Case Study: Accountable Care Organization improves high-risk patient identification and saves $1.5M with ClosedLoop

Accountable Care Organization - Customer Case Study

The Accountable Care Organization (ACO), a network of nine federally qualified health centers serving more than 200,000 patients in the Chicago area, needed a better way to identify high-risk patients and reduce total cost of care. Its existing rules-based health risk assessment (HRA) system was limited by inaccurate patient-reported data, infrequent updates, and a lack of explainability for care managers. ClosedLoop’s AI-based predictive analytics platform was brought in to help improve risk identification.

ClosedLoop analyzed the ACO’s historical HRA, claims, prescription, ADT, and social factor data to generate more accurate and timely risk scores, with daily updates and contributing factors for each patient. In just 24 hours, ClosedLoop improved risk prediction accuracy by 24% over the rules-based approach, and by layering in additional data sources later achieved a 63% improvement in identifying high-risk patients while reducing false positives by 80%. The ACO also projected savings of $1.5 million in the first 12 months through better targeted care management interventions.

Accountable Care Organization

Dave DeCaprio

Co-founder