Artificial intelligence based assessment of clinical reasoning documentation: an observational study of the impact of the clinical learning environment on resident documentation quality.

Journal: BMC medical education
PMID: 40264096

Abstract

BACKGROUND: Objective measures and large datasets are needed to determine aspects of the Clinical Learning Environment (CLE) impacting the essential skill of clinical reasoning documentation. Artificial Intelligence (AI) offers a solution. Here, the authors sought to determine what aspects of the CLE might be impacting resident clinical reasoning documentation quality assessed by AI.

Authors

  • Verity Schaye
    Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA. verity.schaye@nyulangone.org.
  • David J DiTullio
    Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA.
  • Daniel J Sartori
    Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA.
  • Kevin Hauck
    Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA.
  • Matthew Haller
    Department of Medicine, New York University Grossman School of Medicine, New York, NY, USA.
  • Ilan Reinstein
    I. Reinstein is a research scientist, Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York.
  • Benedict Guzman
    Division of Applied AI Technologies, New York University Langone Health, New York, NY, USA.
  • Jesse Burk-Rafel
    J. Burk-Rafel is assistant professor of medicine and assistant director of UME-GME innovation, Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York. At the time this work was completed, he was an internal medicine resident at NYU Langone Health, New York, New York; ORCID: https://orcid.org/0000-0003-3785-2154 .

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