Putting data to work for value-based agreements
Complex medical treatments like gene and immunotherapies have brought us into a new era of healthcare. But these therapies come with risk for both manufacturers and payers. It takes enormous investment for manufacturers to develop treatments, with no guarantee that current healthcare reimbursement systems will let them recoup the cost. For payers, it's the same risk they've faced for decades but at a larger scale: They take on the cost of new treatments without knowing what kind of benefit their patients will see.
Value-based agreements (VBAs) are an increasingly popular response to this challenge. Verpora reported a record number of VBAs published in the U.S. in 2018 by major manufacturers like AstraZeneca and Boehringer Ingelheim and key payers like Harvard Pilgrim. They weren’t limited solely to newer complex treatments, either. Well established brands like Jardiance and Symbicort have seen VBAs, too.
The types of VBAs in use today vary widely, but all work on the same general principle. The manufacturer and payer agree on a set of measures or outcomes against which the “value” of the treatment or therapy will be evaluated. If patients receiving the treatment meet or exceed those outcomes, the manufacturer gets a higher payment; if patients don't see the agreed-upon benefits from treatment, the payer reimburses the manufacturer at a lower rate. In short: Both sides assume a level of risk and have the potential to realize value from the agreement based upon whether the treatment works. However, there are also some challenges with VBAs in three key spaces.
"Both sides have the potential to realize value from the agreement based upon whether the treatment works."
Defining relevant outcomes for VBAs
Deciding which outcomes to write a VBA against is one of the biggest hurdles payers and manufacturers face. Since outcomes depend on available real-world data, it often stalls the creation of VBAs completely. When it doesn’t, outcomes are largely limited by whatever the payer’s existing data can measure. Sometimes metrics are as simple as persistence: Did a patient fill a prescription for at least six of the eight months it was prescribed?
This type of data might indicate whether the treatment has an acceptable side effect profile or not, but it doesn't explain actual outcomes like whether the patient's condition improved. To get a 360-degree longitudinal view of valuable outcomes that are both clinically and patient-relevant, you need more data. VBAs allow both payers and manufacturers to access the same medical, lab and imaging data in addition to the payer data to create a full patient picture.
Defining the VBA patient cohort
There are also challenges in defining the patient cohort within the VBA. Similar to a real-world study, the inclusion and exclusion criteria for a VBA cohort must be agreed upon upfront. That way, those patients can be accurately tracked throughout the term of the contract. But what happens if a patient sees multiple doctors or goes to different pharmacies to fill a prescription? It’s important to capture the patient’s full longitudinal view.
Consider a situation where John Simon filled two prescriptions, John Symon had lab work ordered and Jon Simon has a lab value in his medical record. While these names seem like three separate patients in the VBA cohort, they represent just one patient with misspelled records. Those false negatives prevent the actual John Simon from having all the necessary data to measure his outcomes. False negatives like these are common and create further stumbling blocks in realizing the potential of VBAs. If you can't be sure of the patient cohort you're measuring, then you can’t be confident in the value of the agreement itself.
“If you can't be sure of the patient cohort you're measuring, then you can’t be confident in the value of the agreement itself.”
Delivering on the potential of VBAs at scale
Even if both the outcome and cohort challenges are addressed, most VBAs face additional hurdles in real-time monitoring and management. The parties agree on what patients and metrics they'll examine at the beginning, but one party is often limited in its ability to track performance until the agreement ends. Since payers typically have the data the VBAs are measured against, manufacturers may only get quarterly or annual review periods to find out if the outcome targets were hit and what the financial impact will be. Not only does this make it difficult to develop and implement VBAs at scale, but it also limits the opportunity for real-time interventions that could help improve patient outcomes.
It's time to activate the full power of data to drive greater value from VBAs.
Are you a payer? HealthVerity starts with your data and sets up a unique HealthVerity ID token for each individual. This creates one patient view and a cohesive patient story.
Are you a manufacturer? After a unified patient view is in place, we work with you and the payer to determine what additional data will be needed to assess the agreed-upon outcomes. Will you need lab values? Pharmacy claims? Imaging data? Rather than proxies for outcomes, you can build agreements based upon the data you really want to examine.
Focus on outcomes that aren't just activity-based, but also relevant: clinically, financially and most of all, relevant for the patients themselves. You can access de-identified data to leverage your own analytic tool suite through a private data store we build specifically for you and your VBA partner. Distributed ledger technology makes it possible to do real-time monitoring, all while preserving privacy and allowing for automated adjudication. It's the potential of value-based agreements, realized at scale.
Interested in realizing the potential of value-based agreements?