Medicare Advantage, traditional Medicare, and Part D data analytics for reimbursement and policy strategy - McDermott+

Medicare Advantage, traditional Medicare,
and Part D

data analytics for reimbursement and policy strategy

Data-driven insights across Medicare Advantage, Traditional Medicare and Part D

More than half of Medicare beneficiaries are enrolled in Medicare Advantage (MA), and nearly four out of five Medicare beneficiaries are enrolled in a Part D plan.

As these programs continue to grow, policymakers, plans, providers, manufacturers, and other healthcare stakeholders need rigorous data and subject matter expertise to evaluate how legislative and regulatory proposals may affect patient access, provider and plan revenue, utilization, quality, risk adjustment, and overall Medicare program spending.

McDermott+ combines deep knowledge of Medicare policy with sophisticated analytic skills and comprehensive Medicare datasets, allowing clients to assess trends and policy impacts across traditional Medicare, Medicare Advantage, and Part D.

Medicare Advantage insights for policy, payment, and market strategy

Our Medicare data assets

Dataset Years available What it includes What it enables
Medicare Advantage encounter data 2015 – 2023 100% of Medicare Advantage encounter data, including plan enrollment information Analysis of MA utilization, diagnoses, patient journeys, outcomes, plan-level trends, and comparisons across MA organizations
Medicare fee-for-service claims 2015 – 2024, plus the most recently available quarterly claims data 100% of Medicare fee-for-service claims, including 100% of carrier claims Analysis of utilization, spending, diagnoses, provider practice patterns, sites of service, patient outcomes, and longitudinal trends in traditional Medicare
Medicare prescription drug event data 2015 – 2024 100% of Medicare Part D prescription drug event data, including plan formulary information Analysis of drug spending, volume, utilization, formulary placement, and trends at the national drug code level
Medicare Master Beneficiary Summary File 2015 – 2025 Enrollment and demographic information for all Medicare beneficiaries, including their selected MA and Part D plan (if enrolled) The MBSF file can be used to support claims analysis by demographic group, MA plan and Part D plan

 
Together, these datasets allow us to track patients, providers, services, diagnoses, plans, and prescription drug use over time. This information creates a powerful foundation for understanding how care is delivered across the Medicare program and how policy changes may affect stakeholders. These datasets can also be combined with publicly available information. This includes with MA and Part D plan enrollment, Star Ratings, and benefits to provide a comprehensive picture of the competitive landscape, or with hospital and facility level information to better understand trends among providers.

Medicare Advantage analytics

We use MA encounter and enrollment data to assess care patterns, utilization, diagnoses, outcomes, and plan-level trends. This includes evaluating how new regulatory and legislative proposals related to MA payment policy, risk adjustment, and quality metrics may impact beneficiary access and Medicare spending. We also use this data to compare utilization across MA organizations, and to identify differences in the care provided between MA and traditional Medicare.

Traditional Medicare analytics

We use traditional Medicare (also known as Original Medicare or Medicare fee-for-service) claims to evaluate practice patterns, beneficiary spending and utilization, and identify trends in the Medicare program. This includes evaluating how new regulatory and legislative proposals may impact patient access, provider revenue, and Medicare spending. We routinely use this traditional Medicare data to support device manufacturers, providers, and trade associations in developing comment letters to submit to CMS on proposed rules related to Medicare payment systems. We also use traditional Medicare data as a baseline to compare against trends observed in Medicare Advantage.

Part D analytics

Part D data provides a detailed view of prescription drug utilization, spending, formulary design, and beneficiary access across the Medicare prescription drug benefit.

We use prescription drug event data and formulary information to track drug-level trends including formulary placement and total utilization for individual Part D plans, beneficiary access and cost-sharing for medications, and to evaluate how potential new policies or regulations may impact Part D plans, manufacturers, pharmacies, patients and Medicare spending.

Potential analyses include tracking drug spending and volume at the national drug code level, evaluating formulary placement, comparing utilization across plans or regions, and supporting advocacy related to Part D payment policy, benefit design, and regulatory changes.

When combined, Medicare Advantage encounter data, traditional Medicare claims, and Part D event data offer a comprehensive view of utilization, behavior, and trends across the Medicare program.

How these data can help your organization

We use these datasets to provide a more complete picture of the Medicare program by analyzing traditional Medicare, Medicare Advantage, and Part D separately or together.

We help clients:

  • Evaluate the potential impact of new legislative or regulatory proposals
  • Compare utilization, access, and outcomes across MA and traditional Medicare
  • Analyze plan-, market-, or industry-wide trends
  • Assess changes to MA risk adjustment, payment policy, or Part D benefit design
  • Understand patient journeys for populations of interest
  • Examine variation by geography, site of service, provider type, or plan

These nuanced insights equip clients to effectively pursue business and policy objectives.

Timeline for Medicare Advantage and Part D policy and advocacy

Medicare Advantage and Part D policy development follows a recurring annual cycle. Data analysis can be especially valuable at key points throughout the year.

Timing Key event
Fall CMS typically releases the proposed rule for technical changes to Medicare Advantage and Part D
January/February CMS publishes the advance notice for the upcoming year
April CMS announces MA capitation rates and Part D payment policies
June Deadline for MA and Part D bids
October 1 Plan options and benefits for upcoming year released and marketing begins
Early October New Star Ratings announced
October 15 – December 7 Annual open enrollment period for MA and Part D plans

Frequently asked questions

Does Medicare Advantage encounter data include payment information?

No, CMS is required to blind payment information associated with the encounter records that are made available to researchers. Payment information is only available for traditional Medicare claims data and Medicare Part D prescription drug event data.

Yes, the traditional Medicare and Part D data allows researchers to track patients longitudinally. This includes the ability to identify which patients are enrolled in Part D, and whether the beneficiary is in MA or traditional Medicare. This allows researchers to evaluate Part D use and spending among MA and traditional Medicare beneficiaries.

Yes, each Medicare beneficiary is assigned a beneficiary identifier that remains the same across traditional Medicare, MA, and Part D data sets, allowing for the individual to be tracked across programs over time. For example, analysis can follow an individual who is enrolled in FFS Medicare in one year but enrolls in MA in a later year.

The Medicare claims and encounter data allows researchers to profile patient populations and identify potential areas of unmet needs in the Medicare program. This real-world data can be used to quantify the size of the Medicare patient population that may benefit from new technologies and the top providers that care for those patients. The data can also be used to evaluate patient outcomes and understand care pathways. This research can be performed for both traditional Medicare and MA beneficiaries, to identify how care delivery differs between the two.

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