{"title":"Regulating AI in Nursing and Healthcare: Ensuring Safety, Equity, and Accessibility in the Era of Federal Innovation Policy.","authors":"Y Tony Yang, Richard Ricciardi","doi":"10.1177/15271544251381228","DOIUrl":null,"url":null,"abstract":"<p><p>The rapid integration of artificial intelligence in healthcare, accelerated by the Trump administration's 2025 AI Action Plan and private sector innovations from companies like Nvidia and Hippocratic AI, poses urgent challenges for nursing and health policy. This policy analysis examines the intersection of federal AI initiatives, emerging healthcare technologies, and nursing workforce implications through document analysis of regulatory frameworks, the federal AI Action Plan's 90+ initiatives, and insights from the American Academy of Nursing's November 2024 policy dialogue on AI transformation. The analysis reveals that while AI demonstrates measurable improvements in discrete clinical tasks-including 16% better medication assessment accuracy and 43% greater precision in identifying drug interactions at $9 per hour compared to nurses' median $41.38 hourly wage-current federal policy lacks critical healthcare-specific safeguards. The AI Action Plan's emphasis on rapid deployment and deregulation fails to address safety-net infrastructure needs, implementation pathways for vulnerable populations, or mechanisms ensuring health equity. Evidence from the Academy dialogue indicates that AI's \"technosocial reality\" fundamentally alters care delivery while potentially exacerbating disparities in underserved communities, as demonstrated by algorithmic bias in systems like Optum's care allocation algorithm. The findings suggest that achieving equitable AI integration requires comprehensive regulatory frameworks coordinating FDA, CMS, OCR, and HRSA oversight; community-centered governance approaches redistributing decision-making power to affected populations; and nursing leadership in AI development to preserve patient-centered care values. Without proactive nursing engagement in AI governance, healthcare risks adopting technologies that prioritize efficiency over the holistic, compassionate care fundamental to nursing practice.</p>","PeriodicalId":53177,"journal":{"name":"Policy, Politics, and Nursing Practice","volume":" ","pages":"15271544251381228"},"PeriodicalIF":2.6000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Policy, Politics, and Nursing Practice","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/15271544251381228","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH POLICY & SERVICES","Score":null,"Total":0}
引用次数: 0
Abstract
The rapid integration of artificial intelligence in healthcare, accelerated by the Trump administration's 2025 AI Action Plan and private sector innovations from companies like Nvidia and Hippocratic AI, poses urgent challenges for nursing and health policy. This policy analysis examines the intersection of federal AI initiatives, emerging healthcare technologies, and nursing workforce implications through document analysis of regulatory frameworks, the federal AI Action Plan's 90+ initiatives, and insights from the American Academy of Nursing's November 2024 policy dialogue on AI transformation. The analysis reveals that while AI demonstrates measurable improvements in discrete clinical tasks-including 16% better medication assessment accuracy and 43% greater precision in identifying drug interactions at $9 per hour compared to nurses' median $41.38 hourly wage-current federal policy lacks critical healthcare-specific safeguards. The AI Action Plan's emphasis on rapid deployment and deregulation fails to address safety-net infrastructure needs, implementation pathways for vulnerable populations, or mechanisms ensuring health equity. Evidence from the Academy dialogue indicates that AI's "technosocial reality" fundamentally alters care delivery while potentially exacerbating disparities in underserved communities, as demonstrated by algorithmic bias in systems like Optum's care allocation algorithm. The findings suggest that achieving equitable AI integration requires comprehensive regulatory frameworks coordinating FDA, CMS, OCR, and HRSA oversight; community-centered governance approaches redistributing decision-making power to affected populations; and nursing leadership in AI development to preserve patient-centered care values. Without proactive nursing engagement in AI governance, healthcare risks adopting technologies that prioritize efficiency over the holistic, compassionate care fundamental to nursing practice.
特朗普政府的《2025人工智能行动计划》(2025 AI Action Plan)以及英伟达(Nvidia)和希波克拉底人工智能(Hippocratic AI)等公司的私营部门创新加速了人工智能在医疗保健领域的快速整合,这给护理和卫生政策带来了紧迫的挑战。本政策分析通过对监管框架、联邦人工智能行动计划的90多项举措的文件分析,以及美国护理学会2024年11月关于人工智能转型的政策对话的见解,研究了联邦人工智能举措、新兴医疗保健技术和护理劳动力影响的交集。分析显示,虽然人工智能在离散的临床任务中显示出可衡量的改善——包括药物评估准确性提高16%,识别药物相互作用的准确性提高43%,而护士的时薪中位数为41.38美元,每小时9美元——但目前的联邦政策缺乏关键的医疗保健特定保障措施。人工智能行动计划强调快速部署和放松管制,但未能解决安全网基础设施需求、针对弱势群体的实施途径或确保卫生公平的机制。来自学院对话的证据表明,人工智能的“技术社会现实”从根本上改变了医疗服务,同时可能加剧服务不足社区的差距,正如Optum的医疗分配算法等系统中的算法偏见所证明的那样。研究结果表明,实现公平的人工智能整合需要全面的监管框架来协调FDA、CMS、OCR和HRSA的监督;以社区为中心的治理方法将决策权重新分配给受影响人群;以及在人工智能开发中的护理领导地位,以维护以患者为中心的护理价值观。如果护理人员不积极参与人工智能治理,医疗保健就有可能采用优先考虑效率的技术,而不是护理实践中最基本的整体、富有同情心的护理。
期刊介绍:
Policy, Politics & Nursing Practice is a quarterly, peer-reviewed journal that explores the multiple relationships between nursing and health policy. It serves as a major source of data-based study, policy analysis and discussion on timely, relevant policy issues for nurses in a broad variety of roles and settings, and for others outside of nursing who are interested in nursing-related policy issues.