Development and validation of a pragmatic natural language processing approach to identifying falls in older adults in the emergency department.

Journal: BMC medical informatics and decision making
Published Date: Jul 22, 2019

Abstract

BACKGROUND: Falls among older adults are both a common reason for presentation to the emergency department, and a major source of morbidity and mortality. It is critical to identify fall patients quickly and reliably during, and immediately after, emergency department encounters in order to deliver appropriate care and referrals. Unfortunately, falls are difficult to identify without manual chart review, a time intensive process infeasible for many applications including surveillance and quality reporting. Here we describe a pragmatic NLP approach to automating fall identification.

Authors

  • Brian W Patterson
    UW Health, Madison, USA.
  • Gwen C Jacobsohn
    BerbeeWalsh Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Manish N Shah
    BerbeeWalsh Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Yiqiang Song
  • Apoorva Maru
    BerbeeWalsh Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Arjun K Venkatesh
    Department of Emergency Medicine, Yale University, Yale-New Haven Hospital, New Haven, CT.
  • Monica Zhong
    BerbeeWalsh Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Katherine Taylor
    BerbeeWalsh Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Azita G Hamedani
    BerbeeWalsh Department of Emergency Medicine, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA.
  • Eneida A Mendonca
    University of Wisconsin-Madison, USA.

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