Health systems rely on commercial prediction algorithms to identify and help patients with complex health needs. We show that a widely used algorithm, typical of this industry-wide approach and affecting millions of patients, exhibits significant racial bias: At a given risk score, Black patients are considerably sicker than White patients, as evidenced by signs of uncontrolled illnesses. Remedying this disparity would increase the percentage of Black patients receiving additional help from 17.7 to 46.5%. The bias arises because the algorithm predicts health care costs rather than illness, but unequal access to care means that we spend less money caring for Black patients than for White patients. Thus, despite health care cost appearing to be an effective proxy for health by some measures of predictive accuracy, large racial biases arise. We suggest that the choice of convenient, seemingly effective proxies for ground truth can be an important source of algorithmic bias in many contexts.

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Background: Racial inequities for patients with heart failure (HF) have been widely documented. HF patients who receive cardiology care during a hospital admission have better outcomes. It is unknown whether there are differences in admission to a cardiology or general medicine service by race. This study examined the relationship between race and admission service, and its effect on 30-day readmission and mortality Methods: We performed a retrospective cohort study from September 2008 to November 2017 at a single large urban academic referral center of all patients self-referred to the emergency department and admitted to either the cardiology or general medicine service with a principal diagnosis of HF, who self-identified as white, black, or Latinx. We used multivariable generalized estimating equation models to assess the relationship between race and admission to the cardiology service. We used Cox regression to assess the association between race, admission service, and 30-day readmission and mortality. Results: Among 1967 unique patients (66.7% white, 23.6% black, and 9.7% Latinx), black and Latinx patients had lower rates of admission to the cardiology service than white patients (adjusted rate ratio, 0.91; 95% CI, 0.84–0.98, for black; adjusted rate ratio, 0.83; 95% CI, 0.72–0.97 for Latinx). Female sex and age >75 years were also independently associated with lower rates of admission to the cardiology service. Admission to the cardiology service was independently associated with decreased readmission within 30 days, independent of race. Conclusions: Black and Latinx patients were less likely to be admitted to cardiology for HF care. This inequity may, in part, drive racial inequities in HF outcomes.

Individual physicians are widely believed to play a large role in patients’ decisions about end-of-life care, but little empirical evidence supports this view. We developed a novel method for measuring the relationship between physicians’ characteristics and hospice enrollment in a nationally representative sample of Medicare patients with poor-prognosis cancer—for whom palliative treatment and hospice would be considered standard of care—who died in the period 2006–11. We found that the proportion of a physician’s patients that were enrolled in hospice was a strong predictor of whether or not their other patients would enroll in hospice. The magnitude of this association was larger than that of other known predictors of hospice enrollment that we examined, including patient medical comorbidity, age, race, and sex. Patients cared for by medical oncologists, and those cared for in not-for-profit hospitals, were significantly more likely to enroll in hospice than other patients. These findings suggest that physician characteristics are one of the strongest predictors of whether a patient receives hospice care—which mounting evidence indicates can improve care quality and reduce costs. Interventions geared toward physicians, both by specialty and by previous history of patients’ hospice enrollment, may help optimize appropriate hospice use.

Role of the Funder/Sponsor: The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.