Search AMIA.org

This study aims to develop and evaluate an approach using large language models (LLMs) and a knowledge graph to triage patient messages that need emergency care. The goal is to notify patients when their messages indicate an emergency, guiding them to seek immediate help rather than using the patient portal, to improve patient safety.

Despite their potential, Clinical Decision Support (CDS) systems often lack alignment with clinicians' needs and are underutilised in practice. Pilot implementations can help to improve the fit between systems and local needs by engaging users in real-world testing and refinement. Although pilot implementations of CDS have been reported, limited evidence has explored the factors contributing to pilot success.

The objective was to understand the association between people with adequate and inadequate health literacy (HL) in the All of Us cohort.

Nursing documentation burden is a growing point of concern in the United States healthcare system. Documentation in the electronic health record (EHR) is a contributor to perceptions of burden. Efficiency tools like flowsheet macros are one development intended to ease the burden of documentation.

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.

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 [...]

Observational data have been actively used to estimate treatment effect, driven by the growing availability of electronic health records (EHRs). However, EHRs typically consist of longitudinal records, often introducing time-dependent confounding that hinder the unbiased estimation of treatment effect. Inverse probability of treatment weighting (IPTW) is a widely used propensity score method since it provides unbiased treatment effect estimation and its derivation is straightforward. In this study, we aim to [...]