Natural language processing - A surveillance stepping stone to identify child abuse

OBJECTIVE: We aimed to refine a Natural Language Processing (NLP) algorithm that identified injuries associated with child abuse and identify areas in which integration into a real-time clinical decision support (CDS) tool may improve clinical care. METHODS: We applied an NLP algorithm in "silent mode" to all emergency department (ED) provider notes between July 2021-December 2022 (n=353) at one pediatric and eight general EDs. We refined triggers for the NLP, assessed adherence to clinical guidelines and evaluated disparities in degree of evaluation by examining associations between demographic variables and abuse evaluation or reporting to child protective services. RESULTS: Seventy-three cases falsely triggered the NLP, often due to errors in interpreting linguistic context. We identified common false positive scenarios and refined the algorithm to improve NLP specificity. Adherence to recommended evaluation standards for injuries defined by nationally accepted clinical guidelines was 63%. There were significant demographic differences in evaluation and reporting based on presenting ED type, insurance status, and race/ethnicity. CONCLUSIONS: Analysis of an NLP algorithm in "silent mode" allowed for refinement of the algorithm and highlighted areas in which real-time CDS may help ED providers identify and pursue appropriate evaluation of injuries associated with child physical abuse.