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Researchers Propose Framework to Ensure Equitable Healthcare Algorithms

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By Shania Kennedy

The authors further argue that when race and ethnicity are taken into account, various methodological approaches can be used ensure equitable algorithmic performance. When these data are unavailable, imputing them can enhance opportunities to identify, assess, and combat algorithmic bias in clinical and nonclinical settings, they said.

To illustrate their points, the authors describe two applications in which the imputation of race and ethnicity data has the potential to reduce algorithmic bias: equitable disease screening algorithms using machine learning and equitable pay-for-performance incentives.

Currently, there is significant research emphasis on leveraging “big data” from EHRs to develop machine-learning (ML) algorithms that support clinical decision-making. Many of these algorithms are designed to detect or predict medical conditions and outcomes, such as sepsis-related mortality , pancreatic cancer , and postpartum hemorrhage .

Because these tools affect decisions about who receives care, the type of care they receive, and how the care is provided, it is critical that they do not create or perpetuate biases. Racially or ethnically biased algorithms have significant potential to perpetuate worse outcomes for patients who are already at risk for receiving poorer quality care. In the case of a clinical decision support tool for a life-threatening condition, […]

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