# Bias Detection in Emergency Psychiatry: Linking Negative Language to Diagnostic Disparities

arXiv: https://arxiv.org/abs/2509.02651 · HTML: https://mlsanity.com/paper/2509.02651/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 64 references that are in the map.
- First posted on arXiv 2 Sep 2025.

## Builds on (most cited first, 7 of 7)

- [The Ethics of ChatGPT in Medicine and Healthcare: A Systematic Review on Large Language Models (LLMs)](https://mlsanity.com/paper/2403.14473/) (2024)
- [Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python](https://mlsanity.com/paper/2106.07799/) (2021)
- [Distinguishing Clinical Sentiment: The Importance of Domain Adaptation in Psychiatric Patient Health Records](https://mlsanity.com/paper/1904.03225/) (2019)
- [Clinical Text Deduplication Practices for Efficient Pretraining and Improved Clinical Tasks](https://mlsanity.com/paper/2312.09469/) (2023)
- [Assessing the Efficacy of Clinical Sentiment Analysis and Topic Extraction in Psychiatric Readmission Risk Prediction](https://mlsanity.com/paper/1910.04006/) (2019)
- [The Point of View of a Sentiment: Towards Clinician Bias Detection in Psychiatric Notes](https://mlsanity.com/paper/2405.20582/) (2024)
- [Fair Machine Learning for Healthcare Requires Recognizing the Intersectionality of Sociodemographic Factors, a Case Study](https://mlsanity.com/paper/2407.15006/) (2024)

## Built on by (most cited first, 1 of 1)

- [EQUITRIAGE: A Fairness Audit of Gender Bias in LLM-Based Emergency Department Triage](https://mlsanity.com/paper/2605.03998/) (2026)

## Abstract (arXiv)

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.