Dissecting racial bias in an algorithm used to manage the health of populations
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 health costs as a proxy for health needs. Less money is spent on Black patients who have the same level of need, and the algorithm thus falsely concludes that Black patients are healthier than equally sick White patients. Reformulating the algorithm so that it no longer uses costs as a proxy for needs eliminates the racial bias in predicting who needs extra care. Science , this issue p. 447 ; see also p. 421
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 health costs as a proxy for health needs. Less money is spent on Black patients who have the same level of need, and the algorithm thus falsely concludes that Black patients are healthier than equally sick White patients. Reformulating the algorithm so that it no longer uses costs as a proxy for needs eliminates the racial bias in predicting who needs extra care. Science , this issue p. 447 ; see also p. 421
Executive Summary
The article examines racial bias in a widely used health algorithm, revealing that it prioritizes White patients over Black patients with the same level of risk. The bias stems from the algorithm's use of health costs as a proxy for health needs, resulting in Black patients being deemed healthier than equally sick White patients. By reformulating the algorithm to remove cost-based proxies, the racial bias can be eliminated, ensuring more accurate identification of patients requiring extra care.
Key Points
- ▸ Racial bias in health algorithms can lead to unequal treatment of patients
- ▸ The use of health costs as a proxy for health needs can perpetuate bias
- ▸ Reformulating algorithms to remove cost-based proxies can eliminate racial bias
Merits
Exposure of algorithmic bias
The study sheds light on the existence of racial bias in health algorithms, highlighting the need for increased scrutiny and regulation of these tools.
Demerits
Limited scope
The study focuses on a single algorithm, and its findings may not be generalizable to all health algorithms or contexts.
Expert Commentary
The study's findings underscore the importance of critically evaluating the assumptions and methodologies underlying health algorithms. By recognizing the potential for bias in these tools, we can work towards creating more inclusive and equitable healthcare systems. The article's recommendation to reformulate algorithms to remove cost-based proxies is a crucial step towards addressing these disparities. However, it is essential to consider the complexities of healthcare systems and the need for nuanced, context-specific solutions.
Recommendations
- ✓ Develop and implement more equitable health algorithms that account for the social determinants of health
- ✓ Establish regulatory guidelines for the development and deployment of health algorithms to prevent bias and ensure fairness.
