Out of two point four billion people with chronic disease, only one point nine million patients per year participate in
trials annually. That's zero point zero six percent. forty-four point eight percent would volunteer if they could.
Only zero point zero six percent currently participate. This isn't because people don't want to help find cures.
It's because your system was designed by someone who hates sick people.
Trials cost forty-one thousand dollars, require traveling to major medical centers, and reject eighty-six point one percent
of actual patients. You built a hospital you can't get into.
Impressive, in a genocidal sort of way. Meanwhile, the system that's supposed to catch dangerous side effects misses ninety
to ninety-nine percent of them. Only one to ten percent of adverse drug events are ever reported to the F.D.A.
No denominator data. No effect sizes. No way to calculate how often a drug actually hurts people.
The safety monitoring system is a suggestion box that nobody uses.
You don't have a recruitment problem. You have a capacity problem.
The Solution: Consumer Reports for Drugs. Your decentralized framework for drug assessment (D.F.D.A.
(a decentralized F.D.A.)) is an OPEN COORDINATION PROTOCOL.
It connects companies with treatments to patients who need them.
It's H.T.T.P. for clinical trials. A standard that lets all the existing systems talk to each other and share outcomes.
You invented the internet thirty years ago and somehow forgot to plug medicine into it.
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 ninety-five percent of rare diseases with zero approved treatments.
It bars eighty-five percent of patients from the trials that might save them.
We've perfected a system where only the healthy and compliant test cures for the sick and desperate.
It's genius, in a suicidal sort of way. Trials are designed to test drugs on people who don't actually exist.
To get into a study for an antidepressant, you can't have any other pesky problems like anxiety or P.T.S.D.
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? Only fourteen point five percent 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 run on comically small groups.
They'll test a new heart drug on two hundred seventy-five people.
A cancer drug on just twenty people. A diabetes drug on one hundred people.
Then they prescribe the winner to millions. You're basing survival on sample sizes smaller than kindergarten classes.
So instead of studying mythical, perfectly sick unicorns, what if you just collected data from everyone who's actually sick?
The Power of Real-World Evidence (Or: Spying on Sick People for a Good Cause).
This framework is built on a slightly creepy idea: analyzing data from real patients in the real world.
Real-World Evidence, or R.W.E. A fancy term for "watching what happens.".
Skeptics say, "Correlation isn't causation!" They say this while dying of diseases nobody bothered to study properly.
They point to flip-flopping egg advice as proof this is voodoo.
They're not wrong about the old way. Old studies couldn't tell if eggs killed people or if egg-eaters just smoked three
packs a day. But now there's enough data and computing power to make each person their own control group.
Track arthritis pain for a month. Take Turmeric, stop, start again.
The pattern reveals whether it works or if it's coincidence.
A landmark meta-analysis in the New England Journal of Medicine quietly ended this argument while nobody was listening.
Researchers compared observational studies against randomized controlled trials across 19 different treatments.
Cardiac surgery, cancer, ophthalmology, obstetrics. The result: nearly identical effect sizes.
The cheap method and the expensive method found the same answers.
You've been overpaying for data like tourists at an airport restaurant.
Each pair of dots represents the same treatment tested two ways: one observational (cheap, fast, real patients) and one
randomized controlled (expensive, slow, cherry-picked patients).
In nearly every comparison, the confidence intervals overlap.
Both methods found the same treatments work and the same treatments don't.
The Two-Stage Pipeline: Watch First, Then Test. Observational data finds the signals.
But correlation still isn't proof (even you know that). So the framework uses a two-stage pipeline: Stage 1 (Signal Detection):
Aggregate data from millions of patients. Score each treatment-outcome relationship using six Bradford Hill causality criteria:
How strong is the effect? How consistent across people? Does the treatment come before the improvement?
Is there a dose-response pattern? Cost: about ten cents per patient.
Stage 2 (Confirmation): The top signals (the zero point one to one percent most promising) proceed to pragmatic trials embedded
in routine care. Simple randomization. Real patients. Real conditions.
Cost: about nine hundred twenty-nine dollars per patient. This eliminates confounding and proves causation.
Stage 1 filters millions of possibilities for almost nothing.
Stage 2 confirms the winners for one-eightieth the cost of traditional trials.
The result: every treatment gets an evidence grade. Validated: Confirmed by pragmatic trial.
Promising: Strong observational signal, awaiting trial. Signal: Hypothesis only, needs more data.
The cheap stage does the hard work of searching. The expensive stage only runs on candidates that deserve it.
