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Health-related social needs (HRSN) significantly influence health outcomes, yet healthcare organizations face persistent challenges tracking referrals to community-based organizations and interpreting referral outcomes across fragmented clinical and social care systems. Prior studies report low referral fulfillment rates, but much of this evidence derives from single organizations, manual data collection, or incentivized documentation workflows, limiting insight into how information infrastructure shapes what is observable at scale.

Patients living with dementia (PLWD) require attention to social determinants of health (SDoH), but social information is often unavailable or incomplete during care encounters, and clinicians report uncertainty about how to act on this information.

In our recent study we showed that GPT's ability to perform OPS-code extraction from operational reports was equivalent to coding neurosurgeons. In this study we aim to evaluate the effect of context enhancement on GPT-5's code extraction abilities.

Hospital artificial intelligence (AI) is increasingly embedded in electronic health record workflows, cloud inference pipelines, imaging, medication review, triage, early warning, documentation, and operational management. This paper proposes a risk-tiered governance framework and implementation pathway for hospital AI applications that are integrated into clinical and operational workflows.

This study evaluated the effect of a Spatial Awareness Integrated Electronic Health Record (SAI-EHR) prototype on efficiency, usability, and cognitive workload among intensive care nurses performing standardized documentation tasks, compared with a traditional linear flowsheet-style interface (LID-EHR).

Clinical documentation consumes substantial clinician time, potentially detracting from patient care. Generative artificial intelligence (AI) may support drafting discharge summaries and patient referral documents, but feasibility in non-Western-language oncology settings using real-world electronic health record (EHR) data remains insufficiently evaluated. This study assessed feasibility in a Japanese cancer hospital using an enterprise AI system.

Interactive digital self-management (IDSM) interventions may help individuals with chronic respiratory disease self-manage their condition.

This study aimed to examine perspectives on challenges, design requirements, and implementation considerations for developing a granular, patient-facing consent tool that enables meaningful control over sensitive health data sharing in real-world healthcare settings.

Automated patient outreach (APO) approaches offer promising solutions to patient engagement for outstanding health care services. Little is known about APO effectiveness.

Interprofessional teams are central to high-quality patient care. However, identifying the daily frontline clinician who performs direct care-related tasks for a patient requires labor-intensive methodologies. Although electronic health record (EHR) audit logs offer a scalable alternative, their use for identifying frontline clinicians is underdeveloped.