Abstract
Background and objectiveAccurate identification and categorization of injuries from medical records can be challenging, yet it is important for injury epidemiology and prevention efforts. Coding systems such as the International Classification of Diseases (ICD) have well-known limitations. Utilizing computer-based techniques such as natural language processing (NLP) can help augment the identification and categorization of diseases in electronic health records. We used a Python program to search the text to identify cases of scooter injuries that presented to our emergency department (ED).Materials and methodsThis retrospective chart review was conducted between March 2017 and June 2019 in a single, urban academic ED with approximately 80,000 annual visits. The physician documentation was stored as combined PDF files by date. A Python program was developed to search the text from 186,987 encounters to find the string “scoot” and to extract the 100 characters before and after the phrase to facilitate a manual review of this subset of charts.ResultsA total of 890 charts were identified using the Python program, of which 235 (26.4%) were confirmed as e-scooter cases. Patients had an average age of 36 years and 53% were male. In 81.7% of cases, the patients reported a fall from the scooter and only 1.7% reported wearing a helmet during the event. The most commonly injured body areas were the upper extremity (57.9%), head (42.1%), and lower extremity (36.2%). The most frequently consulted specialists were orthopedic and trauma surgeons with 28% of cases requiring a consult. In our population, 9.4% of patients required admission to the hospital.ConclusionsThe number of results and data returned by the Python program was easy to manage and made it easier to identify charts for abstraction. The charts obtained allowed us to understand the nature and demographics of e-scooter injuries in our ED. E-scooters continue to be a popular mode of transportation, and understanding injury patterns related to them may inform and guide opportunities for policy and prevention.
Highlights
Dockless e-scooters saw tremendous growth in 2017 and rapidly gained popularity as a convenient and environmentally-friendly form of transportation [1]
In 81.7% of cases, the patients reported a fall from the scooter and only 1.7% reported wearing a helmet during the event
E-scooters continue to be a popular mode of transportation, and understanding injury patterns related to them may inform and guide opportunities for policy and prevention
Summary
Dockless e-scooters saw tremendous growth in 2017 and rapidly gained popularity as a convenient and environmentally-friendly form of transportation [1]. The International Classification of Diseases (ICD) was developed by the World Health Organization (WHO) and has been adopted by countries to standardize the documentation of clinical diagnoses for a variety of purposes This classification system is used to identify the trends, causes, and outcomes of medical cases, but the use of ICD codes as a primary source of data collection can lead to gaps in information, related to the mechanism of injury [5]. Accurate identification and categorization of injuries from medical records can be challenging, yet it is important for injury epidemiology and prevention efforts Coding systems such as the International Classification of Diseases (ICD) have well-known limitations.
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