Impact Scorecard
What the journey produced
Illustrative synthetic data
A. Live demo metrics
Computed from this session's journey run
Run the journey to populate live demo metrics
B. Illustrative impact model
Simulated Optum HEOR outcome rates
100%
Modeled hospitalizations avoided per year
2,132
= 41,000 affected patients × 100% × (14.8 − 9.6) per 100 patient-years
Illustrative model on simulated data; an upper bound under a stated assumption, not a forecast.
C. Pilot measurement framework
Baselines and targets are set with the customer; nothing is pre-filled
| KPI | Definition | Data source | Baseline | Target |
|---|---|---|---|---|
| Share of affected patients covered by a payer brief | Affected patients in plans with a generated brief ÷ all affected patients | Optum RWD + iZO | ||
| Time to therapy for affected patients | Days from first prescription to first paid fill | Optum Claims | ||
| Pharmacy abandonment rate | Share of approved prescriptions not picked up | Optum Claims | ||
| Evidence coverage of barrier types | Share of active barrier types supported by at least one selected evidence item | iZO Medical Affairs + Optum HEOR | ||
| Time to briefing | Elapsed time from barrier signal to an MLR-ready payer or MSL brief | iZO Context Engine | ||
| KOL coverage of target segments | Share of priority segments with at least one engaged, matched KOL | iZO KOL Intelligence | ||
| MSL briefing relevance (field feedback) | MSL rating of briefing usefulness after scientific exchange | iZO Medical Affairs |
Combined offering for Optum's pharma customers
Optum evidence, made actionable
Optum evidence and data assets made actionable in daily market access and medical workflows.
One connected journey
The iZO Context Engine and domain-trained agents connect barrier, patient, evidence, KOL and briefing steps.
Measured in a pilot
A defined customer pilot to measure impact against agreed baselines.