Predictive analytics system for identifying high-risk patients and reducing preventable hospital readmissions through targeted interventions
A regional hospital system faced high 30-day readmission rates that resulted in financial penalties and indicated suboptimal patient care outcomes. Their existing discharge planning process couldn't effectively identify which patients were most likely to be readmitted, leading to insufficient support for high-risk patients and inefficient allocation of care management resources.
We developed a comprehensive readmission risk model using gradient boosting algorithms that analyzed patient demographics, medical history, comorbidities, social determinants, medication adherence patterns, and discharge circumstances. The system provided risk scores and recommended specific interventions for different risk categories, integrated with the hospital's electronic health record system.
The prediction model achieved 84% accuracy in identifying patients at high risk for 30-day readmission, enabling targeted interventions for the most vulnerable patients. Overall readmission rates decreased by 23% through improved discharge planning and post-discharge care coordination. The program generated $1.8M in annual savings through reduced penalties and improved care efficiency.