Predicting Readmission Risks

Business Goals

• Reduce the number of readmissions
• Upgrade care management operations

Challenge

• One readmission costs ~$20 000 for the healthcare provider. Some patients have 11 readmissions during a 90-day episode, totaling ~$220 000 loss.
• Nurses are overloaded and need to prioritize patient care. One nurse can have ~400 beds.

Results

• In 90 days of the A/B testing period, the number of readmissions dropped by 32% in the experimental group nursed with the AI service, compared to the control group under ordinary care.
• Recalculated to the number of patients, the savings were ~$8 000 000 for the observation period (90 days).

Implementation Details

• 6 years of medical history records and machine learning tools were used to uncover reasons, predict and prevent readmissions.
• 3 groups of patients have been identified according to the readmission risk rate.
• Detected readmission reasons became the foundation for personalized health plans.
• Each group of patients gets a target health plan to reduce readmission risks.
• Each patient is tracked by predictive AI models and allocated to one of the risk cohorts in real-time.
• Nurses get push notifications about patient risks.
• Nurses can add patient notes taken into account by the AI service.