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

Autor: Ioannides KLH; Department of Emergency Medicine, University of California, San Francisco-Fresno Medical Education Program, Fresno, CA, United States of America.; National Clinician Scholars Program, University of California, Los Angeles, CA, United States of America.; Department of Emergency Medicine, University of California, Los Angeles, CA, United States of America., Wang PC; Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, United States of America.; Office of Health Informatics and Analytics, UCLA Health, University of California, Los Angeles, CA, United States of America., Kowsari K; Office of Health Informatics and Analytics, UCLA Health, University of California, Los Angeles, CA, United States of America., Vu V; Office of Health Informatics and Analytics, UCLA Health, University of California, Los Angeles, CA, United States of America., Kojima N; Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, United States of America., Clayton D; Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, United States of America., Liu C; National Clinician Scholars Program, University of California, Los Angeles, CA, United States of America.; Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, United States of America., Trivedi TK; Department of Emergency Medicine, University of California, Los Angeles, CA, United States of America.; Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, CA, United States of America., Schriger DL; Department of Emergency Medicine, University of California, Los Angeles, CA, United States of America., Elmore JG; National Clinician Scholars Program, University of California, Los Angeles, CA, United States of America.; Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2022 Apr 06; Vol. 17 (4), pp. e0266097. Date of Electronic Publication: 2022 Apr 06 (Print Publication: 2022).
DOI: 10.1371/journal.pone.0266097
Abstrakt: 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.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje