Estimation of racial and language disparities in pediatric emergency department triage using statistical modeling and natural language processing

Abstract

Abstract Objectives The study aims to assess racial and language disparities in pediatric emergency department (ED) triage using analytical techniques and provide insights into the extent and nature of the disparities in the ED setting. Materials and Methods The study analyzed a cross-sectional dataset encompassing ED visits from January 2019 to April 2021. The study utilized analytical techniques, including K-mean clustering (KNN), multivariate adaptive regression splines (MARS), and natural language processing (NLP) embedding. NLP embedding and KNN were employed to handle the chief complaints and categorize them into clusters, while the MARS was used to identify significant interactions among the clinical features. The study also explored important variables, including age-adjusted vital signs. Multiple logistic regression models with varying specifications were developed to assess the robustness of analysis results. Results The study consistently found that non-White children, especially African American (AA) and Hispanic, were often under-triaged, with AA children having >2 times higher odds of receiving lower acuity scores compared to White children. While the results are generally consistent, incorporating relevant variables modified the results for specific patient groups (eg, Asians). Discussion By employing a comprehensive analysis methodology, the study checked the robustness of the analysis results on racial and language disparities in pediatric ED triage. The study also recognized the significance of analytical techniques in assessing pediatric health conditions and analyzing disparities. Conclusion The study’s findings highlight the significant need for equal and fair assessment and treatment in the pediatric ED, regardless of their patients’ race and language.