Building Consensus for Predictable Cancer Care Costs: Applying the Delphi Method to Multidisciplinary Stakeholder Collaboration

In this interview, Carole Tremonti, RN, MBA, discusses the critical role of clinical pathways in equitable cancer care, the application of the Delphi Method by the Predictable Cost of Care Working Group to foster multidisciplinary collaboration and transparency in decision-making, and the group’s focus on metastatic non-small cell lung cancer as an initial pilot for creating a cost-predictive model that balances stakeholder consensus.

Transcript:

Carole Tremonti, RN, MBA: I am a nurse change innovator who has been working in the cancer industry for over 24 years, and I'm at this conference because I believe and know that clinical pathways are critical to equitable distribution of care. This is a place where there are all of the thought leaders in this industry that can come together to solve this problem.

Can you provide an overview of the Delphi Method and how your working group arrived at this methodology?

Tremonti: As you can imagine, coming up with a predictable cost of care is not simple. There are many variables that come into play and the members of the Predictable Cost of Care Working Group come to the table with very different perspectives. Some are pathway developers that work in actual direct care settings. Others are in the commercial setting, others are in value-based care arrangements in the payer setting, and then we also have our pharmaceutical sponsors and attendees, who are coming at it again from a completely different perspective of brand engagement. As we began the work together about 6 months ago, it became very evident: how could a group like this come together, given their diverse perspectives, to understand and transparently, in an equitable way, decide the variables that should be included within the Predictable Cost of Care Model that would develop transparency and permit understanding across all of these stakeholders?

In doing so, we researched and found the Delphi Method. The Delphi Method is a fantastic way to do round-robin decision-making to create consensus based on evidence and standards of practice. We created a multi-panel session where we have a steering committee, consisting of 3 members, who are experts in their own right—representing cost, representing the disease we're going to work with, and then also representing clinical care. Then we have a group of panel experts, which is the second group. That is the group that has been, to date, the Predictable Cost of Care Working Group, which is again, representative of all the players across the industry. And then underneath that we have a consensus group, which is 25 members that all come from all of these different fields as well. The reason the 3 groups are important is that when we create what is in the predictable cost of care, we use a round-robin survey methodology and that way, asking everyone independently and asking the questions a little bit in a different way each time, we are able to hone in and create group decision-making on what gets included in the model when we can't come to an agreement.

So from the consensus group, which is 25 members, that information flows up then to the panel experts, who will further refine the recommendations from consensus and then up to the steering committee, who ultimately will be a tiebreaker in the event that that happens. It's a very collaborative model that also has a multidisciplinary representation, and for us that's very critical because we want to make sure that when we're done with this model—which we're very excited about—that it's usable by all of the stakeholders.

In the context of the Predictable Cost of Care Working Group, how has the Delphi Method helped break down traditional silos between stakeholders, and can you provide specific examples of decision-making opportunities that were previously difficult for these diverse groups to collaborate on?

Tremonti: Transparency is critical in decision-making for cancer treatment. Throughout the entire conference, we have heard people talking about how even when we go into the space of using AI—which is a model in itself—understanding exactly what is going into an equation is extremely important. It creates trust, and trust is really the only way that we provide solid care. There are a number of things relative to these stakeholders that have been very difficult to collaborate on previously. One which is drug cost. Those are done in a silo, because pathway developers do not have all of the information that the brand developer may have. There are a number of spaces in which we hit heads in thinking about what to do and what to include, many of them were what costs to include because all indications are different. Sometimes it's a first-line therapy, sometimes it's a new agent that has never been used before, but other times it's a comparative agent. So the biggest problem we had to solve is, what is a transparent source of data when you're coming up with a predictable cost of care that we could all agree upon? For everyone, that was the clinical trial. That is what medical oncologists use to make a decision about treatment. It is what is published evidence, and so coming up with a predictable cost of care permits us, looking at the clinical trial data, to cover a new indication or an indication where we have 2 brands that are competitors of each other, but in this way, we're all using an equal playing field.

What are key insights that you’re hoping panelists cover at the session when discussing the Delphi Method?

Tremonti: There are a lot of really important key insights that we hope come out of our session, particularly to be sure that the difference of representation—what they're excited about, but also what are they concerned about with this kind of model. We really want to be sure to put it out there so that everyone also understands that their perspective is being heard, shared, and addressed. We all think that that is critically important. The second question is will you use the model? Are you a pathway developer? Is this going to be valuable to you? Are you a medical oncologist who uses pathways? Do you want to see what went into this model? How do we create that for you? How do we create and remove any barriers to understanding and be sure that we create this in a very transparent way? Because transparency is something very difficult to do, especially when you're trying to build a large language model. When you're trying to build a large language model, what we need to understand is where are the costs coming from? What are the critical costs, and what is the bar for data? What is the bar for where we are going to get the data from? We want to make sure we talk about that very openly so that everyone is as comfortable as they can be adopting this model.

Looking ahead, which disease states do you think would be ideal candidates for applying the Delphi Method?

Tremonti: Finding an ideal disease state to test this method on wasn't easy as there are so many options. There are literally thousands of archetypes of cancer. The group, though, given the composition of it, did come to a consensus to try for our first pilot, which was metastatic non-small cell cancer. That is an indication that is high-cost, high-volume, and we also felt we could understand the predictable cost relative to it and/or create boundaries around the disease. Additionally, there was data availability, so that was the biggest factor of the question of what could we all focus on? What did we all agree on? Metastatic non-small cell lung cancer was a perfect target for us. So that's where we're focusing on first.

Looking ahead, does the Predictable Cost of Care Working Group plan to incorporate a greater focus on patient-centered outcomes? While the current emphasis is on cost, what future initiatives are in place to ensure patients' perspectives and needs are considered in the model?

Tremonti: One of the controversial subjects that we covered in coming up with the variables that would go into the Predictable Cost of Care was a consideration relative to patients. How were we going to represent patients who are the end user of the treatments we give them? At the outset, we decided that there wasn't enough standard evidence available to us to be able to conclude the patient impact into our initial Predictable Cost of Care. It's extremely important, yet difficult to calculate in a standard way, in a transparent way, that was equal across the playing field because different information is collected from patients in different ways. That being said, we did table it for this first model, yet we have every intention to bring it in in the future, and ways we can do that is through collecting electronic patient-reported outcomes, which is now becoming the standard. It's part of the enhanced oncology model. It is also just standard practice and the way to have a patient's voice and concerns heard. As patient-reported outcomes become more widely used and available, then that means we'll also be able to use that data and pull it in to our methodology.