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Large language models (LLMs) have shown potential in biomedical applications, leading to efforts to fine-tune them on domain-specific data. However, the effectiveness of this approach remains unclear. This study aims to critically evaluate the performance of biomedically fine-tuned LLMs against their general-purpose counterparts across a range of clinical tasks.

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 [...]

To assess the prevalence of recommended design elements in implemented electronic health record (EHR) interruptive alerts across pediatric care settings.

Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to bridge the gap between these 2 categories by building on modern interpretable machine learning (ML) techniques to design interpretable mortality risk scores that are as accurate as black boxes.

To develop a distributed algorithm to fit multi-center Cox regression models with time-varying coefficients to facilitate privacy-preserving data integration across multiple health systems.

We developed and validated a large language model (LLM)-assisted system for conducting systematic literature reviews in health technology assessment (HTA) submissions.

Modernizing and strengthening the US public health data and information infrastructure requires a strong public health informatics (PHI) workforce. The study objectives were to characterize existing PHI specialists and assess informatics-related training needs.

Digital health research involves collecting vast amounts of personal health data, making data management practices complex and challenging to convey during informed consent.

With the global population aging and advancements in the medical system, long-term care in healthcare institutions and home settings has become essential for older adults with disabilities. However, the diverse and scattered care requirements of these individuals make developing effective long-term care plans heavily reliant on professional nursing staff, and even experienced caregivers may make mistakes or face confusion during the care plan development process. Consequently, there is a rigid [...]

To describe the prevalence of and trends in practices that interfere with the exchange of patient health information (potential information blocking) 2 years after implementation of information-blocking regulations.