Medicare Part D Prescribers by Provider, Drug, Pharmacy, and Plan Case Study
FastHSR built Medicare Part D prescriber analytics for prescription drugs provided to Medicare beneficiaries, aggregated by provider, drug, pharmacy, plan ID, and other flexible grouping dimensions.
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Client question
A client needed detailed Part D prescription drug intelligence for Medicare beneficiaries enrolled in Part D. The client wanted outputs similar in spirit to public Medicare Part D prescriber data, but with more flexible grouping dimensions and pharmacy and plan-level detail.
Data foundation
FastHSR used Medicare Part D prescription drug event data and supporting reference files to construct analytic outputs by prescriber, drug, pharmacy, and plan. The work required careful normalization of provider identifiers, drug identifiers, pharmacy identifiers, plan IDs, geography, and calendar-year measurement windows.
- Prescriber layer: provider NPI, specialty, geography, and organization crosswalks where available.
- Drug layer: brand name, generic name, NDC, ingredient, strength, formulation, and therapeutic grouping where needed.
- Pharmacy layer: pharmacy identifier, chain or parent organization, pharmacy geography, and pharmacy type where available.
- Plan layer: Part D contract, plan ID, plan type, and year.
- Beneficiary layer: beneficiary counts and suppression-ready aggregation logic.
Flexible grouping design
The output design allowed the client to aggregate Part D utilization and spending by two, three, or four grouping variables, depending on the business question and minimum-cell requirements.
- Two-way cuts: provider by drug, drug by plan ID, pharmacy by drug, or provider by plan ID.
- Three-way cuts: provider by drug by pharmacy, provider by drug by plan ID, or drug by pharmacy by plan ID.
- Four-way cuts: provider by drug by pharmacy by plan ID.
- Optional geography: patient geography, prescriber geography, or pharmacy geography where appropriate.
- Optional time: annual files, rolling periods, or quarterly summaries where data supported reliable reporting.
Measures
FastHSR produced spending and utilization measures at each aggregation level, with small-cell protections and consistency checks so the data could support product, market, and account-planning workflows.
- Total drug costs.
- Number of beneficiaries.
- Number of claims or prescription events.
- Number of fills or scripts.
- Days of supply.
- Average cost per fill.
- Average cost per beneficiary.
- Market share or mix metrics by provider, pharmacy, drug, or plan where requested.
Data engineering approach
The work required claims-scale programming because detailed Part D grouping can produce very large tables. FastHSR built a reproducible pipeline that standardized identifiers, applied suppression and minimum-cell rules, generated multiple aggregation levels, and reconciled totals across related output files.
- Normalize provider, drug, pharmacy, and plan identifiers.
- Apply drug-name and NDC crosswalks for brand/generic and formulation-level grouping.
- Map pharmacies to chains, parent organizations, and geography where data supported assignment.
- Aggregate claims into requested grouping levels without double counting beneficiaries.
- Validate totals across annual, geography, provider, pharmacy, and plan-level files.
- Package large outputs for product ingestion or downstream analytics.
Findings
The analysis showed that Part D utilization and spending patterns can vary substantially depending on whether the data are viewed through the prescriber, drug, pharmacy, or plan lens. Flexible grouping helped the client identify market patterns that would not be visible in a standard provider-drug summary alone.
The final files supported data-product enhancement, pharmacy and plan analysis, drug market intelligence, and provider-level account planning while preserving suppression and aggregation rules needed for responsible reporting.
Deliverables
- Part D prescriber-by-drug analytic file.
- Provider-by-drug-by-pharmacy analytic file.
- Provider-by-drug-by-plan ID analytic file.
- Provider-by-drug-by-pharmacy-by-plan ID analytic file where cells supported reporting.
- Total costs, beneficiary counts, claims, fills, days supply, and derived spending measures.
- Documentation of grouping variables, suppression logic, identifier crosswalks, and validation checks.
Use cases
- Part D market intelligence by provider, drug, pharmacy, and plan.
- Data-product enhancement using Medicare Part D claims.
- Pharmacy and plan account targeting.
- Drug-level utilization and spending analysis.
- Prescriber behavior and market share analysis.
- Specialty pharmacy, PBM, and payer strategy.
Frequently asked questions
Why add pharmacy and plan ID to provider-drug Part D files?
Provider-drug files show prescribing patterns, but pharmacy and plan dimensions reveal where prescriptions are dispensed and which Part D plans are associated with utilization and spending.
Why produce multiple grouping levels?
Different business questions require different grain. A provider-drug file is useful for broad prescriber behavior, while provider-drug-pharmacy-plan files can support account targeting and product workflows when cell sizes allow.
Can this be refreshed over time?
Yes. The same pipeline can be run for new years, rolling periods, or quarterly refreshes when updated Part D data are available.
For Medicare Part D prescriber analytics, provider-drug-pharmacy-plan files, pharmacy market intelligence, or Part D data-product development, please email us.