Operational AI Adoption in U.S. Inpatient Rehabilitation Facilities: Organizational Selection and Domain Heterogeneity, 2021-2024.

OBJECTIVE: To examine Inpatient Rehabilitation Facilities, market, and regional characteristics associated with operational AI adoption across three functional domains and breadth of use. DESIGN: Retrospective cohort study using multivariable linear probability models. SETTING: United States Inpatient Rehabilitation Facilities (IRFs). PARTICIPANTS: 276 IRFs (providing 766 hospital-years of data) that responded to the American Hospital Association (AHA) Annual Survey (mean response rate of 58%). INTERVENTIONS: Not applicable (observational study). MAIN OUTCOME MEASURE(S): Any operational AI use; domain-specific use (predicting patient demand, staff scheduling, and optimizing operational efficiency); breadth of adoption. RESULTS: Operational AI use more than doubled from 11.1% in 2021 to 24.9% in 2024, led by system-affiliated, non-profit, and teaching facilities. Organizational structure explained 50-67% of adoption variance, exceeding the combined role of market conditions, volume, and region. Optimizing operational efficiency was the dominant domain, reaching 20.0% by 2024, compared with 8.1% for predicting patient demand and 7.0% for staff scheduling. Staff scheduling declined after 2022, suggesting domain-specific implementation barriers beyond resource constraints. CONCLUSIONS: Operational AI in IRFs is spreading along the same organizational lines as earlier health IT, concentrating in system-affiliated, non-profit, and teaching facilities. Reaching smaller, independent, and for-profit IRFs will take targeted financing and shared infrastructure. Staff-scheduling tools lag the most, so wider use of these tools will depend on clear rules and clinician input on how algorithms guide staff schedules.