ArticleJournal of the American Medical Informatics Association : JAMIA2022
Considerations for development of child abuse and neglect phenotype with implications for reduction of racial bias: a qualitative study.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- The Use of Biased Language in the Care of Seriously Ill Children: A Pilot Study.The American journal of hospice & palliative care · 2026Article
- Identifying stigmatizing language in clinical documentation: A scoping review of emerging literature.PloS one · 2024Article
- Initial development of tools to identify child abuse and neglect in pediatric primary care.BMC medical informatics and decision making · 2023Article
- Black and Latinx Primary Caregiver Considerations for Developing and Implementing a Machine Learning-Based Model for Detecting Child Abuse and Neglect With Implications for Racial Bias Reduction: Qualitative Interview Study With Primary Caregivers.JMIR formative research · 2023Article
- Developing machine learning-based models to help identify child abuse and neglect: key ethical challenges and recommended solutions.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Addressing Consequential Public Health Problems Through Informatics and Data Science.Journal of the American Medical Informatics Association : JAMIA · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe 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
methodsWe 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.
resultsThree 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.
conclusionsOur 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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