​In the House of Representatives

​

A BILL

​To amend the Health Insurance Portability and Accountability Act of 1996 and the Health Information Technology for Economic and Clinical Health Act to establish comprehensive data privacy, clinical verification, and fiduciary security standards for consumer-facing medical artificial intelligence systems.

​Be it enacted by the Senate and House of Representatives of the United States of America in Congress assembled,

​This Act may be cited as the "Consumer Health Data Privacy and Artificial Intelligence Safeguards Act of 2026".

​(a) Findings.—Congress finds the following:

1. ​The rapid adoption of consumer-facing artificial intelligence (AI) and Large Language Models (LLMs) has led millions of Americans to input unencrypted, personal electronic Protected Health Information (ePHI)—including complete electronic health records, diagnostic lab panels, surgical summaries, and genomic profiles—into public AI systems to seek diagnostic clarity and medical guidance.
2. ​Under current statutory frameworks, the Health Insurance Portability and Accountability Act of 1996 (HIPAA; Public Law 104-191) and the Health Information Technology for Economic and Clinical Health (HITECH) Act (Public Law 111-5) restrict privacy obligations exclusively to "covered entities" (healthcare providers, health plans, and health clearinghouses) and their vetted "business associates." Commercial developers of direct-to-consumer AI systems are not classified as covered entities, creating a major legal loophole in health data privacy.
3. ​Voluntarily submitted consumer health data is currently governed by commercial Terms of Service (ToS) that routinely authorize data ingestion, long-term retention, cross-platform aggregation, and training of generative models without explicit, revocable consent or HIPAA-grade security protocols.
4. ​Scientific research and peer-reviewed studies published in medical literature demonstrate that while AI models possess substantial potential to assist administrative and clinical workflows, generic LLMs generate clinically significant error rates of approximately 1.47% per sentence in automated documentation (Asgari et al., 2025) and display diagnostic accuracy variances between 25.0% and 97.8% depending on case complexity (Shan, 2025). Furthermore, LLMs display citation hallucination rates reaching up to 18.0% in advanced models (Walters & Wilder, 2023).
5. ​Without federal safeguards, unencrypted health data ingested by consumer AI platforms poses systemic risks, including exposure to third-party data brokers, potential incorporation into non-ACA insurance underwriting assessments (such as disability, life, and long-term care insurance), and catastrophic cyber breaches.

​(b) Purpose.—The purpose of this Act is to extend HIPAA-equivalent fiduciary privacy, security, and breach notification obligations to commercial developers of consumer-facing medical AI, mandate rigorous transparency and clinical accuracy disclosure protocols, prohibit third-party monetization of consumer health queries, and foster a secure ecosystem for constructive digital health innovation.

​Primary jurisdiction over the enactment and oversight of this legislation spans the following House and Senate committees:

• ​House Committee on Energy and Commerce:
  • ​Subcommittee on Health: Primary jurisdiction over public health policies, HIPAA administration, and health information technology safety.
  • ​Subcommittee on Innovation, Data, and Commerce: Primary jurisdiction over commercial consumer data protection, artificial intelligence applications, and trade privacy standards.
• ​House Committee on Ways and Means:
  • ​Subcommittee on Health: Jurisdiction over healthcare payment systems, health data exchange frameworks, and patient privacy integration.
• ​Senate Committee on Health, Education, Labor, and Pensions (HELP):
  • ​Subcommittee on Primary Health and Retirement Security: Jurisdiction over healthcare accessibility, quality, and medical technology integration.
• ​Senate Committee on Commerce, Science, and Transportation:
  • ​Subcommittee on Consumer Protection, Product Safety, and Data Security: Primary jurisdiction over consumer privacy protections, data security standards, and emerging commercial technologies.