Instead of spending fifty-seven million dollars testing one drug, you spend one dollar watching a million treatments and
five hundred dollars confirming the ones that work. Proof It Works (While the F.D.A.
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 cost forty-one thousand dollars.
Oxford pragmatic trials cost five hundred dollars. That's not a typo.
Five hundred dollars. The cost of a nice dinner in Manhattan to save a human life.
Results: They found that steroids cut COVID deaths by thirty percent.
They saved over one million lives globally. It took three months instead of three years.
It cost less than one Super Bowl commercial. The F.D.A.'s response: "But did they file the correct paperwork?".
RECOVERY wasn't a fluke. A Harvard meta-analysis of one hundred and eight embedded pragmatic trials found median costs of
just ninety-seven dollars per patient. The patient-centered outcomes research network, or P.C.O.R.net, ADAPTABLE trial enrolled
fifteen thousand seventy-six patients across forty clinical sites at nine hundred twenty-nine dollars per patient.
This model works across therapeutic areas, across countries, across decades.
The evidence base for cheap, fast, embedded trials isn't one study.
It's one hundred and eight and counting. What You'd See: F.D.A.
dot gov two point zero. The protocol upgrades F.D.A. dot gov from a digital cemetery to something useful.
Cost: Less than one fighter jet that doesn't work. Step One: Type in What's Killing You.
Revolutionary feature: A search box. The F.D.A.'s current website doesn't have this because they assume you've already died.
Step Two: See What Actually Works (Based on Reality, Not Theory).
Instead of "This drug is approved for exactly this condition in exactly these patients on exactly Tuesdays," you get: "Here's
what happened to fifty thousand people who tried this: Forty percent got much better.
Thirty percent got somewhat better. Twenty percent saw no change.
Ten percent grew a third nipple (but a useful one)." It's like Consumer Reports, but for not dying.
Treatment Rankings: Every Option, Ranked by Reality Search any condition and see every treatment ranked by real-world effectiveness:
F.D.A. Approved treatments with effectiveness scores from actual patients.
Phase Three and Phase Two 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. Humanity wastes billions repeating the same failed ideas.
Step Three: Join a Trial from Your Couch (While Dying Comfortably).
Current system: Drive five hundred miles to a university hospital, wait six months, get rejected for having the wrong kind
of dying. New system: Click button. Get pills. Report if you die.
Revolutionary! Step Four: Get Drugs Delivered Like Pizza (But More Life-Saving).
Amazon can deliver a banana costume in two hours but experimental medications take six months and require seven forms of
I.D.? Your framework fixes that. Your pharmacy becomes a trial site.
Your doctor becomes a researcher. Your dying becomes data. Step Five: Publish Results.
Current system: five hundred-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 or 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.
Your data helps the next person. Their data helps you. Every pill becomes a tiny experiment.
Every patient becomes a scientist. Every outcome gets recorded.
You should have done this when you invented writing in three thousand B.C.
Outcome Labels: Nutrition Facts for Drugs. Food has nutrition labels.
Cigarettes have warning labels. Drugs have... incomprehensible forty-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: No
marketing spin. No forty-page legal disclaimers. Just clear data about what actually happens to people like you.
Here's what an Outcome Label for depression would actually look like: OUTCOME LABEL: Depression Severity *Based on forty-seven
thousand eight hundred thirty-two participants* Treatments Ranked by Effect Size Rank one: Exercise.
Effect: minus thirty-one point two percent. Sample size: twelve thousand eight hundred forty-seven.
Optimal dose: forty-five minutes per day. Rank two: Bupropion.
Effect: minus twenty-eight point three percent. Sample size: two thousand eight hundred forty-seven.
Optimal dose: three hundred milligrams. Rank three: Sertraline.
Effect: minus twenty-four point seven percent. Sample size: five thousand one hundred twenty-three.
Optimal dose: one hundred milligrams. Rank four: Sleep (seven to nine hours).
Effect: minus twenty-two point one percent. Sample size: thirty-one thousand two hundred four.
Optimal dose: eight point two hours. Rank five: Venlafaxine.
Effect: minus twenty-one point two percent. Sample size: one thousand eight hundred ninety-two.
Optimal dose: one hundred fifty milligrams. Rank six: Omega three.
Effect: minus eighteen point nine percent. Sample size: four thousand five hundred twenty-one.
Optimal dose: two thousand milligrams eicosapentaenoic acid and docosahexaenoic acid, or E.P.A.
plus D.H.A. Rank seven: Meditation. Effect: minus sixteen point four percent.
Sample size: eight thousand nine hundred thirty-two. Optimal dose: twenty minutes per day.
