Automatic uncovering of patient primary concerns in portal messages using a fusion framework of pretrained language models.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Aug 1, 2024

Abstract

OBJECTIVES: The surge in patient portal messages (PPMs) with increasing needs and workloads for efficient PPM triage in healthcare settings has spurred the exploration of AI-driven solutions to streamline the healthcare workflow processes, ensuring timely responses to patients to satisfy their healthcare needs. However, there has been less focus on isolating and understanding patient primary concerns in PPMs-a practice which holds the potential to yield more nuanced insights and enhances the quality of healthcare delivery and patient-centered care.

Authors

  • Yang Ren
    Department of Computer Science, University of South Carolina, Columbia, SC, United States.
  • Yuqi Wu
    Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, T6G 2R3, Canada.
  • Jungwei W Fan
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, MN, USA.
  • Aditya Khurana
    Department of Radiation Oncology, Mayo Clinic, Rochester, MN 55905, United States.
  • Sunyang Fu
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.
  • Dezhi Wu
    UofSC Big Data Health Science Center (BDHSC), University of South Carolina, Columbia, SC, United States.
  • Hongfang Liu
    Department of Artificial Intelligence & Informatics, Mayo Clinic, Rochester, MN, United States.
  • Ming Huang
    College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.

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