Characterization of Stigmatizing Language in Medical Records (2023)

Abstract: Widespread disparities in clinical outcomes exist between different demographic groups in the United States. A new line of work in medical sociology has demonstrated physicians often use stigmatizing language in electronic medical records within certain groups, such as black patients, which may exacerbate disparities. In this study, we characterize these instances at scale using a series of domain-informed NLP techniques. We highlight important differences between this task and analogous bias-related tasks studied within the NLP community (e.g., classifying microaggressions). Our study establishes a foundation for NLP researchers to contribute timely insights to a problem domain brought to the forefront by recent legislation regarding clinical documentation transparency. We release data, code, and models.

## introduction widespread and well-documented disparities in healthcare outcomes between demographic groups exist within the united states @xcite . the sources of these disparities are diverse and complex, with numerous interacting factors contributing to worse outcomes for minority patients @xcite . one source of disparities may stem from latent biases of healthcare providers @xcite . multiple studies have highlighted the tendency for providers to prescribe different treatment plans to black patients compared to white patients despite having similar clinical dispositions @xcite @xcite . elevated implicit bias scores have been associated with these decisions and have been further linked with decreased levels of patient-provider communication @xcite . a major challenge with these biases is that they are invoked unconsciously. how is stigmatizing language in medical records different from other forms of abusive language? prior studies of stigmatizing language in clinical notes have relied on qualitative methods @xcite or refrained from analyzing computational nuances of the problem domain @xcite . modeling tasks such as hate-speech detection @xcite and analyses of social bias encoded within language models @xcite share many similarities with characterizing stigmatizing language in medical records. however, it is not clear a priori where the task of characterizing stigmatizing language in medical records falls within the broader abusive language landscape. in this paper, we demonstrate that characterization of stigmatizing language in medical records most strongly parallels the characterization of linguistic microaggressions @xcite . however, unlike traditional microaggressions, biased language in the clinical domain is concentrated in unremarkable phrases and lacks any indication of the targeted identity group. our analysis establishes a foundation for a novel task that has high importance to both patients and clinicians. ## stigmatizing language in medical records as abusive language clinical stigmatizing language lies in the implicit and directed quadrant of the typology of abusive language introduced by @xcite . physicians generally use a vocabulary of commonplace terms and phrases which have negative implications only when interpreted in certain contexts or by other physicians @xcite . this language almost always places the patient as the target of the stigma, even if they are not the intended recipient @xcite . stigmatizing language in medical records shares many similarities with linguistic microaggressions. both reflect an unconscious bias internalized by the speaker and materialized through thinly veiled innuendo @xcite . this innuendo is not necessarily negative in affect @xcite . one major difference between stigmatizing language in the clinical domain and other forms of abusive language is the notion of necessity. whereas most abusive language is better left unsaid, clinicians have a responsibility to document their interaction with patients @xcite . often, this requires that they characterize sociallystigmatized circumstances (e.g., substance use disorders) and medically-relevant patient eccentricities (e.g., unfounded social histories). minor differences in phrasing may have a large impact on whether a statement is stigmatizing to patients. the idea of stigmatizing language in medical records is relatively new, with @xcite providing the first qualitative evidence of negative language in the medical record. using word counts, @xcite and @xcite later identified a higher prevalence of implicit bias within records of black patients than white patients. @xcite was the first to use machine learning to analyze stigmatizing language in medical records. the authors identified sentences with possible bias using a manually-curated word list and then annotated whether each match was positive, negative, or out-of-context. a logistic regression classifier trained on a bag-of-words representation of the text achieved good performance (f1 of 0.935). unfortunately, the authors did not provide a baseline to indicate how valuable context around the seed terms is for classification. the more general task of identifying biased and abusive language in text has garnered much atten-tion from researchers in recent years @xcite . @xcite was the first to computationally analyze microaggressions. the majority of microaggression research published thereafter has remained confined to using web data @xcite . our study provides an analysis of stigma in an important linguistic domain that differs dramatically from those currently studied in the covert bias research space. ## data we consider two clinical datasets. in addition to covering different clinical specialties, they also feature different demographic compositions. jhm we retrospectively acquired a dataset of 128,343 english-language progress notes written by physicians across 5 clinical specialties within the johns hopkins medicine (jhm) hospital system -internal medicine, emergency medicine, pediatrics, ob-gyn, and surgery. notes were processed in accordance with our institution's privacy policy after approval by our institutional review board (irb). because the notes contain sensitive identifiable information, they are unable to be shared beyond our study team. ## discussion the covert, highly contextual, and nondemographically aligned nature of stigmatizing language in medical records places it in a unique area of the abusive language research landscape. the current reliance on domain experts to identify possible instances of bias using anchor terms is limiting given the adversarial relationship been abusive language and speakers @xcite . it also does not address abstract forms of stigma @xcite or stigmatizing pragmatics @xcite . methods for discovering stigmatizing language in medical records are poised to be highly impactful @xcite . counterfactual analyses may be instrumental for better characterizing the nuance between stigmatizing and non-stigmatizing clinical language @xcite . whether these nuances are uniform across patient populations (e.g., hospital systems, regions) and providers (e.g., nurses, resident physicians) remains an open question not answerable from our datasets alone. likewise, future work is necessary to understand whether clinical knowledge is necessary for models in this domain @xcite .