Your 5 step guide to better underwriting risk prediction accuracy
Step 1: Clients simply drag and drop a prospective member group census file into the interface using Excel or CSV spreadsheets containing the following group and member information:
- Employer group state and zip
- Eligible member names, gender, and dates of birth
- Employee or dependent
Step 2: Utilizing tokenization technology from Datavant, the leading healthcare data connectivity provider, the risk predictor de-identifies each member on the submitted census file.
- 325 million+ patients
- 100 billion+ diagnostic results
and are matched with de-identified lab results in the Registry.
Step 4: Using AI-driven algorithms, the scoring engine predicts member costs by assessing disease progression and severity indicated in the lab data, demographics, and clinical analysis.
Step 5: Individual member and group level risk scores are delivered through the user interface. Group risk scores typically range from 0.5 to higher than 2.