Applying generative AI with retrieval augmented generation to summarize and extract key clinical information from electronic health records.

Journal: Journal of biomedical informatics
PMID: 38880236

Abstract

BACKGROUND: Malnutrition is a prevalent issue in aged care facilities (RACFs), leading to adverse health outcomes. The ability to efficiently extract key clinical information from a large volume of data in electronic health records (EHR) can improve understanding about the extent of the problem and developing effective interventions. This research aimed to test the efficacy of zero-shot prompt engineering applied to generative artificial intelligence (AI) models on their own and in combination with retrieval augmented generation (RAG), for the automating tasks of summarizing both structured and unstructured data in EHR and extracting important malnutrition information.

Authors

  • Mohammad Alkhalaf
    Centre for Digital Transformation, School of Computing and Information Technology, University of Wollongong, Wollongong, Australia.
  • Ping Yu
    Beijing National Laboratory for Molecular Sciences, Key Laboratory of Analytical Chemistry for Living Biosystems, Institute of Chemistry, the Chinese Academy of Sciences (CAS), Beijing 100190, China.
  • Mengyang Yin
    Opal Healthcare, Sydney, Australia.
  • Chao Deng
    School of Mechanical Science & Engineering, Huazhong University Of Science & Technology, 1037 Luoyu Road, Wuhan, China. Electronic address: dengchao@hust.edu.cn.

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