Ambient artificial intelligence scribes: utilization and impact on documentation time.

Objectives: To quantify utilization and impact on documentation time of a large language model-powered ambient artificial intelligence (AI) scribe.

Materials And Methods: This prospective quality improvement study was conducted at a large academic medical center with 45 physicians from 8 ambulatory disciplines over 3 months. Utilization and documentation times were derived from electronic health record (EHR) use measures.

Ambient artificial intelligence scribes: physician burnout and perspectives on usability and documentation burden.

Objective: This study evaluates the pilot implementation of ambient AI scribe technology to assess physician perspectives on usability and the impact on physician burden and burnout.

Materials And Methods: This prospective quality improvement study was conducted at Stanford Health Care with 48 physicians over a 3-month period. Outcome measures included burden, burnout, usability, and perceived time savings.

Transcription-independent TFIIIC-bound sites cluster near heterochromatin boundaries within lamina-associated domains in C. elegans.

Background: Chromatin organization is central to precise control of gene expression. In various eukaryotic species, domains of pervasive cis-chromatin interactions demarcate functional domains of the genomes. In nematode Caenorhabditis elegans, however, pervasive chromatin contact domains are limited to the dosage-compensated sex chromosome, leaving the principle of C.

Development and Validation of an Electronic Health Record-Based Machine Learning Model to Estimate Delirium Risk in Newly Hospitalized Patients Without Known Cognitive Impairment.

Importance: Current methods for identifying hospitalized patients at increased risk of delirium require nurse-administered questionnaires with moderate accuracy.

Objective: To develop and validate a machine learning model that predicts incident delirium risk based on electronic health data available on admission.

Design, Setting, And Participants: Retrospective cohort study evaluating 5 machine learning algorithms to predict delirium using 796 clinical variables identified by an expert panel as relevant to delirium prediction and consistently available in electronic health records within 24 hours of admission.