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00:00:05.040Welcome to Value-Based Voices, a podcast from the Association for Value-Based Cancer Care.
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00:00:15.679Each episode dives into the shifting terrain of cancer care in the United States, exploring what value means in today's clinical, policy, and patient-centered environments.
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00:00:30.079Our mission is to spark informed dialogue, promote transparency, and equip every stakeholder, from providers to payers to patients, with the insight they need to navigate cancer care with clarity, confidence, and purpose.
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00:00:37.200My name is Brian Reid.
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00:00:41.200I'm the principal of Reid Strategic and the editor of the Cost Curve newsletter.
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00:00:43.600And today we're speaking with James Chambers.
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00:00:45.759He's a professor at Tufts Medical Center.
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00:00:50.719He's associated with Tufts Center for the Evaluation of Value and Risk in Health.
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00:00:56.960And he's the director of the SPEC database over at Siever, which is one of the topics we're going to be discussing today.
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00:00:58.159So welcome, James.
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00:01:00.000Thank you for having me, Brian.
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00:01:04.239I'm curious, do academics really get a summer here?
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00:01:07.200Are you having a chance to catch a breath or is it just one thing after another?
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00:01:08.480A little bit, a little bit.
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00:01:13.680My teaching loads are not too big, the best of times, but always a busy summer with research.
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00:01:20.560I'm actually heading off to Scotland next week for a family vacation, so wish me luck on the flight with three kids for that one.
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00:01:25.439Then that you are going to get a true summer if you have to travel with children.
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00:01:25.760Yes.
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00:01:26.719Absolutely.
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00:01:34.640But it it strikes me that the underlying policy issues you're looking at do not take time off, so you probably don't get any relief from that reality.
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00:01:36.000A little bit.
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00:01:47.680So I want to start by talking about kind of it it's probably a little bit of an exaggeration, but not a ton to call it your life's work, the spec database.
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00:01:51.519What is the spec database and why is it important?
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00:01:53.040Good place to start.
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00:01:56.719So I'm a long-retired pharmacist.
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00:02:00.560So I grew up in Northern Ireland and I went around to study economics after that.
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00:02:12.080And when I came to the States, one question that always sort of bothered me a little bit was what influence did the large number of independent insurance companies in the US have on access?
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00:02:20.400So compared to a single payer system where everybody has the same access, whether that be good or bad, what influence does this large number of payers have?
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00:02:26.080And the Spec database, or the specialty drug evidence and coverage database, was born out of that question.
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00:02:37.199It was trying to really quantify what extent do we see variation in access through payer coverage policies in the US focused on commercial pairs.
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00:02:44.479So in 2017 or 2016 was the pilot we received funding to build a database to track specialty drugs.
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00:02:47.360And specialty drugs is a very ill-defined term.
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00:02:55.919It really just means expensive, complex drugs, oncology, rare disease, autoimmune diseases as well.
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00:03:04.319And the database has been running since 2017 routinely, and we track with a lot of detail how payers cover these specialty drugs for their enrolled population.
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00:03:07.919So you've been looking at this since 2016-2017.
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00:03:18.319Before we dive into the oncology specific findings, what's the trend been over the last decade when it comes to coverage and what are the distinctions you're drawing there?
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00:03:22.479Yeah, well, it's a lot of what we do is try to understand the changes.
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00:03:27.280Not to steal my own thunder from our upcoming discussion.
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00:03:31.199We see a lot more restrictions now than we did a number of years ago.
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00:03:33.439So a lot more utilization management.
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00:03:42.319When I refer to a restriction today, what I really mean is some condition on access that goes beyond how the FDA approved the therapy.
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00:03:46.319So a restriction beyond the approved use of that drug.
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00:03:53.439So we've seen more patient subgroup restrictions, more step therapy, more prescriber requirements.
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00:03:59.680And one thing we've tried to understand more recently is our policies just becoming more complex.
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00:04:26.639And in some research we presented at a recent conference and just submitted for publication, we've we tried to understand how much more onerous have these policies become over time, with just in terms of the number of criteria patients must meet, the number of documentation requirements, and the number of restrictions to really understand just how big a deal is this coverage policy step in the patient journey to access a drug.