Rank eight: Fluoxetine. Effect: minus fifteen point eight percent.
Sample size: three thousand four hundred fifty-six. Optimal dose: forty milligrams.
Makes It Worse: Alcohol greater than two per day (plus twenty-three point four percent), sleep deprivation (plus nineteen
point eight percent), social isolation (plus fifteen point two percent).
No Significant Effect: Multivitamin, Probiotics, B-complex, Ashwagandha, five-hydroxytryptophan, or 5-H.T.P.
Notice anything? Exercise beats every drug. Sleep beats most drugs.
Your doctor probably didn't mention that because there's no pharmaceutical rep buying lunch to promote jogging.
This is what happens when you rank treatments by outcomes instead of by marketing budget.
Your Personal Death-Prevention Assistant: The F.D.A.i. Everyone gets a superintelligent doctor that lives in their phone
and doesn't judge them for Googling "is my poop normal?". The F.D.A.i.
(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.". F.D.A.i.: "Based on fifty thousand similar cases, here's what worked: Sixty
percent success: Reattachment surgery plus Drug A. Thirty percent success: Prosthetic foot plus Drug B.
Ten percent success: Hopping lessons plus Prayer. Zero percent success: Essential oils (but your remaining foot will smell
lavender-fresh)". It connects to your wearables, apps, and medical records to find what's killing you while your doctor
is still trying to remember your name. Every doctor who ever lived, in your pocket, and they actually agree on something.
It also does something no doctor can: precision dosing. By analyzing what dose preceded your best outcomes, it generates
personalized recommendations. Not "take some magnesium." Instead: "Your sleep quality was highest after four hundred milligrams
of Magnesium over the previous twenty-four hours." Optimal doses derived from your data, not a study of thirty-six college
students in nineteen ninety-seven. Making It Legal to Not Die.
This solution requires a law: the Right to Trial and F.D.A.
Upgrade Act. It says dying people can try things that might help them not die, and what happens gets written down so other
dying people can see what worked. The fact that this requires a law tells you everything about your species.
The Business Model: How Everyone Profits Except Disease. How Companies Register Treatments (five minutes, zero approval
needed). ANY COMPANY - pharma, supplements, food, interventions - can instantly create a trial: Register treatment on a
public portal (name, ingredients, condition, price). Set treatment price (what you charge patients - covers manufacturing
and delivery). Get automatic liability insurance (built into protocol governance).
Trial goes live immediately - appears in search results, ranked by existing data.
Patients join based on rankings, which allows you to collect zero-cost data on whether it works.
Net cost to company: zero dollars. WHY? Patients pay for treatment (covers manufacturing and delivery).
Patients provide data (eliminates data collection cost of approximately forty-one thousand dollars per patient).
Protocol enables standardized analysis (eliminates analysis cost).
Insurance is built-in (eliminates liability cost). The Payment Flow.
Patient pays: Treatment cost plus Refundable deposit. Patient receives: Subsidy (from a one percent Treaty Fund).
Patient reports: Outcomes via simple app. Deposit refunded: When trial complete.
Net to patient: zero dollars to negative fifty dollars (they might profit).
Example. Patient joins trial for experimental migraine treatment (one hundred dollars per month).
Pays: one hundred dollar treatment plus fifty dollar deposit equals one hundred and fifty dollars.
Receives: one hundred and twenty-five dollar subsidy. Out of pocket: twenty-five dollars.
Completes trial, reports outcomes. Gets back: fifty dollar deposit.
Net: plus twenty-five dollar profit plus potentially cures migraines.
Company receives. One hundred dollars per month from patient.
Manufacturing cost: twenty dollars per month. Profit: eighty dollars per month per patient.
Plus: Free clinical trial data worth forty-one thousand dollars per patient in traditional system.
Why This Creates Unlimited Research Capacity. Traditional model start: research ideas.
Decentralized protocol start: research ideas. Traditional model gatekeeping: grant committees select the few.
Decentralized protocol gatekeeping: register instantly. Traditional model enrollment: recruit from limited pool.
Decentralized protocol enrollment: patients decide by joining.
Traditional model cost: fifty-seven million dollars per trial.
Decentralized protocol cost: zero dollars per trial. Traditional model throughput: approximately ten trials per year per
company. Decentralized protocol throughput: unlimited trials simultaneously.
This is why ninety-five percent of rare diseases have no treatments.
Traditional funding can't afford to test everything. This decentralized protocol enables testing EVERYTHING because: There
is no approval bottleneck (instant registration). There is no funding bottleneck (patients pay for treatment).
