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This study aims to compare the effectiveness of 2 ambient AI scribe technologies in reducing physician burnout, improving workflow satisfaction, and enhancing documentation efficiency through a randomized crossover trial.

To develop and evaluate a human-LLM (Large Language Model) collaborative approach for systematic ontology updating, demonstrated with the Dietary Lifestyle Ontology (DILON).

Research on artificial intelligence (AI)-based clinical decision-support (AI-CDS) systems has returned mixed results. Sometimes providing AI-CDS to a clinician will improve decision-making performance, sometimes it will not, and it is not always clear why. This scoping review seeks to clarify existing evidence by identifying clinician-level and technology design factors that impact the effectiveness of AI-assisted decision-making in medicine.

To detail patient challenges, and how technology support addressed them, in a remote patient activation intervention for hemodialysis patients (n = 93) from trained patient mentors (n = 26).

We present a set of definitions and a conceptual model to support primary and consulting physician schedule integration into the electronic health record (EHR) in an inpatient setting and show how utilization of this functionality supports patient-centered communication.

Physician and advanced practice provider (APP) well-being is a critical focus in healthcare. Emerging technology such as generative artificial intelligence (GAI) scribes reduces physician and APP administrative burden created by electronic health records. Early adopters of this technology have demonstrated promising improvements in clinical documentation, well-being, and cognitive load. However, further exploration across professional roles is warranted.

Our study aims to assess the time-cost burden reduction of transitioning from manual case reporting to electronic case reporting (eCR) for COVID-19 among healthcare organizations (HCOs) over a 1-year period.