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00:04:34.560So that that's interesting because I think a lot of times when we talk about access, it's it's treated almost as a binary, right?
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00:04:37.199Is this drug covered or is it not?
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00:04:42.800And it strikes me that what you're doing with Spec is to look for some of those distinctions.
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00:04:44.720So can you talk a little bit?
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00:04:51.920You went through some of them, but when it comes to what is good coverage versus bad, and and perhaps good and bad is not the right way.
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00:04:54.000If you've got a better way of framing it, let me know.
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00:05:00.959But like what are the elements that go into this spectrum of, you know, this is what coverage is?
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00:05:06.800Well, when I first started this work, you know, the question, the first variable was covered or not.
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00:05:09.600And it soon became clear that that's not the question here.
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00:05:12.959The question is for whom is the drug covered for?
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00:05:14.800You know, the devil is in the details.
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00:05:25.199So what we're finding is, and we'll talk about it maybe a little bit later, cell engine therapies, US pairs very rarely exclude a drug from coverage, at least a specialty drug.
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00:05:32.240So I focus on the, like I said, the specialty, not really the formulary drugs, where you see more formulary exclusions over time.
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00:05:42.079But for these specialty therapies, so medical benefit drugs, these infused medications or specialty pharmacy drugs, very rarely do pairs exclude them entirely.
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00:05:45.920What we do see is pairs putting certain limits on access.
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00:05:58.399Now, those limits may be related to the journey the patient's had through previous treatments, meaning that the patient must first try an alternative drug before they can access the drug that the physician would like to prescribe.
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00:06:06.079For oncology and for rare disease more generally, we do see a lot of what we refer to as patient subgroup restrictions.
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00:06:16.720So this is a requirement for the patient to meet certain clinical criteria, typically symptom-related, so the duration of symptoms or the severity of symptoms, or maybe even genetic criteria.
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00:06:30.160So what the payers are doing is you know putting limits on access, oftentimes narrower than the FDA approval, which is providing some access, but not to the full scope of that FDA label.
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00:06:36.879And it strikes me that you you can look at the FDA label as one kind of objective comparator.
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00:06:44.240You've also looked at uh professional guidelines as, you know, is that another source of truth?
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00:06:50.959Um obviously you've you've documented and we'll we'll talk about this kind of deviations from that FDA-approved label.
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00:06:55.040Um, what are you finding when it comes to comparisons to professional guidelines?
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00:06:56.000Yeah, a good point.
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00:07:02.480Maybe to step back a little bit as well and maybe remind everybody, you know, the FDA approves in safety and efficacy.
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00:07:06.079What a payer does is adjudicate medical necessity.
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00:07:09.199So they're making different they're making a different decision, essentially.
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00:07:14.480So the fact that there's differences there maybe isn't wholly surprising given that that's the case.
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00:07:20.240The FDA, for instance, bases the decision solely on the pivotal trial or largely on the pivotal trial.
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00:07:24.720Payers have to account for other things, other treatments, the cost, and so on.
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00:07:31.519So we've been trying to think hard about how we can get a sense of what's driving the access criteria.
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00:07:33.920Um, guidelines is one of those things, of course.
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00:07:51.279So one a recent study published last year was to understand the relationship between the utilization management tools, these patient subgroup restriction step therapy, not benchmarked against the FDA label, but against the clinical guidelines, so the clinical consensus of how that disease should be managed.
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00:07:55.759And we found a theme of my work, which is variation here.
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00:08:01.519Some payers were much more aligned with the clinical guidelines than other payers were.
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00:08:16.319What I'm increasingly focused on is the relationship between how the drugs covered and the pivotal clinical trial eligibility criteria.
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00:08:38.480So what we're beginning to see more of, if that's fair to say, is when the FDA considers the entirety of the clinical trial, they have to decide on what is the what's the approved use, which almost always is some form of an extrapolation from that clinical trial because the clinical trial is such a controlled setting, very specific patients.
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00:08:49.360So what we're beginning to see for recently approved products, especially, is alignment of the coverage restrictions with some of the eligibility criteria from the clinical trials.
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00:08:58.480So this relationship of you know how patients have access to the drug and the strength of the evidence that supports that judgment from the pair.
