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This study compared the time efficiency of the hospital admission process using personal mobile devices to traditional walk-in methods, thereby assessing the effectiveness of the mobile admission process.

This study aims to evaluate the impact of using a large language model (LLM) for generating draft responses to patient messages in the electronic health record (EHR) system on clinicians and support staff workload and efficiency.

Interruptive alerts in clinical decision support (CDS) systems are intended to guide clinicians in making informed decisions and adhering to best practices. However, these alerts can often become a source of frustration, contributing to alert fatigue and clinician burnout. Traditionally, alert burden is often assessed by evaluating total firing counts, which can overlook the true impact of highly interruptive workflows. This study demonstrates how an alert burden metric was used [...]

[This corrects the article DOI: 10.1093/jamiaopen/ooaf007.].

To determine if natural language processing (NLP) and machine learning (ML) techniques accurately identify interview-based psychological stress and meaning/purpose data in child/adolescent cancer survivors.

To explore patients' use of patient portals to access lab test results, their comprehension of lab test data, and factors associated with these.

This study aimed to develop and evaluate an artificial intelligence (AI)-driven system for forecasting Pediatric Emergency Department (PED) overcrowding and optimizing physician shift schedules using machine learning operations (MLOps).

To semantically enrich the laboratory data dictionary of the Study of Health in Pomerania (SHIP), a population-based cohort study, with LOINC to achieve better compliance with the FAIR principles for data stewardship.

Accurate, complete allergy histories are critical for decision-making and medication prescription. However, allergy information is often spread across the electronic health record (EHR); thus, allergy lists are often inaccurate or incomplete. Discrepant allergy information can lead to suboptimal or unsafe clinical care and contribute to alert fatigue. We developed an allergy reconciliation module within Mass General Brigham (MGB)'s EHR to support accurate and intuitive reconciliation of discrepancies in the allergy [...]