Use reimbursement science to improve coverage decisions

Healthcare innovation is challenging, and becoming more so as the health system rapidly evolves. In addition to overcoming the technical hurdles to finding a new drug, device, or diagnostic that is genuinely better than what we have now, product developers must demonstrate more compelling evidence of effectiveness, safety and value than was true even a few years ago.

On the other side of the equation, payers, health systems and other key decision makers are facing significantly increased pressure to improve outcomes and reduce costs of care. For example, approvals of a handful of highly effective and extremely expensive drugs for cancer and hepatitis treatment have led to a year-over-year tripling of patients who spend upwards of $50,000 per year on medications, according to a recent Express Scripts survey. While these exciting treatments reflect successful innovation, they come with a hefty price tag, and require a response from value-driven payers and health systems.

Read: Seven strategies for managing the rise in specialty drug costs

TunisMaking informed and defensible decisions about which new technologies to cover, for what patients, and at what price is a major challenge for public and private payers. This is due in part to the fact that there are not yet well defined standards  to evaluate scientific evidence of relative effectiveness and value when comparing new treatments to existing alternatives.

The uncertainty resulting from this lack of standards also makes it impossible for life sciences companies to efficiently use resources for clinical studies intended to generate the evidence that payers need to make these decisions.

To address this challenge, we believe we need a new and sustained commitment to develop the field of “reimbursement science,” which would seek to advance the scientific basis for coverage and payment decisions. This term is adapted from the concept of “regulatory science,” which the FDA and others describe as the process of developing new tools, standards, and approaches to assess the safety, efficacy, quality, and performance of all FDA-regulated products.

Next: The purpose of reimbursement science

The purpose of reimbursement science

The purpose of this work is to speed innovation, improve regulatory decision making, and get products to people in need. The FDA’s support of regulatory science provides an opportunity to develop a rigorous scientific and evidentiary framework that reflects multiple stakeholder priorities and social objectives. It is essential to the ability of the FDA to make consistent and defensible regulatory decisions and to provide product developers with the clarity and predictability needed to plan their investments in clinical studies.

As reimbursement decisions become equally or more influential to products’ success as regulatory decisions, it is imperative that the scientific framework for these decisions becomes more sophisticated and transparent.  It is considerably more difficult to achieve this in the reimbursement space, since many organizations are making purchasing decisions, whereas the FDA is the primary body entrusted with regulatory decisions in the U.S.  

Reimbursement science, like regulatory science, would facilitate a social-scientific dialogue related to decision making around coverage and payment decisions. It would focus on developing new tools, standards and approaches to assess the comparative effectiveness and value of products covered by public and private health plans. And much like regulatory science, the purpose of this work would be to improve health outcomes, improve decision making, and get useful products to patients in need.  In addition, reimbursement science would aim to promote the evidence that will help to ensure that healthcare resources are spent wisely.

Next: Prospects for reimbursement science

Prospects for reimbursement science

So how to move this work forward? In the absence of any obvious existing public platform to conduct this work, the Center for Medical Technology Policy (CMTP), an independent, non-profit that aims to make healthcare more effective and affordable by improving the quality, relevance, and efficiency of healthcare research, established the Green Park Collaborative (GPC) as a public-private forum to define and develop the field of reimbursement science.

The work is being pursued by taking on efforts in specific therapeutic areas such as diabetes, oncology, chronic wounds, or conducting projects on broader methodological issues such as pragmatic approaches to late-phase drug trials.

All of this work requires an equal commitment to technical rigor and sustained stakeholder engagement, since the underlying notion of reimbursement science is a blend of statistical principles and the realities of clinical and policy decision making.  Determining when evidence is “adequate,” “sufficient,” or “permits conclusions” is ultimately a judgment about the available evidence made by individuals or groups, not an inherent property of the evidence itself.

For this reason, the work is highly collaborative, involving a whole range of industry leaders, public and private payers, and researchers, as well as clinicians and patients. Including all stakeholders in this work is critical, as there are competing interests in determining what level of evidence is credible, relevant, and ultimately, adequate to inform a thoughtful reimbursement decision.  

Developing core outcome sets
Recently, we have begun work on a number of methods issues that GPC participants have brought to our attention. One is on the potential value of greater standardization in the clinical outcomes measured and reported in clinical trials intended to inform clinical and health policy decisions.  

Over the past several years, there has been increased interest in the development of “core outcomes sets.” These are defined as a standardized set of outcomes that should be measured and reported, as a minimum, in all clinical trials in specific areas of health or healthcare.

The notion is that if drug and diagnostic developers measured the same outcomes using the same instruments in as many studies as possible, it would be considerably easier for payers and plans to assess comparative effectiveness and value. Currently, product developers and academic researchers measure a wide range of different outcomes with different instruments, and without much input from payers, clinicians, and patients about what matters most. Building consensus around six to eight cross-cutting outcomes for each major clinical condition and class could dramatically improve the quality and relevance of future studies, as well as the overall body of evidence in each therapeutic area.

Next: Opportunities to use “real-world evidence”

Opportunities to use “real-world evidence”

A second area of active early work within the GPC is on developing a framework for identifying what “good” looks like with respect to real-world evidence.

Real-world evidence is developed primarily using data harvested from electronic health records, claims information, and other activities that generate data as a byproduct of the routine delivery of care. The ability to use this data to answer questions of comparative effectiveness and value represents a tremendous opportunity to efficiently generate useful knowledge. The quality of real-world data and the studies conducted with this data vary dramatically, and a number of groups have developed systems intended to separate the wheat from the chaff.  

Despite this work, and the exponential increase in the production of real-world evidence studies, there is not yet a clear consensus on how to efficiently and reliably identify those studies that deserve serious attention from clinical and policy decision makers. This creates uncertainty on the part of both potential producers and potential users of this evidence.

A clear and common framework for this research would on the one hand provide guidance to industry about the rigor of studies they need to develop. On the other, it could provide payers a clearer and more efficient way to make a whole range of coverage decisions and shape care incentives.

The work of reimbursement science is neither simple nor easy. There are complex methodological challenges. There are natural tensions among all the stakeholders that must be resolved. Trust, transparency, and consistency are all critical to the process. This is the hard work ahead, however, if we want to spur transformative health care innovation and ensure we can continue to pay for it.

Sean Tunis, MD, MSc is the founder, president, and chief executive officer of the Center for Medical Technology Policy in Baltimore, Maryland.