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00:09:07.600So the guidelines is a great benchmark for us to use, but we're thinking broadly about you know, where are payers getting this from and how are they formulating those decisions?
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00:09:12.639So let's let's let's hit that and then we'll we'll get into some oncology-specific conversations.
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00:09:16.480Where are payers getting, you know, getting getting this?
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00:09:19.440Well, it it depends on on the drug.
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00:09:33.200Now, think of a disease such as psoriasis or rheumatoid arthritis Crohn's disease or MS, like a large autoimmune condition, which's driving the access criteria, as many people would guess, would be preferred treatments.
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00:09:40.639So, in other words, the payer has negotiated a good price or featurable price for a drug, and that is the payer's preferred treatment.
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00:09:45.039So patients are driven or directed towards those preferred treatments.
00:09:45.039 -->
00:09:55.679Now, for things like celling therapies, rare disease drugs, oncology, you often don't have the therapeutic substitutes that you have in other diseases.
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00:10:00.720So what you begin to see then is these limits on, say, the clinical criteria.
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00:10:12.080So for oncology, the ECOG, which is a performance status embedded in clinical trials, we would sometimes see that the payers would have an e-cog score threshold in the policy.
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00:10:19.120So patients can access the drug, but only if they have an ECOG status of between zero and one or zero and two, say.
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00:10:26.720And that may or may not perfectly reflect the inclusion criterion for that drug's pivotal clinical trial.
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00:10:30.240Okay, so we've opened the oncology box here.
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00:10:34.240You had a publication a couple of months ago in JCO practice.
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00:10:50.240I think the first question I want to set some context because, again, we think about access restrictions broadly, and the thought has been that you know oncology has always been a little bit outside of the usual boxes when it comes to restrictions.
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00:10:52.639Obviously, that's changing, we'll talk about that.
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00:11:02.240But how does oncology compare as a subset to the kind of overall universe of specialty drugs in terms of these restrictions?
00:11:02.720 -->
00:11:06.799Yeah, so not to give away the fines of the study, but it's changing.
00:11:06.799 -->
00:11:16.159But historically, at least, oncology has been the least managed major therapeutic area of all of the diseases we track, which is you know very broad.
00:11:16.159 -->
00:11:19.519It's specialty drugs in general across diseases.
00:11:19.519 -->
00:11:30.399Um my colleague Peter Newman and I published a paper over ten years ago talking about the special status or the exceptional status that cancer has in healthcare.
00:11:30.399 -->
00:11:32.000And you know, there's good reason for that.
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00:11:38.480I think you know, there's stronger advocacy, there's a perceived real urgency for treatment for cancer.
00:11:38.480 -->
00:11:46.639Um, and historically, like I say, pairs have been very reluctant to put limits on access to oncology therapies.
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00:12:00.399In fact, in my first papers, when I was trying to document or statistically examine what factors predict how pairs would behave, I excluded oncology because it was almost a perfect predictor of no restrictions.
00:12:00.399 -->
00:12:02.879So back in 2016-2017.
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00:12:15.200So we don't do that any longer, but um, oncology, like I say, just to recap on that, has been generously is the wrong word, it's been the least managed of the major therapeutic indications.
00:12:16.320 -->
00:12:18.720So that's the historical perspective.
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00:12:20.720That was the case in 2016.
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00:12:22.320Where are we?
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00:12:31.919Again, you published on this, where are we now in 2026, or or or you know, as you know, the the last time you gave the in-depth look at the data?
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00:12:33.120Yeah, good question.
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00:12:40.399Well, the confusion answer is oncology is still the least managed therapeutic area that we track.
00:12:40.399 -->
00:12:46.559However, we're seeing a quite a significant uptick in the in the rate of these restrictions.
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00:13:04.639So just to give you a sense, whenever we did this analysis, we looked at drugs approved drugs approved, uh sorry, coverage policies from 2017 through 2024, meaning coverage policies that were current in 2017, and then we tracked those changes in policies over time and examined the same policies in 2024.
00:13:04.639 -->
00:13:10.000So we tracked the same drugs for the same diseases over time, so a homogeneous sample.
00:13:10.000 -->
00:13:19.519What we found in the most recent year was that non-oncology therapies had some form of restriction 73% of the time.
