About the Health & AI Policy Index

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 meets at least one of the following:

• Directly regulates AI (e.g., safety, risk management, transparency, data governance, bias/equity).
• Indirectly constrains or enables AI in clinical or administrative settings (e.g., HIPAA/OCR guidance, CMS coverage/coding, liability/credentialing).
• Establishes widely referenced standards or guidance used by health organizations and vendors.

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. Two 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 (binding law/reg or mandatory guidance).
  • Medium Impact: materially influences practice but indirect (privacy expansions, reporting, procurement standards).
  • Low Impact: exploratory/advisory (task forces, principles, studies).

Status & updates

• Status: where available, items indicate proposal/adoption/effective phases or analogous lifecycle markers.
• Updates: the database is refreshed on a weekly basis.

Limitations

• Summaries are for awareness and do not constitute legal advice.
• Coverage is selective; the index prioritizes clarity and practical relevance over exhaustiveness.

Team & Acknowledgements

• As HAPI’s Founder/Editor, Will Moss maintains the registry, translates dense rules into plain language, and tracks real-world impact on patients, providers, and developers. Will has a background in health policy and AI governance.
• Clément Roze and Daniel Wei assisted with website development.
• HAPI is entirely funded by the Windreich Department of Artificial Intelligence and Human Health, Mount Sinai.

How to cite

Please cite as: Health & AI Policy Index (HAPI), Windreich Department of Artificial Intelligence and Human Health, Mount Sinai. Include the item permalink and “Last Updated” date.

Contact

Questions, corrections, or suggested additions? Reach out to william.moss@mssm.edu.