Predictive modeling of U.S. health care spending in late life
Name
nihms-976042.pdf
Description
Accepted version
Size
287.82 KB
Format
Unknown
Checksum (MD5)
2c8dea9995f25900446c6f55744becee
Author(s)
Einav, Liran
Finkelstein, Amy
Millainathan, Sendhil
Obermeyer, Ziad
Date Issued
June 2018
Journal
Science
Publisher
American Association for the Advancement of Science (AAAS)
Version
Author's final manuscript
Abstract
2017 © The Authors. 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.”
MIT Department
Massachusetts Institute of Technology. Department of Economics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1126/SCIENCE.AAR5045
