AI & Machine Learning Operationalization (MLOps) Software
Your hospital struggles with staff coverage, labor costs, and employee burnout. Polaris analyzes and automates scheduling to deliver remarkable efficiencies and savings. We use AI to predict your department's peaks and valleys, then load your data to identify the perfect staff schedule for your patient volume. Clinicians today face tremendous challenges with staffing. Predicting staffing to meet patient volume is complex, typically relying on human analysis and historical trends vs. data. Producing optimal staff schedules is notoriously difficult and time-consuming. Faulty staffing models lead to excessive labor costs, including the high costs of external flex labor providers. And inefficient scheduling negatively impacts satisfaction and work/life balance. (63% of RNs report burnout. Average costs for caregiver turnover: $5M/ year. ) Our AI methodology optimizes staffing in three ways. 1) Through machine learning, we can predict the number of providers needed to meet patient demand. 2) We identify the best distribution of existing providers. 3) And finally, we generate an optimal schedule based on the recommended distribution. Our schedule balances provider preferences, organizational policies, and regulatory constraints. Here's how our process works: First, Polaris users provide the relevant historical data. The AI “machine” integrates your historical data with AI databases to generate a highly accurate hour-by-hour volume prediction for your department. Next, we set scheduling parameters and review your shift distribution. Finally, we generate a staff schedule that makes the best use of your existing resources. Predict patient volume. Optimize staff scheduling. Reduce employee burnout. Save millions in inefficient staffing costs. Instantly, with the Polaris AI engine.
- Revenue
- $660K
- Customers
- -
- Year founded
- 2020
- Funding
- -
- Team size
- 6
- Location
- United States
