March 13, 2017

You Don't Get What You Pay For

The enormous interest in getting good “value” for every dollar spent on health care, whether by individuals, insurers, government, or anyone else neglects certain basic realities—for example, that medical care isn’t a consumer good like toasters: it’s a very sophisticated service provided by highly trained professionals;  and that health insurance by its very nature makes the operation of a free market impossible. There’s still another basic reality that is even more often neglected, and that is the widespread belief that “you get what you pay for.” Or, if you pay less for one treatment than another, the cheaper one is necessarily inferior. Any claims that the two are of equal quality are suspect. And claims that the cheaper one is higher quality are, on their face, deemed outlandish.

Translated into practice, this means that patients and doctors alike tend to assume that more is better. More x-rays (or, as plain radiographs, CT scans, MRIs, and PET scans are collectively known, “imaging studies”), more medications, more doctors is superior care and must result in better outcomes. As a result, I’m not at all surprised that changing physician behavior and patient expectations has proved difficult, even when professional guidelines assert that less is more. And unfortunately (unfortunate since, from a geriatric perspective, less often is more), a new study that purports to show that greater spending per hospitalized patient fails to improve outcomes is hardly convincing.

Previous retrospective studies, especially those comprising the Dartmouth Atlas of Health Care, have shown that expenditures on apparently similar patients differ by geographic region, by hospital, and within regions—without any measurable difference in outcomes. But the Dartmouth Atlas has been criticized for working backwards from death even though death could not have been predicted in advance, it has been criticized for failing to adequately consider differences between the patient population in different locales, and it has been critiqued for not acknowledging that patient preference might account for some of the observed differences in health care utilization and, as a result, in cost. The new study asks whether physicians working in the same hospital nonetheless exhibit differences in their pattern of test- and treatment-ordering and whether that variation results in different outcomes for their patients. Looking at over 1.3 million hospitalizations occurring at over 3000 hospitals and involving 72,000 physicians, they found large variability in expenditures and no difference in outcomes—just like the Dartmouth Atlas findings.

The authors were careful to look at Medicare Part B spending because this is involves services that are at the discretion of physicians (Part A spending is determined largely by the DRG, the reason for admission, and is set by Medicare) and is a “proxy” for the intensity of resource use by physicians. They were careful to confine their analysis to Medicare fee-for-service beneficiaries who were age 65 or older and hospitalized for an acute medical condition. And they examined separately the behavior of general internists and hospitalists. They made some adjustments to account for differences among patients, including age (in 5-year increments), sex, race/ethnicity, median income, and existing comorbidities, and other adjustments to account for differences among physicians, including age (also in 5-year increments), sex, and site of medical school education. They found that the variation in spending across physicians within a hospital was greater than across hospitals. Among hospitalists, adjusted spending was more than 40 percent higher among doctors in the highest spending quartile compared with the lowest quartile. And higher expenditures had no effect on either the 30-day readmission rate or mortality, the two measures of quality used to examine outcomes.

Regrettably, this study has a number of glaring weaknesses. First, there are the odd omissions: the authors report on the gap between the highest and lowest quartiles of hospitalists but not the corresponding figure for general internists, even though nearly twice as many patients were cared for by internists than by hospitalists. Next, it’s not clear that the two outcomes examined—mortality and readmission rate—are good indicators of quality. Or rather, even if the two groups were indistinguishable based on these two measures, perhaps one group fared far better than the other on some other measure that wasn’t looked at, say quality of life. Finally, the study wasn’t randomized and it wasn’t prospective, allowing for the possibility that there were important differences between the patients on whom much money was spent and those on whom less was spent. In fact, maybe the patients on whom more resources were expended were sicker. If they were sicker but had the same mortality rate and readmission rate as those on whom fewer resources were spent, then arguably they fared better than their counterparts!

So where do we go from here? Contrary to the prevailing wisdom, the answer may not lie with “big data.” Too many things are going on at once with these patients to be able to reliably conclude that ceteris paribus, all things being equal, overall expenditure on tests and treatments had no bearing on outcomes. I think it would make sense to look at a small number of detailed case examples—20 or 30 patients of the same age with the same admitting diagnosis, matched for severity of illness, co-morbidities, race, ethnicity, and socioeconomic class, some of whom are cared for by prolific test-orderers and some of whom are not—following them prospectively over time to see what happens to them. And the study would try to ascertain why various choices were made, perhaps by interviewing the patients and/or their doctors, perhaps by gleaning the answer from free text in medical records, and what their outcomes turned out to be.

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