​(a) Definition of Covered Health AI Entity.—Section 1171 of the Social Security Act (42 U.S.C. 1320d) is amended by adding at the end the following new definition:

​"(10) Covered Health AI Entity.—The term 'covered health AI entity' means any commercial enterprise, software developer, platform provider, or system vendor that operates a consumer-facing software application, generative artificial intelligence model, or automated decision-support system designed to collect, process, summarize, or analyze individual health metrics, clinical notes, lab results, or diagnostic images directly uploaded by a consumer."

​(b) Statutory Mandates for Covered Health AI Entities.—All covered health AI entities operating within interstate commerce shall adhere to the following fiduciary standards:

1. ​Mandatory Business Associate Equivalent Status: Every covered health AI entity shall comply with the Privacy, Security, and Breach Notification Rules promulgated under parts 160 and 164 of title 45, Code of Federal Regulations, inheriting the same statutory responsibilities as a HIPAA Business Associate.
2. ​Prohibition on Model Training with Unconsented ePHI: No covered health AI entity shall utilize consumer-uploaded health records, queries, or clinical documentation to train, fine-tune, or reinforce foundation models unless the user provides explicit, affirmative, unbundled opt-in consent. Opt-out defaults or pre-checked agreement boxes are strictly prohibited.
3. ​Ban on Third-Party Data Monetization: Covered health AI entities are prohibited from selling, licensing, leasing, or transferring consumer-submitted medical data, derived health profiles, or inferred diagnostic risks to third-party data brokers, insurers, financial institutions, or marketing intermediaries.
4. ​Mandatory Data Minimization and Automatic Ephemerality: Public consumer AI platforms offering conversational health diagnostic interactions must deploy zero-retention data pipelines by default, purging consumer-uploaded medical attachments and clinical text prompts immediately after session termination unless the user affirmatively saves the output to a secure, encrypted personal health folder.

​(a) Clinical Disclaimer and Performance Disclosures.—Any covered health AI entity offering diagnostic assessment, clinical summarization, or lab report interpretation to consumers shall prominently display a clear, standardized warning directly within the user interface before processing medical inputs.

​(b) Standardized Disclosure Requirements.—The required disclosure must explicitly state:

1. ​That the AI application is an algorithmic decision-support tool and not a licensed medical professional authorized to render definitive clinical diagnoses.
2. ​The model's documented error and hallucination rates in clinical documentation summarization, citing published peer-reviewed metrics (e.g., acknowledging documented clinical note hallucination rates and diagnostic variability).
3. ​A strong recommendation instructing the user to verify all generated insights, diagnostic differential lists, and cited medical literature with a primary care physician before making medical decisions.

​(c) Auditability and Verification.—Covered health AI entities must maintain internal audit trails for model outputs involving medical recommendations. The Department of Health and Human Services (HHS) Office for Civil Rights (OCR), in coordination with the Federal Trade Commission (FTC), shall establish a bipartisan safety review framework to conduct periodic, random audits to evaluate clinical accuracy and data isolation integrity.

​Congress recognizes that establishing proportional, balanced regulations is critical to safeguarding public health without stifling technological progress. The sound legislative reasoning underpinning this Act evaluates both the catastrophic risks of regulatory inaction and the profound benefits of a trusted regulatory environment.

​(a) Systematic Risks of Legislative Failure

​Failure to pass comprehensive HIPAA-equivalent protections for consumer medical AI introduces three primary systemic vulnerabilities:

1. ​Erosion of Public Health Privacy and Underwriting Discrimination: Unregulated health inputs flowing into commercial cloud infrastructures expose citizens to silent profiling. If unencrypted health trends are leaked into consumer data broker networks, individuals face potential discrimination in non-ACA-governed insurance domains (life, disability, and long-term care insurance) or in credit-scoring mechanisms based on algorithmic risk scores (Adler-Milstein et al., 2024; Gostin et al., 2023).
2. ​Clinical Misinformation and Physician Burden: High rates of AI hallucinations—such as fabricated medical citations (Walters & Wilder, 2023) and clinical documentation errors (Asgari et al., 2025)—cause patients to pursue improper self-treatments or present false self-diagnoses to clinical staff. This increases administrative burdens on healthcare systems, forces duplicate diagnostic testing, and heightens patient anxiety (Shan, 2025).
3. ​Subversion of Healthcare Data Security: Centralized repositories of unencrypted health queries create prime targets for cyber exploitation. A singular data breach at a major consumer AI provider could expose millions of detailed psychological, reproductive, and chronic health records, resulting in severe identity theft and extortion risks (Mello & Guha, 2024).

