The problem: Narrow focus during evidence planning may impact coverage

Most evidence planning for a pipeline therapy is targeted at the regulator: Sponsors generate what the U.S. Food & Drug Administration (FDA) needs for the approval process,¹ then hope this same set of evidence answers the questions a U.S. payer may ask when making coverage decisions. In reality, evidence generated primarily with regulatory approval in mind may be incomplete to satisfy both needs. Regulators weigh placebo-controlled efficacy in a trial population, but payers consider comparative value, position in the treatment paradigm, and budget impact against standard of care and in-market treatments.^2,3

A typical regulator-first approach to evidence planning can have downstream impacts that may cause a delay in payer coverage. For example:

• The Academy of Managed Care Pharmacy (AMCP) dossier may be assembled from incomplete data.
• A pharmacy-and-therapeutics (P&T) committee may return objections.
• Key payer questions may remain unanswered at launch.

The result may be a more restrictive payer coverage policy: later-line placement, narrow approved populations, and step therapy. Payers may write these types of conservative policies if the evidence available to them does not support broader access.

Medical and HEOR teams may benefit from a more efficient approach: Write the ideal payer coverage policy early and incorporate it into evidence planning before launch.

What ‘write the policy first’ means in practice for medical and HEOR teams

Our suggestion to “write the policy first” does not mean planning for payers instead of regulators. Instead, both evidence-generation plans can be prepared in parallel. Ideally, this occurs before Phase 2/3 protocol finalization, and, at latest, before pivotal design lock, so that comparator choice, population enrichment, endpoints, and real-world evidence (RWE) plans can still be influenced.

This draft coverage policy can serve as a guidepost that supports identification of evidence that could be used to support payer decision making. The resulting studies may become direct inputs for the dossier that P&T committees and other healthcare decision makers use. In effect, the draft coverage policy functions as a pre-specification of the claims the dossier will later defend.

The process: Writing a medical coverage policy and mapping the evidence

Writing the coverage policy is just the first step of a broader process of evidence evaluation. Below, we describe how the draft policy can be integrated with other aspects of evidence planning.

1. Draft the policy. Write the ideal coverage policy, in payer language: covered population, line of therapy, prior authorization requirements, and step edits (or their absence). Calibrate the policy to (1) the most favorable position a well-evidenced product could credibly support and (2) what a committee could defend if supporting data existed. Keep in mind that coverage is plan- or channel-specific (e.g., commercial, pharmacy benefit managers [PBMs], Medicare, Medicaid, integrated delivery networks [IDNs]). A single “ideal” policy may be useful as an organizing tool, but differences in channels and archetypes should be considered.
2. Pair the coverage policy with an explicit view on net price. Committees defend against a net-cost target, not list price, so each channel should state the net price assumption. Mechanics vary by channel.
• Commercial/PBM: Net price is driven by rebates negotiated for formulary position. These payments shape formulary placement and patient cost sharing, not just access.⁴
• Medicaid: Net price is anchored to the Medicaid Drug Rebate Program’s "best price" floor—the lowest price offered to any purchaser in the average manufacturer price (AMP) quarter. Manufacturers also owe inflation-penalty rebates when AMP outpaces inflation. Since commercial rebate levels drive the Medicaid basic rebate, pulling back commercial rebates is one lever manufacturers use to manage best-price exposure.^5,6
• Medicare Part D: IRA negotiation adds a new anchor: The maximum fair price (MFP) feeds into best price and could reset the Medicaid floor. Commercial payers may use MFP as a public benchmark for their own rebate talks. Separately, new legislation shifts PBM pay in Part D/Medicare Advantage toward flat, disclosed fees instead of price-linked rebates, changing how net price flows through.^7,8,9
• 340B/IDNs: Covered entities get statutory discounts off AMP. Because many can resell at a markup, the program pushes list price up rather than net price down.^10,11
3. Score the policy. Test the draft policy based on product data against the standard a P&T committee would apply: cost (a defensible budget impact model for requested position and population); cost effectiveness (value for spend, reduced utilization, or avoided costs); clinical superiority (to relevant comparator at that position, not placebo); safety (including real-world tolerability); waste (less discontinuation, titration failure, downstream utilization); and quality of life (including patient-reported outcomes). For dimensions the product cannot plausibly demonstrate evidence, revise the policy until every aspect is addressed.
4. Map each criterion to evidence. Every line in the policy is a testable claim. Pair each component of the policy with the research endpoint, comparator, subgroup, or study that applies.
5. Reconcile the policy evidence against the regulatory track. Some claims may already be covered by the regulatory approval program evidence. What remains are the evidence gaps that could be addressed to provide payers with targeted data to support their decision making.
6. Price the risk and reward. Before committing to the evidence plan, forecast what each study decision is worth and what it could cost. Identify where the plan meets the business case.
7. Commit the study decisions. Convert the gaps into study design choices while trials can still respond. Commissioning the comparator, enriched population, patient-reported outcomes, or RWE early, rather than post-launch, may reduce delays and broaden access from the start.

3 examples of medical policy elements and life sciences evidence planning

The table below presents examples of how a policy-first evidence planning process might look for individual lines of coverage policies across three therapeutic areas. These are provided as illustrative examples only, not therapeutic recommendations.

