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Characteristics of electronic health record (EHR) use associated with burnout in inpatient pediatric settings remain unclear. This study aims to (1) measure the relationship between subjective EHR experience and burnout and (2) identify objective EHR- and schedule-based measures associated with EHR experience and burnout.

We examined the intention of lung cancer patients to use virtual reality (VR) to support shared decision-making and treatment preparedness and identified predictors of this intention using the Unified Theory of Acceptance and Use of Technology (UTAUT).

This study evaluates the quality of estimated delivery dates (EDD) captured in electronic health record (EHR) systems, focusing on identifying and quantifying bias in these estimates for patients seeking prenatal care.

Comorbidity algorithms derived from International Classification of Diseases (ICD) codes are central to risk adjustment and cohort characterization, but existing software inconsistently handles mixed ICD-9/10 data and typically operates at the encounter level. We developed medicalcoder to address these limitations.

To describe the implementation process for the Precision Resuscitation with Crystalloids in Sepsis (PRECISE) trial, a pragmatic, multihospital randomized controlled trial (RCT) of a machine learning (ML) algorithm integrated into the electronic health record (EHR).

Large language models (LLMs) offer significant potential for automating clinical trial classification by eligibility criteria. However, the optimal input data remain unclear: while abstracts provide a condensed signal, full-text articles contain substantially more information. Whether this additional signal improves performance or whether accompanying noise negatively affects the model's reasoning capabilities remains unclear.

To extend the PyCox to accommodate time-varying covariates using counting process data, addressing immortal time bias in survival analysis.

Artificial intelligence (AI) adoption in global health informatics is accelerating, yet scaling, sustainability, equity, and environmental challenges limit impact, particularly in Low and Middle Income Countries (LMICs).

Modern biomedical data ecosystems increasingly rely on global cloud platforms to coordinate access to large-scale genomic and clinical datasets. However, operational governance remains largely investigator-centric, shifting the responsibility for complex security, compliance, and infrastructure management to individual laboratories. As data volumes and regulatory requirements expand, this approach fails to scale across the research enterprise. This disjointed approach creates a substantial governance burden and can slow down scientific progress. In centralized [...]

We provide an overview of the transformation of REACHnet's research-quality electronic health records (EHR) dataset into the Multi-State EHR-Based Network for Disease Surveillance (MENDS) dataset to support chronic disease surveillance.