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OBJECTIVE: Assess inter-rater agreement on clinician- and clinical decision support (CDS)-driven diuretic titrations and risk classifications to benchmark a semi-autonomous CDS (OTTO-FM).

OBJECTIVES: The objective of this study is to evaluate the effect of a continuous remote patient monitoring (CRPM) program on reducing 30-day readmissions in a case-versus-retrospective-propensity-matched-control study of heart failure (HF) patients.

BACKGROUND: Patient portals are crucial for successful transition after hospitalization, especially for cancer patients with complex treatment plans.

BACKGROUND: No studies have examined the effect of artificial intelligence (AI)-generated hospital courses on discharge summary documentation time.

BACKGROUND: Electronic health record (EHR) clinical decision support (CDS) systems generate excessive, non-contextual alerts, causing clinician desensitization with override rates exceeding 90%. This alert fatigue disrupts workflows, contributes to burnout, and creates patient safety risks. Traditional survey-based methods fail to capture real-time end-user feedback at scale.

BACKGROUND: The COVID-19 pandemic accelerated the adoption of digital health technologies, including telehealth, electronic medical records, and patient-facing applications. These tools maintained access to care during periods of restriction but exposed gaps in workforce readiness, patient trust, digital literacy, and equity. Despite widespread implementation, limited evidence exists on how patients and health care providers (HCPs) experienced this transition and how these experiences influenced digital health adoption.

[This corrects the article DOI: 10.1093/jamiaopen/ooag100.].

BACKGROUND: Pharmacogenetics is increasingly recognized as essential for optimizing drug therapy and reducing preventable adverse drug events. However, applying pharmacogenetic data in real-world clinical decisions remains challenging. Traditional rule-based clinical decision support systems often fall short. Machine learning-based clinical decision support systems offer a more dynamic solution, combining pharmacogenetic variants, patient-specific clinical data, medication history, and guidelines to generate personalized treatment recommendations.

BACKGROUND: Large language models (LLMs) are rapidly transforming medical education, yet their performance in Allergy/Immunology remains insufficiently characterized. Furthermore, concerns regarding accuracy, consistency, and sensitivity to input format persist.