We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
ARAB HEALTH - INFORMA

Download Mobile App




AI Can Prioritize Emergency Department Patients Requiring Urgent Treatment

By HospiMedica International staff writers
Posted on 13 May 2024
Print article
Image: AI can be as good as a physician at prioritizing which patients need to be seen first (Photo courtesy of 123RF)
Image: AI can be as good as a physician at prioritizing which patients need to be seen first (Photo courtesy of 123RF)

Emergency departments across the world are facing severe overcrowding and excessive demands, but a new study indicates that artificial intelligence (AI) might soon assist in prioritizing patients who require urgent treatment. This research has shown that AI can match the performance of physicians in determining which patients should be seen first.

In this study, researchers at UC San Francisco (San Francisco, CA, USA) utilized anonymized data from 251,000 adult emergency department (ED) visits to test the effectiveness of an AI model. This AI was tasked with extracting and interpreting symptoms from clinical notes to assess the immediacy of patients' treatment needs. The AI's assessments were then compared to the Emergency Severity Index—a 1-5 scale used by ED nurses to triage incoming patients according to the urgency of their conditions. For privacy, the data used were de-identified. The AI technology employed was the ChatGPT-4 large language model (LLM), accessed through UCSF's secure generative AI platform, equipped with extensive privacy measures. To evaluate the AI, researchers used a set of 10,000 matched pairs, totaling 20,000 patients, where each pair consisted of one patient with a severe condition like a stroke and another with a less critical issue such as a broken wrist.

The AI was successful in identifying the more severely ill patient in each pair 89% of the time based solely on symptom data. A focused comparison in a smaller subset of 500 pairs, which also involved physician evaluation, showed the AI's accuracy at 88%, slightly higher than the physician's 86%. Integrating AI into the triage process could potentially alleviate the burden on physicians, allowing them to concentrate on treating the most critical cases and providing a supportive decision-making tool for clinicians handling multiple urgent cases simultaneously. This study stands out as it is among the few that test an LLM with real-world clinical data instead of simulations and is the first to use data from over 1,000 clinical cases and to focus on emergency department visits, where patients present a wide range of medical issues.

“Imagine two patients who need to be transported to the hospital but there is only one ambulance. Or a physician is on call and there are three people paging her at the same time, and she has to determine who to respond to first,” said lead author Christopher Williams. “Upcoming work will address how best to deploy this technology in a clinical setting.”

Related Links:
UC San Francisco

Gold Member
POC Blood Gas Analyzer
Stat Profile Prime Plus
Gold Member
STI Test
Vivalytic Sexually Transmitted Infection (STI) Array
New
Electric Cast Saw
CC4 System
New
Mobile Power Procedure Chair
LeMans P360

Print article

Channels

Surgical Techniques

view channel
Image: (Left) An image of a 3D-printed material implanted in vivo for 4 weeks. (Right) A photo of coral (Photo courtesy of Dr Zhidao Xia and Jesus Cobaleda)

Revolutionary Coral-Inspired Material for Bone Repair Promotes Faster Healing

Bone defects caused by fractures, tumors, and non-healing injuries are major contributors to disability worldwide. Traditionally, doctors have used either a patient’s own bone (autograft) or donor bone... Read more

Patient Care

view channel
Image: The portable biosensor platform uses printed electrochemical sensors for the rapid, selective detection of Staphylococcus aureus (Photo courtesy of AIMPLAS)

Portable Biosensor Platform to Reduce Hospital-Acquired Infections

Approximately 4 million patients in the European Union acquire healthcare-associated infections (HAIs) or nosocomial infections each year, with around 37,000 deaths directly resulting from these infections,... Read more

Health IT

view channel
Image: First ever institution-specific model provides significant performance advantage over current population-derived models (Photo courtesy of Mount Sinai)

Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients

Machine learning algorithms have been deployed to create predictive models in various medical fields, with some demonstrating improved outcomes compared to their standard-of-care counterparts.... Read more

Point of Care

view channel
Image: The acoustic pipette uses sound waves to test for biomarkers in blood (Photo courtesy of Patrick Campbell/CU Boulder)

Handheld, Sound-Based Diagnostic System Delivers Bedside Blood Test Results in An Hour

Patients who go to a doctor for a blood test often have to contend with a needle and syringe, followed by a long wait—sometimes hours or even days—for lab results. Scientists have been working hard to... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.