ArticlePloS one2025
Recommendations to improve race identification in health records: A rapid scoping review.
Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Race is a critical variable in understanding health disparities, yet health databases lack consistent practices for identifying race. This rapid scoping review aimed to examine existing recommendations for identifying race in health databases and highlight gaps in current literature to guide future research. Following the Joanna Briggs Institute methodology and PRISMA-ScR guidelines, searches were conducted in MEDLINE, Embase, and Scopus for relevant literature published between January 2019 and February 2025. Articles were included if they addressed race identification in health databases, were available in English, had full-text access, and were peer-reviewed, knowledge syntheses, or grey literature. All articles were double screened in Covidence, and twenty-one articles were included. Descriptive thematic analysis identified five recommendation categories, including, self-identification and patient-centered practice, standardization across healthcare systems, data quality and completeness, algorithmic and predictive methods, and disaggregated data use and cross sector collaboration. There were common findings on the value of self-identification, cross-system consistency, and tools like natural language processing and imputation models. Some articles emphasized combining multiple strategies to improve system-wide practices, and overall, minimal conflicting evidence was observed. However, gaps remain in operationalizing these recommendations across various healthcare settings. Future directions should prioritize implementation-focused research and cross-jurisdictional comparisons to inform scalable, equity-driven improvements in race data practices. Ultimately, improving the consistency and accuracy of race data will enhance health equity monitoring, guide equitable resource distribution, and inform policies that better reflect the needs of racialized populations.
Indexed as
Identifiers
What Socratic 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 Socratic graph.