About the Health & AI Policy Index

Authorship

Will Moss leads HAPI as a public policy professional focused on AI governance in healthcare. His background includes government affairs experience at the state and federal levels, with a focus on AI health policy. Through HAPI, Will aims to make healthcare AI governance more accessible while supporting safer, more responsible policy development. He is interested in growing the project and collaborating with others working in this space.

Megan Lagerequist is a collaborator on HAPI and the primary author of the operational implications page. She has experience working in healthcare techology, with a focus on data governance in the EHR, and a background in frontier AI capabilities and governance.

For questions, feedback, or collaboration inquiries related to AI policy in healthcare, contact: william.moss@mssm.edu

About HAPI

The Health & AI Policy Index (HAPI) is a curated, research-oriented registry of policies relevant to artificial intelligence in healthcare. It is designed to help clinicians, health systems, payers, developers, and policymakers track meaningful developments across U.S. states, federal agencies, sector regulations, international frameworks, and voluntary standards.

Purpose & scope

• Audience: clinicians, health system leaders, compliance/legal teams, developers, payers, lobbyists, and policymakers.
• Focus: policies that materially affect the development, evaluation, deployment, or oversight of AI in healthcare.
• Geographies: United States (state and federal), plus selected international frameworks with health relevance.

Inclusion criteria

An item is included when it satisfies both of the following criteria:

• AI relevance (such as explicit regulation of artificial intelligence or machine-learning systems).
• Health relevance (including effects on health care delivery, public health operations, or health-related data, safety, or equity).

Policies are excluded when AI is mentioned only in passing, duplicates an existing entry, or is expected to have an insignificant impact on health care.

Data sources & curation

• Primary sources: official statutes, bills, regulations, agency guidance, standards bodies, and public registers.
• Secondary validation: reputable summaries (e.g., law firm memos, industry associations) to corroborate status/interpretation.
• Curation: items are selected for clarity, materiality, and health relevance; marginal items may be excluded for signal-to-noise.

Fields & tags

Each entry includes a concise summary, healthcare implications, dates, jurisdiction, and links to source text. Three tag families enable quick filtering:

• Keyword Tags: Safety & Risk; Privacy & Data; Transparency & Governance; Clinical Quality & Efficacy; Equity & Bias.
• Stakeholder Tags: Providers & Health Systems; Patients & Public; Payers & Purchasers; Developers & Vendors; Regulators & Government.
• Impact: High; Medium; Low.
  • High Impact: directly governs, authorizes, or constrains AI in healthcare.
  • Medium Impact: materially influences practice but indirectly.
  • Low Impact: exploratory or advisory actions.

Operational Implications

For each policy, we surface key actions or rules that influence operations of healthcare providers, payers, vendors, and other stakeholders. A taxonomy of common rules was developed to compare policies across jurisdictions and surface meaningful trends. Not every policy will be the perfect fit for a common rule, so users of HAPI should refer to the specific implications on the policy page and the original text for details.

State Policy (No Law)

This category includes formal state actions related to AI in healthcare such as resolutions, executive orders, or commissions. While these actions do not always create statutory law, they reflect policy intent and can shape future regulation.

Status & updates

• Status: where available, items indicate proposal, adoption, or effective phases.
• Updates: the database is refreshed weekly.

Limitations

• Summaries are for awareness and do not constitute legal advice.
• Coverage is selective and prioritizes clarity over exhaustiveness.

How to cite

Please cite as: Health & AI Policy Index (HAPI). Include the item permalink and “Last Updated” date.