Gaps in Coding

3AI August 18, 2020

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)