Identifying Children at Risk for Maltreatment Using Emergency Medical Services' Data: An Exploratory Study

The objective of this study was to use natural language processing to query Emergency Medical Services (EMS) electronic health records (EHRs) to identify variables associated with child maltreatment. We hypothesized the variables identified would show an association between the Emergency Medical Services encounter and risk of a children maltreatment report. This study is a retrospective cohort study of children with an EMS encounter from 1/1/11-12/31/18. NLP of EMS EHRs was conducted to generate single words, bigrams and trigrams. Clinically plausible risk factors for child maltreatment were established, where presence of the word(s) indicated presence of the hypothesized risk factor. The EMS encounters were probabilistically linked to child maltreatment reports. Univariable associations were assessed, and a multivariable logistic regression was conducted to determine a final set of predictors. 11 variables showed an association in the multivariable modeling. Sexual, abuse, chronic condition, developmental delay, unconscious on arrival, criminal activity/police, ingestion/inhalation/exposure, and <2 years old showed positive associations with child maltreatment reports. Refusal and DOA/PEA/asystole held negative associations. This study demonstrated that through EMS EHRs, risk factors for child maltreatment can be identified. A future direction of this work include developing a tool that screens EMS EHRs for households at risk for maltreatment.