Dr Sachin Jain Discusses Data's Role in Monitoring Value-Based Payment Models

Sachin Jain, MD, MBA, FACP, is the president and CEO at SCAN Group and SCAN Health Plan.

Transcript:

The paper you co-authored discussed the need to ensure that value-based payment models do not create incentives that work against vulnerable populations. How can data help overcome this problem?

Jain: Yeah. Ultimately, at the foundation of this concern is risk adjustment. I think there's always an unintended consequence when you introduce a payment model that rewards organizations for managing health care utilization, there's always a risk that organizations will selectively apply those payment models to certain populations, unless, of course, they're rewarded for actually taking on that higher degree of complexity. Of course, it's very hard to identify that complexity, oftentimes, when we don't actually have the data that you need to have to appropriately risk adjust or identify cohorts of populations that could benefit from more intensive services and that warrant higher levels of reimbursement. With a better core of data, there's opportunities to, I think, create a higher degree of specificity around who actually exists within a population, and then pay organizations accordingly.

I think one of the areas where we've seen real challenges with this is actually the Medicare Advantage star rating system, where you're rewarded for completing a number of preventative screenings on patients as well as providing certain patient experience levels. Ultimately, some populations are harder to serve than others, but there's not a lot of adjustment for that in the formulas that calculate those star ratings. So, while the star ratings are directionally fantastic and are appropriate, they introduce this challenge of potentially rewarding organizations for actually serving healthier populations. Again, I think we have an opportunity to do better and a better core of data could actually help us do that.