Algorithms are widely used in healthcare to support diagnosis, treatment, risk assessment and decisions about who receives resources. When they are biased, they can worsen outcomes for racial and ethnic minority groups and other marginalised populations. For example, an algorithm that used race to estimate kidney function gave higher results for Black patients, delaying their referral for organ transplants. Another, used to manage chronic diseases, effectively required Black patients to be sicker than White patients to qualify for extra care.
To address this, two US federal agencies, the Agency for Healthcare Research and Quality and the National Institute on Minority Health and Health Disparities, brought together a diverse expert panel. The panel reviewed the evidence and heard from stakeholders, patients and community members. It developed a framework covering the whole life cycle of an algorithm, from defining the problem and choosing the data to development, deployment and monitoring.
The framework is built on five guiding principles: promote health equity at every stage of an algorithm’s life cycle; ensure algorithms and their use are transparent and explainable; authentically engage patients and communities and earn their trust; explicitly identify fairness issues and trade-offs; and establish accountability for fair and equitable outcomes.
Learn more about this article here: https://doi.org/10.1001/jamanetworkopen.2023.45050
Reference:
Chin, M. H., Afsar-Manesh, N., Bierman, A. S., Chang, C., Colón-Rodríguez, C. J., Dullabh, P., Duran, D. G., Fair, M., Hernandez-Boussard, T., Hightower, M., Jain, A., Jordan, W. B., Konya, S., Moore, R. H., Moore, T. T., Rodriguez, R., Shaheen, G., Snyder, L. P., Srinivasan, M., Umscheid, C. A., & Ohno-Machado, L. (2023). Guiding principles to address the impact of algorithm bias on racial and ethnic disparities in health and health care. JAMA Network Open, 6(12), e2345050.