Improving Large Language Models' Summarization Accuracy by Adding Highlights to Discharge Notes: Comparative Evaluation.

Journal: JMIR medical informatics
Published Date:

Abstract

BACKGROUND: The American Medical Association recommends that electronic health record (EHR) notes, often dense and written in nuanced language, be made readable for patients and laypeople, a practice we refer to as the simplification of discharge notes. Our approach to achieving the simplification of discharge notes involves a process of incremental simplification steps to achieve the ideal note. In this paper, we present the first step of this process. Large language models (LLMs) have demonstrated considerable success in text summarization. Such LLM summaries represent the content of EHR notes in an easier-to-read language. However, LLM summaries can also introduce inaccuracies.

Authors

  • Mahshad Koohi Habibi Dehkordi
    Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, United States.
  • Yehoshua Perl
    Dept of Computer Science, NJIT, Newark, NJ, USA.
  • Fadi P Deek
    Department of Informatics, New Jersey Institute of Technology, Newark, NJ, United States.
  • Zhe He
    School of Information, Florida State University, Tallahassee, FL, USA.
  • Vipina K Keloth
    Department of Biomedical Informatics and Data Science, Yale School of Medicine, Yale University, New Haven, CT, USA.
  • Hao Liu
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.
  • Gai Elhanan
    College of Computing, New Jersey Institute of Technology, Newark, NJ 07102-1982, USA.
  • Andrew J Einstein
    Division of Cardiology, Department of Medicine, Columbia University Medical Center and New York-Presbyterian Hospital, New York, New York; Department of Radiology, Columbia University Medical Center and New York-Presbyterian Hospital, New York, New York.