Racial and Gender Discrimination in Hand Surgery Letters of Recommendation

Bradford, Perry S; Akyeampong, Daniel; Fleming, Mark; Dacus, A. Rashard; Chhabra, Anikar; DeGeorge, Brent R.

doi:10.1016/j.jhsa.2021.07.009

Cited by 30 publications

(21 citation statements)

References 24 publications

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“…When preparing this article, we were struck by the lack of research on the content and importance of letters of recommendation for nurses, although colleagues in medicine have studied these areas. 4,13,15 The processes and outcomes of providing recommendation letters to nursing students and colleagues should be formally studied, and nursing should consider adopting standardization for letters of recommendation to eliminate variability and to provide more concrete data for decision makers. 17 There are times when letters of recommendation need to be more comprehensive; the higher the rank or aspirations of the applicant, the more detailed the letter should be.…”

Section: Discussionmentioning

confidence: 99%

How to Write Powerful Letters of Recommendation

Wendler¹,

Vortman²

2023

AJN, American Journal of Nursing

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Section: Discussionmentioning

confidence: 99%

How to Write Powerful Letters of Recommendation

Wendler¹,

Vortman²

2023

AJN, American Journal of Nursing

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“…If a racial category was over 50%, it was recorded as the race of the cardiologist. 16 If the race or sex was not able to be obtained for a given cardiologist, it was recorded as other. The sex or race determination was consistently done by author P.K.…”

Section: Methodsmentioning

confidence: 99%

Patient‐Centered Insights and Biases Regarding Cardiologists Via Online Review Platform Analysis

Mark

Oswald

Kundar

et al. 2023

JAHA

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Background Online cardiologist reviews, such as those on the Yelp website, are a frequently used method for patients to find a cardiologist. It remains unknown how bias may influence such reviews. Our objectives for this study were to (1) determine which cardiologist‐ or practice‐related factors influence the overall rating of cardiologists and patient satisfaction and (2) discover any associations between sex and race with the overall rating of cardiologists or with cardiologist‐ or practice‐related factors. Methods and Results Cardiologist Yelp reviews from practices in the United States from 2007 to 2020 were analyzed. A total of 563 reviews were coded for positive and negative themes. Binary logistic regression was used to determine whether certain factors increased the likelihood of high ratings. Chi‐squared tests were used to determine associations between sex and race with certain factors and overall cardiologist ratings. Cardiologists were more likely to receive higher ratings when reviewers noted the characteristics of competency/knowledge base and thoroughness, positive interactions with staff, and when the cardiologist's name was mentioned in the review. Negative interactions with staff were associated with lower ratings. Female cardiologists received lower ratings and more negative mentions of cardiologist–patient communication than expected. White and Black cardiologists received lower ratings than expected compared with other racial groups. Conclusions Patient‐perceived cardiologist competency, thoroughness, and positive staff interactions were associated with positive reviews in online assessments. Sex and racial differences were also found. Further research must be done to confirm these findings and to understand the association of online reviews with clinical care and patient outcomes.

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“…Notwithstanding excellent overall prediction accuracy, some deep learning systems have been found to have higher rates of underdiagnosis for darker-skinned individuals, such as in the detection of melanoma from skin photographs, or segmentation of cardiac magnetic resonance imaging and pathologies on chest radiographs. 4 A similar example within ophthalmology is the wide variation in diagnostic accuracy (73% vs 60.5%) for light-skinned vs dark-skinned individuals in a deep learning model designed to detect diabetic retinopathy on color fundus photographs. 5 Racial differences between dark-vs light-skinned individuals may also be associated with phenotypic variations such as skin tones, retinal fundus pigmentation, optic disc size, and retinal vessel caliber.…”

