Closing the Health Policy Implementation Gap With Artificial Intelligence

Area of Science:

• Health Policy
• Artificial Intelligence
• Public Health

Background:

• Health care policies frequently face implementation challenges due to workforce shortages, fragmented data systems, and administrative burdens.
• The Budget Reconciliation Act of 2025 includes Medicaid work requirements, highlighting the need for effective implementation strategies.

Purpose of the Study:

• To propose a framework for utilizing artificial intelligence (AI) tools to support the effective implementation of health care policies.
• To examine the potential of AI in overcoming challenges associated with implementing policies like Medicaid work requirements.

Main Methods:

• The study proposes a framework for AI-augmented policy implementation, using Medicaid work requirements as a case example.
• Identifies opportunities for AI, including automating eligibility screening, data analysis for compliance, risk identification, and communication enhancement.

Main Results:

• AI can streamline policy processes, identify individuals needing proactive support, improve communication, and facilitate monitoring and adaptation.
• Addressing data availability and AI limitations like misinformation is essential for successful AI augmentation.

Conclusions:

• AI-augmented implementation can significantly reduce unintended consequences of policies such as Medicaid work requirements.
• Rigorous evaluation of state-led AI innovations is vital for advancing effective approaches and preventing harm.
• Federal support for state AI initiatives, including expertise and data infrastructure, is recommended to facilitate Medicaid enrollment.