Search AMIA.org

This study aims to develop and validate of a span-based annotation framework for clinical named entity recognition (NER) using large language models (LLMs) based on Korean emergency department clinical notes.

Identifying social determinants of mental health (SDOMH) in patients with opioid use disorder (OUD) is crucial for estimating risk and enabling early intervention. Extracting such data from unstructured clinical notes is challenging due to annotation complexity and requires advanced natural language processing (NLP) techniques. We propose the Human-in-the-Loop Large Language Model Interaction for Annotation (HLLIA) framework, combined with a Multilevel Hierarchical Clinical-Longformer Embedding (MHCLE) algorithm, to annotate and predict SDOMH [...]

This study describes the utilization and experiences of artificial intelligence (AI)-generated draft responses to patient messages in pediatric ambulatory clinicians and contextualizes their experiences in relation to those of adult specialty clinicians.

To evaluate large language models (LLMs) for extracting temporal relations from pediatric rare disease clinical reports to enable automated patient timeline creation.

To evaluate effectiveness of open-source generative models in producing high-quality tabular synthetic data using a Health and Demographic Surveillance System (HDSS) dataset from rural Kenya, as a proof of concept in a low- and middle-income (LMIC) setting.

This study evaluates and enhances language access services for Limited English Proficiency (LEP) patients in a large urban health system by integrating interpreter services into the Electronic Health Record (EHR), aiming to reduce care disparities and improve the digital experience for both patients and clinicians.

In a sample of patients with diagnosed alcohol use disorder (AUD), we examined the prevalence of both stigmatizing (eg, "alcohol abuse") and highly stigmatizing, non-medical descriptors (eg, "alcoholic") documented in electronic health records (EHRs). We also assessed whether the use of these terms-entered by clinicians when documenting AUD using search tools and picklists-varied across patient subgroups within a regional health system.

To present the Artificial Intelligence-Ready Mount Sinai (AIR·MS) platform-unified access to diverse clinical datasets from the Mount Sinai Health System (MSHS), along with computational infrastructure for AI-driven research and demonstrate its utility with 3 research projects.

To evaluate the transcription accuracy of 6 German-capable speech-to-text (STT) systems in simulated emergency medical services (EMS) environments, focusing on clinically relevant performance under noisy and multilingual field conditions.