3 ways to reduce implicit bias in predictive analytics for better health equity

Image courtesy of Arcadia

The COVID-19 pandemic has brought into sharp focus the racioethnic and socioeconomic disparities inherent in the U.S. healthcare system. These disparities take the form of increased adverse health outcomes and reduced quality of life for affected groups.

For example, a study of cities that reported COVID-19 deaths by race and ethnicity found that 34% of deaths were among non-Hispanic Black people. This group accounts for just 12% of the total U.S. population, according to the U.S. Centers for Disease Control and Prevention (CDC), citing “long-standing systemic health and social inequities” among the reasons for the racial and ethnic disparities in COVID-19 deaths.

This heightened awareness around inequities and disparities in healthcare has also resulted in some much-needed attention to similar bias-related problems in the growing sector of healthcare artificial intelligence …

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