ArticleStigma and health2025
A Scoping Review of Methodological Approaches to Detect Bias in the Electronic Health Record.
Article in Stigma and health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- The Lifecycle of Electronic Health Record Data in HIV-Related Big Data Studies: Qualitative Study of Bias Instances and Potential Opportunities for Minimization.Journal of medical Internet research · 2025Article
- Identifying stigmatizing and positive/preferred language in obstetric clinical notes using natural language processing.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- CARE-SD: classifier-based analysis for recognizing provider stigmatizing and doubt marker labels in electronic health records: model development and validation.Journal of the American Medical Informatics Association : JAMIA · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
As health systems move to make electronic health records (EHRs) accessible to patients, there is a need to examine if, and the extent to which, bias toward patients may be evident in these records. This scoping review aimed to summarize the scientific literature on methods used to detect biased language about patients in the EHR and the nature and object of the biases detected. A comprehensive literature search was conducted of PubMed, CINAHL, Web of Science, APA PsycInfo, and SOCIndex for peer-reviewed English language studies conducted in the United States published on or before December 22, 2022. Seven studies were included in this review. Four methods were identified: natural language processing methods including machine learning-based (
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What OpenQuestion holds
Registered trials
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.