Generative Artificial Intelligence to Transform Inpatient Discharge Summaries to Patient-Friendly Language and Format.

Journal: JAMA network open
PMID: 38466307

Abstract

IMPORTANCE: By law, patients have immediate access to discharge notes in their medical records. Technical language and abbreviations make notes difficult to read and understand for a typical patient. Large language models (LLMs [eg, GPT-4]) have the potential to transform these notes into patient-friendly language and format.

Authors

  • Jonah Zaretsky
    Division of Hospital Medicine, Department of Medicine, NYU (New York University) Langone Health, New York, New York.
  • Jeong Min Kim
    Department of Physical Medicine and Rehabilitation, Korea University Guro Hospital, Korea University College of Medicine, 148, Gurodong-ro, Guro-gu, Seoul, 08308, Korea.
  • Samuel Baskharoun
    Department of Medicine, NYU Long Island School of Medicine, Mineola.
  • Yunan Zhao
    Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China.
  • Jonathan Austrian
    Division of Hospital Medicine, Department of Medicine, NYU (New York University) Langone Health, New York, New York.
  • Yindalon Aphinyanaphongs
    Department of Population Health, New York University, New York.
  • Ravi Gupta
    Department of Orthopaedics, Government Medical College and Hospital, Chandigarh, India.
  • Saul B Blecker
    Division of Hospital Medicine, Department of Medicine, NYU (New York University) Langone Health, New York, New York.
  • Jonah Feldman
    Department of Medicine, NYU Long Island School of Medicine, Mineola.

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