Examination of Stigmatizing Language in the Electronic Health Record

Gracie Himmelstein,David Bates,Li Zhou

doi:10.1001/jamanetworkopen.2021.44967

Abstract

Stigmatizing language in the electronic health record (EHR) may alter treatment plans, transmit biases between clinicians, and alienate patients. However, neither the frequency of stigmatizing language in hospital notes, nor whether clinicians disproportionately use it in describing patients in particular demographic subgroups are known. To examine the prevalence of stigmatizing language in hospital admission notes and the patient and clinician characteristics associated with the use of such language. This cross-sectional study of admission notes used natural language processing on 48 651 admission notes written about 29 783 unique patients by 1932 clinicians at a large, urban academic medical center between January to December 2018. The admission notes included 8738 notes about 4309 patients with diabetes written by 1204 clinicians; 6197 notes about 3058 patients with substance use disorder by 1132 clinicians; and 5176 notes about 2331 patients with chronic pain by 1056 clinicians. Statistical analyses were performed between May and September 2021. Patients' demographic characteristics (age, race and ethnicity, gender, and preferred language); clinicians' characteristics (gender, postgraduate year [PGY], and credential [physician vs advanced practice clinician]). Binary indicator for any vs no stigmatizing language; frequencies of specific stigmatizing words. Linear probability models were the main measure, and logistic regression and odds ratios were used for sensitivity analyses and further exploration. The sample included notes on 29 783 patients with a mean (SD) age of 46.9 (27.6) years. Of these patients, 1033 (3.5%) were non-Hispanic Asian, 2498 (8.4%) were non-Hispanic Black, 18 956 (63.6%) were non-Hispanic White, 17 334 (58.2%) were female, and 2939 (9.9%) preferred a language other than English. Of all admission notes, 1197 (2.5%) contained stigmatizing language. The diagnosis-specific stigmatizing language was present in 599 notes (6.9%) for patients with diabetes, 209 (3.4%) for patients with substance use disorders, and 37 (0.7%) for patients with chronic pain. In the whole sample, notes about non-Hispanic Black patients vs non-Hispanic White patients had a 0.67 (95% CI, 0.15 to 1.18) percentage points greater probability of containing stigmatizing language, with similar disparities in all 3 diagnosis-specific subgroups. Greater diabetes severity and the physician-author being less advanced in their training was associated with more stigmatizing language. A 1 point increase in the diabetes severity index was associated with a 1.23 (95% CI, .23 to 2.23) percentage point greater probability of a note containing stigmatizing language. In the sample restricted to physicians, a higher PGY was associated with less use of stigmatizing language overall (-0.05 percentage points/PGY [95% CI, -0.09 to -0.01]). In this cross-sectional study, stigmatizing language in hospital notes varied by medical condition and was more often used to describe non-Hispanic Black patients. Training clinicians to minimize stigmatizing language in the EHR might improve patient-clinician relationships and reduce the transmission of bias between clinicians.

Highlights

Health care clinicians spend many hours interacting with the electronic health record (EHR),[1,2] which has become the primary means of communication between clinicians in the same practice, hospital, hospital network, and, increasingly, across systems via health information exchanges.[3]
The diagnosis-specific stigmatizing language was present in 599 notes (6.9%) for patients with diabetes, 209 (3.4%) for patients with substance use disorders, and 37 (0.7%) for patients with chronic pain
In the sample restricted to physicians, a higher post-graduate year (PGY) was associated with less use of stigmatizing language overall (−0.05 percentage points/PGY [95% CI, −0.09 to −0.01])

Summary

Introduction

Health care clinicians spend many hours interacting with the electronic health record (EHR),[1,2] which has become the primary means of communication between clinicians in the same practice, hospital, hospital network, and, increasingly, across systems via health information exchanges.[3]. The EHR’s important role in clinician-clinician communications and clinician-patient relationships raises concerns about the use of stigmatizing language in medical records. Stigmas mark or signal that someone is less worthwhile and merits inferior treatment.[5] Stigmas are not personal preferences but shared social constructions often communicated through language.[6] Stigmatizing language generally takes 3 forms: (1) marking or labeling someone as other; (2) assigning responsibility (ie, blame); and (3) invoking danger or peril.[6] All 3 forms of stigmatizing language may appear in the EHR. Some examples are familiar to clinicians: patients with substance use disorders labeled substance abusers; patients described as noncompliant or poorly controlled, emphasizing patient responsibility for their illness; and distressed patients being called belligerent or combative or implying purposeful efforts to endanger health care staff

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Discussion

Conclusion