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To build natural language processing (NLP) strategies to characterize measures of genetic counseling (GC) efficiency and classify measures according to phase of GC (pre- or post-genetic testing).

To guide research data services (RDS) teams in managing researcher variability (eg, differing deadlines, funding, expertise) when honest-brokering data, we present a framework based on operations management principles and a food service analogy.

Stigmatizing language (SL) in Electronic Health Records (EHRs) can perpetuate biases and negatively impact patient care. This study introduces a novel method for automatically detecting such language to improve healthcare documentation practices.

Electronic health records (EHRs) lack a widely adopted standard for recording transgender and gender diverse (TGD) status, complicating research on TGD health. Computational models have been developed to identify TGD individuals in EHRs; however, gaps remain in understanding which components contribute to stronger phenotyping approaches. This scoping review evaluates EHR-based models for identifying TGD individuals, focusing on identifier types, performance, external validation, and ethical reporting to guide best practices.

The use of real-world data (RWD) in artificial intelligence (AI) applications for healthcare offers unique opportunities but also poses complex challenges related to interpretability, transparency, safety, efficacy, bias, equity, privacy, ethics, accountability, and stakeholder engagement.

To identify a brief scale to accurately assess digital skills among older adults for use in identifying need for support to use digital health tools.

By October 1, 2024, over 450,000 COVID-19 manuscripts were published, with 10% posted as unreviewed preprints. While they accelerate knowledge sharing, their inconsistent quality complicates systematic studies.

Systematic reviews in comparative effectiveness research require timely evidence synthesis. With the rapid advancement of medical research, preprint articles play an increasingly important role in accelerating knowledge dissemination. However, as preprint articles are not peer-reviewed before publication, their quality varies significantly, posing challenges for evidence inclusion in systematic reviews.

Clinician implicit bias can impede patient-centered communication, leading to health care inequities. While the field of implicit bias education is evolving with advances in technology, clinicians' perspectives remain underexplored. This study investigated clinicians' perceptions of educational strategies to complement communication feedback technology in the implementation of an implicit bias education intervention.

This perspective explores how ambient artificial intelligence (AI) scribes could support documentation and quality improvement (QI) of structured, team-based provider-to-provider communication in acute care settings.