E-scooter related injuries: Using natural language processing to rapidly search 36 million medical notes

PLoS One. 2022 Apr 6;17(4):e0266097. doi: 10.1371/journal.pone.0266097. eCollection 2022.

Abstract

Background: Shareable e-scooters have become popular, but injuries to riders and bystanders have not been well characterized. The goal of this study was to describe e-scooter injuries and estimate the rate of injury per e-scooter trip.

Methods and findings: Retrospective review of patients presenting to 180 clinics and 2 hospitals in greater Los Angeles between January 1, 2014 and May 14, 2020. Injuries were identified using a natural language processing (NLP) algorithm not previously used to identify injuries, tallied, and described along with required healthcare resources. We combine these tallies with municipal data on scooter use to report a monthly utilization-corrected rate of e-scooter injuries. We searched 36 million clinical notes. Our NLP algorithm correctly classified 92% of notes in the testing set compared with the gold standard of investigator review. In total, we identified 1,354 people injured by e-scooters; 30% were seen in more than one clinical setting (e.g., emergency department and a follow-up outpatient visit), 29% required advanced imaging, 6% required inpatient admission, and 2 died. We estimate 115 injuries per million e-scooter trips were treated in our health system.

Conclusions: Our observed e-scooter injury rate is likely an underestimate, but is similar to that previously reported for motorcycles. However, the comparative severity of injuries is unknown. Our methodology may prove useful to study other clinical conditions not identifiable by existing diagnostic systems.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Accidents, Traffic*
  • Emergency Service, Hospital
  • Humans
  • Motorcycles
  • Natural Language Processing*
  • Retrospective Studies

Grants and funding

Drs. Ioannides, Liu and Trivedi were supported by the National Clinician Scholars Program at the University of California, Los Angeles. Drs. Trivedi and Liu were additionally supported by the Veterans Affairs (VA) Office of Academic Affiliations through the VA/National Clinician Scholars Program at the University of California, Los Angeles. The contents do not represent the views of the US Department of Veterans Affairs or the United States Government. Drs. Ioannides and Schriger were supported by the Department of Emergency Medicine at the David Geffen School of Medicine at the University of California, Los Angeles. Mr. Wang had generous institutional support from the UCLA Department of Medicine. Drs. Kowsari, Vu, and Wang had generous institutional support from the UCLA Office of Health Informatics and Analytics. The UCLA Clinical and Translational Science Institute also provided logistical support. The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.