This repository preserves Department of Health and Human Services (HHS) AI strategy documents and policy guidance from the 2023-2025 period, including materials developed during my tenure as a data scientist at the Health Resources and Services Administration (HRSA).
I contributed to HHS AI strategy development as a member of the HHS Artificial Intelligence Community of Practice, which brought together data scientists, policy experts, and program leaders across HHS agencies to shape federal AI governance frameworks for healthcare programs.
Purpose of this archive:
- Preserve public policy documents from this period for researchers and practitioners
- Provide reference materials for federal AI governance frameworks
- Document the evolution of healthcare AI policy at the federal level
- Ensure continued public access to these resources
Position: Data Scientist, Health Resources and Services Administration (HRSA/HHS)
Period: December 2023 - March 2025
Contributions to HHS AI Strategy:
- Active member of HHS Artificial Intelligence Community of Practice (AI CoP)
- Participated in cross-agency working groups developing AI governance frameworks
- Contributed data science and analytics perspective to policy discussions
- Advised on algorithmic fairness, transparency, and equity considerations in federal healthcare programs
- Supported integration of AI/ML solutions into HRSA Health Center Program operations
Context: During this period, HHS was developing comprehensive AI strategy and governance frameworks across its agencies (FDA, CDC, CMS, HRSA, NIH, etc.). The AI Community of Practice facilitated coordination, shared best practices, and developed unified approaches to responsible AI deployment in healthcare.
This repository contains publicly available HHS AI strategy materials, including:
- HHS AI Strategy documents
- Ethical AI principles and guidelines
- Algorithmic fairness frameworks
- Transparency and accountability standards
- Agency-level AI implementation plans
- AI use case inventories
- Risk assessment frameworks
- Model governance procedures
- Federal AI policy alignment (OMB, NIST)
- Healthcare-specific AI considerations
- Health equity implications of AI systems
- Patient safety and clinical decision support standards
- Documents federal AI policy evolution during critical 2023-2025 period
- Provides baseline for analyzing policy changes across administrations
- Enables longitudinal study of federal healthcare AI governance
- Reference materials for implementing federal AI standards
- Frameworks applicable to state/local health agencies and healthcare organizations
- Best practices from federal AI governance development
- Primary source documents for federal AI policy analysis
- Evidence of inter-agency coordination approaches
- Case study in healthcare AI governance at scale
This period saw significant federal activity in AI governance:
Key Federal Actions:
- Executive Order 14110 (October 2023) - "Safe, Secure, and Trustworthy AI"
- OMB M-24-10 (March 2024) - Federal agency AI governance guidance
- NIST AI Risk Management Framework - Applied to federal healthcare programs
- HHS AI Strategy - Department-wide coordination and standards
HHS Specific Context:
- 1,400+ Federally Qualified Health Centers using predictive analytics
- Medicare/Medicaid programs increasingly incorporating AI/ML
- FDA regulating AI/ML medical devices and clinical decision support
- NIH funding AI research in precision medicine and health equity
My work at HRSA occurred at the intersection of these policy developments and operational program needs, particularly ensuring AI systems in safety-net healthcare programs addressed health equity and patient safety.
/ai-strategy-documents/ # HHS-wide AI strategy materials
/agency-guidance/ # Agency-specific AI implementation guidance
/frameworks/ # Algorithmic fairness and governance frameworks
/policy-context/ # Federal policy and regulatory documents
/meeting-materials/ # AI Community of Practice documentation (public)
Note: All documents in this repository were publicly available on HHS websites. No confidential or non-public information is included.
Important Legal and Professional Disclaimers:
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Public Information Only: This repository contains only publicly available documents that were accessible on official HHS websites. No confidential, internal, or non-public information is included.
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Personal Archive: This is a personal archive created for research and reference purposes. It does not represent official HHS policy communication or current guidance.
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Point-in-Time Snapshot: These documents reflect HHS AI strategy and guidance as of 2023-2025. Current HHS AI policies may differ. Always consult official HHS websites for current guidance.
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No Official Endorsement: This archive and my commentary do not represent the official position of HHS, HRSA, or any federal agency, past or present.
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Attribution: All documents are properly attributed to their original HHS sources. I claim no authorship of federal policy documents, only participation in their development through the HHS AI Community of Practice.
For Current HHS AI Guidance: Please visit official HHS agency websites:
- HHS AI: https://www.hhs.gov/
- HRSA: https://www.hrsa.gov/
- FDA AI/ML: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-aiml-enabled-medical-devices
- CMS AI: https://www.cms.gov/
While these documents reflect 2023-2025 HHS priorities, many frameworks remain relevant:
Enduring Principles:
- Algorithmic fairness and bias mitigation in healthcare
- Transparency and explainability requirements
- Patient safety considerations for AI systems
- Health equity implications of predictive models
- Data governance and privacy protections
Applicable Use Cases:
- Clinical decision support systems
- Predictive analytics for population health
- Healthcare fraud detection
- Resource allocation algorithms
- Health risk stratification models
Organizations implementing healthcare AI systems can reference these frameworks regardless of current federal policy, as they represent evidence-based best practices developed through extensive multi-agency collaboration.
My Current Research & Consulting Focus:
- Algorithmic bias and patient safety (Johns Hopkins Bloomberg School of Public Health)
Publications & Presentations:
- HHS AI Strategy contributions (2023-2025)
- Black in Data Week 2023: "Data Science Project Template & Best Practices"
- AcademyHealth 2024: "HRSA-Funded Health Centers Reduce Healthcare Expenditures"
If referencing this archive in research:
Hobby, A. (2025). HHS AI Strategy & Policy Documents Archive (2023-2025). GitHub repository. https://github.com/AndreaHobby/hhs-ai-strategy-archive
For individual HHS documents, please cite the original HHS source.
Andrea Hobby, DrPH Student
Johns Hopkins Bloomberg School of Public Health
Expertise: Algorithmic bias in healthcare AI, health equity analytics, federal health policy, AI governance frameworks
Consulting Services: Available for AI governance framework development, algorithmic fairness evaluation, and health equity analytics. See research collaboration services.
Connect:
- GitHub: @AndreaHobby
- LinkedIn: Andrea Hobby
This work reflects the collaborative efforts of the HHS Artificial Intelligence Community of Practice, which included data scientists, policy experts, medical officers, and program leaders across HHS agencies during 2023-2025.
Special thanks to my colleagues at HRSA who supported data-driven policy development and responsible AI implementation in safety-net healthcare programs.
Public Domain - Federal Government Documents: Most documents in this repository are federal government works and therefore in the public domain under U.S. law (17 U.S.C. § 105).
Repository Structure & Documentation: The organization, selection, and documentation of this archive are licensed under MIT License to enable others to create similar policy archives.
This archive demonstrates my commitment to transparency, public access to government information, and evidence-based healthcare AI governance. I believe federal AI policy development should be documented and accessible to researchers, practitioners, and the public.
Created: January 2026
Last Updated: [Current Date]