Section: Related Article Page 543mentioning

confidence: 99%

“…The effects of conscious and unconscious biases in medicine have rightly become an important area of research, education, and advocacy. It has been well-documented that clinicians can be biased by patients’ gender, sexual orientation, weight, race, ethnicity, etc, which can ultimately affect diagnoses and subsequent treatment decisions . With the number of applications of artificial intelligence (AI) algorithms in clinical medicine increasing, so has the attention paid to their potential biases .…”

Section: Introductionmentioning

confidence: 99%

“…It has been welldocumented that clinicians can be biased by patients' gender, sexual orientation, weight, race, ethnicity, etc, which can ultimately affect diagnoses and subsequent treatment decisions. [1][2][3][4][5][6] With the number of applications of artificial intelligence (AI) algorithms in clinical medicine increasing, so has the attention paid to their potential biases. [6][7][8] Because AI can be deployed at a higher scale and across more populations than individual clinicians, the transferred harm from their biases is greater.…”

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Association of Biomarker-Based Artificial Intelligence With Risk of Racial Bias in Retinal Images

et al. 2023

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ImportanceAlthough race is a social construct, it is associated with variations in skin and retinal pigmentation. Image-based medical artificial intelligence (AI) algorithms that use images of these organs have the potential to learn features associated with self-reported race (SRR), which increases the risk of racially biased performance in diagnostic tasks; understanding whether this information can be removed, without affecting the performance of AI algorithms, is critical in reducing the risk of racial bias in medical AI.ObjectiveTo evaluate whether converting color fundus photographs to retinal vessel maps (RVMs) of infants screened for retinopathy of prematurity (ROP) removes the risk for racial bias.Design, Setting, and ParticipantsThe retinal fundus images (RFIs) of neonates with parent-reported Black or White race were collected for this study. A u-net, a convolutional neural network (CNN) that provides precise segmentation for biomedical images, was used to segment the major arteries and veins in RFIs into grayscale RVMs, which were subsequently thresholded, binarized, and/or skeletonized. CNNs were trained with patients’ SRR labels on color RFIs, raw RVMs, and thresholded, binarized, or skeletonized RVMs. Study data were analyzed from July 1 to September 28, 2021.Main Outcomes and MeasuresArea under the precision-recall curve (AUC-PR) and area under the receiver operating characteristic curve (AUROC) at both the image and eye level for classification of SRR.ResultsA total of 4095 RFIs were collected from 245 neonates with parent-reported Black (94 [38.4%]; mean [SD] age, 27.2 [2.3] weeks; 55 majority sex [58.5%]) or White (151 [61.6%]; mean [SD] age, 27.6 [2.3] weeks, 80 majority sex [53.0%]) race. CNNs inferred SRR from RFIs nearly perfectly (image-level AUC-PR, 0.999; 95% CI, 0.999-1.000; infant-level AUC-PR, 1.000; 95% CI, 0.999-1.000). Raw RVMs were nearly as informative as color RFIs (image-level AUC-PR, 0.938; 95% CI, 0.926-0.950; infant-level AUC-PR, 0.995; 95% CI, 0.992-0.998). Ultimately, CNNs were able to learn whether RFIs or RVMs were from Black or White infants regardless of whether images contained color, vessel segmentation brightness differences were nullified, or vessel segmentation widths were uniform.Conclusions and RelevanceResults of this diagnostic study suggest that it can be very challenging to remove information relevant to SRR from fundus photographs. As a result, AI algorithms trained on fundus photographs have the potential for biased performance in practice, even if based on biomarkers rather than raw images. Regardless of the methodology used for training AI, evaluating performance in relevant subpopulations is critical.

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Racial and Gender Discrimination in Hand Surgery Letters of Recommendation

Cited by 30 publications

References 24 publications

How to Write Powerful Letters of Recommendation

How to Write Powerful Letters of Recommendation

Patient‐Centered Insights and Biases Regarding Cardiologists Via Online Review Platform Analysis

Association of Biomarker-Based Artificial Intelligence With Risk of Racial Bias in Retinal Images

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