The use of algorithms to aid in decision making in health care has had notable success, with algorithmic predictions often more accurate than clinical assessments.
As the use of algorithms increases across health care applications, authors Irineo Cabreros and colleagues note that algorithms can lead to unintended racially discriminatory practices, raising concerns about the potential for algorithmic bias.
So, how can the health care industry promote racial and ethnic equity in algorithms?
Cabreros and colleagues argue that race and ethnicity must be measured, either directly through self-report or indirectly through imputation. The authors proceed to make recommendations for imputing methods that can mitigate potential biases.
Today in Health Affairs Forefront, Michael Losak and coauthors argue that student loan forgiveness for front-line health care workers would have a minimal impact on macroeconomic trends but would have far-reaching effects for the millions who have willingly placed themselves in harm’s way for the
past two years.
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