At CPC+CBEx 2025, Gordon Kuntz discussed how the Predictable Cost of Care (PCC) Working Group is expanding its economic models to include patient-centric metrics by developing a parallel track that measures patient time, healthcare interactions, and variability in coverage, aiming to make the model more transparent, verifiable, and reflective of real patient impact.
How is the Predictable Cost of Care (PCC) Working Group incorporating patient-centric metrics into its economic models?
Gordon Kuntz: In the first iteration of the Predictable Cost of Care Working Group, one of the main pieces of feedback we got from our pharma sponsors was the desire to have some measure of patient impact in the Predictable Cost of Care model. It's been really challenging to think about that, but we've added Alan Balch this year to help us to think through those issues.
It's difficult to add it to the economic model, but we're looking to do a parallel track where there will be additional information about the impact to patients. What we're challenged with is dealing with the variability that exists in patient deductibles, coverage policies, and all those sorts of things.
We're really looking for something that measures their time and their number of interactions with the health care system resulting from different kinds of therapies.
Could you share some of the most significant findings from Phase 2 of the working group?
Kuntz: In Phase 2 of the working group, we're looking to actually build out the model. Phase 1 was about defining what the attributes of the model should be and defining the dimensions of the model, what should be included, what shouldn't be included. In Phase 2, we're looking to build out the model, that work is already underway.
As we explore how to do that—and we've got a model that will be coming out this fall—what’s been interesting is really understanding where the challenges are. We thought we had figured out everything, but as you start to do the work, it turns out there are some things we had omitted and we're discovering those and fixing as we go.
In Phase 2, we will be looking to create a model that can be used by pharma to submit their data to pathway developers and others.
What challenges have emerged in aligning the diverse perspectives of stakeholders around the development of these cost models?
Kuntz: In Phase 2, one of the main activities that we're working on is to identify specific cost elements. We have a framework now, but we're plugging in those specific cost elements. Some are pretty straightforward. If there's pre-testing required, we can look up the code for that and we can find out the Medicare reimbursement. We want everything to be transparent, scalable, and easily verified by all parties concerned. It increases trust in the whole process and makes the whole process transparent and easily accessible.
When we get to things that are much more complex, like side effect management, the challenge has been in determining a good, reliable, independently verifiable source for things like how much it costs to manage a grade 3 neutropenia or a grade 4 dehydration episode. How often do those occur?
Pharma has some information about how often they occur from their clinical trials. It looks like they may have some information that we're going to be able to tap into for what the root costs are for those, making sure we can get that at a level of granularity that allows it to be independently verifiable and, again, trustworthy to all the consumers of this information—all those who are going to be using it. That's probably the biggest challenge we've had.
What themes or discussions at CPC+CBEx this year do you think will have the greatest impact on pathway development moving forward?
Kuntz: What I'm most excited about at this year's CPC+CBEx are the developments in artificial intelligence (AI). We've been talking about AI. It had become a bit of a tired subject for us because we kept talking about the future of AI and what might happen. We're finally at the point, last year and this year, where we can talk about some very specific things that are happening in the field of AI and analytics.
What we're seeing is a lot of discussion around how those tools and those technologies are being used to streamline the physician's process, but also to streamline pathway development and real-world data access.
