Estimating the Prevalence of Child Abuse and Neglect Among Adolescents in Primary Care Through Diagnoses Codes and Free-Text EHR Clinical Notes

Abstract

Introduction: Adolescents' child abuse and neglect experiences are often under-documented in primary care, leading to missed opportunities for interventions. This study compares the prevalence of child abuse and neglect cases identified by diagnostic codes versus a natural language processing approach of clinical notes.

Method: We retrospectively analyzed data from 8,157 adolescents, using ICD-10 codes and a natural language processing algorithm to identify child abuse and neglect cases and applied topic modeling on clinical notes to extract prevalent topics.

Results: The natural language processing approach identified more cases of child abuse and neglect cases (n = 294) compared to ICD-10 codes (n = 111). Additionally, topic modeling of clinical notes showed the multifaceted nature of child abuse and neglect as captured in clinical narratives.

Discussion: Integrating natural language processing with ICD codes has the potential to enhance the identification and documentation of child abuse and neglect, which could lead to earlier and more targeted interventions and coordinated care.

Keywords: International Classification of Diseases; Mistreatment; artificial intelligence; child; primary health care.