Do they speak like me? Exploring how perceptions of linguistic difference may influence patient perceptions of healthcare providers

Lee, Donghee N.; Hutchens, Myiah J.; George, Thomas J.; Wilson-Howard, Danyell; Cooks, Eric J.; Krieger, Janice L.

doi:10.1080/10872981.2022.2107470

Cited by 6 publications

(2 citation statements)

References 44 publications

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“…Socioeconomic status (SES) Bias Work to limit the misunderstanding between patients and providers due to cultural and linguistic differences; keep the positive aspects of the social determinants of health (SDoH) in mind [12,[30][31][32].…”

Section: Clinical Practice Strategiesmentioning

confidence: 99%

“… Selection/Sampling/Population Bias Review and keep in mind basic clinical epidemiological principles (e.g., selection bias , which affects “real-world” study interpretation and generalizability to a representative population) [ 13 , 27 ]. Clinical Practice Strategies Socioeconomic status (SES) Bias Work to limit the misunderstanding between patients and providers due to cultural and linguistic differences; keep the positive aspects of the social determinants of health (SDoH) in mind [ 12 , 30 , 31 , 32 ]. Learned/training/clinical data Boas Engage in continuous monitoring and updating to focus on detecting trends in the MLAGs decisions, instead of the learned bias for improvement; use training data that accurately represent populations under-represented in health care systems [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

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The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective

Franklin,

Stephens,

Piracha

et al. 2024

Life

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Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward.

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Section: Clinical Practice Strategiesmentioning

confidence: 99%