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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 strategi…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-1575· cite & verify
Implementing America's AI Action Plan in Health Care: Impact on Student and Practice-Based EHR Education
Construction site tour of the Skilled Nursing Residence, in Edgartown, on the island of Martha's Vineyard, in Dukes County, Maryland on August 5, 2024 - 43 by USDAgov. Public domain

In brief

This conceptual article argues that national AI policy in the United States, including America's AI Action Plan, is moving quickly but pays too little attention to how AI reaches clinical practice. It says most health care organizations already run production-level AI inside electronic health records, such as ambient documentation, decision support, predictive analytics, chart summarization, and patient-facing portals.

The matter is significant because clinicians are expected to take responsibility for AI-informed decisions, yet the article says their preparation is uneven, especially for students and new graduates. What remains open is how far these gaps affect patient outcomes, since the piece is an argument built on a conceptual framework and cited nursing education and workforce studies, not a new measured trial.

Main points

  1. America's AI Action Plan is not health care-specific, but its implementation has immediate implications for clinical practice.
  2. The EHR functions as AI infrastructure, carrying ambient documentation, clinical decision support, predictive analytics, and chart summarization.
  3. Documentation integrity tools and patient-facing AI portals are also listed among tools already deployed through EHRs.
  4. Nursing education and workforce studies are cited as showing gaps in EHR and AI readiness among students and new graduates.
  5. Health care executives responsible for quality and safety are urged to align policy advocacy, EHR governance, and EHR-based education.

The problem

Clinicians, especially students and new graduates, bear responsibility for AI-informed decisions made through EHR-embedded tools without consistent training or governance support, while national AI policy leaves this gap unaddressed.

The rundown

The article positions itself as addressing a policy and practice gap. It contends that national AI strategies emphasize speed and innovation but do not sufficiently address the EHR as the operational base for health care AI, or whether the clinical workforce can use these tools safely.

It uses a conceptual framework linking three elements: national AI policy momentum, EHR infrastructure, and workforce education. Within this framework, it describes workforce education as essential human infrastructure for health care AI.

For leaders, the recommendation is active engagement in shaping AI implementation. This means coordinating policy advocacy, EHR governance, and EHR-based education so that faster AI adoption improves care rather than amplifying risk.

Sources

  1. Peer-reviewedNursing Administration Quarterly2026-10-01

The debate