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

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 classifi

SUBMITTER: Ioannides KLH 

PROVIDER: S-EPMC8985928 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature