A Decentralized Framework for Drug Assessment

Abstract
A Practical Guide: Get 500 Years of Clinical Research in 20, Avoid the Apocalypse, and Make Humanity Filthy Rich by Giving Papers
Keywords

war-on-disease, 1-percent-treaty, medical-research, public-health, peace-dividend, decentralized-trials, dfda, dih, victory-bonds, health-economics, cost-benefit-analysis, clinical-trials, drug-development, regulatory-reform, military-spending, peace-economics, decentralized-governance, wishocracy, blockchain-governance, impact-investing

Decentralized Trials Save Millions

Out of 2.4 billion people with chronic disease, only 5 million participate in trials annually. That’s 0.2%.

Half of humanity would volunteer if they could. We have 240 times more willing participants than we’re using.

This isn’t because people don’t want to help find cures. It’s because the current system can’t scale. Trials cost $41K per patient, require traveling to major medical centers, and have exclusion criteria that reject 86.1% of actual patients.

We’re not facing a recruitment problem. We’re facing a capacity problem.

The Solution: Amazon for Clinical Trials

Your decentralized framework for drug assessment (dFDA) is a two-sided marketplace that connects companies with treatments to patients who need them. It’s Amazon, but instead of buying stuff you don’t need, you’re getting paid to help find cures.

How Companies Register Treatments (5 Minutes, Zero Approval Needed)

Any company - pharma, supplements, food, interventions - can instantly create a trial:

  1. Register treatment on a public portal (name, ingredients, condition, price)
  2. Set treatment price (what you charge patients - covers manufacturing + delivery)
  3. Get automatic liability insurance (built into platform)
  4. Trial goes live immediately - appears in search results, ranked by existing data
  5. Patients join based on rankings → You collect zero-cost data on whether it works

Net cost to company: $0

Why?

  • Patients pay for treatment (covers manufacturing/delivery)
  • Patients provide data (eliminates data collection cost ~$41k/patient)
  • Platform handles analysis (eliminates analysis cost)
  • Insurance is built-in (eliminates liability cost)

The Payment Flow (Or: How Everyone Profits Except Disease)

Patient pays: Treatment cost + Refundable deposit Patient receives: Subsidy (from a 1% Treaty Fund) Patient reports: Outcomes via simple app Deposit refunded: When trial complete Net to patient: $0 to -$50 (they might profit)

Example

  • Patient joins trial for experimental migraine treatment ($100/month)
  • Pays: $100 treatment + $50 deposit = $150
  • Receives: $125 subsidy
  • Out of pocket: $25
  • Completes trial, reports outcomes
  • Gets back: $50 deposit
  • Net: +$25 profit + potentially cures migraines

Company receives

  • $100/month from patient
  • Manufacturing cost: $20/month
  • Profit: $80/month per patient
  • Plus: Free clinical trial data worth $41k/patient in traditional system

Why This Creates Unlimited Research Capacity

Traditional model (bottleneck)

Research ideas → Grant committees → Selected few → $57M per trial → ~10 trials/year per company

Decentralized marketplace (unlimited):

Research ideas → Register instantly → Patients decide by joining → $0 per trial → Unlimited trials simultaneously

This is why 95% of rare diseases have no treatments. Traditional funding can’t afford to test everything. This decentralized marketplace can test EVERYTHING because:

  • No approval bottleneck (instant registration)
  • No funding bottleneck (patients pay for treatment)
  • No data collection bottleneck (patients provide data)
  • No disease too rare (if 100 patients exist globally, trial can run profitably)

Traditional trial: Pharma spends $57M, tests most profitable drug only Decentralized trial: Pharma spends $0, tests everything including supplements, food, lifestyle, off-patent drugs

A 1% Treaty Fund doesn’t fund pragmatic clinical trials directly. It subsidizes participation, which unlocks a self-sustaining marketplace that funds ALL research.

How It Works: Just Let People Try Stuff (Carefully)

Here’s a thought that apparently never occurred to anyone in Washington:

What if sick people could just… try treatments?

And then we could… write down what happens?

And then other sick people could… see what worked?

Your current system leaves 95% of rare diseases with zero approved treatments and ensures 85% of patients are barred from the trials that might save them. We’ve managed to perfect a system where only the healthy and compliant are allowed to test cures for the sick and desperate. It’s genius, in a suicidal sort of way.

This is because trials are designed to test drugs on people who don’t actually exist. To get into a study for an antidepressant, for example, you can’t have any other pesky problems like anxiety or PTSD. You can’t have a history of drug or alcohol use. You can’t be on other medications. You have to be the perfect kind of sick. The result? One investigation found that only 14.5% of real-world patients with depression would actually qualify. The rest of us are too messy for their pristine, clean data.

On top of excluding everyone with a pulse, these “definitive” studies are run on comically small groups. They’ll test a new heart drug on 275 people, a cancer drug on just 20 people, and a diabetes drug on 100 people, then prescribe the winner to millions.

A chart showing a study for Wellbutrin where only 36 patients were in the final analysis, yet the drug is prescribed to millions.

We’re basing the survival of our species on a sample size smaller than a kindergarten class. So, instead of studying these mythical, perfectly sick unicorns, what if we just collected data from everyone who’s actually sick?

The Power of Real-World Evidence (Or: Spying on Sick People for a Good Cause)

Your decentralized framework for drug assessment is built on a simple, powerful, and slightly creepy idea: we can learn more, faster, by analyzing data from real patients in the real world. This is called Real-World Evidence (RWE), which is a fancy term for “watching what happens.”

Skeptics will say, “But that’s just observational research! Correlation isn’t causation!” They’ll point to flip-flopping diet advice about eggs and cholesterol as proof that this is all voodoo.

They’re not wrong about the old way. Old observational studies were flawed because they couldn’t easily separate correlation from causation. Did the people who ate eggs also smoke three packs a day? The studies couldn’t always tell.

But here’s what’s changed: we now have so much data and such powerful computers that we can overcome these problems. With modern data from wearables, apps, and electronic health records, a single person can become their own control group. We can track someone’s arthritis pain for a month, have them take Turmeric for a month, then stop, then start again. By doing this, we can see if the Turmeric is actually doing something or if it’s just a coincidence.

Modern statistical methods are so good that a meta-analysis in the New England Journal of Medicine found that the results from high-quality observational studies are generally the same as those from expensive, slow randomized controlled trials.

A chart comparing effect sizes from observational studies and randomized trials for mortality.

A chart comparing effect sizes from observational studies and randomized trials for various outcomes.

We have the tools to get reliable answers from real-world data. We just have to actually use them.

Introducing FDA.gov 2.0: Now With 80% Less Death

Your decentralized framework for drug assessment upgrades FDA.gov from a digital cemetery to something useful. Cost: Less than one fighter jet that doesn’t work.

Here’s the revolutionary process:

Step 1: Type in What’s Killing You

Enter condition

Revolutionary feature: A search box. The FDA’s current website doesn’t have this because they assume you’ve already died.

Step 2: See What Actually Works (Based on Reality, Not Theory)

View ranked treatments

Instead of “This drug is approved for exactly this condition in exactly these patients on exactly Tuesdays,” you get:

“Here’s what happened to 50,000 people who tried this:

  • 40% got much better
  • 30% got somewhat better
  • 20% no change
  • 10% grew a third nipple (but a useful one)”

It’s like Consumer Reports, but for not dying.

Treatment Rankings: Every Option, Ranked by Reality

Treatment Rankings Example

Search any condition and see every treatment ranked by real-world effectiveness:

  • FDA Approved treatments with effectiveness scores from actual patients
  • Phase 3 and Phase 2 trials you can join right now
  • Experimental options with preliminary data
  • One-click access to join available trials

No more guessing which treatment your doctor half-remembers from a conference. Just clear rankings based on what actually worked for people like you. The current system never publishes negative results, so we waste billions repeating the same failed ideas. This ends that particular brand of expensive stupidity.

Step 2.5: See the Future (With Financially-Backed Predictions)

To complement the real-world data from past patients, your decentralized framework for drug assessment will integrate a powerful forecasting tool: live prediction markets.

This isn’t just polling experts; it’s a marketplace where researchers, doctors, investors, and even the public can bet real money on a trial’s future outcomes.

For any given trial, you’ll see real-time odds on questions like:

  • Effectiveness: “What are the chances this treatment will reduce tumor size by >50% in the next 6 months?” Market Price: 38%
  • Adverse Events: “What is the probability of patients experiencing severe nausea?” Market Price: 22%
  • Completion: “What are the odds this trial will be fully enrolled by its target date?” Market Price: 71%

Why Trust This?

  1. Skin in the Game: These aren’t cheap-talk opinions. Bettors lose money if they’re wrong, creating a powerful incentive for accuracy.
  2. The Ultimate Fact-Checker: Your decentralized institutes of health (DIH) network automatically resolves these bets using the actual, aggregated, and anonymized patient data collected through your decentralized framework for drug assessment. If the market predicts a 22% rate of nausea, and the real-world data shows 40%, the market learns and adjusts, or bettors go broke.

This gives patients an invaluable second layer of information: not just what has happened, but what the world’s collective, financially-incentivized intelligence thinks will happen.

Step 3: Join a Trial from Your Couch (While Dying Comfortably)

Join trials

Current system: Drive 500 miles to a university hospital, wait 6 months, get rejected for having the wrong kind of dying.

New system: Click button. Get pills. Report if you die.

Revolutionary!

Step 4: Get Drugs Delivered Like Pizza (But More Life-Saving)

Get treatment

Amazon can deliver a banana costume in 2 hours but experimental medications take 6 months and require seven forms of ID?

Your framework fixes that. Your pharmacy becomes a trial site. Your doctor becomes a researcher. Your dying becomes data.

Step 5: Publish Results

Report outcomes

Current system: 500-page case report forms that ask questions like “Rate your suffering on a scale of mauve to burnt sienna.”

New system:

  • “Are you dead?” Yes/No
  • “If no, how dead do you feel?” Slider bar
  • “Any new body parts?” Check all that apply

Step 6: Everyone Benefits from Everyone’s Suffering

Improve rankings

Your data helps the next person. Their data helps you. It’s socialism, but for not dying.

Every pill becomes a tiny experiment. Every patient becomes a scientist. Every outcome gets recorded.

It’s what we should have been doing since we invented writing.

The Partnership Approach: Building Rails, Not Trains

Here’s what we’re NOT doing: building a government platform that competes with existing medical technology companies.

Here’s what we ARE doing: establishing an open protocol that lets all the existing platforms talk to each other.

Think of it like the internet. We didn’t build one website that everyone has to use. We built HTTP - the protocol that lets all websites connect. Same idea.

How your framework gets funded: An implementation of this framework is one of many campaigns competing for funding from the 1% Treaty Fund via Wishocracy. It has no independent budget authority. If a particular implementation gets captured or fails to deliver, the community can fund alternative implementations instead. This prevents the “new FDA” problem.

The Players Already in the Game

Companies like Medable ($521M raised), Science 37 (~$100M raised), and others have already built decentralized clinical trial platforms. They’re good at it. They have users. They have infrastructure.

Why compete with them? Partner instead.

