A Natural Language Processing Model to Identify Confidential Content in Adolescent Clinical Notes.

Journal: Applied clinical informatics
PMID: 36898410

Abstract

BACKGROUND: The 21st Century Cures Act mandates the immediate, electronic release of health information to patients. However, in the case of adolescents, special consideration is required to ensure that confidentiality is maintained. The detection of confidential content in clinical notes may support operational efforts to preserve adolescent confidentiality while implementing information sharing.

Authors

  • Naveed Rabbani
    Department of Clinical Informatics, Lucile Packard Children's Hospital, Palo Alto, CA, USA; Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA. Electronic address: nrabbani@stanford.edu.
  • Michael Bedgood
    California Department of Public Health, Richmond, California, United States.
  • Conner Brown
    Information Services Department, Lucile Packard Children's Hospital, Stanford, Palo Alto, California, United States.
  • Ethan Steinberg
    Biomedical Informatics Research, Stanford University, Palo Alto, USA.
  • Rachel L Goldstein
    Division of Adolescent Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States.
  • Jennifer L Carlson
    Division of Adolescent Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States.
  • Natalie Pageler
    Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States.
  • Keith E Morse
    Division of Pediatric Hospital Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA.

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