Identification of Child Survivors of Sex Trafficking From Electronic Health Records: An Artificial Intelligence Guided Approach.

Autor:	Murnan AW; College of Nursing, University of Cincinnati, Cincinnati, OH, USA., Tscholl JJ; Department of Pediatrics, The Ohio State University College of Medicine, Columbus, OH, USA.; Division of Child and Family Advocacy, Center for Family Safety and Healing, Nationwide Children's Hospital, Columbus, OH, USA., Ganta R; Information Technology Research and Innovation, Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, USA., Duah HO; College of Nursing, University of Cincinnati, Cincinnati, OH, USA., Qasem I; College of Nursing, University of Cincinnati, Cincinnati, OH, USA., Sezgin E; Information Technology Research and Innovation, Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, USA.; Center for Biobehavioral Health, The Abigail Wexner Research Institute, Nationwide Children's Hospital, Columbus, OH, USA.
Jazyk:	angličtina
Zdroj:	Child maltreatment [Child Maltreat] 2024 Nov; Vol. 29 (4), pp. 601-611. Date of Electronic Publication: 2023 Aug 06.
DOI:	10.1177/10775595231194599
Abstrakt:	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. Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Databáze:	MEDLINE
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