Skip to main content
NEW
On Demand icon On Demand

The Algorithm Will See You Now: Policy Essentials for AI in Healthcare

Type
Policy
The American Medical Informatics Association (AMIA) is excited to announce a partnership with Washington University in St. Louis School of Medicine (WashU Medicine) to enhance US policymakers' expertise around AI’s impact on healthcare. This one-day course, taught by leading health informatics educators, aimed to equip legislators and their staff to [...]

Members Only

On Demand icon On Demand

Biomedical Informatics Career Journeys in the Pharmaceutical Industry

Type
Careers in Informatics
Presented by the Industry Partnership Council Merck & Co., Inc. is a leading pharmaceutical company that is focused on advancing science to deliver medicines and vaccines that save lives. This panel will focus on different ways biomedical and health informatics experts contribute to this important mission. Specifically, panelists will discuss [...]
On Demand icon On Demand

25x5 Spotlight on Solutions: Real-World Wins in Reducing Documentation Burden

Type
Member Interests
Join us for a dynamic 25x5 webinar highlighting innovative, real-world approaches to reducing documentation burden in healthcare. This special session features select poster presentations recognized as 25x5 Stars in Reducing Documentation Burden at the 2025 AMIA Clinical Informatics Conference. Each presenter will share their impactful work in a rapid, engaging [...]
On Demand icon On Demand

JAMIA Journal Club Webinar - July 2025

Type
JAMIA Journal Club
Credits
1.00
CME
1.00
CNE
Alert Design in The Real World: A Cross-Sectional Analysis of Interruptive Alerting at 9 Academic Pediatric Health Systems Read the abstract Moderator Presenter Statement of Purpose Design elements critical for CDS have been recommended in the literature. However, adherence to these recommendations is not clear. In this work, we assessed [...]
On Demand icon On Demand

JAMIA Journal Club Webinar - June 2025

Type
JAMIA Journal Club
Credits
1.00
CME
1.00
CNE
Large Language Models Are Less Effective at Clinical Prediction Tasks Than Locally Trained Machine Learning Models Read the abstract Moderator Presenter Statement of Purpose Over the past several decades, medicine has been increasingly aided by artificial intelligence (AI) and particularly machine learning (ML). While model development has advanced, it is [...]