Dissecting racial bias in an algorithm used to manage the health of populations

@article{Obermeyer2019DissectingRB,
  title={Dissecting racial bias in an algorithm used to manage the health of populations},
  author={Ziad Obermeyer and Brian W. Powers and Christine Vogeli and Sendhil Mullainathan},
  journal={Science},
  year={2019},
  volume={366},
  pages={447 - 453}
}
Racial bias in health algorithms The U.S. health care system uses commercial algorithms to guide health decisions. Obermeyer et al. find evidence of racial bias in one widely used algorithm, such that Black patients assigned the same level of risk by the algorithm are sicker than White patients (see the Perspective by Benjamin). The authors estimated that this racial bias reduces the number of Black patients identified for extra care by more than half. Bias occurs because the algorithm uses… 
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References

SHOWING 1-10 OF 51 REFERENCES
Racial bias in pain assessment and treatment recommendations, and false beliefs about biological differences between blacks and whites
TLDR
It is shown that a substantial number of white laypeople and medical students and residents hold false beliefs about biological differences between blacks and whites and this work demonstrates that these beliefs predict racial bias in pain perception and treatment recommendation accuracy.
Does Diversity Matter for Health? Experimental Evidence from Oakland
TLDR
The findings suggest black doctors could reduce the black-white male gap in cardiovascular mortality by 19 percent and the effect of physician workforce diversity on the demand for preventive care among African American men.
Socioeconomic inequalities in health. No easy solution.
TLDR
Health insurance coverage alone is not likely to reduce significantly SES differences in health, and attention should be paid both in policy decisions and in clinical practice to other SES-related factors that may influence patterns of health and disease.
Racial/ethnic differences in physician distrust in the United States.
TLDR
Racial/ethnic differences in physician distrust are less uniform than previously hypothesized, with substantial geographic and individual variation present.
Tuskegee and the Health of Black Men
TLDR
It is found that the disclosure of the Tuskegee Study of Untreated Syphilis in the Negro Male in 1972 is correlated with increases in medical mistrust and mortality and decreases in both outpatient and inpatient physician interactions for older black men.
The challenge of multiple comorbidity for the US health care system.
TLDR
One area in which some initial progress is being made to reduce the burden of multiple chronic conditions on society is advancing evidence-based clinical decision making in the care for patients with comorbidities.
Adjusting for social risk factors impacts performance and penalties in the hospital readmissions reduction program
TLDR
Accounting for social risk factors can have a major financial impact on safety-net hospitals, and adjustment for these factors could reduce negative unintended consequences of the HRRP.
The Paradox of Automation as Anti-Bias Intervention
A received wisdom is that automated decision-making serves as an anti-bias intervention. The conceit is that removing humans from the decision-making process will also eliminate human bias. The
Big Data's Disparate Impact
Advocates of algorithmic techniques like data mining argue that these techniques eliminate human biases from the decision-making process. But an algorithm is only as good as the data it works with.
...
...