Healthcare Predictive Analytics Platform

A regional healthcare network consisting of 5 hospitals and 30 clinics was facing a critical challenge with high patient readmission rates. 30-day readmissions were costing the organization millions annually in penalties and reduced reimbursements under value-based care models.

The clinical staff lacked tools to identify high-risk patients who were likely to be readmitted, making it difficult to provide proactive interventions. Patient data was scattered across multiple Electronic Medical Record (EMR) systems, making comprehensive risk assessment nearly impossible.

Care coordination between hospitals, clinics, and home health services was poor due to lack of data sharing and visibility. The organization needed a solution to predict readmission risk and coordinate care more effectively.

We developed an end-to-end predictive analytics platform that leveraged machine learning to identify patients at high risk of readmission:

1. Data Integration: Built secure data pipelines to consolidate patient data from multiple EMR systems (Epic, Cerner) while maintaining HIPAA compliance.

2. ML Risk Model: Developed a gradient boosting model using patient demographics, medical history, medications, lab results, and social determinants of health to predict 30-day readmission risk with 85% accuracy.

3. Real-Time Alerts: Integrated the model with EMR systems to flag high-risk patients automatically and trigger care coordinator notifications.

4. Care Coordination Dashboard: Created a Tableau dashboard for care teams to track high-risk patients, interventions, and outcomes across the entire network.

5. Mobile Application: Built a mobile app for care coordinators to access patient information and update intervention status in the field.

The platform processes thousands of patient records daily and provides risk scores within seconds.