How AI, Data Analytics, and Outcomes-Based Contracts Are Shaping Health Care: Laura Bobolts, PharmD, BCOP

As value-based care continues to evolve, industry experts are calling for a stronger focus on outcomes-based contracts and smarter use of real-world data, says Laura Bobolts, PharmD, BCOP, senior vice president of clinical strategy and growth at OncoHealth. With artificial intelligence (AI) enhancing processes like prior authorization, health care organizations are finding new ways to improve efficiency while preserving the critical role of clinical judgment in complex cases.

This transcript was lightly edited; captions were auto-generated.

Transcript

Can you share examples of value-based care models that have successfully integrated advanced oncology therapies and demonstrated improved patient outcomes?

Value-based care is a hot topic in oncology. Whether you see value-based care agreements between the provider and the payer, or whether you have novel companies coming to the market that are in the middle, providing value-based care recommendations to a provider on behalf of the payer, or getting involved with primary care physicians to make sure that therapy has a value to it and it’s not just provided at a fee-for-service level. We're seeing more and more eagerness to get into the value-based care landscape. But I would say if you've seen one value-based care agreement, you've probably seen one from a payer standpoint. They’re all so very different. How do you determine what is a metric to measure? What defines high-quality, valuable care to a member? [It’s] very complicated.

To give you an example: I've seen value-based care agreements taking a look at therapies for cancer patients on pathway and off pathway so [as] to incentivize on-pathway use and disincentivize off-pathway use. Maybe also incentivize peer-to-peer interactions. But what you're hearing is that I'm not saying outcomes-based contracts. That's what we'd love to see more in oncology for value-based care agreements, where the therapy is paid and reimbursed based on an outcome that is achieved by the patient. That complete response, that progression-free survival benefit, whatever it may be that the outcome is determined, we would love to see more and more of that, especially with our cellular gene therapies that are so expensive and novel in the market. But in today's day and age, we're just not seeing enough of that in oncology as we are in other disease states.

How are advancements in data analytics and real-world evidence supporting more informed decision-making and cost management in oncology care?

[For] payers and groups helping payers with their spending in oncology, you're absolutely looking at analytics on the daily. You need to look at analytics to measure your strategies that you put in place. Is that step therapy strategy effective? Is it worth it? We know all our strategies can come with an admin burden, so you need the analytics to measure the success of any program that you put into place, and for real-world evidence, that's where AMCP is so integral. They actually have an initiative in place right now looking at how to expand the use of real-world evidence, how to educate payers on the value of real-world evidence. I've been a part of those focus groups. It's been a joy to be a part of that and to learn, and I would love to see more real-world evidence generated in the community, because we have so many unanswered questions in oncology.

For example, my colleague, Susan [Susan Wojcicki, PharmD, clinical pharmacy lead, Humana], at a talk at AMCP yesterday, mentioned in multiple myeloma [that] most of our studies don't look at patient populations over the age of 65. Over the age of 75 are elderly patients. How do they respond to 4-drug treatments [and] the first 3-drug treatments? That's something that could be answered with real-world evidence. We need to see more of that topic.

As the oncology landscape continues to evolve, what emerging trends or innovations do you foresee playing a pivotal role in balancing cost control with expanded access to transformative treatments?

To balance cost control and expand access, we might see AI being involved more in the prior authorization space, in the payer space, especially even in oncology more, to help with taking a look at the criteria. Can we use also machine learning to pull out the criteria through the medical records? Sometimes I look through 90 pages of medical records to see if a therapy is appropriate for a cancer patient. Can we get more efficient at doing that with AI?

I think we're going to get there over time; it's almost so new I think of AI like the cloud. Where is it? Where is it? What is it? Is it a machine? Am I going to see a robot somewhere? It's almost like this mythical thing, but I think over time, we're going to see more people dipping their toes into using AI to become more efficient in the prior authorization space. Not to use AI to make decisions. Especially in oncology, you need humans to make decisions with complex cases that are so very sensitive with cancer patients. But can AI be leveraged to make you more efficient to expedite those prior authorization reviews so that you can reduce the time in which you can get the patient and answer as fast as possible?