Telehealth Scale and Artificial Intelligence Adoption Tiers Across Clinical and Operational Domains in US Hospitals: Cross-Sectional Study

Clinical Snapshot

Key Findings

• P Value: Not applicable (machine learning framework)

• Effect Size: Steep increase in predicted clinical AI adoption at lower telehealth volumes, plateauing at higher volumes

• Primary Outcome: Telehealth volume was strongest predictor of both clinical and operational AI adoption tiers

• Nnt Or Sensitivity: 91.4% of hospitals with no clinical AI adoption were telehealth nonreporters

• Confidence Interval: Not reported (machine learning approach)

Clinical Application

Telehealth scale may serve as practical marker for AI readiness assessment in hospital settings Limited direct applicability due to different healthcare structure, though principles may inform Australian Digital Health Strategy and telehealth expansion under Medicare Benefits Schedule Hospital administrators and health system leaders planning digital transformation initiatives

Abstract

BACKGROUND: Telehealth expansion and artificial intelligence (AI) adoption are often described as parallel dimensions of health system digital transformation. However, whether telehealth scale is associated with hospital AI adoption and whether this relationship varies across hospital settings remain unclear. OBJECTIVE: This study examined the association of telehealth scale with clinical and operational AI adoption tiers in US hospitals and assessed whether these patterns differed by telehealth reporting behavior and geography. METHODS: This cross-sectional study included 6173 US acute care hospitals using linked 2024 American Hospital Association Annual Survey and Information Technology Supplement data and 2023 Healthcare Cost Report Information System data. Telehealth scale was parameterized using log-transformed telehealth volume, a telehealth nonreporting indicator, and a reported-zero telehealth indicator. Clinical and operational AI adoption tiers were derived from hospital-reported AI capability items and classified into 3 tiers. Both outcomes were modeled using multioutput gradient-boosted tree classifiers, and model behavior was interpreted using Shapley additive explanations, partial dependence plots, and stratified analyses by the Core-Based Statistical Area category. RESULTS: Telehealth volume was the strongest predictor of both clinical and operational AI adoption tiers and had a larger contribution to the clinical AI model. Telehealth nonreporting was common, occurring in 57% (3521/6173) of hospitals, and was concentrated among hospitals in the lowest clinical AI adoption tier, accounting for 91.4% (3145/3441) of hospitals with no reported clinical AI adoption. Higher telehealth volume was associated with a steep increase in predicted clinical AI adoption tiers at lower telehealth volumes, followed by a plateau at higher volumes. At similar telehealth volumes, rural hospitals showed weaker telehealth-attributed contributions to predicted clinical AI adoption tiers than metropolitan hospitals. Supplementary analyses suggested that telehealth reporting status and telehealth intensity reflected related but distinct structural processes. CONCLUSIONS: Telehealth scale was strongly associated with hospital AI adoption tiers, especially clinical AI adoption tiers. These findings suggest that telehealth capacity may serve as a practical hospital-level marker of broader digital readiness for AI adoption, but the cross-sectional design does not establish whether telehealth expansion precedes or causes AI adoption. Hospitals with telehealth nonreporting and rural hospitals may face additional structural barriers that limit the translation of digital capacity into AI maturity. Policies to reduce inequities in hospital AI adoption may therefore need to pair telehealth expansion with implementation support, interoperability capacity, and organizational resources.

References

1. 1.Liu, L. (2026). Telehealth scale and artificial intelligence adoption tiers across clinical and operational domains in US hospitals: Cross-sectional study. Journal of Medical Internet Research, 28(6), e96762. https://doi.org/10.2196/96762