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TRUVACE RECORD VERSION
record: TRV-2026-1092
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-09-15T06:55:00.425205Z
status: published
lens: g_space
sector: health
headline: Hospital Artificial Intelligence Tools and Inpatient Utilization and Costs in Older Adults with Alzheimer's Disease and Related Dementias
dek: Background Hospitals are increasingly adopting artificial intelligence and machine learning (AI/ML) tools to support clinical decision making and care management. However, evidence on how hospital AI/ML adoption relates to inpatient utilization and spending among clinically complex populations remains limited. Older adults with Alzheimer's disease and related dementias (ADRD) experience higher rates of readmissions and potentially avoidable hospitalizations. Methods Cross-sectional study was conducted using 2023…
gain_title: Among Medicare fee-for-service beneficiaries aged 65+ with ADRD hospitalized in 2023, greater hospital adoption of patient-related AI/ML tools was associated with lower odds of frequent hospitalizations, 30-day readmissions, and preventable acute hospitalizations, with inpatient risk prediction tools also linked to no 
problem_title: (none)
trace_subject: (none)
gain_reading: Among Medicare fee-for-service beneficiaries aged 65+ with ADRD hospitalized in 2023, greater hospital adoption of patient-related AI/ML tools was associated with lower odds of frequent hospitalizations, 30-day readmissions, and preventable acute hospitalizations, with inpatient risk prediction tools also linked to no 
gain_evidence: Greater hospital adoption of patient-related AI/ML tools was associated with lower odds of frequent hospitalizations, 30-day readmissions, and preventable acute hospitalizations. | Inpatient risk prediction and high-risk outpatient identification tools were consistently associated with lower inpatient utilization. | Inpatient risk prediction was associated with lower total Medicare spending
problem_reading: (none)
problem_evidence: (none)
quick_read: A 2023 cross-sectional study of 340,509 Medicare fee-for-service beneficiaries aged 65 or older with Alzheimer's disease and related dementias examined whether hospital adoption of patient-related AI/ML tools was associated with inpatient utilization and spending, using four adoption indicators for predicting inpatient risks, identifying high-risk outpatients, monitoring health, and recommending treatments.

The findings matter because ADRD patients have higher rates of readmissions and potentially avoidable hospitalizations, and the study suggests risk-prediction tools may lower utilization without increasing overall spending, but cost increases linked to specific tool types and the observational design leave uncertainty about causal effects and generalizability beyond the 2023 FFS hospitalized ADRD population.
limitation: Cross-sectional design examines associations in 2023 claims and cannot establish causality, and findings are limited to Medicare FFS beneficiaries aged 65+ with ADRD who had at least one hospitalization in 2023.
tag: Evidence-backed gain
key_points: Study used 2023 inpatient claims linked to Medicare Beneficiary Summary File and American Hospital Association Annual Survey IT Supplement. | Sample included 340,509 FFS beneficiaries aged 65 years or older with ADRD who had at least one inpatient hospitalization in 2023. | Hospital AI/ML adoption measured by four indicators: predict inpatient health risks, identify high-risk outpatients, monitor patient health, and recommend treatments. | Outcomes examined were frequent hospitalization, 30-day readmission, preventable acute and chronic hospitalizations, total Medicare payments, and beneficiary out-of-pocket spending.
rundown: The analysis linked 2023 Medicare FFS inpatient claims for 340,509 beneficiaries aged 65+ with ADRD to hospital-level AI/ML adoption data from the American Hospital Association IT Supplement, using four patient-related indicators and multivariable regression adjusting for beneficiary and hospital characteristics.

Results showed heterogeneity by tool function: inpatient risk prediction was associated with lower total Medicare spending, while high-risk outpatient identification was associated with higher Medicare spending, and treatment recommendation tools were associated with higher beneficiary OOP spending.
sources:
- peer_reviewed | Journal of the American Geriatrics Society | https://doi.org/10.1111/jgs.70704 | 2026-09-14
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