Cary Michael P, Bessias Sophia, McCall Jonathan, Pencina Michael J, Grady Siobahn D, Lytle Kay, Economou-Zavlanos Nicoleta J
Duke University School of Nursing, Durham, North Carolina, USA.
Duke University School of Medicine, Durham, North Carolina, USA.
J Nurs Scholarsh. 2025 Jan;57(1):130-139. doi: 10.1111/jnu.13007. Epub 2024 Jul 29.
The concept of health equity by design encompasses a multifaceted approach that integrates actions aimed at eliminating biased, unjust, and correctable differences among groups of people as a fundamental element in the design of algorithms. As algorithmic tools are increasingly integrated into clinical practice at multiple levels, nurses are uniquely positioned to address challenges posed by the historical marginalization of minority groups and its intersections with the use of "big data" in healthcare settings; however, a coherent framework is needed to ensure that nurses receive appropriate training in these domains and are equipped to act effectively.
We introduce the Bias Elimination for Fair AI in Healthcare (BE FAIR) framework, a comprehensive strategic approach that incorporates principles of health equity by design, for nurses to employ when seeking to mitigate bias and prevent discriminatory practices arising from the use of clinical algorithms in healthcare. By using examples from a "real-world" AI governance framework, we aim to initiate a wider discourse on equipping nurses with the skills needed to champion the BE FAIR initiative.
Drawing on principles recently articulated by the Office of the National Coordinator for Health Information Technology, we conducted a critical examination of the concept of health equity by design. We also reviewed recent literature describing the risks of artificial intelligence (AI) technologies in healthcare as well as their potential for advancing health equity. Building on this context, we describe the BE FAIR framework, which has the potential to enable nurses to take a leadership role within health systems by implementing a governance structure to oversee the fairness and quality of clinical algorithms. We then examine leading frameworks for promoting health equity to inform the operationalization of BE FAIR within a local AI governance framework.
The application of the BE FAIR framework within the context of a working governance system for clinical AI technologies demonstrates how nurses can leverage their expertise to support the development and deployment of clinical algorithms, mitigating risks such as bias and promoting ethical, high-quality care powered by big data and AI technologies.
As health systems learn how well-intentioned clinical algorithms can potentially perpetuate health disparities, we have an opportunity and an obligation to do better. New efforts empowering nurses to advocate for BE FAIR, involving them in AI governance, data collection methods, and the evaluation of tools intended to reduce bias, mark important steps in achieving equitable healthcare for all.
设计中的健康公平概念包含多方面的方法，该方法将旨在消除人群群体间有偏见、不公正且可纠正差异的行动作为算法设计的基本要素。随着算法工具在多个层面越来越多地融入临床实践，护士在应对少数群体历史边缘化及其与医疗环境中“大数据”使用的交叉所带来的挑战方面具有独特的地位；然而，需要一个连贯的框架来确保护士在这些领域接受适当的培训并具备有效行动的能力。
我们引入医疗保健领域公平人工智能的偏见消除（BE FAIR）框架，这是一种全面的战略方法，融合了设计中的健康公平原则，供护士在寻求减轻偏见并防止医疗保健中使用临床算法产生歧视性做法时采用。通过使用“现实世界”人工智能治理框架中的示例，我们旨在引发关于使护士具备支持BE FAIR倡议所需技能的更广泛讨论。
借鉴美国卫生信息技术国家协调办公室最近阐明的原则，我们对设计中的健康公平概念进行了批判性审视。我们还回顾了描述人工智能（AI）技术在医疗保健中的风险及其促进健康公平潜力的近期文献。在此背景基础上，我们描述了BE FAIR框架，该框架有可能使护士通过实施治理结构来监督临床算法的公平性和质量，从而在卫生系统中发挥领导作用。然后，我们研究促进健康公平的领先框架，以为BE FAIR在地方人工智能治理框架中的实施提供参考。
BE FAIR框架在临床人工智能技术的有效治理系统背景下的应用表明，护士如何能够利用其专业知识来支持临床算法的开发和部署，减轻诸如偏见等风险，并促进由大数据和人工智能技术推动的符合伦理的高质量护理。
随着卫生系统了解善意的临床算法如何可能使健康差距长期存在，我们有机会也有义务做得更好。使护士能够倡导BE FAIR、让他们参与人工智能治理、数据收集方法以及旨在减少偏见的工具评估的新努力，是实现全民公平医疗保健的重要步骤。
