Document Type
Article
Publication Date
8-2023
Publisher
SAGE Publications
Source Publication
Child Maltreatment
Source ISSN
1077-5595
Original Item ID
DOI: 10.1177/107755952311945
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.
Recommended Citation
Murnan, Aaron; Tscholl, Jennifer J.; Ganta, Rajesh; Duah, Henry Ofori; Qasem, Islam; and Sezgin, Emre, "Identification of Child Survivors of Sex Trafficking From Electronic Health Records: An Artificial Intelligence Guided Approach" (2023). College of Nursing Faculty Research and Publications. 1212.
https://epublications.marquette.edu/nursing_fac/1212
Comments
Published version. Child Maltreatment, Vol. 29, No. 4 (November 2024): 601-611. DOI. © 2023 The Authors, published by SAGE Publications.
Henry Duah was affiliated with University of Cincinnati at the time of publication.