Appendix P — ✍️ dFDA via Executive Order

Introduction

Achieving the ambitious goals of the Decentralized FDA (dFDA) initiative, drastically accelerating medical progress and reducing costs, may require innovative implementation strategies. While congressional legislation could provide the clearest authority and funding, recent precedents from the 2025 Trump administration, particularly the establishment and operation of the Department of Government Efficiency (DOGE), suggest a potential pathway leveraging executive action to initiate and drive the dFDA’s creation without an initial congressional mandate.

This document outlines how mechanisms similar to those used for DOGE could be applied to establish the dFDA, acknowledging the significant legal and funding challenges inherent in such an approach.

Core Precedent: The DOGE Model

The DOGE initiative demonstrated the use of executive power to:

  1. Establish New Structures via Executive Order: DOGE was formally created by Executive Order 14158 on January 20, 2025, not through new legislation.
  2. Repurpose Existing Agencies: The order renamed and restructured the existing United States Digital Service (USDS) into the “United States DOGE Service,” forming the core operational unit.
  3. Mandate Agency-Wide Changes: Subsequent EOs directed sweeping actions across agencies concerning spending, personnel, and regulation, such as the Workforce Optimization Initiative (E.O. 14210) and the Cost Efficiency Initiative (E.O. 14222).
  4. Leverage Technology and Centralized Oversight: DOGE emphasized creating centralized technological systems for monitoring and control, embedding “DOGE Teams” within agencies.
  5. Influence Funding and Priorities: Executive directives led to actions like freezing or terminating research grants at agencies like the NSF and NIH, aligning spending with administration priorities (e.g., anti-DEI) without new laws specifically mandating those cuts.

A comprehensive overview of DOGE, its structure, actions, and the controversies it faced can be found on its Wikipedia page.

Applying the Model to dFDA Implementation

A similar strategy could be employed to initiate the dFDA:

  1. Establishment via Executive Order:

    • An EO could formally establish the “Decentralized FDA Initiative” or a “Center for Regulatory Innovation” within HHS or the Executive Office of the President.
    • This EO would define the dFDA’s mission, scope (initially perhaps focusing on specific disease areas or intervention types), and mandate cooperation from relevant agencies (FDA, NIH, CMS, ONC).

  2. Repurposing Existing Infrastructure & Expertise:

    • Instead of creating a new agency from scratch, the EO could designate an existing entity or component within HHS (e.g., parts of FDA’s digital health center, ONC, or leveraging infrastructure/teams from the All of Us Research Program) as the core dFDA Infrastructure operator, potentially renaming it.
    • This leverages existing budgets, personnel slots, and infrastructure, mirroring the USDS transformation into the U.S. DOGE Service.

  3. Mandating Digital Platform Development & Standards:

    • Given the dFDA’s nature as a tech platform, the EO would direct the designated entity to build and operate the decentralized clinical trial infrastructure.
    • It could mandate the adoption of specific data standards (like FHIR), interoperability protocols, and security requirements across participating agencies and trial sponsors, similar to how DOGE EOs mandated specific technological systems for financial oversight.

  4. Centralized Coordination & Oversight:

    • Establish a small “dFDA Coordination Office” via the EO, responsible for driving implementation.
    • Mandate the creation of embedded “dFDA Liaison Teams” within FDA, NIH, and CMS, tasked with aligning agency actions with the dFDA initiative and reporting back, mirroring the DOGE Team structure.

  5. Accelerating Regulatory Adaptation via Executive Action:

    • The EO could direct the FDA and HHS to prioritize and expedite rulemaking and guidance development to implement the regulatory modifications identified in regulatory/regulations-to-modify-or-rescind.md.
    • This uses existing agency authority, spurred by executive direction, rather than waiting for Congress to pass specific laws for each regulatory change.

  6. Influencing Funding & Reimbursement:

    Summary of Potential Funding Sources (Annualized Estimates):

    Potential Funding Source/Authority Approximate Annual Budget/Authority (FY24 Basis) Notes
    NIH Office of the Director (incl. Common Fund) ~$2.3 Billion Discretionary/flexible funds for cross-cutting initiatives.
    ARPA-H ~$1.5 Billion Flexible funding for high-risk, high-reward health projects.
    CMMI (CMS Innovation Center) ~$1.0 Billion (annualized) ~$10B per decade authority for testing payment/delivery models.
    FDA IT/Data Modernization (Part of overall FDA Budget) Existing funds could be prioritized (e.g., +$105M requested in FY24).
    Estimated Total Annual Potential ~$4.8 Billion Excludes FDA budget prioritization, represents total available funds.

