Predictive modeling of U.S. health care spending in late life

End-of-life health care spending In the United States, one-quarter of Medicare spending occurs in the last 12 months of life, which is commonly seen as evidence of waste. Einav et al. used predictive modeling to reassess this interpretation. From detailed Medicare claims data, the extent to which spending is concentrated not just on those who die, but on those who are expected to die, can be estimated. Most deaths are unpredictable; hence, focusing on end-of-life spending does not necessarily identify “wasteful” spending. Science, this issue p. 1462 The United States spends a lot on people in the last year of life, but those at high risk of death account for relatively little spending. That one-quarter of Medicare spending in the United States occurs in the last year of life is commonly interpreted as waste. But this interpretation presumes knowledge of who will die and when. Here we analyze how spending is distributed by predicted mortality, based on a machine-learning model of annual mortality risk built using Medicare claims. Death is highly unpredictable. Less than 5% of spending is accounted for by individuals with predicted mortality above 50%. The simple fact that we spend more on the sick—both on those who recover and those who die—accounts for 30 to 50% of the concentration of spending on the dead. Our results suggest that spending on the ex post dead does not necessarily mean that we spend on the ex ante “hopeless.”