Search AMIA.org

To compare patient trust in educational health contentand the physician providing it, when authorship is attributed to a physician or generative artificial intelligence (AI).

: Although interest in using sensors in long-term care (LTC) settings is growing, little guidance exists on how to engage stakeholders in planning. This study aimed to (1) develop a systematic, community-engaged process for co-designing sensorization plans - defining goals and parameters (e.g., data collection, sharing) to support decision making prior to sensor deployment, (2) apply this process with LTC stakeholders, and (3) examine stakeholder perspectives across sensor approaches.

This retrospective cohort study evaluated the effect of an artificial intelligence (AI) scribe on patient instructions documentation in ambulatory internal medicine settings.

To evaluate whether increasing agentic architectural complexity via self-reflection or multiagent design improves clinical code status extraction (Do Not Resuscitate [DNR], Do Not Intubate [DNI], Comfort Measures Only [CMO]) across open-source large language models (LLMs) of varying capabilities.

To systematically review and characterize methodological heterogeneity in sepsis case detection using the Medical Information Mart for Intensive Care III (MIMIC-III) and eICU-CRD databases.

Smart healthcare innovations driven by Virtual Human Twin (VHT) technology enable continuous patient monitoring. However, standards to assess their stage of development or a unified framework for performance comparisons are still lacking. This study aims to quantitatively analyze VHT-related studies, evaluate their development levels, and align them to the VHT roadmap.

Ovarian cancer (OvCa) patients and caregivers face complex, evolving information needs, yet digital health platforms often lack tailoring. This study developed and validated computational personas to support a tailored recommender system (Health E-Librarian with Personalized Recommendations [HELPeR]) and address the cold-start problem, in which limited information about new users reduces the system's ability to generate relevant recommendations.

To investigate the longitudinal effects of an ambient artificial intelligence (AI) scribe use in primary care on outcomes related to documentation time, note writing patterns, and clinician financial productivity.