Step 1: Data Aggregation → Step 2: Defining Similarity → Steps 3 & 4: Routing + Outcome Packets → Step 5: Synthesis → Capstone: Every Component Exists
Similarity defined. Routed to your exact cohort. Outcome packets extracted.
Now what?
You vote.
What do 1,000 people with your exact issue say? 97% say Treatment A worked. 3% say it didn't. That's your answer. Not a doctor's guess. Not a decade-old study. Real-time insight from people exactly like you—tallied in milliseconds.
The core concept is almost embarrassingly simple: vote and see what worked.
Why This Matters
One person says "I lasted 28 months"—that's hope.
A hundred say it? That's data.
A thousand? That's evidence.
Synthesis is where distributed observations become collective intelligence. Where individual outcomes become population-level insight. Where the network's quadratic potential converts to actionable truth.
The core is vote. But each network may layer on thousands of rules, weights, and methods—recency adjustments, confidence intervals, outlier filters, Bayesian priors. The sophistication is optional. The foundation is universal: now that you have real-time insight from everyone with your exact issue, tally the results and see what actually worked.
The Five Synthesis Methods
QIS doesn't mandate one synthesis algorithm. Networks choose. Users pick. Competition tunes. Here are the five methods—all running on your device, all using math that's existed for decades:
1Simple Vote
Most common outcome wins. Treatment A worked for 62% of similar patients. Same logic behind every poll since democracy existed.
Time: 2ms • Complexity: O(k)
2Weighted Recency
Newer packets = heavier votes. Timestamp determines weight. Like Amazon ratings where last week's reviews count more than last year's.
Time: 10ms @ 100k packets • Complexity: O(k log k)
3Rule-Based
If side_effect_severity > 3, flag high risk. Union all reported symptoms. No ML. Just if/else logic. Transparent and auditable.
Time: 5ms • Complexity: O(k)
4Bayesian Update
Prior: 80% survive 2 years. Update with 100 packets. Posterior: 78% with 95% confidence interval. On-device stats libraries handle it.
Time: 50ms • Libraries: jStat, bayes.js
5Ensemble
Run all methods. Merge results. Vote on treatment, average survival months, flag rule violations. Same logic as ML model ensembles.
Time: 100ms • Best-of-all accuracy
Competition does the tuning. One network uses median-only synthesis. Another uses recency-weighted Bayesian. Users migrate to whichever saves more lives. The market optimizes the algorithm. Read more: The Three Elections →
The Code Is Simple
Here's what weighted outcome voting actually looks like:
const packets = receivedPackets; // 100-500 typical
// Weight by similarity (closer biology = higher weight)
const weightedOutcomes = packets.map(p => ({
outcome: p.survival_months,
weight: p.similarity_score
}));
// Weighted average
const totalWeight = weightedOutcomes.reduce((sum, o) => sum + o.weight, 0);
const synthesized = weightedOutcomes.reduce(
(sum, o) => sum + (o.outcome * o.weight), 0
) / totalWeight;
// Result: weighted average survival for people like you
console.log(`Expected: ${synthesized.toFixed(1)} months`);
That's it. No distributed coordination. No consensus rounds. No waiting for network agreement. The packets arrived—your device computes locally.
Phones Don't Care About Scale
The timing claims aren't theoretical. Modern smartphones perform trillions of operations per second—the iPhone's A17 Pro alone hits 5+ teraflops. Synthesis is trivial:
| Operation | Algorithm | Complexity | Real-World Time |
|---|---|---|---|
| Simple vote count | Linear scan | O(k) | < 1ms per 10k items |
| Weighted sort | Timsort (JS default) | O(k log k) | ~100ms per 1M items |
| Median finding | Quickselect | O(k) average | < 10ms per 100k items |
| Bayesian posterior | Beta-binomial conjugate | O(k) | < 50ms with jStat |
Battery impact? Negligible. Less than rendering a Snapchat filter. Less than scrolling TikTok for 10 seconds. Synthesis is computationally invisible.
Proof: It's Already Everywhere
You don't need to imagine whether this works. You use synthesis every day:
Amazon Reviews
Weighted average of ratings. Recency matters. Verified purchases count more. Same math, different domain.
Uber ETAs
Aggregate historical trip times. Weight by conditions. Return a prediction. Runs in milliseconds, 500,000 times per second globally.
Election Tallies
Count votes. Machines process 300 ballots per minute. The math is trivial. The process is auditable.
Sports Statistics
Points per game. Win percentage. Batting average. Every sports app synthesizes distributed outcomes into aggregate stats.
Insurance Actuarial Tables
Survival probabilities by age. Risk-adjusted premiums. The math has worked for centuries.
On-Device ML Inference
TensorFlow.js runs neural networks in browsers. ONNX Runtime delivers millisecond inference on phones. The compute exists.
You synthesize survival the same way you synthesize product ratings.
Except survival isn't a sport. It's not a rating. It's oxygen.
Why No One Does It—Yet
If synthesis is this simple, why isn't everyone doing it?
Because there's nothing to synthesize.
The math is trivial. Your phone could tally a million votes before you finish reading this sentence. But you can't vote on insight that doesn't exist. And right now, no one is:
1. Defining Similarity at Scale
Experts define similarity daily—but in their heads, for one patient at a time. No network captures these templates. No system turns "patients like you" into a queryable routing key.
2. Routing to Exact Cohorts
DHTs and vector databases route billions of queries. But none of them route by expert-defined medical similarity. The infrastructure exists. The application doesn't.
3. Extracting Real-Time Outcome Packets
Your phone aggregates health data constantly. But it doesn't package outcomes into shareable packets. It doesn't publish what worked. The data exists—locked in silos, never flowing.
Synthesis is the easy part. The hard part was building a pipeline that delivers real-time insight from your exact cohort—similarity defined by domain experts, routed at scale, outcome packets returned in one trip. Once that exists, synthesis is just math. QIS builds the pipeline. The vote follows.
The Complete Chain
Synthesis is the final step. Here's the full QIS pipeline—every component production-proven:
Define Similarity
Expert-curated templates specify what "similar" means. NCCN guidelines. ICD-10 codes. Trial inclusion criteria. Doctors do this daily.
Aggregate Locally
HealthKit, FHIR APIs, wearables. 150+ health data types. On-device extraction. Your phone does this now.
Route by Similarity
DHT publish/subscribe. O(log N) hops. BitTorrent handles 170M+ monthly users. DHTs do this at scale.
Receive Outcome Packets
Built into the routing itself—DHT FIND_VALUE returns stored data, vector queries return metadata. One trip. No separate fetch needed.
Synthesize Locally
Vote, weight, aggregate. jStat for Bayesian. TensorFlow.js for ML. Math does this in milliseconds.
If you can define similarity (doctors do), aggregate locally (phones do), route by hash (DHTs do), pull packets (APIs do), and synthesize (math does)—then QIS isn't theory.
It's engineering. Every piece exists. Every step works today.
The Challenge Stands
Show me the step that doesn't work.
Show me the component that fails in production.
Can't? Then the only question left isn't can we?
It's when?
Step 1: Data Aggregation → Step 2: Defining Similarity → Steps 3 & 4: Routing + Outcome Packets → Step 5: Synthesis → Capstone: Every Component Exists