Under-coding in Risk Adjustment data

How do we solve this problem?

HiLabs offers a proactive approach to detect data errors corresponding to under-coding using a combination of Medical Ontologies and historical data as input in the AI engine. The AI engine also called MCode has been built on 30+ M members record from CMS data and analyzes every possible slice of data. It also gives the client the flexibility to customize according to the health plans needs and data. It also provides the client with auditable facts.

Eliminates the need for current rule based approach

Health plans currently work on deterministic checklists when submitting the claims data. They compare against historical Hierarchical Condition Categories (HCCs) benchmarks to review the data of members with risk score in top 20 percentile mark

Auto learning of billing patterns

AI-based Analysis on every possible slice of data thus eliminating the need of deterministic checklists based on medical charts. It also shows errors which are unknown to the user

Your analysis is no longer limited to your own data!

Our solution comes with out-of-the-box knowledge base of 2 billion plus HCC coding patterns of the entire Medicare FFS population of the country

Supervised and Unsupervised training


High ROI

The tool enables organizations with Medicare risk-based contracts to achieve significantly higher revenue without significant increase in their investment of resources. MCode does not require medical charts to detect anomalies as required by the existing solutions in the market

Easy data ingestion with insured security

Quick data ingestion on our MGest platform which is compatible with most of the data source systems in the industry. Data security insured through virtual or cloud systems

Let’s make an impact

We at HiLabs are making businesses better for several of the country’s health plan. We understand your needs, are flexible to customization and are ready to bring in the change through our rich experience in this industry

About HiLabs, A cohort of experienced Engineers, Data Scientists and Physicians.

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