Identifying Asset Characteristics and Payer Concerns

When developing a market access strategy, understanding how a new medical asset's features influence payer concerns is crucial. This lesson will show you how specific characteristics of an asset trigger particular concerns for payers.

By the end of this lesson you'll be able to identify how asset characteristics trigger specific payer concerns within the MEA selection tool.

Core idea

The MEA selection tool uses a set of asset characteristics – specific features of your medical product – to predict potential payer concerns. Think of it like a diagnostic tool: you input symptoms (asset characteristics), and it suggests potential issues (payer concerns) that need to be addressed. This process helps you anticipate what questions and objections payers might raise.

The tool considers seven key asset characteristics:

1. Treatment type: Is it a one-off treatment, chronic, or episodic? This affects how payers view long-term costs and administration.
2. Evidence maturity: How strong is the clinical evidence supporting the asset? Payers are highly sensitive to uncertainty.
3. Population size: How many patients will use this treatment? A larger population can lead to greater budget impact.
4. Cost level: Is the treatment very high, high, or moderate cost? This directly impacts affordability concerns.
5. Comparator landscape: What existing treatments are available? This influences how payers assess value for money.
6. Subgroup profile: Is the patient population homogeneous, mixed, or heterogeneous? This can raise questions about the treatment's effectiveness across different patient groups.
7. Indication profile: Does the treatment address a single indication, or are there multiple or possible expansions? This impacts market potential and pricing strategies.

Each characteristic, when present, can activate one or more specific payer concerns. For example, if your asset has "Uncertain" evidence maturity, it will likely trigger concerns about a weak or uncertain evidence base and immature or surrogate outcomes.

Example

Let's consider a new medical asset and see how its characteristics translate into predicted payer concerns using the tool's logic.

Task: Identify the activated payer concerns for an asset with the following profile:

1. Treatment type: Chronic
2. Evidence maturity: Uncertain
3. Population size: Large
4. Cost level: High
5. Comparator landscape: Active comparator
6. Subgroup profile: Heterogeneous
7. Indication profile: Single

Here's how the tool would process this profile:

• Evidence maturity = Uncertain:
  • weak_evidence (weak or uncertain evidence base)
  • immature_outcomes
  • limited_patient_centredness
• Population size = Large:
  • large_pop (large eligible population → unsustainable budget impact)
  • uncertain_uptake (high uncertainty in uptake, duration, or eligibility)
  • util_controls (utilization controls needed)
• Cost level = High:
  • high_cost_pt (high cost per patient (affordability))
• Comparator landscape = Active comparator:
  • icer_threshold (ICER exceeds acceptable threshold)
• Subgroup profile = Heterogeneous:
  • subgroup_uncertain (uncertain subgroup claims)
  • modest_effect (modest or marginal effect size)
  • ce_subgroup (acceptable CE only in restricted subgroup)

Notice how a single asset characteristic can trigger multiple concerns, and multiple characteristics can trigger the same concern (e.g., "ICER threshold" is triggered by both "Cost level" and "Comparator landscape").

Common mistakes

• Ignoring the "why": Don't just note what concern is triggered, but understand why that characteristic leads to that concern. For instance, a "Large" population size triggers budget impact concerns because more patients mean higher overall spending.
• Overlooking combined triggers: Some concerns are activated only when multiple characteristics are present (e.g., "ICER threshold" is more likely if "Cost level" is high and there's an "Active comparator"). Always consider the full profile.

Key takeaways

• The MEA selection tool uses seven asset characteristics to predict payer concerns.
• These characteristics include treatment type, evidence maturity, population size, cost level, comparator landscape, subgroup profile, and indication profile.
• Each characteristic can trigger one or more specific payer concerns, such as "weak evidence" or "high cost per patient."
• Understanding these triggers helps anticipate payer objections and build a proactive market access strategy.