Advanced Risk Adjustment

Leverage AI powered algorithms to identify suspect conditions. Incorporate data from multiple sources including unstructured and structured data in EMRs, labs, HIEs, and claims in a unified data model that provides a comprehensive 360-member view.

Improve coding accuracy by automatically developing worklists that help coders review dropped and suspected conditions for members. Analyze the accuracy of evidence and add notes for physicians to review.

Identify coding gaps at the point of care. Review supporting evidence and coder notes, and write back the accepted open coding gaps into EMRs in a streamlined workflow that eliminates documentation duplication.

Reduce the risk of non-compliance during RADV audits by enabling seamless communication between coders and physicians after the patient visit and before claims are submitted to ensure accurate clinical documentation. Automate task creation for reviewing physician responses and documentation requests with physician querying capabilities.