Gaps in Coding

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Leading healthcare insurance provider

Problem Statement

  • To identify any revenue leakage due to gaps in HCC coding and subsequent risk-adjusted payments that are based on each member’s HCCs
  • Business objective was to develop an analytics process to predict any gaps in coding/HCC at each member level to plug any revenue leakage

Analytics Led Approach

  • An advanced next-gen HCC Gaps in coding analytics solution was developed :
    • Leverages machine learning algorithms to predict and identify missing and suspected HCCs and ICDs at member level
    • Provides a detailed predictive score-based map of all potential coding gaps for a member in a calendar period
    • Enables effective coding corrections by focused targeting of provider for filling coding gaps and behavior modification

Business Impact

  • Creation of HCC/HHS-HCC output file with member level HCC/HHS-HCC codes in order to maximize capitated payments

Critical Success Factors

  • Ability to pro-actively segment and target less likely members to ensure increased completion rates Advanced process to identify potential coding gaps among MA member
  • Enabled targeted approach toward coding correction via provider
  • $3.8mn - $18.8mn worth of potential annual revenue leakage identification per 31,374 members (assuming acceptance of 10 %– 50% of identified gaps)

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