George Platt, MSOM, PAHM
Chief Executive Officer and President
Health Plans Trust Affinitē™ Risk to Optimize Hierarchical Condition Category (HCC) Scores
Designed by clinical experts, Affinitē Risk applies artificial intelligence and machine learning to streamline the complexity and ensure the accuracy of HCC risk adjustment.
Frequent updates to the Centers for Medicare and Medicaid’s Hierarchical Condition Category (CMS-HCC) model for risk adjustment require ongoing improvements to a health plans’ method for calculating risk scores.
If these changes are missed or implemented poorly, health plans can incur higher costs due to missed reimbursable expenses and potentially impact the quality of care for chronically ill members.
Vital Data Technology’s Affinitē™ Risk module solves this challenge by providing a cloud-based solution – instantly updated with new risk criteria. This modular approach enables plan executives to identify member-specific risk stratification and alleviate financial and care gaps.
Key Ways Affinitē™ Risk Helps You Maximize Risk Adjustment
Compute DIY HCC per member daily on rolling 12 month
Data Science Artificial Intelligence and Machine Learning driven predictability of HCC scores
Real-time forensic analysis of risk acuity gaps with real time engagement with affected provider’s patients/members
Development of CareFlow rules for risk stratification and engagement
Speed time-to-value with pre-built risk adjustment calculations for CMS HCC, the Elixhauser Comorbidity Index and the Charlson Comorbidity Index