Prepping for the Inflation Reduction Act: Why Manufacturers Need a Plan for Rapid RWE Generation
Since the passing of the Inflation Reduction Act (IRA) in August 2022, the healthcare industry has been bracing for the impact of several key provisions. Three of these policies—price negotiations, inflation-adjusted rebates, and the out-of-pocket spending cap—will be at least partially in place by the close of 2023.
As manufacturers prepare for the effects of these policies on their revenue stream, it’s become clear that real-world evidence will play a pivotal role in their strategy. Manufacturers will need to quickly conduct real-world analyses on the value and comparative effectiveness of their products—and they will need the ability to focus on specific subpopulations.
Let’s learn more about what the IRA requires.
Tight Turnarounds for Price Negotiation
The first IRA provision of note establishes the Medicare Drug Price Negotiation Program, which allows Medicare to negotiate the prices of selected high-cost, single source drugs without generic or biosimilar competition. Medicare is focusing on drugs that have been on market for a long time (more than 9 years for small-molecule drugs, and 13 years for biological drugs). Plasma-derived drugs and drugs with an orphan designation are excluded.
By September 2023, Medicare will select the first batch of 10 drugs from among the top 50 covered drugs with more than a $200 million annual drug spend. Although manufacturers of likely candidates are no doubt already planning for their potential inclusion on this list, they might not be fully aware of the tight timelines. Once these drugs are officially announced on September 1, manufacturers will have only one month to submit data for CMS consideration in negotiating a maximum fair price: all manufacturer-specific data is due on October 2.
In the spring of 2024, these manufacturers will face another tight turnaround, as there is only one month between the day CMS extends an initial pricing offer (February 1) and the day manufacturers must propose a counteroffer (March 2).
Comparative Effectiveness Research and RWE
So, what kind of data will manufacturers need to gather and submit? In negotiating the maximum fair price, CMS has stated that it will consider comparative effectiveness research (CER), with an emphasis on patient subpopulations, such as the terminally ill, the elderly, and those with disabilities.
It’s important to note that CMS is prioritizing real-world evidence and will not accept the use of quality-adjusted life years (QALYS) to measure the value of a treatment. Manufacturers are likely to be a bit flummoxed by the IRA’s guidelines, as mandatory price negotiation is new for the U.S. Even in global markets, such negotiations tend to happen at launch, rather than a decade after market entry.
The good news is that each impacted manufacturer should have years of real-world evidence (RWE) to marshal. The bad news is that traditional analytics and programming—either in-house or outsourced—will not be flexible enough to meet these tight timelines or automatically generate the necessary documentation. Manufacturers will need to interrogate multiple data sources simultaneously and will need full oversight of the questions asked and answered. Most importantly, manufacturers will need to be adept with their analyses to accommodate the quick turnaround.
To ensure that negotiated prices land as close to the cap as possible, manufacturers will need general evidence that demonstrates how their treatment addresses an otherwise unmet medical need. They’ll also need CER that shows the value of the treatment for specific sub-populations, including those that may have been excluded or underrepresented in the treatment’s initial clinical trials. These data sets should include any incremental improvements in both clinical and economic outcomes.
The Instant Health Data (IHD) analytics platform from MMIT’s sister company, Panalgo, can provide fit-for-purpose data for the Medicare Drug Price Negotiation Program. Manufacturers can iterate analyses from custom queries in just a few days, without the need for on-staff analysts. In addition to CER, IHD can support characterization of the patient journey, demonstrate unmet need for relevant subpopulations, and help manufacturers model the impact of price on adherence and sales.
In general, manufacturers are going to need these capabilities sooner rather than later, regardless of whether their treatment is selected as part of the first grouping. Most—if not all—top manufacturers will eventually have treatments that are impacted by the Medicare Drug Price Negotiation Program. The IRA details the selection of progressively larger groupings of Part D (and eventually Part B) drugs in later years. The ability to immediately analyze real-world data is paramount.
Forecasting and Commercialization Strategy
The second IRA policy necessitating immediate planning is the Inflation Rebate Provision, which requires manufacturers to pay Medicare a rebate if the price for a single-source or biological drug increases faster than the rate of inflation, as measured by the Consumer Price Index for All Urban Consumers (CPI-U). This provision begins in 2023, and CMS has already released (and revised) a list of the first 20 drugs whose manufacturers will be required to pay a rebate, which will be calculated by multiplying the pricing difference by the number of units sold in Medicare.
Many experts argue that Inflation Rebate Provision will drive higher launch prices, as manufacturers of new market entrants will need a different strategy for recouping their R&D costs. Without the ability to adjust prices over time, manufacturers might be forced to frontload costs at launch.
In any case, manufacturers of both new and existing drugs will need additional RWE to optimize market access and prepare for reimbursement negotiations. Just as manufacturers of top-selling drugs must prepare for the future window of eligibility for the Price Negotiation Program, manufacturers of new entrants must be able to make an air-tight case for formulary inclusion—especially if they plan on establishing a higher price at launch.
If a higher launch price is warranted, manufacturers will also need real-world data to conduct accurate forecasting for the drug’s lifecycle. Identifying cost savings based on improved outcomes for a particular subpopulation can help manufacturers gain coverage and avoid utilization management restrictions. Essentially, the analysis of RWE becomes imperative for any treatment, even new ones not subject to price negotiations.
Out-of-Pocket Spending Analysis
The third IRA policy of note to manufacturers is the out-of-pocket cap on spending for Medicare Part D enrollees. By 2025, the IRA imposes a hard cap of $2,000 for beneficiaries’ out-of-pocket spending, with future years indexed to the rate of increased part D costs. Significantly, manufacturers will be required to provide a 20% price discount on brand-name drugs to patients who are spending more than the out-of-pocket cap.
This cap is generally viewed as advantageous for manufacturers, as it is likely to increase medication adherence and reduce the number of patients who eventually abandon treatment due to financial concerns. Manufacturers can use real-world data to prepare for the potential impact of the out-of-pocket cap on their target populations. By quantifying the impact of patient expenditure on overall medication adherence and treatment initiation, manufacturers can determine if lowering out-of-pocket costs could potentially improve the number of patients staying on a therapy.
The need for real-world data to generate relevant, persuasive evidence for CMS and payer consideration in price negotiations, formulary placement, and value discussions is more pressing than ever before. Given the time component of many of the IRA’s new policies, manufacturers must be able to conduct this research as efficiently as possible—without outsourcing complex programming.
For more information on automating CER research for patient subpopulations, learn more about Panalgo’s IHD Analytics platform. A longer version of this blog was recently published on the Panalgo site.
