Observational studyJournal of the American Medical Informatics Association : JAMIA2019
Underserved populations with missing race ethnicity data differ significantly from those with structured race/ethnicity documentation.
Observational study in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 5 of them syntheses that pooled 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.
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
49 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Improving Clinical Documentation with Artificial Intelligence: A Systematic Review.Perspectives in health information management · 2024Pooled it
- Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023Pooled it
- Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications.Applied clinical informatics · 2022Pooled it
- The quality of social determinants data in the electronic health record: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Can antiepileptic efficacy and epilepsy variables be studied from electronic health records? A review of current approaches.Seizure · 2021Pooled it
- Barriers to identifying residents with dementia for embedded pragmatic trials: A call to action.Journal of the American Geriatrics Society · 2022Trial
- Enhancing the Observational Medical Outcomes Partnership Common Data Model to Support Health Equity Research: Expanding Race and Ethnicity Data Representation.Medical care · 2026Article
- Patient and carer perceptions and experiences of technology-facilitated appointment reminders in healthcare: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Racial disparities in multiple sclerosis quality of care metrics.Multiple sclerosis (Houndmills, Basingstoke, England) · 2026Article
- Race and Ethnicity Data in the Electronic Health Records: New Insights Through Comparison with American Community Survey Microdata.Journal of racial and ethnic health disparities · 2026Article
- Integration of fairness-awareness into clinical language processing models.Communications medicine · 2026Article
- Evaluating Methods for Imputing Race and Ethnicity in Electronic Health Record Data.Health services research · 2025Article
- Comparing Multiple Imputation Methods to Address Missing Patient Demographics in Immunization Information Systems: Retrospective Cohort Study.JMIR public health and surveillance · 2025Article
- Medically Tailored Grocery Deliveries to Improve Food Security and Hypertension in Underserved Groups: A Student-Run Pilot Randomized Controlled Trial.Healthcare (Basel, Switzerland) · 2025Article
- Where are my Black professors? The research culture preventing Black researchers from thriving in UK institutions.Wellcome open research · 2025Review
- Recommendations to improve race identification in health records: A rapid scoping review.PloS one · 2025Article
- Using Natural Language Processing to Inform Targeted Rural and Urban Hispanic VA Suicide Prediction Models.Rural mental health · 2025Article
- Contextualized race and ethnicity annotations for clinical text from MIMIC-III.Scientific data · 2024Article
- A multi-state analysis on the effect of deprivation and race on PICU admission and mortality in children receiving Medicaid in United States (2007-2014).BMC pediatrics · 2024Article
- Characterizing atrial fibrillation symptom improvement following de novo catheter ablation.European journal of cardiovascular nursing · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
objectiveWe aimed to address deficiencies in structured electronic health record (EHR) data for race and ethnicity by identifying black and Hispanic patients from unstructured clinical notes and assessing differences between patients with or without structured race/ethnicity data. MATERIALS AND
methodsUsing EHR notes for 16 665 patients with encounters at a primary care practice, we developed rule-based natural language processing (NLP) algorithms to classify patients as black/Hispanic. We evaluated performance of the method against an annotated gold standard, compared race and ethnicity between NLP-derived and structured EHR data, and compared characteristics of patients identified as black or Hispanic using only NLP vs patients identified as such only in structured EHR data.
resultsFor the sample of 16 665 patients, NLP identified 948 additional patients as black, a 26%increase, and 665 additional patients as Hispanic, a 20% increase. Compared with the patients identified as black or Hispanic in structured EHR data, patients identified as black or Hispanic via NLP only were older, more likely to be male, less likely to have commercial insurance, and more likely to have higher comorbidity. DISCUSSION: Structured EHR data for race and ethnicity are subject to data quality issues. Supplementing structured EHR race data with NLP-derived race and ethnicity may allow researchers to better assess the demographic makeup of populations and draw more accurate conclusions about intergroup differences in health outcomes.
conclusionsBlack or Hispanic patients who are not documented as such in structured EHR race/ethnicity fields differ significantly from those who are. Relatively simple NLP can help address this limitation.
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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.