Practice-Based Learning and Improvement: Improving Morbidity and Mortality Review Using Natural Language Processing.

Journal: The Journal of surgical research
PMID: 36427445

Abstract

INTRODUCTION: Practice-Based Learning and Improvement, a core competency identified by the Accreditation Council for Graduate Medical Education, carries importance throughout a physician's career. Practice-Based Learning and Improvement is cultivated by a critical review of complications, yet methods to accurately identify complications are inadequate. Machine-learning algorithms show promise in improving identification of complications. We compare a manual-supplemented natural language processing (ms-NLP) methodology against a validated electronic morbidity and mortality (MM) database, the Morbidity and Mortality Adverse Event Reporting System (MARS) to understand the utility of NLP in MM review.

Authors

  • Molly Kobritz
    Northwell Health North Shore/Long Island Jewish General Surgery, Manhasset, New York; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York. Electronic address: mkobritz@northwell.edu.
  • Vihas Patel
    Department of Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY.
  • David Rindskopf
    City University of New York, Graduate School And University Center, New York, New York.
  • Lyudmyla Demyan
    Northwell Health North Shore/Long Island Jewish General Surgery, Manhasset, New York; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York.
  • Mark Jarrett
    Northwell Health, Manhasset, NY, US.
  • Gene Coppa
    Northwell Health North Shore/Long Island Jewish General Surgery, Manhasset, New York; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York.
  • Anthony C Antonacci
    Northwell Health North Shore/Long Island Jewish General Surgery, Manhasset, New York; Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York.

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