Developing a machine learning model to detect diagnostic uncertainty in clinical documentation.

Journal: Journal of hospital medicine
PMID: 36919861

Abstract

BACKGROUND AND OBJECTIVE: Diagnostic uncertainty, when unrecognized or poorly communicated, can result in diagnostic error. However, diagnostic uncertainty is challenging to study due to a lack of validated identification methods. This study aims to identify distinct linguistic patterns associated with diagnostic uncertainty in clinical documentation.

Authors

  • Trisha L Marshall
    Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
  • Lindsay C Nickels
    Digital Scholarship Center, University of Cincinnati Libraries and College of Arts and Sciences, Cincinnati, Ohio, USA.
  • Patrick W Brady
    Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
  • Ezra J Edgerton
    Digital Scholarship Center, University of Cincinnati Libraries and College of Arts and Sciences, Cincinnati, Ohio, USA.
  • James J Lee
    Digital Scholarship Center, University of Cincinnati Libraries and College of Arts and Sciences, Cincinnati, Ohio, USA.
  • Philip A Hagedorn
    Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.

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