环境式AI与人工文书员对急诊医师文书负担和临床生产力的影响：最新证据综述
Posted inClinical Updates Emergency Medicine Superintelligence

Medical Scribe and Ambient Artificial Intelligence Impact on Emergency Physician Documentation Burden and Clinical Productivity: A Contemporary Evidence Review

This review synthesizes recent evidence demonstrating that both human and ambient AI scribes reduce emergency physician documentation time without diminishing clinical productivity, highlighting evolving technology adoption and future research needs.
环境式 AI 记录员在急诊医师中的应用：多中心证据综合显示可减轻文书负担
Posted inClinical Updates Emergency Medicine Superintelligence

Ambient Artificial Intelligence Scribe Use and Objective Documentation Effort Among Emergency Physicians: A Multisite Evidence Synthesis

AI scribes reduce active documentation time among emergency physicians, delay note initiation without prolonging completion, and yield longer notes without increasing short-term readmissions, supporting efficient clinical documentation across diverse ED settings.
Evaluating Blood-Based Biomarkers GFAP and UCH-L1 for Diagnosing Intracranial Injury in Mild Traumatic Brain Injury Patients in the Emergency Department
Posted inClinical Updates Emergency Medicine Neurology

Evaluating Blood-Based Biomarkers GFAP and UCH-L1 for Diagnosing Intracranial Injury in Mild Traumatic Brain Injury Patients in the Emergency Department

This study validates plasma biomarkers GFAP and UCH-L1 as sensitive but nonspecific tools to help rule out intracranial injury in mild traumatic brain injury patients, potentially improving emergency department efficiency while highlighting the need for larger studies.
Derivation and Validation of Claims-Based Operational Definitions for Cardiogenic Shock: Enhancing Diagnostic Accuracy and Epidemiologic Surveillance
Posted inCardiology Clinical Updates Emergency Medicine news

Derivation and Validation of Claims-Based Operational Definitions for Cardiogenic Shock: Enhancing Diagnostic Accuracy and Epidemiologic Surveillance

This review synthesizes evidence on the derivation and validation of a claims-based algorithm for identifying cardiogenic shock (CS), demonstrating its high diagnostic performance and enabling robust large-scale epidemiologic and long-term outcome research.