Operations
Improving Operations Across a Hospital Network
Use machine learning to optimize healthcare staffing and improve operational efficiencies.
Challenge
- 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.
Solution
- 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.
Outcomes
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Increase Operational Efficiency
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Increased Model Accuracy
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Reduce Response Time to Demand Surges
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Reduced Data Centralization Time & Costs
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