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

Jared Kaltwasser
AUGUST 02, 2018
healthcare spending ethics,euthenasia spending,dying medical bills,hca news

Models Improving

Predictive modeling is getting better and better. While it might not be able to predict long-term deaths with sufficient accuracy, there are signs that its short-term capabilities are impressive.

A paper published earlier this summer in NPJ Digital Medicine looked at the possibilities of deep-learning models based on data from electronic health records. Among other findings, the study suggested that the deep-learning model outperformed the augmented Early Warning Score (aEWS) at predicting mortality at 24 hours after hospital admission.

“If a clinical team had to investigate patients predicted to be at high risk of dying, the rate of false alerts at each point in time was roughly halved by our model,” wrote the authors, who represented the University of California San Francisco, the University of Chicago, Stanford University and tech giant Google.

In one case, the aEWS model gave a cancer patient with fluid in her lungs a 9.3 percent chance of dying while in the hospital, but the deep-learning model rated her chance of death during the hospitalization at 19.9 percent. The patient died 10 days into her stay.

Finkelstein, at MIT, said if prediction models were able to become much more accurate, it would require society to evaluate how it wants to deal with people with low chances of survival. That’s more of a philosophical and ethical question than an economics one, she said.

For Jennings, this brave new world could prompt questions like this: What percentage of survival is too low to be worthy of healthcare spending? The answer could vary from society to society and might even depend on the relative wealth of a country, he said.

There are risks in trying to set a standardized threshold of which patients receive care and which don’t.

If, for instance, a policy was enacted that people with less than a 37 percent chance of survival were not entitled to expensive end-of-life interventions, it would be imperative that physicians agreed with the threshold, Jennings said.

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“How you can get the practitioners of medicine to obey the rules that the policymakers lay down?” he said. “If you make those rules too stringent, you violate the conscience of physicians.”

Any policy that encouraged physicians to violate their consciences would be bad for medicine. If physicians reacted by trying to game the system, that would be bad too.

Generally speaking, end-of-life decisions have been left to patients and families in the U.S., and they sometimes refuse life-sustaining treatment because it doesn’t accord with their values or wishes. That kind of model is good for patients and the economy, Jennings said.

However, the use of machine-learning and artificial intelligence technologies as guides for end-of-life decisions ought to be carefully thought through, he added.

“It is a different thing to use artificial intelligence capabilities to make determinations and set rules about what individual physicians and insurance companies will facilitate going forward,” he said. “I think computers can help us very much understand data. I don’t think that computers ought to set the rules for extremely complicated life decisions.”

Finkelstein said for now the lesson from her study is that if “wasteful spending” at the end of life is an issue, it certainly isn’t an economic emergency. Rather than trying to decide which patients are worthy of spending, she said, we ought to try and learn which types of treatments are worthwhile.

“We should stop focusing on calling spending that occurs at what is ex-post the end of life ‘wasteful’ and do the hard work of identifying interventions and types of care that actually seem to produce no benefits to patients,” she said.

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