Closing the Health Policy Implementation Gap With Artificial Intelligence.

Implementing America's AI Action Plan in Health Care: Impact on Student and Practice-Based EHR Education.

National artificial intelligence (AI) policy in the United States is accelerating rapidly through initiatives such as America's AI Action Plan, creating urgency for health care organizations to respond. Although these policies are not health care-specific, their implementation has immediate implications for clinical practice because AI is increasingly operationalized through electronic health record (EHR)-embedded workflows. This article argues that a critical policy and practice gap exists: National AI strategies emphasize speed and innovation while insufficiently addressing health care's core operational reality-the EHR as AI infrastructure-and the preparedness of the clinical workforce to safely use AI tools. Using a conceptual framework that links national AI policy momentum, EHR infrastructure, and workforce education, this article demonstrates that most health care organizations already deploy production-level AI through EHRs, including ambient documentation, clinical decision support, predictive analytics, chart summarization, documentation integrity tools, and patient-facing AI portals. As these tools become routine, clinicians increasingly function as the "human in the loop," bearing responsibility for AI-informed decisions without consistent training or governance support. The article highlights evidence from nursing education and workforce studies demonstrating gaps in EHR and AI readiness, particularly among students and newly graduated clinicians. It concludes that workforce education constitutes essential "human infrastructure" for health care AI. Health care executives-especially those accountable for quality and safety-must actively engage in shaping AI implementation by aligning policy advocacy, EHR governance, and EHR-based education to ensure that accelerated AI adoption improves care rather than amplifies risk.

The Value Opportunity from Artificial Intelligence in U.S. Health Care Spending.

The analysis considered the savings potential from full national implementation of three specific AI technologies - machine learning (ML), natural language processing (NLP), and generative AI (genAI) - across administrative and medical expense categories for payers and providers (but excluded onetime implementation costs).

Artificial Intelligence in Primary Health Care Centers: Opportunities, Challenges, and Implications for Nursing Practice

AI is heralded as a transformative force for the primary health care (PHC) sector, offering enhanced clinical decision-making, reduced administrative burden, increased patient engagement, and improved efficiency. This paper explores the opportunities, challenges, ethical issues, and impacts of AI implementation in PHC centers. Existing literature demonstrates positive impacts including improved diagnostic accuracy and precision, increased efficiency, and better patient outcomes. However, wide adoption is hindered by barriers such as data standardization and quality issues, poor organizational readiness, low physician acceptance, and the intention-implementation gap. Serious ethical issues include algorithmic bias, threats to data privacy, dehumanization of care, professional accountability concerns, and insufficient governance frameworks. For nurses, AI presents transformative opportunities, yet concerns remain regarding professional redundancy and loss of therapeutic relationships. Responsible AI implementation in PHC therefore requires nurses as equal stakeholders, with appropriate education and context-specific ethical guidelines.

Harnessing data science and artificial intelligence to advance implementation research and practice.

Implementation science aims to bridge the gap between research evidence and routine health care practice by understanding and optimizing the integration of evidence-based interventions. In this paper, we identify seven persistent challenges limiting implementation progress, including (1) overwhelming volume of implementation materials (e.g., reports, interviews, surveys); (2) contextual variability; (3) complex interactions between contextual factors, interventions, and outcomes; (4) interest holder engagement constraints; (5) equity and access barriers; (6) insufficient or biased data; and (7) concerns around data security, trust, and ethics. We explore how advances in data science and artificial intelligence (AI) offer promising solutions to these challenges by enhancing evidence extraction and synthesis, contextual analysis, interest holder engagement, and adaptation throughout the implementation process. Using a live project in precision oncology as a practical example, we demonstrate how AI tools, such as large language models, clustering algorithms, and sentiment analysis, can support implementation research and practice, from concept (e.g., barriers, facilitators, strategies), extraction, and barrier identification to process mapping. We also introduce ImpleMATE, an AI-enabled platform that integrates implementation science knowledge with dynamic learning health system workflows, enabling continuous knowledge extraction, decision support, and feedback for implementation improvement. While AI offers significant potential to accelerate and scale up implementation efforts, we emphasize the need for ethical oversight, transparency, and human collaboration to ensure responsible, equitable, and impactful application in practice. SPANISH ABSTRACT http://links.lww.com/IJEBH/A661.

Designing Nursing Policy Intelligence for 2040: Japan as a Leading Case

ABSTRACT Aim To propose design requirements for decision‐grade nursing policy intelligence that can translate nursing workforce and professional development signals into implementable policy action for the 2040 horizon. Background Health systems planning for 2040 face population ageing, fiscal constraint and rising care complexity. The central problem is not only estimating nursing supply but converting signals on workforce flows, career development, workload, supervision capacity, retention and service outcomes into timely, legitimate decisions. Japan is used as a leading case because these challenges are already visible. Sources of Evidence This non‐empirical policy analysis used a structured, purposive source‐selection strategy. Searches of two bibliographic nursing and health databases for literature published from January 2014 to 5 May 2026 were supplemented by targeted searches of international and Japanese policy repositories and citation chasing. Sources were charted by evidence type, policy focus, geographic scope and policy relevance. Discussion The analysis argues that nurses’ policy engagement is necessary but insufficient unless health systems also build a governed translation capability. Using career development and continuing professional development as a practical use case, the paper proposes seven design requirements: co‐production through boundary roles; alignment with decision windows; public data stewardship; transparent scenario modelling; rapid‐cycle products; implementation feedback; and safeguards. Conclusion Decision‐grade nursing policy intelligence offers an intelligence‐to‐action pathway for workforce reform under demographic constraint. Japan provides a useful case for specifying adaptable governance conditions. Implications for nursing Intelligence‐to‐action pathways can help nurse managers link professional development, deployment, supervision capacity, workload sustainability and retention. Implications for nursing policy Policymakers and system leaders should treat nursing policy intelligence as shared infrastructure for aligning workforce signals with budget, regulatory and implementation decisions.

Artificial Intelligence Interventions to Support Frontline Healthcare Workers in Low-Resource Settings: A Review