Using large language model to guide patients to create efficient and comprehensive clinical care message.

Siru LiuAileen P WrightAllison B McCoySean S HuangJulian Z GenkinsJosh F PetersonYaa A Kumah-CrystalWilliam MartinezBabatunde CarewDara MizeBryan SteitzAdam Wright

Published in: Journal of the American Medical Informatics Association : JAMIA (2024)

LLMs can generate follow-up patient messages designed to clarify a medical question that compares favorably to those generated by healthcare providers.

Keyphrases

healthcare
end stage renal disease
ejection fraction
newly diagnosed
palliative care
prognostic factors
autism spectrum disorder
quality improvement
health insurance