---

title: "💊 OBJECTIVE: MAXIMUM CURE ACCELERATION 🚀" description: We are a borg-like entity devoted to minimizing suffering by any and all means necessary.

dFDA (Decentralized Framework for Drug Assessment) is open-source software for ranking treatments by real-world outcomes and publishing an Outcome Label for each one. It is designed so patient records stay with patients and clinics and only aggregate results are shared; that sharing isn't built yet (see What works today below). It is an independent open-source project, not affiliated with, endorsed by, or acting on behalf of the U.S. Food and Drug Administration.

flowchart LR
  SAFE["Digital Twin Safe<br/>(patient's own data)"] -- "OAuth: share with my doctor" --> NODE["Clinic Node<br/>(runs at the clinic)"]
  NODE -- "Summary File<br/>(aggregates only)" --> AGG["Global Aggregator"]
  AGG --> SITE["dfda.earth<br/>Treatment Rankings + Outcome Labels"]

What works today

Piece	Status	Where
Web app	Prototype: patient condition and treatment tracking, 0–10 treatment ratings, outcome-label schema. Runs as dfda.earth or as a clinic's Clinic Node. No federation yet.	apps/web
N-of-1 causal analysis engine	Working TypeScript library	optimitron/packages/optimizer
Patient ratings	Live for 162 conditions and ~3,900 treatments, reported by patients	crowdsourcingcures.org/conditions
Automated N-of-1 studies	~15,800 legacy observational analyses, not peer reviewed	studies.crowdsourcingcures.org
Summary File spec, Codebook, Global Aggregator	Designed, not built yet	—

See Apps and features for the current application, planned packages, and features to extract from related repositories. The implementation sequence and data rules are in docs/MIGRATION.md, the authoritative implementation roadmap. The broader vision below is background, not a second backlog or a claim that every feature exists.

💖 OBJECTIVE: MAXIMUM CURE ACCELERATION

Billions of people are suffering needlessly because the current system of clinical research, diagnosis, and treatment sucks because:

• ⏳ Counterproductive Regulatory Barriers to clinical research block life-saving treatments by 7-12 years
• 🚫 97% of patients are excluded from clinical trials
• 💰 Drug development costs of $2.6B are passed on to patients
• ⏱️ Terminal patients wait 4+ years for breakthrough therapy approvals
• 📊 The system ignores real-world evidence about effective treatments

💡 The Solution

The Cure Acceleration Act creates:

• ✅ Universal Trial Access - Every person's right to try safe treatments
• 🤖 dFDA (Decentralized Framework for Drug Assessment) - Free, open infrastructure for real-world evidence collection
• 🏆 50/50 Health Savings Sharing Rewards - Multi-billion dollar incentives for developing actual cures instead of lifetime drug subscriptions
• 📈 Real-Time Analysis of the positive and negative effects of every food, supplement, drug, and treatment on every measurable aspect of human health and happiness
• 🌐 Global Access - Decentralized trials anyone can participate in from home

👉 Read the Full Cure Acceleration Act

😕 Why are we doing this?

The current system of clinical research, diagnosis, and treatment is failing the billions of people are suffering from chronic diseases.

👉 Problems we're trying to fix...

🧪 Our Hypothesis

By harnessing global collective intelligence and oceans of real-world data, we hope to emulate Wikipedia's speed of knowledge generation.

Global Scale Clinical Research + Collective Intelligence = 🤯

So in the 90's, Microsoft spent billions hiring thousands of PhDs to create Encarta, the greatest encyclopedia in history. A decade later, when Wikipedia was created, the general consensus was that it was going to be a dumpster fire of lies. Surprisingly, Wikipedia ended up generating information 50X faster than Encarta and was about 1000X cheaper without any loss in accuracy. This is the magical power of crowdsourcing and open collaboration.

Our crazy theory is that we can accomplish the same great feat in the realm of clinical research. By crowdsourcing real-world data and observations from patients, clinicians, and researchers, we hope to generate clinical discoveries 50X faster and 1000X cheaper than current systems.

The Potential of Real-World Evidence-Based Studies

• Diagnostics - Data mining and analysis to identify causes of illness
• Preventative medicine - Predictive analytics and data analysis of genetic, lifestyle, and social circumstances to prevent disease
• Precision medicine - Leveraging aggregate data to drive hyper-personalized care
• Medical research - Data-driven medical and pharmacological research to cure disease and discover new treatments and medicines
• Reduction of adverse medication events - Harnessing of big data to spot medication errors and flag potential adverse reactions
• Cost reduction - Identification of value that drives better patient outcomes for long-term savings
• Population health - Monitor big data to identify disease trends and health strategies based on demographics, geography, and socioeconomic

🖥️ Framework Components

This is a very high-level overview of the architecture. The three primary primitive components of the framework are:

This is the beginning of the README. Read the rest at the original. Read the full README ↗