Machine learning to predict notes for chart review in the oncology setting: a proof of concept strategy for improving clinician note-writing.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 38700253

Abstract

OBJECTIVE: Leverage electronic health record (EHR) audit logs to develop a machine learning (ML) model that predicts which notes a clinician wants to review when seeing oncology patients.

Authors

  • Sharon Jiang
    MIT-IBM Watson AI Lab, Cambridge, MA, USA.
  • Barbara D Lam
    Division of Hematology, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA, 02215, USA.
  • Monica Agrawal
    Department of Computer Science, Stanford University, Stanford, CA, USA.
  • Shannon Shen
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
  • Nicholas Kurtzman
    Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA 02215, United States.
  • Steven Horng
    Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA dsontag@cs.nyu.edu.
  • David R Karger
    Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
  • David Sontag
    1 Department of Computer Science, New York University , New York, New York.

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