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100,000+ employees worldwide
€47.6 billion in global sales
160+ years of pharmaceutical and science leadership
The client had access to massive volumes of trial, lab, and EHR data, but disconnected sources prevented a unified view for predictive risk analytics. As a result, early warning signals were often missed and AI initiatives slowed.
Vital signs, lab results, and trial data existed across separate systems with limited cross-source visibility.
Risk labels and outcome definitions varied across teams, creating noisy training data.
High-risk cases were a small minority, making standard models less effective.
Without clear explanations, care teams had lower confidence in automated alerts.
Inferenz built a cloud-based analytics hub that unified patient data, engineered predictive features, and deployed an early alert system that surfaced high-risk patients months in advance.
Unified Healthcare Data Hub
Integrated trial, lab, and EHR data into a governed analytics environment.
Data Preparation Framework
Standardized units, aligned IDs, and resolved missing values for model-ready inputs.
Advanced Feature Engineering
Developed trend, rate-of-change, and rolling indicators to detect subtle deterioration signals.
Optimized Risk Modeling
Built and tuned ensemble models designed to improve sensitivity for rare high-risk events.
Explainability Layer
Enabled clinicians to understand the key factors driving each alert.
Early Warning Activation
Operationalized alerts for care teams to intervene proactively before escalation.
150+ vital-sign features helped identify high-risk patients nearly 180 days sooner.
17% reduction in emergencies and unplanned ER visits across pilot sites.
8 million patient records cleaned, merged, and prepared for analytics.
Established a reusable platform for future healthcare AI use cases.
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