physicians that makes them less likely to get back surgery. I can think of a number of reasons:
Source
Tags: Back Pain, Health Economics, Journals, MRI, Self-referral, Surgery
Can technological change make people worse off? Most economists think technical improvements are always good. Producing more of the output with fewer input is considered a more efficient use of resources. But is this the case in the medical field? John Goddeeris shows that this may not always be the case in his 1984 paper.
The Model
Let us assume that individuals maximize utility of the for developed by Arrow (1976):
Here, i indexes the state of illness, where the probability that each stat of illness occurs is pi. Individuals can spend their income on consumer goods, xi, or medical care, mi, where medical care is translated into health by the function hi(mi). A technological advancement is defined as hia(mi)≥hib(mi), for all mi, and strict inequality for some mi.
We can now introduce insurance into the model. Individuals who buy insurance pay a premium equal to π and a coinsurance rate z. The price of the medical premium must be equal to the expected value that the insurance company expects to pay out in medical benefits (less the copayments).
One would think that V*a>V*b, but this may not always be the case. For instance, let us assume that a person can either be healthy or sick (i.e., i=2). Further, assume the following utility functions:
If individuals are endowed with income x0, then:
Assume p=.1, x0=10 and the original technology is:
This means that if medical spending is above 5, health will be partially restored. Goddeeris finds that the optimal coinsurance rate to maximize utility is no coinsurance (i.e., z=0). With no coinsurance, sick individuals choose m2=5. The utility level under the original technology (i.e., V*b) equals -.000476. What happens when there is a positive technological changes as follows:
Again, the author finds that no coinsurance (i.e., z=0) is optimal. With no coinsurance, individuals of course choose m2 = 15. However , tutility level under the new technology (i.e., V*a) equals -.000592. How can this technological improvement have decreased utility?
In this example, the true cost of the innovation is so large relative to its benefits are so large, people only choose to use it since coinsurance is 0. A higher coinsurance rate would have induced individuals to choose m2 = 5. According to Goddeeris, “the larger added expenditures in the ill state leads to an even greater reduction in expected utility. A ero co-insurance rate remains optimal after the innovation. Thus V*a < V*b, and the innovation –which clearly expands productive capabiities and is in fact adopted–is welfare reducing by our standard.”
The reason this occurs, is that individuals act ex post as if their expenditure decisions have no impact on insurance premiums. While no individual person’s actions will affect insurance rates, since all sick individuals act similarly, health insurance premiums increase much more after the technological innovation than before it.
Despite the finding that technology is welfare reducing in this particular case, technological improvement are of course welfare improving in other cases. One question that remains is how to operationally decide when a technology is welfare enhancing and when is it welfare reducing. In which category do MRI machines fall? What about CT scanners?
Tags: Health Insurance, MRI, Technological Change
There is an interesting article at Forbes describing that the housing boom is not the only bubble that may need to burst. Scans per thousand insured people went from 85 to 234 in the U.S. between 1999 and 2007. Author David Whelan describes what happened to one radiologist in Connecticut after Medicare and HMOs cut scan reimbursement rates this year:
Radiologist David Gruen used to spend millions of dollars to replace his General Electric MRI and CT scanners every three years. It was money well spent because the machines were always busy. But a year ago Medicare cut the price it pays for imaging, so Gruen gets paid 15% to 50% less for each order, depending on the type of scan. Health insurers got wise, too, and started imposing a 48-hour review on imaging orders. The doctor hired clerks to battle the HMOs, but his office volume was flat last year, down from 10% growth in prior years. Gruen was forced to take a 20% salary cut. Now his Norwalk, Conn. practice is holding off on buying new machines and stretching the old machines’ life span to five years. “We really do face a crisis,” he says.
In additional, General Electric’s (disclaimer: my former employer) medical division has seen declining profits for the first time in years.
Are Medicare and HMO cuts to imaging hurting patients? The New England Journal of Medicine thinks not. There are side-effects to these scans including increased levels of radiation exposure, especially dangerous for kids. As with any test, there is the probability of a false positive (i.e., that patient does not have the disease, but the test claims they do). “A study from the National Institutes of Health found that 17% of patients getting tested for cancer had at least one false positive chest X ray over a four-year period, and 8% of women had at least one false positive ultrasound for ovarian cancer.” These figures lend some more evidence that Americans may be Overtreated (see my post on Shannon Brownlee’s book of the same name).
Forbes also finds that “a doctor who owns his own machine is four times as likely to order a scan as a doctor who doesn’t.” Financial incentives do make a difference (for more information on how physician financial incentive affect surgery rates, see my working paper “Operating on Commission“).
Tags: financial incentives, MRI
Recent comments