Evidence map›Paper›PMID 36418736›Full record

Observational studyJournal of racial and ethnic health disparities2023

Discrepancies in Race and Ethnicity in the Electronic Health Record Compared to Self-report.

Joann M Samalik, Caren S Goldberg, Zubin J Modi, Emily M Fredericks, Samir K Gadepalli, Sally J Eder, Jeremy Adler

Abstract readObservational Study
PubMed Publisher
In one paragraph

Observational study in Journal of racial and ethnic health disparities, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
30citing papers in PubMed, 1 pooled it
–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

30 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Electronic health record data quality assessment and tools: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2023
    Pooled it
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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.

Joann M SamalikDivision of Pediatric Gastroenterology, 1500 E. Medical Center Dr. C.S. Mott Children's Hospital, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
Caren S GoldbergDivision of Pediatric Cardiology, 1500 E. Medical Center Dr. C.S. Mott Children's Hospital, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
Zubin J ModiSusan B. Meister Child Health Evaluation and Research Center, 2900 Plymouth Rd, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
Emily M FredericksSusan B. Meister Child Health Evaluation and Research Center, 2900 Plymouth Rd, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
Samir K GadepalliSusan B. Meister Child Health Evaluation and Research Center, 2900 Plymouth Rd, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
Sally J EderSusan B. Meister Child Health Evaluation and Research Center, 2900 Plymouth Rd, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA.
Jeremy AdlerDivision of Pediatric Gastroenterology, 1500 E. Medical Center Dr. C.S. Mott Children's Hospital, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA. jeradler@med.umich.edu.ORCID 0000-0002-4857-6098

Funding

Crohn's and Colitis Foundation of America This work was supported in part by the Pediatric Research Organization for Kids with Intestinal Disorders (PRO-KIIDS)
6 · The paper itself

Abstract

objectiveRacial and ethnic disparities are commonplace in health care. Research often relies on sociodemographic information recorded in the electronic health record (EHR). Little evidence is available about the accuracy of EHR-recorded sociodemographic information, and none in pediatrics. Our objective was to determine the accuracy of EHR-recorded race and ethnicity compared to self-report.

methodsPatients/guardians enrolled in two prospective observational studies (10/2014-1/2019) provided self-reported sociodemographic information. Corresponding EHR information was abstracted. EHR information was compared to self-report, considered "gold standard." Agreement was evaluated with Cohen's kappa.

resultsA total of 503 patients (42% female, median age 12.8 years) were identified. Self-reported race (N = 484) was 73% White, 16% Black or African American (AA), 4% Asian, 5% multiracial, and 2% other. Self-reported ethnicity (N = 410) was 9% Hispanic/Latino, and 88% non-Hispanic/Latino. Agreement between self-reported and EHR-recorded race was substantial (kappa = 0.77, 95% CI 0.72-0.83). Race was discordant among 10% (47/476). Hispanic/Latino ethnicity also had strong agreement (kappa = 0.77, 95% CI 0.65-0.89). Among those who self-reported Hispanic/Latino and reported race (N = 21), race was less accurately recorded in the EHR (kappa = 0.26, 95% CI 0-0.54). Race did not match among 43% with recorded race (9/21). Among self-reported racial and/or ethnic minorities, 13% (12/164) were misclassified in the EHR as non-Hispanic White.

conclusionsWe found race and ethnicity are often inaccurately recorded in the EHR for patients who self-identify as minorities, leading to under-representation of minorities in the EHR. Inaccurately recorded race and ethnicity has important implications for disparity research, and for informing health policy. Reliable processes are needed to incorporate self-reported race and ethnicity in the EHR at institutional and national levels.

Indexed as

Electronic Health RecordsEthnicityRacial GroupsSelf ReportBlack or African AmericanChildFemaleHispanic or LatinoHumansMaleMinority GroupsWhiteData qualityDisparitiesElectronic health recordEthnicityRace

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.