{"ID":2875442,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.02651","arxiv_id":"2509.02651","title":"Bias Detection in Emergency Psychiatry: Linking Negative Language to Diagnostic Disparities","abstract":"The emergency department (ED) is a high stress environment with increased risk of clinician bias exposure. In the United States, Black patients are more likely than other racial/ethnic groups to obtain their first schizophrenia (SCZ) diagnosis in the ED, a highly stigmatizing disorder. Therefore, understanding the link between clinician bias exposure and psychiatric outcomes is critical for promoting nondiscriminatory decision-making in the ED. This study examines the association between clinician bias exposure and psychiatric diagnosis using a sample of patients with anxiety, bipolar, depression, trauma, and SCZ diagnoses (N=29,005) from a diverse, large medical center. Clinician bias exposure was quantified as the ratio of negative to total number of sentences in psychiatric notes, labeled using a large language model (Mistral). We utilized logistic regression to predict SCZ diagnosis when controlling for patient demographics, risk factors, and negative sentence ratio (NSR). A high NSR significantly increased one's odds of obtaining a SCZ diagnosis and attenuated the effects of patient race. Black male patients with high NSR had the highest odds of being diagnosed with SCZ. Our findings suggest sentiment-based metrics can operationalize clinician bias exposure with real world data and reveal disparities beyond race or ethnicity.","short_abstract":"The emergency department (ED) is a high stress environment with increased risk of clinician bias exposure. In the United States, Black patients are more likely than other racial/ethnic groups to obtain their first schizophrenia (SCZ) diagnosis in the ED, a highly stigmatizing disorder. Therefore, understanding the link...","url_abs":"https://arxiv.org/abs/2509.02651","url_pdf":"https://arxiv.org/pdf/2509.02651v3","authors":"[\"Alissa A. Valentine\",\"Lauren A. Lepow\",\"Donald Apakama\",\"Lili Chan\",\"Alexander W. Charney\",\"Isotta Landi\"]","published":"2025-09-02T13:53:17Z","proceeding":"q-bio.OT","tasks":"[\"q-bio.OT\",\"cs.LG\"]","methods":"[\"Language Model\"]","has_code":false}