%0 Journal Article
%T Zero-shot learning to extract assessment criteria and medical services from the preventive healthcare guidelines using large language models.
%A Luo X
%A Tahabi FM
%A Marc T
%A Haunert LA
%A Storey S
%J J Am Med Inform Assoc
%V 0
%N 0
%D 2024 Jun 20
%M 38900185
%F 7.942
%R 10.1093/jamia/ocae145
%X <B>OBJECTIVE: </B>The integration of these preventive guidelines with Electronic Health Records (EHRs) systems, coupled with the generation of personalized preventive care recommendations, holds significant potential for improving healthcare outcomes. Our study investigates the feasibility of using Large Language Models (LLMs) to automate the assessment criteria and risk factors from the guidelines for future analysis against medical records in EHR.<BR><B>METHODS: </B>We annotated the criteria, risk factors, and preventive medical services described in the adult guidelines published by United States Preventive Services Taskforce and evaluated 3 state-of-the-art LLMs on extracting information in these categories from the guidelines automatically.<BR><B>RESULTS: </B>We included 24 guidelines in this study. The LLMs can automate the extraction of all criteria, risk factors, and medical services from 9 guidelines. All 3 LLMs perform well on extracting information regarding the demographic criteria or risk factors. Some LLMs perform better on extracting the social determinants of health, family history, and preventive counseling services than the others.<BR><B>CONCLUSIONS: </B>While LLMs demonstrate the capability to handle lengthy preventive care guidelines, several challenges persist, including constraints related to the maximum length of input tokens and the tendency to generate content rather than adhering strictly to the original input. Moreover, the utilization of LLMs in real-world clinical settings necessitates careful ethical consideration. It is imperative that healthcare professionals meticulously validate the extracted information to mitigate biases, ensure completeness, and maintain accuracy.<BR><B>CONCLUSIONS: </B>We developed a data structure to store the annotated preventive guidelines and make it publicly available. Employing state-of-the-art LLMs to extract preventive care criteria, risk factors, and preventive care services paves the way for the future integration of these guidelines into the EHR.