As Predictive Analytics Rise, End-of-Life Spending Is Scrutinized

Jared Kaltwasser
AUGUST 02, 2018
predict death,final year healthcare spending,ethics end of life,hca news

If you want to talk about wasteful spending at the end of life, health policy expert Bruce Jennings, M.A., suggests looking back to the 1960s.

Back then, much of the medical technology we take for granted today was brand new — and much of it was overused.

Nowadays, physicians have learned that many technologies, such as artificial life support, are often used inappropriately.

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“People don’t have any idea how burdensome it is to have continued life support for a patient who is actually trying to die, and the technology is not allowing them to die,” Jennings, an adjunct professor at Vanderbilt University and a senior fellow at the Center for Humans and Nature, told Healthcare Analytics News™.

Avoiding extensive life-sustaining interventions in cases with little or no chance of survival isn’t only good for patients and families; it’s also good for the healthcare system.

“Right now, we save a lot of money by giving people the exit option,” he said. “But it’s probably not enough because too many ordinary people and families have the rights, on paper, to refuse treatment, but they can’t bring themselves to exercise those rights.”

The idea that people are opting for unnecessary or unlikely-to-succeed treatments when the end is near adds up to tens of millions — perhaps billions — of dollars in healthcare spending each year. Indeed, spending in the last year of life has become a hot-button issue in healthcare, premised on the idea that much of the money we spend at the end of life is simply ‘wasted.’

End-of-Life Spending by the Numbers

The data appear to tell a compelling story. In 2014, 2.6 million people died in the U.S., and four of every five of those people were on Medicare, according to a 2015 report from the U.S. Centers for Disease Control and Prevention. Medicare spent $600 billion on patient care that year, and approximately one quarter of that total was believed to have been spent on patients in the final year of life.

Such data have led to this idea: If the country wants to get its healthcare spending under control, it ought to take a look at curbing end-of-life spending.

But scientists at the Massachusetts Institute of Technology, Harvard University and Stanford University recently took a different approach. They sought to use a machine-learning model to analyze Medicare and mortality data to find out how many patient deaths were predictable and how much was spent on patients whose deaths seemed to be written in the statistics.

The results were conclusive: It’s really, really hard to predict death. Even in cases where a patient had a higher likelihood of death than survival, the corresponding spending was relatively small. In total, less than 5 percent of spending went patients whose predicted mortality was above 50 percent, according to the paper.

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In other words, healthcare spending helps people who are sick. People who are sick sometimes die, and therefore people who die account for a significant amount of healthcare spending. But that doesn’t mean spending on the sick — even the very sick — can be counted as waste, the authors argue.

“Our results suggest that spending on the ex post dead does not necessarily mean that we spend on the ex ante ‘hopeless,’” the study concludes.

Jennings sees the findings as an important counter-narrative to the idea that end-of-life spending is fraught with waste. Rather, the report suggests that a high percentage of these people have a legitimate shot at survival, at least statistically.

“If we change the major narrative to… a lot of them have a chance at recovery, it doesn’t seem like too much to spend,” he said.

Savings Hard to Come By

Although the study answers questions about our end-of-life spending, it also raises questions about the role predictive modeling could play in the healthcare decisions of tomorrow.

Amy Finkelstein, Ph.D., an MIT economist who co-authored the study, said she doesn’t expect computer modeling to ever accurately predict death in a way that would allow for the reigning in of end-of-life spending. For one thing, the study indicated that even with the availability of richer data, such as electronic health records, the predictive model fell short at deciphering which patients wouldn’t survive.

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But Finkelstein said even highly accurate modeling wouldn’t easily translate into easy cost savings.

“We do an exercise in the paper where we imagine a hypothetical, better predictor. We produce an artificial ‘oracle’ predictor that puts some weight on our prediction and some weight on realized outcomes (i.e. increasing predicted probabilities if ex post you died),” she said. “Doing so, we can jack up the performance to well above anything in the literature and still not make a dent in the results.”

One reason why end-of-life spending is difficult to curb is that it’s not primarily the result of last-minute interventions. A well-publicized 2016 study in Health Affairs found that most patients don’t see a spike in spending at the end of life. In fact, nearly half of the Medicare recipients in the study had persistent high spending, generally due to having multiple chronic conditions rather than a specific disease. Another 29 percent had “moderate persistent spending,” meaning there was no significant increase in the final year of life, but their spending was less than those with multiple chronic conditions.

Only 22.3 percent of the patients in the study had a significant jump in healthcare spending during their final year in life. Of those, 12.1 percent saw a last-minute increase, as opposed to a steady rise.

“These findings suggest that spending at the end of life is a marker of general spending patterns often set in motion long before death,” concluded the authors, led by Matthew A. Davis, Ph.D., M.P.H., of the University of Michigan.

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