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This study compares the contents of two data standards; the Observational Medical Outcomes Partnership (OMOP) and Fast Healthcare Interoperability Resources (FHIR), highlighting their strength and weaknesses and serve as an initial step toward understanding how each standard supports secondary data analysis.

Evaluate the impact of ambient listening AI scribe technology for drafting notes on patient-reported communication and satisfaction ratings of their physician during ambulatory visits.

This study aimed to characterize patterns of digital health technology (DHT) use and examine the relationships between multimorbidity, DHT adoption, and user-reported frustration, with a focus on identifying socioeconomic and phenotypic determinants of digital health burden.

We aimed to develop a data model and a natural language processing (NLP) pipeline for representing physical activity (PA) in Electronic Health Records (EHRs), and to evaluate transformer- and Large Language Model (LLM)-based classifiers for sentence-level PA attribute classification.

Stigma impacts outcomes across stigmatizing conditions, including substance use disorders (SUDs). Recent policy changes give patients rapid access to clinical notes in the electronic health record (EHR), which may include stigmatizing language. The objective of this study was to assess the perspectives of women with history of pregnancy and SUDs on typical language used in clinical notes.

To demonstrate a large-scale EHR data transformation to the Observational Medical Outcomes Partnership (OMOP) Common Data model in the OCHIN network to support AI/ML analyses within the AIM-AHEAD national research consortium. We highlight customized workflows and infrastructure development processes designed to support OCHIN database use by AI/ML researchers of varied academic backgrounds and skillsets.

Generative information extraction using large language models (LLMs), particularly through prompting combined with few-shot learning, has become a popular method. In many ways such prompts with examples resemble the annotation guidelines long used for manual labeling of data for information extraction, and indeed studies have demonstrated the direct use of these guidelines as effective prompts. However, constructing annotation guidelines is both labor- and knowledge-intensive. Instead, this paper proposes to leverage [...]

To leverage the evaluation of the environmental impact of the Direct AP-HP/Lorah e-referral service offered in Paris (France) hospitals to create recommendations for evaluating the impact of digital health services.

To evaluate the clinical applications and translation readiness of model-based synthetic tabular data in healthcare, and identify gaps in governance reporting that may hinder translation.

People with type 1 diabetes mellitus (T1DM) show glucose variability driven by insulin dosing, meals, activity, and circadian rhythms. Many deep learning approaches treat glucose forecasting and hypoglycemia detection as separate tasks and provide limited transparency.