Your decentralized framework for drug assessment provides:

  • Open protocol for data exchange (like email protocols)
  • Federated data network (data stays in Epic/Cerner/Apple Health systems)
  • Treatment ranking algorithms (open source, anyone can verify)
  • Trial matching services (connects patients to ANY platform)

Existing platforms provide:

  • Patient-facing apps and interfaces
  • Trial management tools
  • Sponsor relationships
  • Regional expertise

The Integration Consortium

Before writing code, form partnerships with:

EHR Vendors (they have the patient data):

  • Epic Systems (31% of US hospital market)
  • Cerner/Oracle Health (26% of US market)
  • Allscripts/Veradigm (ambulatory care)

Tech Giants (they have distribution):

  • Apple Health (billions of devices)
  • Google Health (Android ecosystem)

Existing DCT Platforms (they have expertise):

  • Medable, Science 37, Thread
  • Let them be “Founding Partners” with governance seats

International Partners (already proven it works):

  • NHS/UK Biobank (RECOVERY trial model)
  • NIH All of Us (400k+ participants, genomic data)

Why They’ll Cooperate

For EHR vendors:

  • Reduced certification fees if they enable data portability
  • Public “seal of approval” for interoperability
  • Patient pressure (people want their data to work for them)
  • Eventually: regulatory requirement

For DCT platforms:

  • “Founding Partner” status (branding/marketing value)
  • Access to the $27.2B annual 1% Treaty Fund pool
  • Governance representation
  • Early access to protocol features
  • Revenue sharing on trials they run

For pharma/CROs:

  • 50-95% cost reduction per trial
  • 240x more participants available
  • Faster enrollment
  • Better, more diverse data

Federated, Not Centralized

Data doesn’t move to a central database. It stays where it is:

  • Epic systems keep their data
  • Apple Health keeps its data
  • Cerner keeps its data

Your framework’s protocol just lets you run queries ACROSS systems without moving data. Like TriNetX does with 300M+ patient records.

This solves:

  • GDPR/HIPAA compliance (data never leaves source)
  • Privacy concerns (no central honeypot to hack)
  • Vendor cooperation (they keep control)
  • Patient trust (data doesn’t go to “the cloud”)

The Money Shot: How to Save 95% on Not Killing People

Look at these incredible savings! It’s like Black Friday, but for survival!

Problem to Fix Current Insanity New Reality Money Saved Lives Saved
Clinical Phase Timeline 9.1 years (avg) 2-3 years ~7 years of waiting eliminated ~50,000/year
Cost per trial $57 million $2 million $55 million Infinite ROI
Who can participate 13.9% of patients 100% of patients 86.1% inclusion rate Everyone
Rare diseases with treatments 5% Eventually 100% Priceless Millions

The Itemized Receipt of Eliminated Stupidity

Expense Category Traditional Clinical Trial Your Decentralized Framework Cost Savings What It Actually Is
💾 Data Management $198,014 $10,000 94.9% Excel spreadsheets with fear
✅ IRB Approval $324,081 $5,000 98.5% Permission slips for adults
📝 IRB Amendments $6,347 $0 100% Permission to fix typos
🔍 Source Data Verification $1,486,250 $25,000 98.3% Checking if you lied about dying
🤝 Patient Recruitment $805,785 $15,000 98.1% Facebook ads for dying people
🎯 Patient Retention $76,879 $20,000 74% Bribes to keep dying people interested
👨‍⚕️ Research Associates $2,379,605 $150,000 93.7% People who watch you take pills
👩‍⚕️ Physicians $1,966,621 $100,000 94.9% Doctors pretending to be scientists
🏥 Clinical Procedures $5,937,819 $1,000,000 83.2% Poking you with expensive things
🧪 Laboratory $2,325,922 $500,000 78.5% Testing if your pee is still pee
🏢 Site Recruitment $849,158 $0 100% Bribing hospitals to participate
🏗️ Site Retention $4,461,322 $0 100% Bribing hospitals to not quit
👥 Administrative Staff $7,229,968 $100,000 98.6% People who file the files
📊 Site Monitoring $4,456,717 $0 100% People who watch the people who watch you
🏢 Site Overhead $7,386,816 $0 100% Electricity for the filing cabinets
📎 All Other $17,096,703 $100,000 99.4% Nobody knows but it’s expensive
TOTAL $56,988,007 $2,025,000 95.7% The price of bureaucracy

This eliminates $55 million per trial.

