Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness.

Journal: The journal of trauma and acute care surgery
Published Date: May 1, 2020

Abstract

BACKGROUND: Incomplete prehospital trauma care is a significant contributor to preventable deaths. Current databases lack timelines easily constructible of clinical events. Temporal associations and procedural indications are critical to characterize treatment appropriateness. Natural language processing (NLP) methods present a novel approach to bridge this gap. We sought to evaluate the efficacy of a novel and automated NLP pipeline to determine treatment appropriateness from a sample of prehospital EMS motor vehicle crash records.

Authors

  • Christopher J Tignanelli
    From the Department of Surgery (C.J.T., G.B., G.B.M.), University of Minnesota, Minneapolis, Minnesota; Institute for Health Informatics (C.J.T., G.M.S., R.F., R.M., B.C.K., S.P., E.A.L., G.B.M.), University of Minnesota, Minneapolis, Minnesota; Department of Surgery (C.J.T., J.L.G.), North Memorial Health Hospital, Robbinsdale, Minnesota; North Memorial Health Hospital Emergency Medical Services (A.L.T.), Robbinsdale, Minnesota; and Department of Emergency Medicine (J.W.L.), North Memorial Health Hospital Emergency Medical Services, Robbinsdale, Minnesota.
  • Greg M Silverman
  • Elizabeth A Lindemann
    Department of Surgery, University of Minnesota, Minneapolis, MN.
  • Alexander L Trembley
  • Jon C Gipson
  • Gregory Beilman
  • John W Lyng
  • Raymond Finzel
  • Reed McEwan
    Academic Health Center-Information Systems, Minneapolis, MN, USA.
  • Benjamin C Knoll
    Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.
  • Serguei Pakhomov
    Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.
  • Genevieve B Melton
    Institute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.

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