December 3, 2019 at 5:00 am ET
In the United States, we are engaged in the challenging but important conversation about how to assess the value of pharmaceuticals.
While efforts to improve the frameworks for drug value assessment are emerging, we are missing an important opportunity if we continue to focus all value assessment energy on just drugs. To be truly relevant to sustainable value-based health care decision-making, we must advance methods and tools that can effectively support comparative assessment of a broader array of medical interventions.
As an incubator, the Innovation and Value Initiative is a testing ground for developing new economic modeling and methods. The Open-Source Value Project is a rapid-learning environment, giving economists and other decision makers the ability to apply what we’re learning today in value assessment for pharmaceuticals to future economic models and decision scenarios for other interventions. Below are three key lessons learned in drug assessments that should be foundational in all health care technology assessments.
Consider data beyond clinical trials
Non-clinical data, including real-world data, must be built into value assessment models in a way that can assist all decision makers in developing more precise decisions that affect populations with known differences, including age, ethnicity or even biomarker-based disease subtype.
While the research, regulatory and innovator communities have not yet solved this conundrum of how to incorporate such data into live modeling and decision support, important progress is underway.
The Food and Drug Administration has taken positive steps with draft guidance to enhance diversity in medical device clinical trials. While many organizations applaud the FDA’s direction, they also urge the FDA to include real-world data and evidence to help fill gaps in information to better reflect real-world patient populations.
And research underway at the National Evaluation System for health Technology Coordinating Center to collect and catalyze real-world evidence studies throughout a medical device’s life cycle is intended to accelerate the development, translation and use of real-world evidence. For real-world data to become scientifically valid, real-world evidence though, it is essential that we continue to collaborate to find explicit and accelerated paths forward.
Incorporate patient-defined factors of value
To be relevant, any value assessment model must include evaluation of factors articulated by actual patients experiencing the conditions and include both quantifiable analysis as well as narrative considerations that affect interpretation and application to policy. Currently, the majority of approaches relegate patient factors to end-stage commentary, which is too easily overlooked or discounted by decision makers with the power to affect access.
Patient perspectives on value matter. When included in assessment, the value of a therapy is broadened beyond just unit cost of an intervention, to include considerations such as impact on caregivers and family and ability to work.
Incorporating these value factors into modeling is time consuming and complex. But complexity should not be a barrier or a rationale for omission. Instead, the research community must continue to scientifically test and apply methods to demonstrate the additive importance of incorporating patient perspectives.
Commit to open source development
We must eschew the notion that one method for value assessment exists and instead adopt a transparent, active-learning approach in which all stakeholders collaborate to define and improve methods and models. That includes collaborating with experts in an open-source environment from device manufacturer, surgical societies and other therapeutic communities to investigate the best data sources and methods for evaluating the comparative benefit on clinical, quality-of-life and cost metrics. An open-source approach allows all health care stakeholders to participate in value assessment movement and collaborate on new — more accurate and relevant — methods and models in a manner that speeds economic model development.
By assessing both clinical and real-world evidence, improving our ability to account for patient heterogeneity and conducting research transparently, we can improve our potential to identify and make accessible optimal treatment pathways for patients and their clinicians. We can also help payers and employers gain confidence that their resources are being spent on treatments and care that is most likely to deliver benefits to the diverse populations they care about.
We’ve made progress assessing value in the United States, and there is no reason to think that our focus on pharmaceuticals is misplaced. Indeed, we’ve learned much and along the way highlighted some real challenges and areas for improvement in the collective value discussion.
But to truly make the science and the practice meaningful to those who receive care, pay for care, and care for loved ones, it is time to move to the next level. We need to prioritize methods and models that can help us examine value across therapeutic interventions. And we need models that consider the patient-lived experience, their diversity and the factors that drive both their engagement in treatment and their potential health outcomes.
Most of all, we need the commitment and leadership from all stakeholders to a transparent, iterative environment for improving the science and practice of value assessment.
Jennifer Bright is the executive director of the nonprofit Innovation and Value Initiative.
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