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🏥 Hospital 🛡️ Insurance 🏢 Enterprise 💊 Pharma
Concepts
Prevention Settlement Billable Evidence PSM Causal Attribution
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ReHealth Link
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Live Demo

Experience the 4-Step Loop Demo

Complete four steps in sequence: enter sample data → view clinician-reviewed draft recommendations → run PSM causal attribution → generate an auditable evidence package.

⚠️ Simulation demo · All data and parameters are illustrative, not real patient or project results; no medication, dosage, or treatment advice is provided.
STEP 01
🎯
Predict
Enter patient data
STEP 02
💊
Intervene
Waiting for prediction
STEP 03
⚖️
Attribute
Waiting for intervention
STEP 04
📄
Settle
Waiting for attribution
🎯 Cardiovascular Risk Prediction
Enter patient clinical features for real-time risk scoring and SHAP attribution
27
Prediction Results
Model Output · AUC 0.839 · 16-dim Features
Prediction Complete
—
—
—
Risk Percentile
<200ms
Inference Latency
SHAP Feature Importance
← Enter patient data and click "Run Risk Prediction"
💊 Personalized Intervention Plan
Auto-generated targeted intervention based on risk factors
Intervention Trajectory Log
Structured logging · Required data input for PSM attribution
Trajectory Logged
T=1
Treatment Assignment
—
Intervention Items
12mo
Follow-up Period
—
Timestamp
✅ Treatment assignment (T=1) generated
✅ Baseline risk score linked
✅ Intervention plan structurally logged
✅ Data ready for PSM attribution pipeline
← Confirm intervention plan to generate trajectory log
⚖️ PSM Causal Attribution
Propensity Score Matching · Eliminate Selection Bias · Quantify Causal Effect
PSM finds the most similar control match for each treated patient, eliminating observable selection bias. Only the post-matching effect difference has causal interpretability.
248
Treatment Group N
1:1
Matching Ratio
0.2σ
Caliper Width
16
Covariates
Attribution Results
ATT · Confidence Intervals · SMD Match Quality
Attribution Complete
ATT (Average Treatment Effect on Treated)
Sample
95% CI: [-0.112, -0.062]
p < 0.001 ✓ Statistically Significant
Matching Quality · SMD Comparison
VariablePrePostStatus
Age0.310.04✓
BMI0.240.06✓
SBP0.190.05✓
Smoking0.280.07✓
Baseline Risk0.350.03✓
✅ All covariates SMD < 0.1 post-match, good matching quality
← Click "Run PSM Causal Attribution"
📄 Auditable Evidence Package
References real-world study methodology · PDF + JSON output
Report Generated
PSM Causal Attribution Evidence Report Level 2 · RWE
Report ID—
Intervention TypeCombined
Follow-up12 months
Treatment N248
Post-match SMD0.042 (<0.1 ✓)
Primary Outcome ATTSample parameter
95% CI[-0.112, -0.062]
SecondarySBP -4.3mmHg
Methodology ReferenceReal-world study methods
Download PDF
Export JSON
4-Step Loop Complete
Complete causal chain from prediction to settlement
Loop Complete
🎯
Predict ✓
Risk score output, baseline covariates locked
💊
Intervene ✓
Plan confirmed, trajectory structurally logged
⚖️
Attribute ✓
Illustrative result; review with project data, study design, and limitations
📄
Evidence Package ✓
Auditable evidence package generated for program evaluation and discussion
This is the complete prevention settlement loop
Complete 4-step causal chain, evidence acceptable by payers
← Generate report to see loop summary