Citation

Murnan AW, Tscholl JJ, Ganta R, Duah HO, Qasem I, Sezgin E. Child Maltreat. 2023; ePub(ePub): ePub.

Copyright

(Copyright © 2023, American Professional Society on the Abuse of Children, Publisher SAGE Publishing)

DOI

10.1177/10775595231194599

PMID

37545138

Abstract

Survivors of child sex trafficking (SCST) experience high rates of adverse health outcomes. Amidst the duration of their victimization, survivors regularly seek healthcare yet fail to be identified. This study sought to utilize artificial intelligence (AI) to identify SCST and describe the elements of their healthcare presentation. An AI-supported keyword search was conducted to identify SCST within the electronic medical records (EMR) of ∼1.5 million patients at a large midwestern pediatric hospital. Descriptive analyses were used to evaluate associated diagnoses and clinical presentation. A sex trafficking-related keyword was identified in.18% of patient charts. Among this cohort, the most common associated diagnostic codes were for Confirmed Sexual/Physical Assault; Trauma and Stress-Related Disorders; Depressive Disorders; Anxiety Disorders; and Suicidal Ideation. Our findings are consistent with the myriad of known adverse physical and psychological outcomes among SCST and illuminate the future potential of AI technology to improve screening and research efforts surrounding all aspects of this vulnerable population.

Language: en

Keywords

artificial intelligence; sex trafficking; child health; medical records; natural language processing