Interpreta’s Risk Adjustment module provides an HCC-based risk capability for an added dimension of insight into your patient population. Extending the key learnings of quality analytics, Interpreta’s Risk Adjustment helps both payer and provider organizations identify acute and chronic disease conditions, properly identifying patient risk and treatment gaps to improve at-risk population care.
Interpreta’s platform helps users create a central analytics perspective from a complex and challenging data ecosystem. For risk adjustment, Interpreta ingests structured data through standard claim ingestion and unstructured data through our supplemental data portal or integration with machine learning and natural language processing solutions. Our partners are able to aggregate insights from multiple data sources into a single insight engine for care management and strategic adjustment.
All patients tracked in Interpreta’s Risk Adjustment can be analyzed across an extensive set of parameters for a full picture of patient risk:
- HCC risk scoring with inference and persistence alerts
- Ability to view captured and uncaptured risk gaps
- Clear visibility into previously coded HCC diagnosis
- Evidence of claims inferring new conditions, or confirming existing conditions
Captured & Uncaptured Risk
Patient risk gaps are displayed in the platform in both captured and uncaptured categories. Captured risk gaps are generated from processed claims, generating risk gaps from the Interpreta engine. Uncaptured risk is a combination of both persistence and inference gaps and is derived from current and historical data in the platform. Captured and uncaptured risk summaries guide healthcare professionals into single member investigation to discover uncoded diagnosis.
Persistence risk is based on processed historical claims, after coding, and is formulated with HCC risk logic. The risk score displays the potential risk of a patient population, sub-population or a single member. Persistence risk should be reviewed against historical patient care with particular focus on major patient health outcomes.
Inference risk is based on a broader view of patient health data. Patients commonly see additional primary care physicians, specialists and also have pharmacy and laboratory data that must be analyzed. Inference risk gathers the full data perspective of the patient and examines for potential uncoded diagnosis.
Interpreta’s platform provides a user-driven supplemental data platform allowing the user to upload, view and notate unstructured data impacting both quality and risk analytics. The supplemental data portal is a particularly powerful in inference risk, bringing unstructured data into the portal for capture in quality and risk scoring. Interpreta’s supplemental data portal can be configured to ingest data from outside solutions such as Machine Learning and Natural Language Processing platforms.