Evidence map›Paper›PMID 39401039›Full record

ArticleJAMA network open2024

Race and Ethnicity and Clinician Linguistic Expressions of Doubt in Hospital Admission Notes.

Courtney R Lee, Jaya Aysola, Xinwei Chen, Eden Addisu, Ari Klein, Davy Weissenbacher, Graciela Gonzalez-Hernandez, Gary E Weissman

Abstract read
In one paragraph

Article in JAMA network open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–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

8 citing papers in PubMed.

  1. Article
  2. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Courtney R LeeLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
Jaya AysolaLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
Xinwei ChenLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
Eden AddisuLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.
Ari KleinDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia.
Davy WeissenbacherDepartment of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia.
Graciela Gonzalez-HernandezDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California.
Gary E WeissmanLeonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia.

Funding

Res-Ed ComponentP30AG059302 · NIA · UNIVERSITY OF PENNSYLVANIA · PI Rachel M Werner, Allison Willis · 2018 to 2026
$6.4M
Social Media Mining for PharmacovigilanceR01LM011176 · NLM · UNIVERSITY OF PENNSYLVANIA · PI GONZALEZ HERNANDEZ, GRACIELA, SHEN, LI · 2012 to 2021
$4.5M
NIA NIH HHS P30 AG059302NLM NIH HHS R01 LM011176
6 · The paper itself

Abstract

Importance: Stigmatizing language in electronic health records (EHRs) can generate or reinforce negative stereotypes about patients from minoritized groups and erode their trust and experience. However, less is known about the use of such language to cast doubt on patient clinical history in hospital settings. Objective: To compare the prevalence of language expressing doubt about patient clinical history (eg, experiences and symptoms) in hospital admission notes by patient race and ethnicity. Design, Setting, and Participants: In this cohort study, natural language processing tools were applied to analyze 54 936 admission notes from 1249 clinicians about patients aged 18 years or older at admission and hospitalized at an academic health system in the Northeast US between January 1, 2018, and February 28, 2023. Data were analyzed from September 1, 2022, to July 31, 2023. Exposure: Patient race and ethnicity (non-Hispanic Black, non-Hispanic White, and racial and ethnic minoritized groups excluding non-Hispanic Black [includes multiple racial and ethnic groups, such as Hispanic and Asian]) as recorded in the EHR based on self-report or registrar determination. Main Outcome and Measure: Binary indicator for at least 1 term casting doubt on patient clinical history was defined using epistemic stance, a linguistic construct expressing a writer's degree of certainty in information. Terms were manually validated via iterative review of notes by the study team. Results: Among 56 325 admission notes (mean [SD] age of patients, 55.9 [19.0] years; 30 913 notes among female patients [54.9%]; 25 649 notes among non-Hispanic Black patients [45.5%], 26 442 notes among non-Hispanic White patients [46.9%], and 2985 notes among members of racial and ethnic minoritized groups excluding non-Hispanic Black patients [5.3%]), we analyzed 54 936 admission notes that had no missing data. Among all analyzed admission notes, 39 023 notes (71.0%) contained doubt language. Notes written about non-Hispanic Black patients had increased odds of containing at least 1 word or phrase of doubt (adjusted odds ratio, 1.21; 95% CI, 1.14-1.28; P < .001) compared with notes among non-Hispanic White patients. Compared with notes among non-Hispanic White patients, notes written about members of racial and ethnic minoritized groups excluding non-Hispanic Black patients had similar odds of containing at least 1 term of doubt. Conclusion and Relevance: In this study, language casting doubt on patient clinical history was more likely to be found in notes of non-Hispanic Black patients. These results suggest that with the implementation of policies allowing patients full access to their health records, examining clinical documentation standards may be associated with improved patient experience.

Indexed as

Electronic Health RecordsAdultAgedCohort StudiesEthnicityFemaleHospitalizationHumansLanguageLinguisticsMaleMiddle AgedNatural Language ProcessingPatient AdmissionRacial Groups

Identifiers

PMID39401039
PMCPMC11581534

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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.