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This study aimed to describe the usage and perceptions of physicians and nurses in primary and specialty care on the acceptability, usability, and quality of artificial intelligence (AI)-generated draft replies to patient portal messages.

Ambulatory central line-associated bloodstream infections (A-CLABSIs) cause significant morbidity, are costly, and are a relatively new patient safety target. To calculate A-CLABSI rates, the total ambulatory line-day denominator must be known; however, few hospitals can measure this accurately. Access to this data is vital to the quality improvement process.

Intravenous (IV) and oral acetaminophen have equivalent bioavailability and effectiveness, but IV formulations are significantly more expensive. Clinical decision support (CDS) tools can encourage cost-effective prescribing. We evaluated the effectiveness and acceptability of different CDS strategies on prescribing patterns of acetaminophen.

Clinical data exchange remains fragmented and insufficient to support coordinated, safe care. Decades of technological progress, standards development, and interoperability-focused legislative and regulatory improvements have been incremental, underscoring the need for unifying initiatives to coordinate and improve operations and outcomes.

Clinicians frequently face questions that require rapid, evidence-based answers. Artificial intelligence (AI) tools are increasingly used for this purpose, yet their reliability for clinical decision-making remains uncertain.

A potential limitation of current data segmentation technologies is that pertinent contextual information accessible via the patient's electronic health records (EHRs) is not used to make segmentation inferences.

Protecting sensitive health information, particularly substance use data, is essential to preserving patient trust, improving care engagement, and ensuring compliance with federal confidentiality laws such as 42 CFR Part 2. Manual segmentation of sensitive data remains burdensome and error-prone, creating a need for scalable, automated, and patient-centered data segmentation technologies.

Physician burnout, often driven by documentation burden in electronic health records, remains a major challenge in clinical care. Ambient artificial intelligence (AI) scribes generate draft notes from clinical encounters, offering a scalable approach to reduce administrative workload, yet population-level evaluations remain limited.

This study aimed to develop and validate a targeted set of clinical rules embedded within a clinical decision support system to support safer antimicrobial dosing in patients with impaired renal function and to assist antimicrobial stewardship activities.

Parents of children with rare and serious illnesses often have unmet information needs. Large language models (LLMs) can help parents seek medical information. However, few studies have observed parents' use of LLMs or how they would use it in conjunction with their patient portal.