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Atrial fibrillation (AF) is common among intensive care unit (ICU) patients. Effective management of AF in this setting remains a subject of debate, with current guidelines often derived from outpatient studies. This study aims to evaluate the effectiveness of different AF management strategies-both, rhythm, rate, or no control-in reducing mortality in ICU patients using a deep learning-based causal inference model.

Describe the impact of synchronous vs asynchronous clinical decision support (CDS) on clinician behavior in a single-site randomized, controlled environment.

This study aims to automatically classify physical examinations performed during general practitioner (GP) consultations using a deep learning fusion model. The model distinguishes between two interaction types: Human-Object Activities (HOA), such as blood pressure measurement, and Human-Human Activities (HHA), such as gland palpation.

Increasingly, structured longitudinal electronic health records (EHRs) are being harnessed to predict risk of having present but as yet undetected disease by analyzing "patient trajectories." Trajectory studies explore clinical event associations, characterize disease trajectories, and enhance risk prediction. This scoping review assesses study characteristics and objectives, identifies model types, and appraises model performance and reporting.

To compare the clinical outcomes of sepsis patients when an augmented systemic inflammatory response syndrome (SIRS+) and the Epic sepsis predictive model version 1 (ESPMv1) alert were active in the emergency department at two county hospitals.

For every 30 minutes a provider spends seeing a patient, they spend 36 minutes charting in the electronic health record (EHR). Clinical documentation burden in U.S. healthcare is driven by increasing administrative tasks associated with EHRs, regulatory demands, and workflow inefficiencies. This burden contributes to increased cognitive load, fragmented care, and staff burnout. No comprehensive conceptual framework guides researchers addressing these challenges.

To examine how social media analytics have been applied in research on firearm injury exposure, with a focus on informatics approaches, analytical methodologies, and public health surveillance applications.

Artificial intelligence (AI) has impacted healthcare at urban and academic medical centers in the US. There are concerns, however, that the promise of AI may not be realized in rural communities. This scoping review aims to determine the extent of AI research in the rural US.

We propose Heterogeneity-aware Collaborative One-shot Lossless Algorithm for Generalized Linear Model (COLA-GLM-H), a novel one-shot lossless distributed algorithm that enables the integration of heterogeneous multi-institutional data while preserving patient privacy by avoiding patient-level data sharing.