RBQM · Quality & risk managers
↔ sharedCuRE Caliber
Risk-based quality management — CSM + KRIs computed off live OMOP data.
What it does
Risk-based quality management — CSM + KRIs computed off live OMOP data. RWD-native RBQM can flag EHR-observed events a site has not reported yet, then draft the investigation narrative and CAPA trail.
Key capabilities
- Centralized statistical monitoring
- Live KRI computation
- AI-drafted investigation narratives + CAPA trail
- Structured risk-review workflow for the quality triad
- AI signal clustering → recommended action
- Cross-study risk/threshold priors
- CtQ factors model
- AI-targeted SDV selection
What sets it apart
- RWD-native RBQM can flag EHR-observed events a site has not reported yet, then draft the investigation narrative and CAPA trail.
- The signal engine drives both execution surfaces CuRE owns — Control monitoring-visit planning and Capture targeted SDV — the closed RBQM loop CluePoints can't own end-to-end.
- QTL calibration, patient-profile review, sponsor-CRO oversight, cross-study benchmarking, and duplicate-subject detection extend the RBQM surface.
- Protocol-to-config sandbox turns a protocol and sample data into working KRIs quickly enough for sales and study-startup demos.
- Live computation, not nightly batches — risk signals surface in hours, not days.
Flag the outlier site, live
Run centralized statistical monitoring across a synthetic multi-site study. Each site's Key Risk Indicators are scored against their thresholds and summed into a risk score; move the outlier sensitivity and watch the flags move; expand a site to see its KRI breakdown — including the AE under-reporting signal that catches missed safety events.
A synthetic study across 8 sites — six Key Risk Indicators evaluated per site over their operational data.
A site is flagged when its risk score is a robust z-score (median / MAD) of at least this above its peers. Lower to widen the net; raise to surface only the extreme outliers.
- Screen failure · dropout · enrollment
- Query rate · protocol deviations
- AE under-reporting (sidecar method)
Sites · ranked by additive risk score
| Key risk indicator | Value | Threshold | Weight | Status |
|---|---|---|---|---|
| Screen failure rate | 42% | > 30% | 6 | breach +6 |
| Subject dropout rate | 32% | > 20% | 8 | breach +8 |
| Enrollment rate (actual/planned) | 56% | < 70% | 5 | breach +5 |
| Open query rate per subject | 3.09 | > 2 | 6 | breach +6 |
| Protocol deviation rate | 21% | > 10% | 7 | breach +7 |
| AE under-reporting signal· sidecar13 obs vs 24 expected AEs | 0.008 | < 0.05 | 10 | breach +10 |
Risk score is the sum of breached KRI weights (TransCelerate RBQM · ICH E6(R3) catalog).
Every KRI value, breach, risk score, and z-score is computed in your browser from the synthetic site data — no backend. The KRI codes, thresholds and weights are the seeded CuRE Caliber catalog (TransCelerate / ICH E6(R3)); the AE under-reporting signal is the exact Poisson lower-tail probability that a site's few reported AEs would occur if it truly reported at the study rate — the simaerep concept that, in the product, runs in the oracle-validated calculate-stats sidecar.
Why this is more than a toy
The KRI codes, default thresholds, and additive weights are the seeded CuRE Caliber catalog — recognized TransCelerate RBQM and ICH E6(R3) indicators. Per the platform's analytics-placement boundary (ADR-PLT-044), Caliber itself does only threshold-and-weight math; the oracle-validated statistical methods live in the CuRE Calculate sidecar. The headline AE under-reporting signal here is the exact Poisson lower-tail probability that a site would report so few adverse events if it truly reported at the study rate — a faithful, self-contained stand-in for the sidecar's simaerep method. Everything is computed client-side on synthetic sites; nothing calls a backend.
See CuRE Caliber in action
Every research ecosystem is unique. Let's discuss how CuRE can be configured for your needs.