Why This Works: The Mathematical Impossibility of Committees

Here’s the problem with having ~200 FDA bureaucrats decide what 8 billion people can try when they’re dying:

The Math That Proves Centralization Can’t Work

Let’s calculate how long it would take the FDA to properly evaluate every treatment for every person:

Years for FDA to evaluate everything: 1,369,863,014
Years for decentralized system: 273.97

The committee needs 1.3 million years. The decentralized system needs 100 days.

Why 200 People Can’t Replace 8 Billion

The FDA doesn’t know:

  • What disease you have (they haven’t met you)
  • How your body responds to treatments (genetics are unique)
  • What risks you’re willing to take (some prefer death to side effects, others the reverse)
  • Whether you’d rather die trying or die waiting (only you can answer this)

But they decide for you anyway.

The Solution: Let 8 Billion Experts Make 8 Billion Decisions

Current system

330 Million Americans
        ↓
   200 FDA Bureaucrats (Bottleneck)
        ↓
    17-Year Delays
        ↓
    Millions Die

Decentralized system:

8 Billion Humans
    ↓ ↓ ↓ ↓ ↓ (Parallel Processing)
Millions of Trials Simultaneously
        ↓
    Real-Time Results
        ↓
    Data Saves Everyone

This isn’t ideology. It’s information theory. You mathematically cannot centralize medical decisions for 8 billion unique people. The FDA could work 24/7 for a million years and still be behind.

The only solution is to let patients and doctors make their own decisions, then pool the data so everyone benefits from everyone’s experience.

Real Examples of This Working (While the FDA Wasn’t Looking)

The Oxford Recovery Trial: How the British Accidentally Saved Medicine

During COVID, while America was filling out forms, Oxford University did something crazy: they just tested drugs on dying people to see if they stopped dying.

Cost per patient:

  • Normal clinical trials: $41K
  • Oxford pragmatic trials: $500

That’s not a typo. Five hundred dollars. The cost of a nice dinner in Manhattan to save a human life.

Results:

The FDA’s response: “But did they file the correct paperwork?”

Wikipedia for Whether You’ll Die

The system includes Clinipedia: Wikipedia but instead of arguing about whether tomatoes are fruits, we argue about whether they cure cancer.

Every treatment gets a page. Every outcome gets recorded. Every patient contributes data.

Imagine looking up your disease and seeing:

“Exploding Kidney Syndrome”

From Clinipedia, the encyclopedia of not dying

Current Treatments Ranked by Not-Dying Score

  1. Prayer (3% effective, 0% side effects)
  2. Experimental Drug XJ-47 (67% effective, 30% chance of jazz hands)
  3. Traditional medicine (5% effective, tastes like sadness)
  4. Doing nothing (0% effective, 100% chance of exploded kidneys)
  5. Essential oils (0% effective, room smells nice while you die)

This page based on data from 10,847 patients who contributed their suffering to science

Become a Scientist from Your Couch

The system also lets you create your own studies. Got a theory that wearing socks inside-out cures baldness? Create a study. Get a link. Invite your balding friends to join. The system handles the rest. Your data gets published. Who knows, maybe you’ll win a Nobel Prize. Or maybe you’ll just prove that socks are socks. Either way, humanity learns something without spending two billion dollars.

Your Personal Death-Prevention Assistant: The FDAi

Everyone gets a superintelligent doctor that lives in their phone and doesn’t judge them for Googling “is my poop normal?”

The FDAi (Food and Drug Artificial intelligence) is like Siri, but instead of telling you the weather, it tells you how not to die.