00:13:19.519 -->
00:13:22.879So almost three-quarters of the time, some form of restriction.
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00:13:27.440And for oncology, it was 35% of the time.
00:13:27.440 -->
00:13:33.519Now that seems like a large difference, but when we look at the trend over time, there's quite a notable change.
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00:13:36.399And I have some numbers here to make sure I get it right.
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00:13:42.240So in 2017 we found when we focus on step therapy and these clinical requirements.
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00:13:55.3602017 we had 14.5% of policies had some form of restriction, and in 2024 that increased by 60% to 23% of policies having some form of restriction.
00:13:55.360 -->
00:13:58.399So that is quite a notable increase.
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00:14:07.360And what was driving it, we found, was largely step therapy, which was a form of utilization management typically reserved for non-oncology.
00:14:07.360 -->
00:14:15.679Again, this is requiring patients with rheumatoid arthritis to try Humera before they have access to the more recently approved therapy.
00:14:15.679 -->
00:14:20.320So we were what was driving a lot of this increase in use of UM was step therapy.
00:14:20.799 -->
00:14:28.480So that's driving it, but what's the kind of is there a sense of what the underlying um pressure here is that that that's making this possible?
00:14:28.480 -->
00:14:32.559So again, 10 years ago, cancer had some sort of special status.
00:14:32.559 -->
00:14:35.600Why is it losing its special status?
00:14:35.600 -->
00:14:41.679Again, and we're we're still not at the point where oncology is its own is just another kind of drug.
00:14:42.080 -->
00:14:42.799Yeah, certainly.
00:14:42.799 -->
00:14:45.279It's still unique, I would say that.
00:14:45.279 -->
00:14:48.799Well, first of all, the world has changed a lot since then.
00:14:48.799 -->
00:14:55.279So there's an increased cost pressure in pairs, and I think there's greater scrutiny in oncology in general.
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00:15:01.120Since 2017, there's been many more, there was much more therapeutic competition.
00:15:01.120 -->
00:15:08.879So for some indications where previously we had no substitutable products, we now have substitutable products.
00:15:08.879 -->
00:15:21.759So CML, for instance, um is where we see a number of products where the payers can require the patient to step through a preferred for like a preferred drug before getting to an alternative.
00:15:21.759 -->
00:15:24.960But previously that opportunity didn't exist.
00:15:24.960 -->
00:15:29.519So that's you know, see we so we see more cluttered therapeutic classes.
00:15:29.519 -->
00:15:33.840Another big reason for this is uh biosimilars.
00:15:33.840 -->
00:15:47.519So we've seen a large increase in the number of biosimilars over that time frame, and we see payers now have preferred reference or biosimilar products, so think trustosimab or rituximab.
00:15:47.519 -->
00:15:53.600Um we see the pairs requiring patients to feel a biosimilar before they've access to you say Hysepton.
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00:16:03.840Now, of course, that's a different sort of form of management, it's not requiring a patient to feel a different drug, but we we track that in terms of the different forms of utilization management.
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00:16:10.879Um and just to sort of finish on that point, what we're seeing is not UM across the board.
00:16:10.879 -->
00:16:30.240What we're seeing is UM in certain circumstances, so it is when when you do have substitutable products, or when you do have biosimilars or generics available, or it's a cell, it's it's a cell therapy, or it's a combination immuncology regimen, we're that's where we're beginning to see the UM.
00:16:30.240 -->
00:16:33.120It's not simply across the board, it's quite focused.
00:16:33.519 -->
00:16:41.039I want to pick up on something you'd said earlier that what you're finding is this trend is not particularly consistent.
00:16:41.039 -->
00:16:53.759You know, you look it in in your your paper on oncology, you look across 18 different health plans, and it's not like all of them have steadily over the past 10 years introduced um you know restrictions.
00:16:53.759 -->
00:16:55.679There's a high amount of variability.
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00:16:56.159Absolutely.
00:16:56.159 -->
00:17:01.279Do you have a sense of what's fueling uh you know that that that sort of inner plan variability?
00:17:01.600 -->
00:17:06.960It's a very good question, and it's the second real big part of this analysis was to understand that variation.
00:17:06.960 -->
00:17:17.279And what we found was that the most generous pairs imposed some form of condition and access, some form of UM 12% of the time, the most restrictive pair did so 40% of the time.
