Guiding Principles to Address the Impact of Algorithm Bias on Racial and Ethnic Disparities in Health and Health Care

Author:

Chin Marshall H.1,Afsar-Manesh Nasim2,Bierman Arlene S.3,Chang Christine3,Colón-Rodríguez Caleb J.4,Dullabh Prashila5,Duran Deborah Guadalupe6,Fair Malika7,Hernandez-Boussard Tina8,Hightower Maia9,Jain Anjali3,Jordan William B.10,Konya Stephen11,Moore Roslyn Holliday4,Moore Tamra Tyree12,Rodriguez Richard13,Shaheen Gauher14,Snyder Lynne Page5,Srinivasan Mithuna5,Umscheid Craig A.3,Ohno-Machado Lucila15

Affiliation:

1. University of Chicago, Chicago, Illinois

2. Oracle Health, Austin, Texas

3. Agency for Healthcare Research and Quality, Rockville, Maryland

4. US Department of Health and Human Services Office of Minority Health, Rockville, Maryland

5. NORC at the University of Chicago, Bethesda, Maryland

6. National Institute on Minority Health and Health Disparities, Bethesda, Maryland

7. Association of American Medical Colleges, Washington, DC

8. Stanford University, Stanford, California

9. Equality AI, Park City, Utah

10. American Medical Association, Chicago, Illinois

11. Office of the National Coordinator for Health Information Technology, Washington, DC

12. Prudential Financial, Arlington, Virginia

13. NORC at the University of Chicago, Chicago, Illinois

14. Elevance Health, Indianapolis, Indiana

15. Yale School of Medicine, New Haven, Connecticut

Abstract

ImportanceHealth care algorithms are used for diagnosis, treatment, prognosis, risk stratification, and allocation of resources. Bias in the development and use of algorithms can lead to worse outcomes for racial and ethnic minoritized groups and other historically marginalized populations such as individuals with lower income.ObjectiveTo provide a conceptual framework and guiding principles for mitigating and preventing bias in health care algorithms to promote health and health care equity.Evidence ReviewThe Agency for Healthcare Research and Quality and the National Institute for Minority Health and Health Disparities convened a diverse panel of experts to review evidence, hear from stakeholders, and receive community feedback.FindingsThe panel developed a conceptual framework to apply guiding principles across an algorithm’s life cycle, centering health and health care equity for patients and communities as the goal, within the wider context of structural racism and discrimination. Multiple stakeholders can mitigate and prevent bias at each phase of the algorithm life cycle, including problem formulation (phase 1); data selection, assessment, and management (phase 2); algorithm development, training, and validation (phase 3); deployment and integration of algorithms in intended settings (phase 4); and algorithm monitoring, maintenance, updating, or deimplementation (phase 5). Five principles should guide these efforts: (1) promote health and health care equity during all phases of the health care algorithm life cycle; (2) ensure health care algorithms and their use are transparent and explainable; (3) authentically engage patients and communities during all phases of the health care algorithm life cycle and earn trustworthiness; (4) explicitly identify health care algorithmic fairness issues and trade-offs; and (5) establish accountability for equity and fairness in outcomes from health care algorithms.Conclusions and RelevanceMultiple stakeholders must partner to create systems, processes, regulations, incentives, standards, and policies to mitigate and prevent algorithmic bias. Reforms should implement guiding principles that support promotion of health and health care equity in all phases of the algorithm life cycle as well as transparency and explainability, authentic community engagement and ethical partnerships, explicit identification of fairness issues and trade-offs, and accountability for equity and fairness.

Publisher

American Medical Association (AMA)

Subject

General Medicine

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