Health
Can AI accurately triage ER patients? UCSF study suggests yes
One of the first studies to test whether artificial intelligence can help triage patients in real-world emergency rooms, a new UCSF study suggests that AI could one day help doctors make one of the most important decisions in healthcare. , suggesting it could help decide who to provide emergency medical care to first.
The study, published Tuesday in JAMA Network Open, found that the AI model could accurately prioritize the sickest patients 89% of the time.
In previous research, AI model Although UCSF can accurately triage simulated patient scenarios and small numbers of patient cases, this is one of the first studies to examine patient data from thousands of real emergency patients, UCSF said.
The study analyzed data from approximately 251,000 adult patients who visited UCSF's emergency department between 2012 and 2023. The researchers randomly selected 10,000 of these patients and tasked OpenAI's GPT-4, an AI model, with determining which patients were more seriously unwell. . , based on symptoms noted by emergency physicians for each patient.
They then compared the AI analysis to a patient's urgency score, a number from 1 to 5 assigned by emergency nurses to allocate care according to the most urgent needs. The AI model correctly identified the sicker patient in 89% of the pairs.
AI technology is useful when two patients have a large difference in severity (for example, a stroke patient and a wrist fracture patient), and when two patients are similar (for example, a stroke patient and a wrist fracture patient). patients) were able to more accurately identify sicker patients. A patient with a broken arm and a patient with a broken wrist.
Research has found that the accuracy of AI models is comparable to that of human doctors. To compare the AI's performance with doctors, researchers took a subset of her 500 patient pairs, asked human doctors to identify patients with severe symptoms, and compared their results with the AI's. Did. The AI model was correct 88% of the time, and the doctor was correct 86% of the time.
Although the study's results are promising, it will still be some time before the technology used in the study can be used in practice, said the study's lead author, Dr. Christopher Williams.
It is unclear how this can be integrated into hospital workflows in real time. Additionally, previous research has shown that AI models can have inherent biases based on race and gender, so identify those biases from your programs before deploying them in clinical settings. He said it was important to remove them.
In the future, AI-assisted triage systems could allow doctors to treat the most critically ill patients first.
“As an early study, this is very promising,” Williams said. “If we can identify the most at-risk patients early, we can intervene and treat them early. That's definitely a good thing. (But) in that not all technologies are ready tomorrow. , these findings should be interpreted with caution.”
Contact Catherine Ho: [email protected]
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