Algorithm Bias and Racial and Ethnic Disparities in Health and Health Care
Dec 15, 2023
auto_awesome
Dr. Marshall H. Chin and Dr. Lucila Ohno-Machado discuss addressing algorithm bias in healthcare to reduce racial disparities. They emphasize the importance of diverse input, transparency, equity promotion, stakeholder collaboration, and ongoing monitoring for fair and patient-centered outcomes.
Racial and ethnic biases in healthcare algorithms require proactive guiding principles for equity promotion.
Transparency, explainability, and accountability are essential for addressing bias in healthcare algorithms.
Deep dives
Importance of Addressing Racial and Ethnic Bias in Healthcare Algorithms
Healthcare algorithms play a significant role in decision-making within clinical care and healthcare, with the potential to improve outcomes but also the risk of perpetuating biases. Instances of racial and ethnic biases in algorithms have been observed in various sectors, indicating a critical need to establish guiding principles to tackle these disparities. Algorithms that learn from biased data can themselves become biased, underscoring the importance of addressing these issues proactively to promote health equity.
Creating Guiding Principles for Algorithm Equity
A panel, including diverse stakeholders, was assembled following a congressional inquiry to investigate bias in healthcare algorithms. Through public engagement and expert input, guiding principles were developed to address racial and ethnic biases in algorithms. The principles emphasize promoting equity throughout the algorithm lifecycle, transparency, community involvement, consideration of fairness issues, and accountability for ensuring fairness and equity.
Implementing Frameworks for Algorithmic Equity
To mitigate bias in healthcare algorithms, a shift towards a patient safety model is suggested, focusing on system improvement rather than individual blame. Transparency, explainability, and accountability are highlighted as critical elements for addressing bias in algorithms. Healthcare organizations and stakeholders are encouraged to integrate these principles into practices to actively advance health equity and ensure ongoing monitoring to prevent biases in evolving populations.
Interview with Marshall H. Chin, MD, MPH, and Lucila Ohno-Machado, MD, PhD, MBA, authors of Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care. Hosted by JAMA Network Open Associate Editor Angel N. Desai, MD, MPH. Related Content: