Cost Exposure Analyses
Methodology for building client-specific GLP-1 cost projections with conservative estimates and defensible assumptions.
Overview
Cost exposure analysis quantifies a client's current GLP-1 spend trajectory and projects future costs under both no-intervention and managed containment scenarios. The methodology emphasizes conservative assumptions and confidence intervals to ensure projections are defensible.
Analytical Methodology
Cost exposure analyses are developed in collaboration with the partnerships team based on client-specific data. The approach follows a structured methodology designed for institutional rigor.
Phase 1: Baseline Data Gathering
Analysis begins with claims data from the client's PBM or pharmacy benefits team. Most analyses can be completed with 12-24 months of claims history.
Data elements typically required:
- • GLP-1 claim counts and member counts by month
- • Average cost per claim and net cost after rebates
- • Total enrolled members and pharmacy spend
Phase 2: Trend Analysis
Growth trends and seasonality adjustments are calculated to establish baseline trajectory. Analysis includes month-over-month growth rate, annualized trend projection, utilization velocity, and persistence rate calculations.
Phase 3: Scenario Modeling
Projections model costs under both no-intervention and managed containment scenarios. Conservative, moderate, and optimistic scenarios are developed with confidence intervals for each.
Phase 4: Executive Summary
Final analysis produces an executive summary suitable for CFO-level audiences, including current state, projected trajectory, containment opportunity, and implementation timeline.
Scenario Definitions
Multiple scenarios are always presented to reflect outcome uncertainty. Conservative positioning is maintained throughout.
Assumes lower end of eligible member participation. Used for skeptical audiences and worst-case planning.
Represents historical average across implementations. Used as the expected case baseline.
Assumes higher engagement rates. Referenced only as potential upside for high-engagement populations.
Important: Conservative scenarios are always presented first. Optimistic scenarios are mentioned only as possible upside, never as expected outcomes.
Data Sensitivity
Note: Initial cost exposure analyses can be completed without member-level PHI. Aggregate claims data is sufficient for preliminary analysis. Member-level data is only required during implementation phases.
Presentation Guidelines
Projections are always presented as ranges, never point estimates. The following guidelines maintain credibility and set appropriate expectations.
- ✓Use ranges: “We project savings of $1.2M to $1.8M annually” not “We'll save $1.5M.”
- ✓Acknowledge uncertainty: Reference population characteristics, engagement rates, and clinical factors.
- ✓Explain assumptions: Walk through eligibility criteria and success rate assumptions used in modeling.
- ✓Propose pilot validation: Offer a controlled pilot to validate projections before full deployment.
Handling Data Variations
Real-world data often requires adjustments. Standard approaches are applied when data is incomplete or unusual.
Missing Rebate Data
Standard rebate assumptions are applied, or net-of-rebate figures are requested from the PBM.
Limited History
Available data is annualized with seasonality adjustment. Projections are flagged as based on limited history.
Recent Benefit Changes
Data is segmented before/after changes. Post-change data is used for trend projections.
Questions about cost exposure methodology or analysis approach?
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