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Payer landscape analyzer

Analyzes the payer landscape for a therapeutic area and geography — segmenting payers by coverage type, mapping formulary tier dynamics, prior authorization requirements, coverage restrictions, decision-maker influence…

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Purpose

Produce a structured payer segment profile for a given therapeutic area and geography — segmenting payers by coverage type, mapping the decision-makers who control formulary access, and specifying tier dynamics, prior authorization requirements, coverage restrictions, and reimbursement pathways. Replaces scattered, anecdotal payer knowledge with a repeatable framework that market access and pricing teams can act on for launch planning, pricing, and account strategy.

When to Use

Invoke this skill when the user asks to:

  • Analyze the payer landscape for a drug, indication, or therapeutic area
  • Segment payers by coverage type (commercial, Medicare, Medicaid, managed Medicaid)
  • Understand formulary tier placement and prior authorization dynamics for a product
  • Map payer decision-makers and their influence before an account engagement or advisory board
  • Prepare payer strategy inputs for a pre-launch or launch market access plan
  • Assess coverage restrictions and reimbursement pathways in a specific geography

Inputs Required

Before starting, collect:

  • Therapeutic area / indication: The disease state and target patient population
  • Product profile: Drug type (small molecule, biologic, specialty, buy-and-bill vs. pharmacy benefit), stage (pre-launch, launched), and key differentiators
  • Geography: Country and, for the US, national vs. specific state/region focus
  • Competitive context: Standard of care and key competitors already reimbursed
  • Anticipated price / cost position: WAC, expected net, or "high-cost specialty" positioning
  • Benefit type: Pharmacy benefit, medical benefit, or both
  • Strategic objective: What the analysis feeds — pricing decision, launch plan, account targeting, or contracting strategy

If price position or benefit type is missing, infer from the product profile (e.g., infused biologic → medical benefit / buy-and-bill) and state the assumption.

Instructions

Step 1: Segment the Payer Universe by Coverage Type

Divide the landscape into distinct payer segments and profile each. For the US, use the following segments (adapt for ex-US single-payer or regional systems as needed):

  • Commercial (Employer & Individual): National plans, regional plans, and self-insured employer groups. Note the largest carriers in the geography and their covered-life share.
  • Medicare (Part D & Part B): Distinguish Part D (pharmacy benefit, formulary-driven) from Part B (medical benefit, physician-administered). Note MA-PD plan dynamics.
  • Medicaid (FFS & Managed Medicaid): Fee-for-service state programs vs. managed Medicaid MCOs. Flag Medicaid Drug Rebate Program implications and state-specific PDLs.
  • Integrated Delivery Networks / IDNs & Health Systems: Where the system controls both coverage and site of care.
  • PBM Layer: Which PBMs (and their formulary templates) sit above the plans in this geography — this often drives access more than the plan itself.

For each segment capture: approximate covered-life share, benefit type control, and formulary decision speed.

Step 2: Map Decision-Makers and Influence

For each payer segment, identify the roles that control or influence access and rank their decision influence (High / Medium / Low):

  • P&T Committee: Clinical formulary decisions and tier placement
  • Chief Medical Officer / Medical Director: Clinical policy and PA criteria authorship
  • Pharmacy Director / Chief Pharmacy Officer: Formulary strategy and cost management
  • PBM Formulary & Trade Teams: Rebate negotiation and formulary template placement
  • VP Market Access / Contracting: Net pricing and value-based arrangements
  • State Medicaid Pharmacy / DUR Board (Medicaid): PDL placement and clinical criteria

For each, note what they optimize for (clinical evidence, budget impact, rebate value) and the evidence or economic argument that moves them.

Step 3: Analyze Formulary Tier Dynamics

For the product in this therapeutic area, model likely tier placement per segment:

  • Expected tier: Preferred brand, non-preferred brand, specialty tier, or excluded
  • Tier drivers: Presence of preferred competitors, class dynamics, generic/biosimilar availability
  • Cost-share implications: Copay vs. coinsurance and the resulting patient out-of-pocket exposure
  • Competitive tier positioning: Where standard of care and key competitors sit today

Flag whether the class is a "managed class" (aggressive utilization management) or open access.