There is no data collection bottleneck (patients provide data).
No disease is too rare (if one hundred patients exist globally, a trial can run profitably).
Traditional trial: Pharma spends fifty-seven million dollars, tests most profitable drug only.
Decentralized trial: Pharma spends zero dollars, tests everything including supplements, food, lifestyle, and off-patent
drugs. A one percent Treaty Fund doesn't fund pragmatic clinical trials directly.
It subsidizes participation, which unlocks a self-sustaining ecosystem that funds ALL research.
The Money Shot: How to Save ninety-five percent on Not Killing People.
For the Clinical Phase Timeline, the current insanity is nine point one years on average, while the new reality is two to
three years, eliminating seven years of waiting and saving approximately fifty thousand lives per year.
For the cost per trial, the current insanity is fifty-seven million dollars, while the new reality is two million dollars,
saving fifty-five million dollars with an infinite R.O.I. Regarding who can participate, the current insanity is thirteen
point nine percent of patients, while the new reality is one hundred percent of patients, an eighty-six point one percent
inclusion rate for everyone. For rare diseases with treatments, the current insanity is five percent, while the new reality
is eventually one hundred percent, which is priceless and saves millions.
The Itemized Receipt of Eliminated Stupidity. This eliminates fifty-five million dollars per trial.
The Partnership Approach: Building Rails, Not Trains. Here's what you're NOT doing: building a government platform that
competes with existing medical technology companies. Here's what you ARE doing: building an open protocol that lets all
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 H.T.T.P., the protocol that lets all websites connect.
Same idea. How your D.F.D.A. (a decentralized F.D.A.) gets funded: An implementation of this framework is one of many campaigns
competing for funding from the one percent 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 F.D.A."
problem. The Players Already in the Game. Multiple companies have already built decentralized clinical trial platforms.
They've collectively raised hundreds of millions in venture funding.
They're good at it. They have users. They have infrastructure.
Why compete with them? That would be very human of you. Partner instead.
The protocol provides: An open protocol for data exchange (like email protocols).
A federated data network (data stays in Epic, Cerner, or 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.
Federated, Not Centralized. Data doesn't move to a central database.
It stays where it is. Everyone keeps their toys. 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 asking every library in the world a question without stealing their books.
Federated data networks already do this with three hundred million plus patient records.
This solves: G.D.P.R. and H.I.P.A.A. 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"). Why This Works: The Mathematical Impossibility of Committees.
About two hundred F.D.A. bureaucrats decide what eight billion people can try when dying.
Here's the math on why that can't work: The F.D.A.'s processing capacity: two hundred committee members, working eight hours
a day, making one decision per hour equals one thousand six hundred decisions per day.
The decisions that actually need making: ten thousand known diseases times one hundred potential treatments times eight
hundred million genetically distinct patient profiles equals eight hundred billion decisions.
At one thousand six hundred decisions per day, the F.D.A. would need one million three hundred sixty-nine thousand eight
hundred sixty-three years to evaluate everything. A decentralized system where eight billion people each make one health
decision per day processes the same load in one hundred days.
The F.D.A. 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. This isn't ideology. It's information theory.
You mathematically cannot centralize medical decisions for eight billion unique people.
The Future: Where Death Becomes Embarrassing. 2027: The Beginning of the End of Dying Slowly.
F.D.A. dot gov becomes actually useful. Millions join trials from home.
The first "Wikipedia disease" gets cured entirely through crowdsourced trials.
The F.D.A. claims credit. 2030: Big Pharma Pivots or Dies. Pharmaceutical companies realize they can't charge ten thousand
dollars for pills that cost one dollar to make when everyone can see the data.
Some adapt. Some become museums. The gift shop sells expired painkillers at original markup.
2035: The Great Revelation. Humanity could have done this all along.
The internet existed since 1990. Computers since 1950. Five thousand years of letting people die while filling out forms.
History books will call this "The Paperwork Age." Children will laugh.
2050: Death Becomes Opt-In. Diseases are mostly solved. Death becomes a choice, like smoking or voting Republican.
The F.D.A.'s job becomes preventing people from becoming immortal too quickly (traffic is bad enough).
The last F.D.A. form is filled out. It's immediately lost. Nobody notices.
How Your D.F.D.A. (a decentralized F.D.A.) Actually Works. This approach achieves forty-four point one times More Efficiency
through: Cost per patient is nine hundred twenty-nine dollars versus the current forty-one thousand dollars.
Time to results is 2 years versus the current 17 years. Patient access is universal access versus eighty-six point one percent
excluded currently.