Journal of Urgent Care Medicine

Clinicians Rewrite Vast Majority of AI-Drafted Portal Messages

Researchers from Dartmouth found that when using large language models (LLMs) to draft responses to patient portal messages, clinicians may spend more time editing the AI responses than it would take to write them manually in the first place. Using more than 610,000 patient portal messages, the researchers created a training dataset of 144,000 clinician–patient conversations and tested multiple commercial and locally hosted LLMs against expert-written responses. In the study —which was presented at the …

LLMs Save Time For ED Discharge Notes

When using a large language model (LLM) assistant for generating emergency department (ED) discharge notes at an academic 2,400-bed tertiary hospital in South Korea, physicians gained some worthwhile efficiencies, according to a comparative effectiveness study published in JAMA Network Open. Six emergency physicians created 300 manual notes, 300 LLM drafts, and 300 LLM-assisted notes from 50 patient cases, which were judged on a Likert scale ranging from 1 to 5. Compared with manual documentation, LLM-assisted …

Will AI Discharge Summaries Help To Reduce Burnout?

AI-generated reports using large language models (LLMs) seem inevitable in healthcare, especially when it comes to the time-consuming task of writing up patient discharge summaries, a commentary post in JAMA Internal Medicine states. Right now, the responsibility of compiling summaries is one of the many administrative burdens leading to burnout among clinicians, and leveraging LLM tools could reduce that burden if done thoughtfully and with appropriate data integration. In a related article, researchers found no …

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