MVision

What is its unique value proposition?

MVision offers the flexibility to try-out various options of HCC modeling, powerful and scalable "what-if" analysis capabilities and also an accelerator to build big data (Hadoop/Spark) based RASS for CMS. The tool offers Impact analysis and production advisory under the what-if-analysis capability. With this tool, users can identify average impact on RAF score (and associated dollar amount), visualize impacts, identify net scoring of HCCs and carry out impact analysis at beneficiary level.

Interactive user configurable logic

User configurable logic to define variables related to interactive diseases. For example, beneficiaries with both diabetes mellitus (HCC 15 to 19) and congestive heart failure (HCC 80) are considered for additional risk adjustment scoring

Allows users to identify multiple metrics

  • Average impact on RAF score and associated dollars

  • Visualize/drill-down RAF/dollars impact by various parameters related to beneficiaries

  • Net scoring of HCCs based on number of beneficiaries

  • Impact analysis at each beneficiary level

Disease hierarchy implementation

MVision has been customized to include data elements related to HCC coding and Risk Adjustment Modeling.
Disease hierarchies address situations when multiple levels of severity for a disease, with varying levels of associated costs, have been reported for a beneficiary

Interactive Variable Configuration

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Example of customized parameters while running a model for RAF scoring

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Impact analysis

Pre-configured Hierarchies of HCCs

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Need for RASS Data Validation

RASS takes in data from various sources and subsequently several data transformations, HCC modeling and application of RAF scores are on the billions of records.

Integration with MCheck

MCheck offers a scalable and easy to maintain solution that can be easily plugged into input/output points of the overall process.

Limitations of traditional Data Validation processes

  • The manual nature of the deterministic approach which is therefore subject to substantial error

  • Organizations take months to define and fine-tune such rules

  • Performance of a deterministic data quality analysis being severely impacted due to the enormity of data


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