Step 4: Detail Utilization Management & Coverage Restrictions

Specify the utilization management (UM) hurdles the product will likely face by segment:

  • Prior Authorization (PA): Likely criteria (diagnosis confirmation, labs, specialist prescriber, prior therapy documentation)
  • Step Therapy: Which agents must be tried and failed first, and typical step count
  • Quantity Limits / Dosing edits
  • Site-of-Care restrictions (for medical benefit / infused products)
  • Reauthorization requirements: Response-based continuation criteria

For each restriction, note severity (access-blocking vs. administrative friction) and the clinical evidence needed to negotiate softer criteria.

Step 5: Map Reimbursement Pathways

Trace how the product gets paid for in each segment:

  • Pharmacy benefit path: PBM adjudication → pharmacy dispense → member cost-share
  • Medical benefit path: Buy-and-bill or specialty pharmacy → J-code / miscellaneous code → claim submission → provider reimbursement (ASP+ dynamics for Part B)
  • Coding readiness: Whether a permanent J-code exists or a miscellaneous code applies at launch
  • Patient support needs: Copay assistance, PA support hubs, appeals support implied by the access profile

Step 6: Synthesize the Payer Segment Profile

Assemble a prioritized view: rank segments by access opportunity and difficulty, and flag the 2–3 segments that most determine commercial success. State the single largest access barrier and the highest-leverage lever to address it.

Output Format

  • Structure: Segment-by-segment profile with a summary priority table up front, followed by detailed sections per analysis dimension
  • Length: 1,000–1,400 words
  • Format: Structured document with a summary table (payer segment × covered-life share × benefit type × expected tier × UM severity × decision influence) plus supporting prose and bulleted detail
  • Sections: Payer Segmentation → Decision-Maker & Influence Map → Formulary Tier Dynamics → Utilization Management & Restrictions → Reimbursement Pathways → Synthesized Segment Priority & Key Barrier
  • Tone: Market access strategist — precise, evidence-grounded, decision-oriented; uses correct payer terminology without over-explaining basics

Quality Checklist

Before delivering output, verify:

  • [ ] Every payer segment (commercial, Medicare, Medicaid/managed Medicaid, plus PBM layer) is profiled — none omitted
  • [ ] Each segment has a decision-influence ranking tied to specific roles, not generic "the payer decides"
  • [ ] Expected formulary tier is stated per segment with named tier drivers
  • [ ] UM section specifies concrete PA/step-therapy criteria, not just "expect prior auth"
  • [ ] Both pharmacy benefit and medical benefit pathways are addressed where the product profile warrants it
  • [ ] Synthesis names the 2–3 highest-priority segments and the single largest access barrier

Notes

  • For US analyses, the PBM layer frequently controls access more than the plan itself — always surface it even when the user only names insurers
  • For ex-US geographies, replace commercial/government segmentation with the relevant national HTA and reimbursement bodies; the same dimensions (decision-makers, restrictions, pathways) still apply
  • Distinguish medical benefit vs. pharmacy benefit early — it determines whether buy-and-bill, site-of-care, and J-code dynamics dominate the analysis
  • When product data is pre-launch and uncertain, model the likely landscape based on class analogs and state the analog used

How to use this skill

This file is a Claude Code skill in Anthropic's standard format — markdown with YAML frontmatter at the top.

Installing in Claude Code:

  • ~/.claude/skills/<folder-name>/SKILL.md — auto-loads in every session anywhere on your machine (recommended for skills you'll use across multiple projects)
  • <your-project>/.claude/skills/<folder-name>/SKILL.md — auto-loads only when working in that project (recommended for project-specific skills)

Restart Claude Code or open a new session to register.

Using in Claude.ai, ChatGPT, Gemini, or other AI tools:

  1. Drag this file into a new chat, or copy and paste its contents
  2. Send your task or request as your next message — the AI will apply the skill where it fits

For Claude Code skill details: https://docs.anthropic.com/en/docs/claude-code/skills