Is it possible to automatically assess pretreatment digital rectal examination documentation using natural language processing? A single-centre retrospective study.

Journal: BMJ open
PMID: 31324681

Abstract

OBJECTIVES: To develop and test a method for automatic assessment of a quality metric, provider-documented pretreatment digital rectal examination (DRE), using the outputs of a natural language processing (NLP) framework.

Authors

  • Selen Bozkurt
    Department of Biostatistics and Medical Informatics, Akdeniz University Faculty of Medinice, 48000 Antalya, Turkey.
  • Kathleen M Kan
    Department of Urology, Stanford University School of Medicine, Stanford, USA.
  • Michelle K Ferrari
    Department of Urology, Stanford University School of Medicine, Stanford, USA.
  • Daniel L Rubin
    Department of Biomedical Data Science, Stanford University School of Medicine Medical School Office Building, Stanford CA 94305-5479.
  • Douglas W Blayney
    Stanford University, School of Medicine, Stanford, CA.
  • Tina Hernandez-Boussard
    Stanford Center for Biomedical Informatics Research, Stanford, California 94305, USA.
  • James D Brooks
    Department of Urology, Stanford School of Medicine, CA.

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