    Note: This table summarizes potential funding pools that could be influenced by executive prioritization. The actual amount redirectable to dFDA would be significantly less than the total budget figures shown and subject to legal and administrative constraints.

NIH Funding Fulfilling Goals Addressable by dFDA

While the dFDA Infrastructure requires dedicated funding for its creation and operation, a significant portion of the existing NIH budget is already allocated towards activities whose goals directly align with the dFDA’s core function: efficient and large-scale clinical evaluation of interventions. The dFDA proposes a method to achieve these existing mandates more effectively.

Institute/Center/Office FY2024 Budget (Approx.) Mandated Purpose/Activities Potentially Fulfilled More Efficiently by dFDA How dFDA Enhances Fulfillment
National Cancer Institute (NCI) $7.2 Billion Conducting & supporting clinical trials for cancer prevention & treatment. Faster, cheaper, larger-scale trials; real-world evidence generation.
National Institute of Allergy & Inf. (NIAID) $6.3 Billion Clinical trials for vaccines, infectious disease treatments, immunotherapies. Rapid trials during outbreaks (like RECOVERY); lower cost vaccine evaluation.
National Heart, Lung, Blood Inst. (NHLBI) $4.2 Billion Clinical trials for cardiovascular, lung, and blood disorders. Large pragmatic trials embedded in care; long-term RWE studies.
National Institute on Aging (NIA) $4.3 Billion Clinical trials for Alzheimer’s disease, related dementias, aging interventions. Lower cost trials for complex, long-duration studies; diverse population recruitment.
National Inst. Neurological Disorders (NINDS) $2.6 Billion Clinical trials for neurological treatments (Stroke, Parkinson’s, MS, etc.). Efficient testing of neuro-therapeutics; better RWE for neurological conditions.
National Inst. Diabetes & Kidney (NIDDK) $2.3 Billion Clinical trials for diabetes, digestive, liver, kidney, urologic diseases. Large-scale trials for common chronic diseases; integration with lifestyle data.
National Institute of Mental Health (NIMH) $2.2 Billion Clinical trials for mental health interventions and treatments. Decentralized trials for hard-to-reach populations; RWE on psychotherapeutics.
National Institute on Drug Abuse (NIDA) $2.0 Billion Clinical trials for addiction treatments and prevention strategies. Community-based decentralized trials; RWE on substance use interventions.
National Inst. Child Health & Dev. (NICHD) $1.8 Billion Clinical trials related to pediatric, reproductive, developmental health. Efficient trials in vulnerable populations; long-term developmental tracking.
National Eye Institute (NEI) $0.9 Billion Clinical trials for vision disorders and treatments. Decentralized trials for vision tests; RWE on eye treatments.
National Inst. Arthritis & Skin (NIAMS) $0.7 Billion Clinical trials for arthritis, musculoskeletal, and skin diseases. RWE for chronic conditions; efficient testing of biologics/therapies.
National Center Adv. Translational Sci (NCATS) $0.9 Billion Direct Mandate: Improving clinical trial efficiency & speed; translation. Direct Alignment: dFDA is a tool for NCATS’ mission; NCATS funds could support platform use.
NIH Common Fund $0.7 Billion Large-scale, cross-cutting initiatives (could include trial platforms). Potential funding source for dFDA-like infrastructure pilots or methods research.
National Inst. Dental & Craniofacial (NIDCR) $0.5 Billion Clinical trials for oral, dental, craniofacial health interventions. Efficient trials for dental procedures/products; RWE collection.
National Inst. Deafness & Comm. (NIDCD) $0.5 Billion Clinical trials for hearing, balance, communication disorders. Decentralized testing of hearing aids/implants; RWE on communication therapies.
National Inst. Minority Health (NIMHD) $0.5 Billion Supporting research & trials addressing health disparities. Enhances recruitment/participation of diverse populations in trials via decentralization.
Subtotal (Clinical Trial Focused ICs/Activities) ~$39.6 Billion Portion dedicated to clinical evaluation varies greatly by IC.

