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Updated: Oct 6, 2025

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
Published on: May 10, 2019
Considerations for development of child abuse and neglect phenotype with implications for reduction of racial bias: a
Aviv Y Landau1, Ashley Blanchard2, Kenrick Cato3
1Data Science Institute, Columbia University, New York, New York, USA.
Objective:
The study provides considerations for generating a phenotype of child abuse and neglect in Emergency Departments (ED) using secondary data from electronic health records (EHR). Implications will be provided for racial bias reduction and the development of further decision support tools to assist in identifying child abuse and neglect.
Materials And Methods:
We conducted a qualitative study using in-depth interviews with 20 pediatric clinicians working in a single pediatric ED to gain insights about generating an EHR-based phenotype to identify children at risk for abuse and neglect.
Results:
Three central themes emerged from the interviews: (1) Challenges in diagnosing child abuse and neglect, (2) Health Discipline Differences in Documentation Styles in EHR, and (3) Identification of potential racial bias through documentation.
Discussion:
Our findings highlight important considerations for generating a phenotype for child abuse and neglect using EHR data. First, information-related challenges include lack of proper previous visit history due to limited information exchanges and scattered documentation within EHRs. Second, there are differences in documentation styles by health disciplines, and clinicians tend to document abuse in different document types within EHRs. Finally, documentation can help identify potential racial bias in suspicion of child abuse and neglect by revealing potential discrepancies in quality of care, and in the language used to document abuse and neglect.
Conclusions:
Our findings highlight challenges in building an EHR-based risk phenotype for child abuse and neglect. Further research is needed to validate these findings and integrate them into creation of an EHR-based risk phenotype.
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