Medicare Advantage

Medicare Advantage (MA), also known as Medicare Part C, is the private health plan alternative to traditional Medicare fee-for-service coverage, administered by CMS-approved commercial health plans. Medicare beneficiaries who choose Medicare Advantage enroll in a plan offered by an insurer such as UnitedHealthcare, Humana, CVS/Aetna, or Centene rather than receiving coverage directly from the federal government. Medicare Advantage plans receive monthly capitation payments from CMS for each enrolled member — the payment amount is risk-adjusted using the CMS-HCC model based on each member's demographic characteristics and documented chronic conditions. Plans must cover all Original Medicare benefits and may offer supplemental benefits such as dental, vision, hearing, and fitness programs. Medicare Advantage is the fastest-growing segment of the US healthcare market, covering more than 33 million beneficiaries as of 2025 — over half of all Medicare-eligible Americans. The financial stakes of MA data quality are enormous: a single RAF score point difference across a plan's membership can represent millions of dollars in capitation revenue annually. CMS audits MA plans through Risk Adjustment Data Validation (RADV) audits, which review medical records to verify that submitted HCC diagnoses are supported by clinical documentation from face-to-face encounters with eligible provider types. Plans with high error rates face recoupment of overpaid capitation amounts, making accurate risk adjustment data pipelines a direct revenue integrity concern. Healthcare data engineers working with Medicare Advantage data build and maintain the complete risk adjustment data lifecycle: claim ingestion from provider encounters, ICD-10 to HCC mapping using the CMS crosswalk, RAF score calculation using the CMS-HCC V28 model coefficients, encounter data submission to CMS through the Encounter Data Processing System (EDPS), and RADV audit support pipelines that retrieve supporting medical records. Key MA-specific data elements include the CMS contract ID (H-number), the plan benefit package (PBP) identifier, the risk adjustment processing system (RAPS) submission identifier, and the dual eligibility code that affects both capitation rates and member cost-sharing. Stars ratings, which depend heavily on HEDIS quality measure performance, are also a primary focus for MA data engineering teams because they determine quality bonus payments from CMS.

Member and enrollment data governs who receives care and who pays for it — making it foundational to every downstream healthcare analytics workflow. Data engineers who understand member terminology build eligibility pipelines that prevent coverage gaps, correctly identify dual-eligible members, and support accurate risk adjustment submissions to CMS. Enrollment errors directly affect capitation payments and can trigger CMS corrective action plans.

• Member eligibility verification and enrollment tracking
• Medicare Advantage and Medicaid managed care reporting
• COBRA, CHIP, and dual-eligible member management
• Enrollment reconciliation and CMS submissions
• Member 360 data model and longitudinal analysis
• Epic ADT feed parsing for real-time member eligibility updates
• Snowflake member dimension table with SCD Type 2 for full enrollment history
• Databricks streaming enrollment reconciliation pipeline for CMS capitation reporting

• Member ID formats vary significantly across payers and source systems — standardize to a consistent composite key (payer_id + member_id) in your Snowflake member master to support cross-payer analytics.
• Enrollment gaps (periods with no active coverage) must be distinguished from true terminations — model using effective_start_dt and effective_end_dt with explicit gap-detection logic rather than a single status flag.
• Duplicate member records are common when members re-enroll after coverage gaps — implement probabilistic matching on (last_name + dob + gender + zip) to link records to a single master member ID in your MDM layer.