Note: Table excludes ICs primarily focused on basic science (like NIGMS, NHGRI) or infrastructure (NLM, NIBIB), and the remaining smaller ICs/activities, though they may have some clinical components. Budgets are approximate FY24 enacted levels. Sources: NIH Almanac, FASEB NIH Budget Data

Discussion:

The table highlights that a substantial portion of NIH’s budget (~$39.6 Billion across these selected ICs) is allocated to Institutes whose mandates inherently involve funding or supporting clinical trials to find treatments for specific diseases or conditions.

  • The dFDA offers a Method, Not a New Goal: The dFDA Infrastructure represents a potentially revolutionary method for achieving the existing, congressionally mandated goals of these Institutes – finding effective treatments – but doing so faster, cheaper, and at a larger scale.
  • Efficiency Argument: Instead of funding numerous traditional, site-based clinical trials costing tens of thousands per patient, these ICs could potentially achieve their clinical evaluation objectives more efficiently by funding trials run on the dFDA Infrastructure at a fraction of the cost (~$500-$1000 per patient).
  • Targetable Spending: While not all ~$39.6B is spent solely on clinical trials (basic science is also funded), a significant fraction (conservatively estimated earlier at $10-15B+ annually across NIH) is directed towards clinical evaluation. It is this portion of spending whose purpose could be directly and more efficiently fulfilled by leveraging the dFDA.
  • Synergy: This approach allows ICs to continue focusing on their specific disease areas (cancer, infectious disease, aging, etc.) while utilizing a shared, efficient, cutting-edge platform for the clinical trial phase, maximizing the impact of their appropriated funds.

Therefore, while direct reallocation of funds between appropriations accounts via executive order is problematic, there is a strong argument that the executive branch could direct NIH to fulfill its existing mandates for clinical evaluation by prioritizing the use of the more efficient dFDA Infrastructure, thereby achieving congressional goals more effectively with the allocated funds.

  1. Personnel & Expertise:

    • Prioritize hiring or detailing personnel with platform development, data science, cybersecurity, and decentralized trial expertise to the lead dFDA entity and liaison teams.

Anticipated Challenges

This executive action pathway faces significant hurdles:

  • Funding: Building and operating the full dFDA initiative requires substantial funding. While the core technology platform’s upfront cost is estimated at ~$38-46M, the broader initiative components (integration, plugins, legal, etc.) add significant costs depending on execution success. Total upfront costs for the full initiative could range from ~$50M (best case) to ~$274M (medium case) or even over $2.2 Billion (worst case), with ongoing operational costs scaling accordingly (see dFDA Cost Benefit Analysis Sections 3.1-3.4 for details). Funding the medium or worst-case scenarios significantly exceeds amounts typically addressable by executive reallocation alone. Dedicated congressional appropriations would almost certainly be necessary for full-scale operation.
  • Statutory Authority: While regulatory modifications can often occur under existing law, creating a fundamentally new pathway for intervention approval that competes with or bypasses traditional FDA routes might be argued to require new statutory authority from Congress.
  • Legal Challenges: As seen with DOGE (Wikipedia overview), this approach would likely face lawsuits questioning the scope of executive authority, the repurposing of funds, potential Appointments Clause violations for key personnel, and procedural challenges under the Administrative Procedure Act.

Conclusion

While requiring congressional action for full funding and unambiguous legal authority seems probable in the long term, the DOGE precedent illustrates a potential strategy for initiating the dFDA using executive orders. By repurposing existing structures, mandating technological development and regulatory adaptation, and centralizing coordination, an administration could significantly accelerate the dFDA’s foundational stages without waiting for specific enabling legislation. This approach shifts the focus from asking Congress for permission to demonstrating feasibility and value, potentially building momentum for future legislative support. However, the inherent funding limitations and significant legal risks must be carefully considered.

Appendix: Draft Executive Order Text

TRANSFORMING THE FDA INTO A DECENTRALIZED, PATIENT-CENTRIC PLATFORM TO ACCELERATE MEDICAL INNOVATION AND EXPAND ACCESS TO SAFE, EFFECTIVE TREATMENTS

It is hereby ordered as follows:

Section 1. Purpose

The Food and Drug Administration (FDA) must transition from a centralized, gatekeeping model to a decentralized global autonomous algorithmic FDA (dFDA). This transformation will:

  • Eliminate artificial barriers between patients and treatments that have demonstrated basic safety.
  • Reduce costs by automating clinical research, leveraging decentralized trials, and aggregating global real-world data.
  • Empower patients with personalized, quantitative assessments of all available interventions.
  • Accelerate medical progress by 80X through automation and decentralization of clinical trials.

