Evidence map›Paper›PMID 35024857›Full record

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.

Aviv Y Landau, Ashley Blanchard, Kenrick Cato, Nia Atkins, Stephanie Salazar, Desmond U Patton, Maxim Topaz

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. The Use of Biased Language in the Care of Seriously Ill Children: A Pilot Study.The American journal of hospice & palliative care · 2026
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  6. Addressing Consequential Public Health Problems Through Informatics and Data Science.Journal of the American Medical Informatics Association : JAMIA · 2022
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Aviv Y LandauData Science Institute, Columbia University, New York, New York, USA.ORCID 0000-0003-3715-7709
Ashley BlanchardNew York Presbyterian Morgan Stanley Children's Hospital, Columbia University Irving Medical Center, New York, New York, USA.
Kenrick CatoDepartment of Emergency Medicine, School of Nursing, Columbia University, New York, New York, USA.
Nia AtkinsColumbia College, Columbia University, New York, New York, USA.
Stephanie SalazarColumbia School of Social Work, Columbia University, New York, New York, USA.
Desmond U PattonData Science Institute, Columbia School of Social Work, Columbia University, New York, New York, USA.
Maxim TopazData Science Institute, Columbia University School of Nursing, Columbia University, New York, New York, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Child AbuseRacismChildDocumentationElectronic Health RecordsHumansPhenotypeQualitative Researchchild abuse and neglectclinical decision support toolelectronic health recordspediatric emergency departmentsracial bias

Identifiers

PMID35024857
PMCPMC8800508

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.