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The use of ambient AI documentation tools is rapidly growing in US hospitals and clinics. Such tools generate the first draft of clinical notes from scribed patient-provider conversations, which clinicians can then review and edit before signing into electronic health records (EHR). Understanding how and why clinicians make modifications to AI-generated drafts is critical to improving AI design and clinical efficiency, yet it has been under-studied. This study aims to [...]

Biobanks are essential for intelligent medicine but face fragmentation and heterogeneity. No standardized framework exists for assessing biobank data value using public information; this study addresses this gap.

Using 15 years of hospital survey data (2008-2023), we examined rural-urban trends in electronic health records (EHR) adoption and advanced capabilities for data sharing, public health reporting, and care improvements.

This review aims to identify the contribution of user experience features and underlying technical features to sustained engagement in unguided chatbots for improving health-related behaviors.

Charles Friedman's Fundamental Theorem of Biomedical Informatics holds that a person working in partnership with an information resource outperforms that same person unassisted. Since its publication, advances in artificial intelligence (AI), adaptive learning systems, and large-scale data infrastructures have transformed the biomedical ecosystem, extending informatics beyond clinical care into domains such as public health, consumer health, translational science, and the broader life sciences. Such expansion has further underscored the importance [...]

This study examined whether attendance at online digital health research appointments in the American Women Assessing Risk Epidemiologically (AWARE) study was associated with (1) participant age, (2) scheduling factors (time of day, day of week, month), (3) appointment confirmation, and (4) HIV behavioral risk factors.

Traditional readmission risk models relying on static discharge data have limited predictive performance and fail to capture patients' recovery trajectories after hospitalization. We sought to identify optimal modeling parameters for dynamically predicting readmission risk using post-discharge step-count data from remote monitoring devices.

Phase II of MVP-CHAMPION, a federal collaboration between the Veterans Affairs Healthcare System (VA) and the Department of Energy (DoE), leveraged large-scale clinical, geo-spatial, and genetic data with state-of-the-art artificial intelligence (AI), and high-performance computing (HPC) to improve value in healthcare.

This study evaluated the optimization of a medication-related clinical decision support system (MRCDSS) in a 1000-bed general hospital in China by analyzing clinician responses.