Section 1A. The Problem

The current medical research system is failing billions of patients worldwide:

Additional critical issues include:

Section 2. Definitions

  1. “Decentralized Autonomous FDA (dFDA)”: A blockchain-secured, AI-driven platform that continuously analyzes anonymized real-world data (RWD) from global clinical trials, electronic health records, and patient-reported outcomes to rank treatments by safety and efficacy.
  2. “Decentralized Clinical Trials (DCTs)”: Trials conducted via telemedicine, wearable sensors, and direct-to-patient supply chains, with no physical trial sites.
  3. “N-of-1 analysis”: A personalized assessment of an individual’s response to a treatment, calculated by comparing their baseline health data to post-intervention outcomes.

Section 3. Policy

3.1 Establishment of the Decentralized Autonomous FDA

Within 180 days, the Secretary of Health and Human Services (HHS) shall:

  1. Develop and deploy the dFDA Infrastructure, integrating:
  • Blockchain technology for immutable, transparent data sharing (e.g., Hyperledger Fabric).
  • Open Source: The dFDA Infrastructure will be developed as open-source software, with publicly accessible code repositories to foster transparency, community contributions, and independent audits.
  • Machine-learning models trained on the entire universe of clinical and RWD, including international datasets (EU EMA, Japan PMDA).
  • Automated adverse-event detection using Natural Language Processing (NLP) of social media, EHRs, and pharmacy records.
  • Transform fda.gov into an “Amazon for Decentralized Trials” where anyone can effortlessly create or join a study, modeled after successful platforms like the Oxford RECOVERY trial.
  • Implement standardized, automated protocols to reduce setup time and costs.
  • Create a system where patients enter their condition and receive a ranked list of the most effective treatments based on comprehensive clinical and real-world data.
  • Provide every patient with a personal FDAi AI agent that can call them to collect data about food and drug intake, analyze time-series data using causal inference, and create n-of-1 studies to be aggregated in the global database.

Key Platform Features:

  1. Digital Twin Safe: Secure storage and control of personal health data
  2. Automated Data Collection: Integration with wearables and health apps
  3. Real-Time Analysis: Continuous monitoring and pattern identification
  4. Outcome Labels: Standardized treatment effect reporting
  5. Study Creation Tools: Enable anyone to launch and manage clinical trials
  6. Global Data Integration: Aggregate and analyze worldwide treatment outcomes

Cost Reduction Targets:

Cost Category Current Cost Target Cost Savings
Data Management $198,014 $10,000 94.9%
IRB Approvals $324,081 $5,000 98.5%
Patient Recruitment $805,785 $15,000 98.1%
Clinical Procedures $5,937,819 $1,000,000 83.2%
Administrative $7,229,968 $100,000 98.6%
Total Trial Cost $56,988,007 $2,025,000 95.7%
  1. Partner with the National Institute of Standards and Technology (NIST) to ensure algorithmic fairness, reproducibility, and compliance with CFR Title 21.
  2. Collaborate with the World Health Organization (WHO) to adopt global data-sharing standards (e.g., HL7 FHIR).

3.2 Patient Access to Decentralized Clinical Trials

  1. Right to Trial: Patients may participate in DCTs for any treatment that has completed Phase I safety testing, at their own expense if necessary.
  • Providers and trial sponsors may not be held liable for off-label use in DCTs (pursuant to 21 U.S.C. § 396).
  • AI-Powered IRB To expedite and enhance the safety review process, a safety-validated fine-tuned large language model (LLM) will act as the primary IRB for DCT protocols and approve all DCT protocols if they meet basic safety criteria (per 45 CFR § 46.109).
    1. Streamlined Study Creation: Drug manufacturers or researchers will have access to a simple online platform to propose and create studies, akin to a third-party vendor registration process on e-commerce platforms.
  • Direct Intervention Delivery: Upon a new participant joining a trial, the primary responsibility of the study creator will be the prompt and efficient delivery of the intervention directly to the participant, their pharmacy, or healthcare provider.
    1. Crowdfunding and Direct Payments: Patients may crowdfund or directly pay for experimental treatments without triggering FDA “marketing” restrictions (21 CFR § 312.7).
    2. Cost-Effective Trial Model: Based on the Oxford RECOVERY trial which tested 18 treatments for just $3M—about $500 per subject, 80x cheaper than traditional trials—by integrating into EHRs and using simple online data entry. With adequate funding, this model could test hundreds of thousands of treatments, dramatically accelerating drug discovery.

