Natural Language Processing (NLP): Identifying Linguistic Gender Bias in Electronic Medical Records (EMRs).

Abstract

With the rise of feminism, women report experiencing doubt or discrimination in medical settings. This study aims to explore the linguistic mechanisms by which physicians express disbelief toward patients and to investigate gender differences in the use of negative medical descriptions. A content analysis of 285 electronic medical records was conducted to identify 4 linguistic bias features: judging, reporting, quoting, and fudging. Sentiment classification and knowledge graph with ICD-11 were used to determine the prevalence of these features in the medical records, and logistic regression was applied to test gender differences. A total of 2354 descriptions were analyzed, with 64.7% of the patients identified as male. Descriptions of female patients contained fewer judgmental linguistic features but more fudging-related linguistic features compared to male patients (judging: OR 0.69, 95% CI 0.54-0.88, p < 0.01; fudging: OR 1.38, 95% CI 1.09-1.75, p < 0.01). No significant differences were found in the use of reporting (OR 0.95, 95% CI 0.61-1.47, p = 0.81) and quoting (OR 0.99, 95% CI 0.72-1.36, p = 0.96) between male and female patients. This study highlights how physicians may express disbelief toward patients through linguistic biases, particularly through the use of judging and fudging language. Both male and female patients may face different types of systematic bias from physicians, with female patients experiencing more fudging-related language and less judgmental language compared to male patients. These differences point to a potential mechanism through which gender disparities in healthcare quality may arise, underscoring the need for further investigation and action to address these biases.