Data Dependencies Between the Four Steps
"Predict→Intervene→Attribute→Settle" has become a frequent phrase in preventive medicine. But in most contexts, it's just a slogan without complete engineering implementation behind it.
Understanding this chain requires recognizing one thing: these four steps are not independent feature modules, but a data causal chain — each step's output is the next step's required input.
- Predict→Attribute:Baseline risk score is PSM's key covariate. Without credible risk prediction, PSM matching lacks its most important confounder control variable.
- Intervene→Attribute:PSM needs explicit treatment assignment records (T=1/0). Without structured intervention trajectory records, PSM cannot execute. "Intervened but didn't record" equals not intervening.
- Attribute→Settle:The billable evidence report's core content is PSM's ATT and confidence intervals. Without causal attribution, effect numbers lack causal interpretability and payers won't accept them.
ReHealth Core's Positioning:The market has prediction tools, intervention platforms, and statistical software. What's missing is infrastructure that connects all four steps' data into a complete causal chain. This is exactly why ReHealth Core exists.
"Predict→Intervene→Attribute→Settle" is not a feature list — it is a data causal chain. ReHealth Core's value is not in any individual step, but in the four-step data connectivity that is unique in the industry.