Definitions: GLP-1, glucagon-like peptide 1; RA, rheumatoid arthritis; TNF, tumor necrosis factor.

For these examples, a pattern holds true: The commercially valuable policy line is the one a default regulatory-focused evidence package does not address. Under a typical planning strategy, a placebo-controlled pivotal trial earns approval, but it may result in later-line coverage. Earning-improved coverage requires deciding, at the trial design stage, to generate the payer-relevant evidence that a regulatory-focused package would not otherwise produce.

Risks and rewards of including payer considerations during evidence planning

A payer-forward study decision is an investment with a clinical trajectory attached, and it should be evaluated carefully. A head-to-head trial against the entrenched competitor could be the best path to ideal coverage but carries the risk that the product underperforms the comparator. Before committing to any evidence strategy, the clinical, medical, and commercial teams should consider the same three numbers:

• The status quo forecast: Revenue and access under the default package: The position the regulator-focused evidence supports, the policy payers might write, and the expected uptake of the treatment given the coverage language.
• The upside scenario: The forecast if the payer-focused evidence wins: an earlier coverage line, broader population, and/or lack of step edit, quantified as covered lives and revenue at the improved position.
• The downside scenario: The forecast if the study produces unfavorable results: What a failed head-to-head comparison could do to the status quo and upside scenarios, and if it jeopardizes approval.

This holistic risk-framing changes the conversation from “should we run this study” to “here are the risks and the potential gains.” Using this framework, the evidence plan becomes a portfolio in which a commercial organization can evaluate funding each study based on potential value.

Why ‘policy-first’ evidence planning works for medical and HEOR teams

A draft policy of desired coverage becomes a key variable to inform evidence-planning strategy early, while trial design can still change. Writing the ideal payer coverage policy and mapping the evidence can ensure payers have what they need to make informed and (ideally) favorable decisions regarding coverage.

Payer-forward thinking also solves a coordination problem among evidence-planning teams with fundamentally different focuses during the process. Medical/HEOR teams typically own the scientific claims and evidence map; Market Access owns the commercial forecast and policy language; and Clinical Development owns protocol changes. A single shared artifact reduces siloed planning and moves stakeholders toward optimization of strategy outcomes.

Where Milliman comes in

¹ U.S. Food & Drug Administration. (2022, August 8). Development & approval process: Drugs. Retrieved September 2, 2026, from https://www.fda.gov/drugs/development-approval-process-drugs.

² AMCP (n.d.). Formulary management. Retrieved September 10, 2026, from https://www.amcp.org/concepts-managed-care-pharmacy/formulary-management.

³ Angus, D.C., et al. (2024). The integration of clinical trials with the practice of medicine: Repairing a house divided. JAMA: The Journal of the American Medical Association, 332(2), 153–162. Retrieved September 23, 2026, from https://doi.org/10.1001/jama.2024.4088.

⁴ U.S. Government Accountability Office. (2023 September). Medicare Part D: CMS should monitor effects of rebates on plan formularies and beneficiary spending. Retrieved September 10, 2026, from https://www.gao.gov/assets/gao-23-105270.pdf.

⁵ Code of Federal Regulations. (2026, August 13). Title 42: § 447.505 Determination of best price. National Archives. Retrieved September 10, 2026, from https://www.ecfr.gov/current/title-42/chapter-IV/subchapter-C/part-447/subpart-I/section-447.505.

⁶ Levy, J.F., Socal, M.P, & Ballreich, J.M. (2024). Strategic manufacturer response to the Medicaid rebate cap removal. JAMA Health Forum, 5(11). Retrieved September 23, 2026, from https://doi.org/10.1001/jamahealthforum.2024.3624.

⁷ Cates, J., Holcomb, K., Klaisner, J., & Swenson, R. (2023, September 12). Medicare price negotiation: A paradigm shift in Part D access and cost. Milliman. Retrieved September 10, 2026, from https://www.milliman.com/en/insight/medicare-price-negotiation-paradigm-shift-part-d-access-cost.

⁸ Steinzor, P. (2026, April 28). Rebates, reference pricing, and the road to 2026 midterms. AJMC. Retrieved September 10, 2026, from https://www.ajmc.com/view/rebates-reference-pricing-and-the-road-to-2026-midterms.

⁹ Cubanski, J., Neuman, T., & Freed, M. (2023, January 24). Explaining the prescription drug provisions in the Inflation Reduction Act. KFF. Retrieved September 10, 2026, from https://www.kff.org/medicare/explaining-the-prescription-drug-provisions-in-the-inflation-reduction-act/.

¹⁰ U.S. Government Accountability Office. (2015, June 5). Medicare Part B drugs: Action needed to reduce financial incentives to prescribe 340B drugs at participating hospitals. Retrieved September 10, 2026, from https://www.gao.gov/products/gao-15-442.

¹¹ Paragon Health Institute. (2026, January 14). PBM 101: What they are and how they affect drug prices. Retrieved September 10, 2026, from https://paragoninstitute.org/private-health/pbm-101-what-they-are-and-how-they-affect-drug-prices.