End-of-life healthcare spending in the United States may not be the low-hanging fruit it's often thought to be, according to a predictive modeling study recently published in Science.
As it turns out, death is "highly unpredictable," and less than 5% of spending is accounted for by individuals with predicted mortality above 50%.
The findings offer new insight into the oft-quoted statistic that 25% of Medicare spending occurs in the last year of life, a fact seized upon by those looking for ways to eliminate waste and lower healthcare spending.
"I think we need to be more careful in drawing the distinction between a fact and its implications," said economist Amy Finkelstein, PhD, from the National Bureau of Economic Research and Massachusetts Institute of Technology, both in Cambridge, Massachusetts.
"That one quarter of all Medicare spending occurs in the last 12 months of life is correct, but the implication that is commonly drawn from that — that clearly this money is waste — is incorrect because it presumes implicitly that at the time we are spending the money, we know with high probability that the recipient of the spending is going to die."
Finkelstein and colleagues used Medicare claims data from a random sample of 20% of enrollees to build a machine-learning model of annual mortality risk. Machine learning extends traditional regression analysis to account for not just the independent effect of multiple variables but also for the interaction effect of multiple variables.
Their main analysis focused on enrollees alive on January 1, 2008, while a second analysis focused on the moment of admission to the hospital, "which is a more decision-relevant vantage because you're admitting someone and you have to diagnose them and start spending a lot of money," Finkelstein said.
Even from the vantage point of admission to hospital, where annual mortality was about 20%, the 95th percentile of annual death probabilities was still only 67%, and less than 4% of those who ended up dying in the subsequent year had a predicted mortality above 80% at the time of admission.
"What the study shows is that it's not correct to look at people who died and compute their spending, but rather you need to look at people who are at high predicted risk of death and see how much spending happened on them and whether it affected outcomes," Anupam Bapu Jena, MD, PhD, the Ruth L Newhouse associate professor of health care policy at Harvard Medical School, Boston, Massachusetts, told theheart.org | Medscape Cardiology. Jena was not involved in the study.
"You also need to ask how often it is that we really know a person is at death's door, where a lot of money is spent. It turns out that the proportion of healthcare spending going to those people, where we might think spending is wasteful, is really low," he explained.
"We have to recognize that our ability to predict isn't that good, so even if there's a 10% chance we could be wrong, that means that 1 out of 10 patients you see in the ICU [intensive care unit] and think isn't going to make it and where you recommend to the family that they withdraw care, could have made it. For some doctors, that kind of error could occur once a week," said Jena, who is also a hospitalist at Massachusetts General Hospital in Boston.
"Maybe 90% sounds good, but it's not for a life-or-death situation."
What Works and for Whom?
Rather than focusing on impressive-sounding statistics and rough averages, "we need to roll up our sleeves and do the hard work," said Finkelstein.
"It would be easier if there was some global solution like cutting end-of-life spending, but it turns out it's not so easy as to just say we're spending a lot of money on people who die and maybe we should stop," she said. "But rather, we actually need to do the important and challenging work that is, in essence, the work of health economists and medical researchers, of looking policy by policy and medical intervention by medical intervention at what kind of spending is actually generating value, saving lives, and improving quality of life, and what is not."
To Jena, a follow-on trial to this study might be one that either randomly assigns patients — "which might be difficult for a variety of reasons" — or quasi-randomly assigns different individuals to different hospitals that vary in terms of intensity of the care they provide.
"Then you can stratify the analysis by these various risk groups that these researchers and others have studied and see what happens to patients who happen to be treated in a hospital or healthcare system that tends to be more intensive compared to one that is less intensive," he said.
Jena added, however, that just looking at hospital care is "a bit of a red herring" because the costs explored in this analysis and reflected in the 25%-of-spending-in-the-last-year-of-life statistic represent both "inpatient and outpatient care, multiple hospitalizations and consultant visits, imaging, et cetera. So it would really be best to look at people exposed to two different healthcare systems that vary in their care intensity."
Finkelstein and Jena declared no conflicts of interest related to this study.
Science. 2018;360:1462-1465. Abstract
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Cite this: Predictive Modeling Highlights Value of Late-Life Healthcare Spending - Medscape - Jul 06, 2018.
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