3.3 Elimination of Economic Barriers

  1. Abolish Pharma User Fees: The FDA shall cease collection of Prescription Drug User Fee Act (PDUFA) and Generic Drug User Fee Act (GDUFA) fees as these are ultimately passed on to the patient in the form of higher costs for medicines. Funding gaps shall be offset by:
  • Reallocating 50% of savings from automated trial oversight to FDA operations.
  • Grants from the National Institutes of Health (NIH) for high-priority public health tools (e.g., antibiotics, Alzheimer’s drugs).

3.4 Real-World Data Sharing Mandate

  1. Open Outcomes Platform: The FDA shall create a secure portal for patients and providers to upload de-identified “before and after” health data (e.g., lab results, wearable device metrics, symptom diaries) for all food, drug, and supplement use.
  • Data contributors retain ownership and may revoke access at any time (GDPR compliance required).
  • The dFDA shall analyze this data in real time to update treatment rankings.
  • Global, real-time data sharing will empower patients, researchers, and policymakers with unprecedented access to outcomes information.
    1. N-of-1 Analysis: For every patient, the dFDA shall automatically generate:
  • A personalized dashboard showing their response to interventions (e.g., “Drug X reduced your migraine frequency by 42%”).
  • Aggregated N-of-1 results to calculate population-level effect sizes (e.g., “Drug X works for 68% of female patients aged 30–50”).

3.5 Quantitative Treatment Rankings

  1. Dynamic Safety/Efficacy Scores: The dFDA shall publish continuously updated rankings of all treatments for every condition, including:
  • Off-label uses (e.g., “Metformin for longevity”).
  • Alternative therapies (e.g., “Medical cannabis for chronic pain”).
  • Comparative effectiveness vs. current standards of care (e.g., “Drug Y is 1.3x more effective than Drug Z but costs 8x more”).
    1. Outcome Labels: Replace static drug labels with concise, data-driven summaries:
  • “Positive effects: 23% reduction in mortality (95% CI: 19–27%).
  • Negative effects: 8% risk of severe nausea (95% CI: 6–10%).
  • Probability of success for your profile: 72%.”

Section 4. Implementation

  1. The HHS Secretary shall establish a dFDA Task Force with representatives from NIH, CDC, FTC, and patient advocacy groups (e.g., PatientsLikeMe, OpenTrials).
  2. Within 365 days, the FDA shall:
  • Sunset all legacy review processes for DCTs and RWD-based approvals.
  • Launch a public API for third-party developers to build dFDA-compliant apps.
    1. The Federal Trade Commission (FTC) shall enforce penalties against entities that withhold or manipulate treatment outcome data (15 U.S.C. § 45).
    2. FDA X-Prize Program:
  • Launch an FDA X-Prize to fund the development of components for the decentralized, automated clinical trial platform.
  • Allocate $100 million in prizes for breakthrough innovations in decentralized trial automation, patient recruitment, and real-world data integration.
  • Prizes have proven effective where bureaucratic procurement has failed, creating competitive incentives for rapid innovation.
  • The open-source model will allow anyone, individuals, organizations, or governments, to improve and integrate with the platform, overcoming the stagnation typical of closed-source projects.
  1. Platform Development Milestones:

  1. Patient Interface:

    • Condition input system
    • Treatment ranking display
    • Trial enrollment portal
    • Automated outcome reporting

  2. Provider Tools:

    • EHR integration
    • Automated data collection
    • Real-time decision support

  3. Research Tools:

    • Study creation interface
    • Data analysis dashboard
    • Global research database

Expected Improvements:

Metric Current Target
Time to Treatment 17 years 2 years
Trial Cost $57M $2M
Patient Access 15% 100%

Section 5. General Provisions

  1. This order does not impair existing patient rights under the Right to Try Act (Pub. L. 115-176).
  2. $1.2 billion is hereby reallocated from the NIH budget to fund dFDA development.
  3. This order is effective immediately.

Rationale & Legal Authority

Precedent: Estonia’s blockchain-based health system (X-Road) and the UK’s Decentralized Trials & Research Alliance demonstrate feasibility.

Expected Outcome:

  • A 10x increase in treatment approvals (30→300/year)
  • 90% cost reduction in drug development within 5 years
  • 80x acceleration in medical progress through automation and decentralization
  • Universal patient access to clinical trials
  • Real-time treatment effectiveness data
  • Global coordination of medical research