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Despite advances in interoperability, patient-supplied medical histories commonly use paper-based forms. Dissatisfaction with this inefficiency has led to the "Kill the Clipboard" initiative from the Centers for Medicare & Medicaid Services to advance digital alternatives. During the coronavirus disease 2019 (COVID-19) pandemic, patient-controlled vaccination record sharing demonstrated that Quick Response (QR) codes and digital data standards provide an effective option. Building on this, several nations are using three standards to [...]

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.

Falls are the leading cause of injury and injury-related death among older adults. Clinical decision support (CDS) may improve clinician referral and patient access to fall prevention exercise.

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.