EM Technology and AI Research & News
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Alcohol Intervention Meets AI Chatbot
Stanford researchers are using generative AI to help young adults recover from alcohol use disorder, through a collaboration between Dr. Brian Suffoletto and the Technology & Digital Solutions team.
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A Model for Geriatric Care in the ED
Emergency departments are seeing more older adult patients. Stanford’s new Level 1 Geriatric ED offers a scalable model that uses smart design, teamwork, and real-time data to improve care and efficiency.
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The Human Touch in the Age of AI
Christian Rose, MD, director of the Missingness in Action conference on missing data discusses the possibilities and pitfalls in AI automation and eavesdropping.
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Strategically Planning for the Future
Dr. Matthew Strehlow, Executive Vice Chair, shares how Stanford Emergency Medicine’s strategic plan brings together the university and hospital to advance care through true collaboration.
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Critical Care Above the Clouds
Alfredo Urdaneta, MD, guides the medical care for Stanford Life Flight transport, where in-flight, in-air conditions present unique challenges.
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Stanford EM Innovation Symposium
The fourth annual virtual Stanford EM Innovation Symposium (StEMI X) drew more than 750 registrants from 40 countries to hear from 30+ experts on AI and innovation in EM. Check out key takeaways and lessons learned.
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Crafting Precision Emergency Medicine
Stanford Emergency Medicine led the 2023 SAEM Consensus Conference on Precision Emergency Medicine, producing two landmark papers that chart a path toward a new era in emergency care.
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The Future of Emergency Medicine
Stanford Department of Emergency Medicine Chair Dr. Andra Blomkalns shares insight on the future of the specialty and the opportunities to lead innovation throughout medicine.
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Breaking the Virtual Care Barrier
Stanford’s Virtual Visit Track (VVT) in the emergency department revolutionizes patient care, enabling remote consultation by board-certified emergency medicine physicians, resulting in shorter stays, satisfied patients, and fewer return visits.
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Quality, Equity, and AI in Emergency Cardiac Care
Dr. Maame Yaa (Maya) Yiadom and her team tested an AI model against clinicians in detecting acute coronary syndrome, revealing screening gaps and the balance between human and AI judgment in precision care.
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Sepsis Test Results in Hours, Not Days
Samuel Yang, MD, associate professor of emergency medicine, is accelerating the diagnosis of bloodstream infections, including a novel approach to quickly determine the susceptibility of bacterial pathogens to antibiotics.
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Reading People's Faces
Stanford is exploring if a video algorithm can predict hospital admission likelihood by assessing patients’ visual cues, in a project co-led by Ryan Ribeira, MD, site principal investigator.
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Predicting Falls Post-ED
Dr. Brian Suffoletto and his team are using AI and digital tools to predict and prevent falls in older adults after they leave the emergency department.
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Precision Cardiac Care
Kenton Anderson, MD, and a national collaborative of transesophageal echocardiography (TEE) experts pioneer the use of TEE during cardiac arrest, providing real-time images of the heart’s aortic valve.
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Operation AI
Dev Dash, MD, aims to reshape emergency medicine operations through the use of large language models for admissions assessment, critical care cohorting, and more.
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What Digital Health Tells Us About Disease
Dr. Christine Ngaruiya uses Natural Language Processing to reveal gender gaps in noncommunicable diseases and leads global efforts linking health and climate change to drive policy and action.
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Patient Monitoring and Machine Learning
Dr. David Kim and his team are creating software that combines real-time EHR and monitor data to give clinicians clearer, more precise insights during emergency visits.
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How to Evaluate the Ethics of AI
A study led by Christian Rose, MD, and Jennifer Newberry, MD, JD explores the techno-ethical complexities of applying precision medicine in the volatile emergency care environment.
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Using AI to Save Lives in Rural Alaska
Brian Rice, MD, uses machine learning to analyze medevac utilization in remote areas of Alaska where air transport is the lifeline for emergencies.
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How digital tools are heading off alcohol-related health problems
Brian Suffoletto, MD, associate professor of emergency medicine, views interactions with patients in the Emergency Department as valuable opportunities to identify specific risks and then facilitate positive behavior changes post-ED discharge using digital devices.