You: “I have diabetes and my foot fell off.” FDAi: “Based on 50,000 similar cases, here’s what worked:

  • 60% success: Reattachment surgery + Drug A
  • 30% success: Prosthetic foot + Drug B
  • 10% success: Hopping lessons + Prayer
  • 0% success: Essential oils (but your remaining foot will smell lavender-fresh)”

It does this by connecting to all your data, from wearables, apps, and your medical records, and looking for patterns. It’s automated root cause analysis for your own body, finding what’s wrong while your doctor is still trying to remember your name. It’s like having every doctor who ever lived in your pocket, except they actually agree on something.

The Digital Twin Safe: Your Medical Data’s Panic Room

Digital Twin Safe

Right now, your medical data is scattered across:

  • 17 different doctors who don’t talk
  • 5 insurance companies that hate you
  • 3 pharmacies that can’t spell your name
  • 1 veterinarian (long story)

The Digital Twin Safe puts it all in one place. It’s like a Swiss bank account, but for your cholesterol levels.

You control who sees what:

  • Insurance companies: “He’s mostly alive”
  • Doctors: Everything
  • Researchers: Anonymized data
  • Facebook: Absolutely nothing, Mark

Outcome Labels: Nutrition Facts for Drugs

Food has nutrition labels. Cigarettes have warning labels. Drugs have… incomprehensible 40-page inserts written by lawyers having seizures.

Outcome Labels fix that - clear, data-driven summaries showing exactly what happens when real people try a treatment:

Outcome Labels Example

Based on thousands of real patients with real outcomes:

Cognitive Improvements

  • Memory Recall: +35%
  • Cognitive Function: +28%
  • Executive Function: +22%
  • Hippocampal Volume: +15%

Side Effects

  • Immune Response: +12%
  • Headache: +9%
  • Fatigue: +7%

No marketing spin. No 40-page legal disclaimers. Just clear data about what actually happens to people like you.

Example: DEPRESSION CRUSHER 3000™

Outcome Label

Based on 50,000 humans who tried this:

  • 📈 Depression: ⬇️ 60% reduction
  • 😴 Sleep: ⬆️ 30% improvement
  • 🍆 Sex drive: ⬇️ 90% reduction (sorry)
  • 🎺 Spontaneous jazz hands: 15% chance
  • 💀 Death: 0.01% (better than depression!)

This label based on actual humans, not laboratory rats who’ve never paid rent

The Future: Where Death Becomes Embarrassing

2027: The Beginning of the End of Dying Slowly

FDA.gov becomes actually useful. Millions join trials from home. The first “Wikipedia disease” gets cured entirely through crowdsourced trials. The FDA claims credit.

2030: Big Pharma Pivots or Dies

Pharmaceutical companies realize they can’t charge $10,000 for pills that cost $1 to make when everyone can see the data. Some adapt. Some become museums.

2035: The Great Revelation

We realize we could have done this all along. We had the internet since 1990. We had computers since 1950. We had writing since 3000 BC.

We spent 5,000 years letting people die while we filled out forms.

History books will call us “The Paperwork Age” and children will laugh at our stupidity.

2050: Death Becomes Opt-In

Diseases are mostly solved. Death becomes a choice, like smoking or voting Republican. The FDA’s job becomes preventing people from becoming immortal too quickly (traffic is bad enough).

The last FDA form is filled out. It’s immediately lost. Nobody notices.

How Your Framework Actually Works

Black box model animation showing how this framework works

Your decentralized framework for drug assessment achieves 82× More Efficiency through:

  • Cost per patient: $500 vs the current $41K
  • Time to results: 2 years vs the current 17 years
  • Patient access: Universal access vs 86.1% excluded currently
Metric Current FDA System A Decentralized Framework for Drug Assessment Improvement
Approach 📋 Central Planning 🛒 Open Platform Market-based
Cost per Patient $41,000 $500 Parameter(82.0, source_ref=‘recovery-trial-82x-cost-reduction’, confidence=‘high’)x cheaper
Time to Results 17 years 2 years 8.5X faster
Patient Access 15% 100% 6.7X more access
Innovation Impact 70% approval drop after 1962 Competition drives innovation Reversed decline