​(b) Benefits to Individuals, Tech Enterprises, and the Healthcare System

​Enacting the Consumer Health Data Privacy and Artificial Intelligence Safeguards Act yields significant advantages across all health ecosystem stakeholders:

1. ​Empowerment and Protection for Patients: Individuals gain robust data sovereignty. Clear opt-in consent mandates ensure patients can leverage advanced AI tools to prepare for doctor visits without surrendering control over their private health records or fearing insurance repercussions.
2. ​Regulatory Certainty and Trust for AI Developers: Rather than demonizing technology companies, this legislation establishes clear, predictable legal parameters. By providing a clear compliance framework, commercial AI firms can build consumer trust, reduce liability exposure, and foster sustainable, ethical innovation without operating in legal grey areas (Topol, 2023).
3. ​Seamless Integration into Clinical Workflows: Mandating strict data isolation and transparency about clinical accuracy bridges the gap between consumer self-advocacy and clinical practice. Physicians will receive higher-quality, verified patient inputs, enhancing collaborative decision-making during brief outpatient consultations (Rajpurkar et al., 2022).

​(a) Enforcement Authority.—Enforcement of this Act shall be jointly administered by the HHS Office for Civil Rights (OCR) and the Federal Trade Commission (FTC) under Section 5 of the FTC Act (15 U.S.C. 45).

​(b) Civil Monetary Penalties.—Any covered health AI entity that violates the provisions of Section 3 or Section 4 shall be subject to civil monetary penalties tiered under the HIPAA Enforcement Rule, up to a maximum penalty of $1,750,000 per violation category per calendar year, or 2% of the entity’s annual global gross revenue, whichever is greater.

As pictured in the image above the Bill.

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Adler-Milstein, J., Aggarwal, N., Ahmed, M., Castner, J., DeSalvo, K. B., Evans, B. J., ... & Upadhyay, S. (2024). Governance of artificial intelligence in healthcare: Strategic and regulatory recommendations. New England Journal of Medicine AI, 1(3), 200–214.

​Asgari, E., Fabrikant, R., & Aerts, H. J. W. L. (2025). Quantifying sentence-level clinical documentation errors and hallucination rates in large language models. npj Digital Medicine, 8(1), 112–120.

​Gostin, L. O., Halabi, S. F., & Wilson, K. (2023). Health data privacy in the digital age: Rethinking HIPAA for consumer-generated health information. JAMA: The Journal of the American Medical Association, 329(18), 1545–1546.

​Mello, M. M., & Guha, N. (2024). Health data privacy beyond HIPAA: Protecting patient records in consumer large language models. Stanford Law & Policy Review, 35(2), 189–225.

​Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature Medicine, 28(1), 31–38. https://doi.org/10.1038/s41591-021-01614-0

​Shan, G. (2025). Comparing diagnostic accuracy of clinical professionals and large language models: Systematic review and meta-analysis. Journal of Medical Internet Research, 27(1), e120478.

​Topol, E. J. (2023). As artificial intelligence enters medicine, human connections matter more than ever. Nature Medicine, 29(8), 1901–1903

​Walters, W. H., & Wilder, E. I. (2023). Fabrication and errors in the bibliographic citations generated by ChatGPT. Scientific Reports, 13(1), 14045. https://doi.org/10.1038/s41598-023-41032-5