Machine Learning in Healthcare

Healthcare companies are using machine learning to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from readmission risk and occupancy rates to marketing, in order to make data-driven decisions that lead to increased profitability.

Use cases in Healthcare

Disease Propensity

Outreach to patients without analytics is like trying to tie your shoes in the dark. Unfortunately, waiting until they seek care results in higher costs, and potentially poorer outcomes, for everyone.

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Drug Delivery Optimization

To increase product adoption, pharmaceutical firms ship millions of drug samples to doctors and hospitals. The orders can be consolidated when the same location requests two or more drug samples. DataRobot can predict which drug samples should wait for consolidation, reducing the overall cost of delivery.

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Estimating Sepsis Risk

Sepsis is a serious condition that often occurs suddenly and with life-threatening impact. Identifying patients most at risk for developing sepsis may mean the difference between life and death.

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Hospital Readmission Risk

Proactively identifying hospital readmittance means increasing quality of care, decreasing costs, and improving the lives of patients.

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Modeling ICU Occupancy

Forecasting ICU occupancy means being prepared for incoming patients and not staffing empty beds.

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