Description
- Abstract:
- We combine 2021–2024 data on artificial intelligence (AI) adoption across U.S. shortterm general hospitals with national measures of hospital finances, volume, employment, and measured quality. Using synthetic difference-in-differences, we find that AI adoption is followed by approximately 3% higher net patient revenue, 3% higher total paid hours, and 7% higher patient volume. Total and clinical expenses also rise. By contrast, estimates for administrative expenses, administrative hours, and employee full-time equivalents are imprecise under inference clustered at the hospital-system level. Measured risk-adjusted mortality declines for several conditions, but unadjusted mortality and claims-based clinical-process measures do not show corresponding improvements, while documented severity increases. The results therefore point most clearly to operational expansion, throughput, and richer documentation; they do not establish administrative cost savings, per-unit productivity gains, or lower underlying mortality.
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Citation
Daniel R. Arnold, Jonathan Cantor, and Christopher M. Whaley,
"AI Adoption, Hospital Throughput, and Employment"
(2026).
Center for Advancing Health Policy through Research (CAHPR) Digital Collection.
Brown Digital Repository. Brown University Library.
https://doi.org/10.26300/mc71-av50
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Center for Advancing Health Policy through Research (CAHPR) Digital Collection
This collection contains research outputs produced by members of the Center for Advancing Health Policy through Research (CAHPR). Collection DOI: https://doi.org/10.26300/mshb-sp27...