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Using AI algorithms can exacerbate health disparities if care or resources are allocated away from underserved populations. We evaluated an algorithm for its potential to worsen health disparities across different clinical use cases.

Missed healthcare appointments, or "Did Not Attends" (DNAs), pose challenges for healthcare systems. Technology-facilitated reminder systems, such as SMS and phone calls are often implemented to improve attendance. This scoping review aimed to map the evidence on patient and carer perspectives and experiences of technology-enabled appointment reminders in healthcare and, secondly, examine how equity-relevant characteristics were reported.

The 2025 American College of Medical Informatics (ACMI) Symposium, themed Charting the Future of ACMI, provided a forum for fellows to reflect on the 2020-2025 ACMI Strategic Plan and define future priorities. Discussions centered on: (1) mobilizing ACMI's expertise to guide the responsible use of artificial intelligence (AI) in medical informatics, (2) advancing informatics education and mentorship across career stages, and (3) strengthening local, national, and global interdisciplinary partnerships. Proposed [...]

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 [...]