About the Health & AI Policy Index

Authorship

Will Moss is the founder and editor of the Health & AI Policy Index (HAPI), a public, nonpartisan registry tracking policies shaping artificial intelligence in healthcare. He works at the Windreich Department of Artificial Intelligence and Human Health at Mount Sinai, where he focuses on AI governance, health policy, and regulatory strategy.

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.

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.

Team

HAPI is maintained by Will Moss.

How to cite

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