Stigmatizing Language, Patient Demographics, and Errors in the Diagnostic Process, 2024, Katherine C. Brooks, MD et al

Stigmatizing language (SL) is widespread throughout medical documentation.1 It is more likely to be found in the records of Black patients,2,3 patients with public insurance,2 and patients with certain comorbidities.3 We investigated associations between SL, errors in the diagnostic process, and demographics for hospitalized patients.

his multicenter, retrospective cohort study was conducted as part of the Utility of Predictive Systems for Diagnostic Errors (UPSIDE) study.4 Using a structured adjudication tool, UPSIDE assessed the presence of diagnostic errors and diagnostic process errors among patients who died or were transferred to the intensive care unit within 48 hours of hospital admission at 29 hospitals from January to December 2019. Diagnostic errors were defined as missed opportunities to make a correct or timely diagnosis. Diagnostic process errors were diagnostic failure points occurring across the Diagnostic Error Evaluation and Research categories.5 Race, ethnicity, and other demographic variables were collected administratively at study sites. Full UPSIDE methods are described elsewhere.6 This study was approved by the University of California San Francisco institutional review board; informed consent was waived because of use of retrospective data.

As a secondary aim, reviewers identified the presence of SL throughout physician, nursing, and ancillary staff notes. Stigmatizing language was defined as containing one of the following features: questioning of patient credibility, racial or social class stereotyping, expressions of disapproval toward patients, and descriptions of difficult patients.

Univariate analysis between SL, diagnostic errors, and demographics was performed using χ2 testing with Rao-Scott second-order correction, taking into account the sampling design of the UPSIDE study.6 We used generalized estimating equations to fit logistic regression models with clustering by hospital, sampling weights, exchangeable working correlations, and robust SEs to calculate unadjusted and adjusted odds ratios (ORs) quantifying the associations between SL and the presence of diagnostic process errors. Analyses were performed in R, version 4.3.2 and Stata, version 17.0. One-sided P < .05 indicated significance.

Results
After excluding 81 hospital admissions with missing data, 2347 were included in our subanalysis. Diagnostic errors were identified in 536 (23.2%) and SL found in 131 (5.1%). Presence of SL varied among sites (median, 4.0%; IQR, 1.1%-8.1%).

In adjusted multivariate analysis of diagnostic process errors, SL was associated with delays in care at presentation (OR, 1.9; 95% CI, 1.3-2.9) and communication with patients and caregivers (OR, 3.8; 95% CI, 1.2-12.0)

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