Closing the Health Policy Implementation Gap With Artificial Intelligence.

Authors

Emma E McGinty, Yongkang Zhang, Fei Wang, John Z Ayanian, William L Schpero

Published in

JAMA health forum. Volume 7. Issue 8. Pages e262515. Aug 07, 2026. Epub Aug 07, 2026.

Abstract

Health care policies often fail to achieve their goals due to implementation challenges attributable to workforce constraints, fragmented health information systems, and administrative complexity. This Special Communication proposes a framework for how artificial intelligence (AI) tools could support effective health care policy implementation, using the implementation of Medicaid work requirements under the Budget Reconciliation Act of 2025 as an example.
Opportunities for AI-augmented health care policy implementation include executing key policy processes, such as generating eligibility screening tools, reviewing documentation, and linking and analyzing data for indicators of policy compliance; identifying individuals at risk of adverse consequences from implementation failure who should receive proactive support; enhancing policy communication to diverse audiences; facilitating implementation monitoring; and learning from and adapting implementation across all of these domains. To realize the potential of AI augmentation, the field needs to overcome challenges related to data availability, as well as the limitations of AI tools themselves, such as hallucinated false information.
AI-augmented health care policy implementation has the potential to meaningfully limit unintended consequences from implementation of Medicaid work requirements. Rigorous evaluation of state-led innovations in AI-augmented implementation of Medicaid work requirements is key to advancing effective approaches and mitigating the potential for harm. The federal government should support state efforts with AI expertise, data infrastructure, and partnerships with preferred vendors who demonstrate that their AI-augmented digital assistants and other AI tools effectively facilitate Medicaid enrollment among eligible individuals.

PMID:
42566202
Bibliographic data and abstract were imported from PubMed on 07 Aug 2026.

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