Time-series models prioritize sites and subjects at risk so your team can act earlier—with reasoning you can verify.
Time-series models prioritize sites and subjects at risk so your team can act earlier—with reasoning you can verify.
Watch how dynamic interactions like chart zooms and live filters bring insights to your fingertips.
Predictive signals spotlight risk patterns early—before they show up in deviations, queries, or missed timelines. Teams shift from reacting to fire drills to preventing them entirely, protecting quality and cycle times.
Focus on the KPIs that matter—screen failures, dropouts, data timeliness, protocol deviations—predicted from historical and real-time patterns. Helps teams see operational drift early and reallocate resources with confidence.
Confidence ranges help teams judge when action is urgent versus optional. When signals narrow or widen, central monitors understand risk volatility instantly and route follow-up appropriately.
The system highlights shifts that predict coming deviations, delays, or underperformance. These early warnings let study teams intervene before issues cascade.
Use forward-looking signals to anticipate site needs, staffing, data review load, and expected monitoring intensity. Ensures the right resources land where they’re needed most.
Toggle between weekly and monthly forecasts to support both executive planning and daily operational decisions. Aligns study teams, data leads, and QA around a shared forward view.
Historical performance provides context for why the model predicts a future shift. Teams understand what is driving the forecast and why, supporting human-in-control decision making.
Early warnings that mature with your portfolio and make study delivery more predictable.
More Studies, Better Foresight
Learn From What Came Before
Stronger Control Over Timelines
Design out avoidable risk before FPFV. AI-augmented protocol analysis surfaces CtQs, quantifies complexity, predicts deviation hot-spots, and reduces amendment drag, so you lock in patient-safe, inspection-ready design from day one.
Operationalize RBQM with verifiable math. AI-guided RACTs, calculation transparency, RBAC-secured workflows, and cross-study analytics deliver proportionate controls, clear KRI/QTL governance, and faster, defensible decisions at scale.
Move beyond manual cleaning and fragmented reviews. With time-series anomaly detection, protocol-aware deviation checks, and structured reasoning, the MyRBQM® Portal highlights what truly requires attention.
Stay ahead of issues, not behind them. Portfolio-wide, AI-enhanced dashboards fuse KRIs, QTLs, and deviation signals to cut escalation latency and focus monitors on high-value actions, fully aligned with ICH expectations.
See the full patient story—fast. Subject Profiles synthesize visits, AEs, ConMeds, labs, and trendlines, accelerating case review and improving safety decisions with transparent traceability and RBAC-controlled access.
Fast, secure connectivity with your clinical platforms through ready-made endpoints and low-overhead, real-time or batch data sync.
Pre-engineered, validated connectors to major EDC systems — reducing integration time while enabling secure, real-time or scheduled data sync.
Seamlessly connect your enterprise data platforms with secure, governed, low-overhead sync — without restructuring your existing data lake.
Our platform and processes meet internationally recognized security, availability, and confidentiality standards. ISO 27001:2022 certification and SOC 2 examinations provide independently verified assurance of our information security controls.
Our clinical-grade AI is independently certified through Microsoft’s Healthcare AI program, ensuring transparent, human-guided oversight, responsible AI practices, and secure integration with Azure’s validated infrastructure.
The MyRBQM Portal is built in alignment with 21 CFR Part 11 and GAMP 5, ensuring data integrity, traceability, and suitability for use in regulated clinical research environments.
Understand factor contributions over time, then approve and route actions in one flow.
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