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Operational AI Adoption in U.S. Inpatient Rehabilitation Facilities: Organizational Selection and Domain
Arjun Teotia1, Zachary Henley2, Prabuddha Prakash3
1Henry Ford Health System, MI; Michigan State University, MI.
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.
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