ArticleJAMIA open2026
Spatial bias in quality of estimated delivery date records in the electronic health record.
Article in JAMIA open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study evaluates the quality of estimated delivery dates (EDD) captured in electronic health record (EHR) systems, focusing on identifying and quantifying bias in these estimates for patients seeking prenatal care. Materials and Methods: We measured spatial and racial biases in the completeness and concordance of EDDs recorded in the EHR for patients seeking prenatal care. Element presence and agreement were compared across the catchment area to identify spatial patterns of bias in EDD records. Results: We obtained 38 477 unique pregnancy episodes in the EHR representing 33 081 unique patients between 2018 and 2022. Clear spatial and racial biases emerged in the completeness and concordance of EDDs ( Discussion: This work demonstrates spatial and racial biases in quality of EDDs recorded in the EHR and, therefore, the need to prioritize bias assessment within EHR data quality (DQ) frameworks. Continued efforts would support equitable improvements in EHR-based research by improving the reliability of current health service analyses and informing the development of more inclusive and effective EHR-based technologies. Conclusion: Including bias related to socioeconomic characteristics as a core axis of EHR DQ would extend existing frameworks to address potential inequities embedded in EHR data.
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