Search AMIA.org

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.

Interpretive clinical artificial intelligence (AI), in which a clinician interprets an image or signal and an AI system offers a competing or confirming read, is increasingly routine in radiology, pathology, dermatology, and endoscopy. Oversight of these systems assumes that independent human judgment occurred before AI influence entered the decision. The informatics infrastructure, meaning the electronic health record, its audit logs, and the reporting systems built on them, does not verify [...]

Clinician trust in artificial intelligence (AI) is a frequently cited barrier to adoption, yet there is limited empirical evidence on how transparency features influence trust. This study aimed to address this evidence gap.

Electronic health record (EHR) audit logs capture clinician-EHR interaction patterns, but most audit log research relies on aggregated measures (eg, total time). We investigated how audit logs could be modeled using large language model (LLM) architectures to learn underlying workflow sequences during clinician-EHR interactions.

The objective of this study was to construct a harmonized SNOMED Clinical Terms (SNOMED CT) components quality assurance framework for identifying, categorizing, and assessing data quality issues in SNOMED CT components.

Design, implement, and evaluate a standards-based architecture for secure federated analytics across heterogeneous Trusted Research Environments (TREs) that aligns with the Five Safes Framework, without requiring TREs to modify their internal governance, infrastructure, or operational practices.