Time-to-therapy distribution
The story isn't the median — it's the right tail. Pre-Forus, 28% of starts took 14+ days; on-Forus, 3%. The tail is where abandonment lives.
Funnel by payer segment
| Stage | pre-Forus | on-Forus | Δ |
|---|
What we are not claiming
The objection this report exists to answer
"Your providers are self-selected — practices that adopt Forus are more organized, so your lift is selection, not causation." Correct instinct. Three defenses, in order of strength:
- Within-provider difference-in-differences. Each practice serves as its own control: its trailing 6-month pre-adoption funnel vs. its on-Forus funnel, differenced against contemporaneous trends in matched non-adopting practices. Removes time-invariant practice quality entirely.
- Adoption-cohort event study. Lift measured relative to each practice's adoption date. If "good practices adopt" drove the result, we'd see improvement before adoption. We don't — the kink is at week zero.
- Geography-staggered comparison. Rollout timing varied by region for operational reasons unrelated to practice quality — a natural experiment for the persuadable skeptic.
Known limitations, stated plainly
- Practices contaminate each other (word-of-mouth adoption), so errors are clustered at the practice level, widening the CIs you see.
- In-flight prescriptions are handled with survival methods; naive conversion rates would flatter recent cohorts and we don't use them.
- Persistence requires fill-data linkage that is incomplete for out-of-network pharmacies; coverage is reported, not assumed.
Privacy posture
All patient-level processing occurs inside the HIPAA boundary; this report contains only de-identified aggregates with small-cell suppression (n<11 suppressed). No patient-level data leaves the platform, including to the brand partner.
One-pager: the proof engine
Forus's revenue is concentrated in a handful of pharma partners, which means renewals are the business — and every brand team will eventually send its own analysts at the lift claims. "Trust our dashboard" loses that meeting. A defensible causal methodology wins it, and it's also the spine of the Series C narrative.
- A one-screen brand report in the client's units: time-to-therapy, patients started, first-pass approval — with the selection-bias defense built in, not appended.
- The honest null, by design. Credibility with a skeptical analytics team compounds across the life of a contract.
- A methodology tab that pre-answers the three attacks any competent reviewer will make.
Templated per-brand, refreshed monthly, with a self-serve cut by payer × geography × indication. The DiD/event-study machinery becomes a shared causal library every client engagement reuses — methodology as a product asset, not a per-deal scramble.
I've spent years on both sides of this table: defining success metrics and measurement for product launches at Meta and Uber, and earlier, presenting analytic results to paying enterprise clients as a Director of Solutions — including the part where their analysts try to take the numbers apart. Building reports that survive hostile review is the job I've done longest. — Jeff Pinto · jeff@jeffpinto.com · jeffpinto.com