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

• Published in: Journal of pediatric health care Vol. 39; no. 2; pp. 189 - 197
• Main Authors: Lee, Rachel Y., Landau, Aviv Y., Heider, Paul M., Hanson, Rochelle F., Espeleta, Hannah C., Cato, Kenrick D., Topaz, Maxim
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.03.2025
• Subjects: Adolescent; Advanced Practice Nursing; artificial intelligence; Child; Child Abuse - diagnosis; Child Abuse - statistics & numerical data; Electronic Health Records - statistics & numerical data; Female; Humans; International Classification of Diseases; Male; Mistreatment; Natural Language Processing; Pediatrics; Prevalence; primary health care; Primary Health Care - statistics & numerical data; Retrospective Studies; International Classification of Diseases; primary health care; Mistreatment; artificial intelligence; child
• ISSN: 0891-5245; 1532-656X; 1532-656X
• DOI: 10.1016/j.pedhc.2024.10.016

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. 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. 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. 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.