Most healthcare AI only predicts. How does Propensity Score Matching help ReHealth AI produce payer-accepted causal evidence, making prevention outcomes billable for the first time?
There's a fundamental flaw in healthcare AI that's been ignored for too long: almost all healthcare AI products only predict — they don't attribute causality.
Risk prediction is useful — telling a physician that this patient has a 23% probability of heart attack in the next three years is valuable information. But prediction alone can't answer a more critical question: "If we intervene, does the risk actually decrease?"
The answer to this question determines whether preventive medicine can be billed.
Correlation ≠ Causation: Healthcare AI's Core Dilemma
A classic example: we observe that patients taking a certain antihypertensive have 30% lower heart attack rates than those who don't. Does this mean the drug works?
Not necessarily. Patients who take medication may inherently be more health-conscious, more likely to get regular checkups, maintain healthier lifestyles — factors that independently reduce cardiac risk. Drug effects and "patients being healthier" are confounded together and can't be separated.
❌ Correlation Analysis (Insufficient)
Observed: Treatment group has lower incidence
Can't rule out: Treatment group was healthier to begin with. Can't conclude: The drug itself reduced risk.
✓ Causal Attribution (What We Need)
Proves: The intervention itself changed outcomes
Control confounders, construct counterfactual comparison groups, statistically verify the intervention's independent effect. This is payer-accepted evidence.
PSM: Constructing "Parallel Worlds" Statistically
PSM (Propensity Score Matching) is the core method for solving this problem. Its insight: if we can't run randomized controlled trials (RCTs), we can use statistical methods on observational data to construct a comparison group that approximates random assignment.
Core Insight
For each patient who "received intervention," find a patient from the "no intervention" group who is highly similar on all important characteristics. Compare this closely matched pair — one received intervention, one didn't — and the outcome difference is the true causal effect of the intervention.
PSM Matching Process
Treatment Patient A
60yr M Hypertension Smoker
↔ Match
Control Patient B
61yr M Hypertension Smoker
→
Risk Difference
= Intervention Effect
Highly similar pair — the only difference is whether they received intervention
Why PSM Is Our Core Method at Seed Stage
Causal Analysis Roadmap
Seed
PSM — low data requirements, established methodology, rapidly generates clinically accepted evidence
Series A
DID (Difference-in-Differences) — controls time-dimension confounders, suited for long-term follow-up data
Series B+
Synthetic control + instrumental variables — handles more complex causal scenarios, builds higher evidence grades
How Causal Evidence Becomes Settlement Basis
When PSM proves that "patients receiving ReHealth AI intervention programs had X% lower cardiovascular incidence over three years compared to similarly characterized non-intervention patients," this number becomes a causal evidence report backed by statistical significance.
This report enables: insurers to evaluate incorporating preventive intervention programs into coverage; hospitals to apply for preventive program reimbursement qualification; enterprises to demonstrate the actual ROI of health management investment to employees.
Prevention has a billing basis for the first time.
ReHealth Core: Complete System from Prediction to Causal Evidence
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