- Data stored at each hospital on different legacy systems, creating mismatches between the structure of each set.
- Reducing hospital gridlock required advanced machine learning, but siloed data and the risk of model leakage became a compliance concern.
- Rising demands during COVID meant that operational data quickly became stale given changing conditions.
- Devron deployed to learn on data at each of the system’s 7 hospitals for real-time data ingestion and faster and insights on operational efficiencies.
- As repeated improvements increase model accuracy, the operations team quickly leverages insights across facilities to respond to constraints as they emerge.
- With faster more accurate models, the Health System could divert patients and allocate personnel across the network to mitigate gridlock before critical constraints emerged.
Increase Operational Efficiency
Increased Model Accuracy
Reduce Response Time to Demand Surges
Reduced Data Centralization Time & Costs