AI Adoption, Hospital Throughput, and Employmenttechnical_reportsWe 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.Daniel R. ArnoldAuthorJonathan CantorAuthorChristopher M. WhaleyAuthor2026-08-01artificial intelligencelabor substitutionhospital performanceWorking PaperAll rights reservedIn CopyrightAll Rights Reserved10.26300/mc71-av50