Quantifying Clinician Bias and its Effects on Schizophrenia Diagnosis in the Emergency Department of the Mount Sinai Health System
- URL: http://arxiv.org/abs/2509.02651v1
- Date: Tue, 02 Sep 2025 13:53:17 GMT
- Title: Quantifying Clinician Bias and its Effects on Schizophrenia Diagnosis in the Emergency Department of the Mount Sinai Health System
- Authors: Alissa A. Valentine, Lauren A. Lepow, Lili Chan, Alexander W. Charney, Isotta Landi,
- Abstract summary: In the United States, schizophrenia (SCZ) carries a race and sex disparity that may be explained by clinician bias.<n>We investigated the effects of clinician bias on SCZ diagnosis while controlling for known risk factors and patient sociodemographic information.
- Score: 35.98898736539817
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the United States, schizophrenia (SCZ) carries a race and sex disparity that may be explained by clinician bias - a belief held by a clinician about a patient that prevents impartial clinical decision making. The emergency department (ED) is marked by higher rates of stress that lead to clinicians relying more on implicit biases during decision making. In this work, we considered a large cohort of psychiatric patients in the ED from the Mount Sinai Health System (MSHS) in New York City to investigate the effects of clinician bias on SCZ diagnosis while controlling for known risk factors and patient sociodemographic information. Clinician bias was quantified as the ratio of negative to total sentences within a patient's first ED note. We utilized a logistic regression to predict SCZ diagnosis given patient race, sex, age, history of trauma or substance use disorder, and the ratio of negative sentences. Our findings showed that an increased ratio of negative sentences is associated with higher odds of obtaining a SCZ diagnosis [OR (95% CI)=1.408 (1.361-1.456)]. Identifying as male [OR (95% CI)=1.112 (1.055-1.173)] or Black [OR (95% CI)=1.081(1.031-1.133)] increased one's odds of being diagnosed with SCZ. However, from an intersectional lens, Black female patients with high SES have the highest odds of obtaining a SCZ diagnosis [OR (95% CI)=1.629 (1.535-1.729)]. Results such as these suggest that SES does not act as a protective buffer against SCZ diagnosis in all patients, demanding more attention to the quantification of health disparities. Lastly, we demonstrated that clinician bias is operational with real world data and related to increased odds of obtaining a stigmatizing diagnosis such as SCZ.
Related papers
- Intersectional Fairness in Vision-Language Models for Medical Image Disease Classification [25.30858592524878]
Cross-Modal Alignment Consistency (CMAC-MMD) is a training framework that standardises diagnostic certainty across intersectional patient subgroups.<n>In the dermatology cohort, the proposed method reduced the overall intersectional missed diagnosis gap (difference in True Positive Rate, $$TPR) from 0.50 to 0.26.<n>For glaucoma screening, the method reduced $$TPR from 0.41 to 0.31, achieving a better AUC of 0.72 (vs. 0.71 baseline)
arXiv Detail & Related papers (2025-12-17T09:47:29Z) - MoodAngels: A Retrieval-augmented Multi-agent Framework for Psychiatry Diagnosis [58.67342568632529]
MoodAngels is the first specialized multi-agent framework for mood disorder diagnosis.<n>MoodSyn is an open-source dataset of 1,173 synthetic psychiatric cases.
arXiv Detail & Related papers (2025-06-04T09:18:25Z) - Detecting clinician implicit biases in diagnoses using proximal causal inference [17.541477183671912]
We propose a causal inference approach to detect the effect of clinician implicit biases on patient outcomes in large-scale medical data.<n>We test our method on real-world data from the UK Biobank.
arXiv Detail & Related papers (2025-01-27T05:48:15Z) - Fair Machine Learning for Healthcare Requires Recognizing the Intersectionality of Sociodemographic Factors, a Case Study [41.94295877935867]
Socioeconomic status (SES) is commonly included in machine learning models to control for health inequities.
Within an intersectional framework, patient SES, race, and sex were found to have significant interactions.
Increased SES is associated with a higher probability of obtaining a schizophrenia diagnosis in Black Americans.
Whereas high SES acts as a protective factor for SCZ diagnosis in White Americans.
arXiv Detail & Related papers (2024-05-30T19:10:35Z) - Evaluating Echo State Network for Parkinson's Disease Prediction using
Voice Features [1.2289361708127877]
This study aims to develop a diagnostic model capable of achieving both high accuracy and minimizing false negatives.
Various machine learning methods, including Echo State Networks (ESN), Random Forest, k-nearest Neighbors, Support Vector, Extreme Gradient Boosting, and Decision Tree, are employed and thoroughly evaluated.
ESN consistently maintains a false negative rate of less than 8% in 83% of cases.
arXiv Detail & Related papers (2024-01-28T14:39:43Z) - Perceptual Features as Markers of Parkinson's Disease: The Issue of
Clinical Interpretability [0.0]
Up to 90% of patients with Parkinson's disease (PD) suffer from hypokinetic dysathria (HD)
This paper provides large and robust insight into analysis of 5 Czech vowels of 84 PD patients.
arXiv Detail & Related papers (2022-03-21T09:46:48Z) - VIRDOC: Statistical and Machine Learning by a VIRtual DOCtor to Predict
Dengue Fatality [0.0]
Clinicians conduct routine diagnosis by scrutinizing signs and symptoms of patients in treating epidemics.
The success of the therapeutic regimen relies largely on the accuracy of interpretation of such sign-symptoms, based on which the clinician ranks the potent causes of the epidemic and analyzes their interdependence to devise sustainable containment strategies.
This study proposed an alternative medical front, a VIRtual DOCtor (VIRDOC), that can self-consistently rank key contributors of an epidemic and also correctly identify the infection stage, using the language of statistical modelling and Machine Learning.
arXiv Detail & Related papers (2021-04-29T12:06:05Z) - Clinical Outcome Prediction from Admission Notes using Self-Supervised
Knowledge Integration [55.88616573143478]
Outcome prediction from clinical text can prevent doctors from overlooking possible risks.
Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets.
We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
arXiv Detail & Related papers (2021-02-08T10:26:44Z) - Integrative Analysis for COVID-19 Patient Outcome Prediction [53.11258640541513]
We combine radiomics of lung opacities and non-imaging features from demographic data, vital signs, and laboratory findings to predict need for intensive care unit admission.
Our methods may also be applied to other lung diseases including but not limited to community acquired pneumonia.
arXiv Detail & Related papers (2020-07-20T19:08:50Z) - Hemogram Data as a Tool for Decision-making in COVID-19 Management:
Applications to Resource Scarcity Scenarios [62.997667081978825]
COVID-19 pandemics has challenged emergency response systems worldwide, with widespread reports of essential services breakdown and collapse of health care structure.
This work describes a machine learning model derived from hemogram exam data performed in symptomatic patients.
Proposed models can predict COVID-19 qRT-PCR results in symptomatic individuals with high accuracy, sensitivity and specificity.
arXiv Detail & Related papers (2020-05-10T01:45:03Z) - Towards Causality-Aware Inferring: A Sequential Discriminative Approach
for Medical Diagnosis [142.90770786804507]
Medical diagnosis assistant (MDA) aims to build an interactive diagnostic agent to sequentially inquire about symptoms for discriminating diseases.
This work attempts to address these critical issues in MDA by taking advantage of the causal diagram.
We propose a propensity-based patient simulator to effectively answer unrecorded inquiry by drawing knowledge from the other records.
arXiv Detail & Related papers (2020-03-14T02:05:54Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.