00:17:17.279 -->
00:17:19.759So quite a significant difference there.
00:17:19.759 -->
00:17:34.000You know, a big takeaway from my work in general is that it really does matter what health plan you have for some of these diseases, not just oncology, but broader than that, um, which has implications, of course, for patients and also how physicians behave.
00:17:34.000 -->
00:17:39.680If they have to tailor the treatment choice, not just to the patient, but also to the patient's insurance coverage.
00:17:39.680 -->
00:17:41.680So, what is it about the plan?
00:17:41.680 -->
00:17:46.640So with a sample of 18 pairs, these are the largest 18 commercial pairs in the US.
00:17:46.640 -->
00:17:52.720So it was when we built the data set, it was any pair with at least 1% market share got into the sample.
00:17:52.720 -->
00:17:59.680So we have all of the national pairs in there and a number of the largest regional payers in there.
00:17:59.680 -->
00:18:04.319It is hard to find characteristics that are predictive here.
00:18:04.319 -->
00:18:10.400I mean, certainly available budget is important, so you know the resources available to the plan.
00:18:10.400 -->
00:18:16.160Um, we've stratified by national versus regional pair and not find a strong signal.
00:18:16.160 -->
00:18:19.519We've discussed with various people about competition in a state.
00:18:19.519 -->
00:18:35.039So we think about some of the regional plans, those that are you know that imposed utilization most often are those where there's the least competition, but when and compared to states where there's lots of competing plans and they they can't be restrictive in order to be competitive.
00:18:35.039 -->
00:18:39.920So it is difficult to answer that question concretely.
00:18:39.920 -->
00:18:50.720One thing I would say is that we there's a large number of blue cross-blue shield plans in our sample, and we find surprising variation across the Blue Cross Blue Shield state plans.
00:18:50.720 -->
00:18:59.279So you know, some of those Blue Cross-Blue Shield plans are among the most generous of the payers, and some are the among the most restrictive of the payers.
00:19:00.160 -->
00:19:07.680I am shocked to hear that the US healthcare system can be confusing and there are not good answers for massive uh variations.
00:19:07.680 -->
00:19:21.440Um and and I hate to say that the takeaway here is buyer beware, but it it really does feel like to your point, um, you simply there's very few rules of thumb that that you can use to even kind of predict in advance how something's gonna be handled.
00:19:21.680 -->
00:19:33.920Yeah, I mean there's certainly predictors that are we can find at the drug level, so things like if if there's competition, recency in the markets, the cost of the drug, if there's a safety warning, did the FDA expedite the drug.
00:19:33.920 -->
00:19:37.680So there's things at the drug level, but for the payers it's a little bit more confusing.
00:19:37.680 -->
00:19:41.440We don't have the sample size to really get about like we would like we would like to.
00:19:42.000 -->
00:19:49.519Which is striking because your sample is you know 18, which is pretty broad, and you've you've you're capturing you know the vast majority of the largest payers.
00:19:49.519 -->
00:19:53.839Um so ownership gears, you've mentioned cell therapy a couple of times.
00:19:53.839 -->
00:19:55.759You had a paper last year.
00:19:55.759 -->
00:20:03.039How does are there should we think about cell therapy differently than we think about oncology as a whole?
00:20:03.039 -->
00:20:08.799Um, you know, what what what are the factors that go into how and where cell therapy gets restricted?
00:20:09.119 -->
00:20:14.240Well, cell gene therapies, I mean, it's it's a fascinating subset of specialty therapies in me.
00:20:14.240 -->
00:20:25.279I mean, I remember I'm a lot older now than I was when I was in pharmacy school, but I remember here in the the beginnings of gene therapy when I was in pharmacy school, and this it was science fiction, I mean, when we heard about these things.
00:20:25.279 -->
00:20:27.599I mean, truly, like this is this would never happen.
00:20:27.599 -->
00:20:29.039And now, and and now we're there.
00:20:29.039 -->
00:20:32.079So cell and gene therapies are different for lots of reasons, of course.
00:20:32.079 -->
00:20:35.839They're very expensive, but they're also one-time treatments.
00:20:35.839 -->
00:20:41.200So all of the cost is up front, and very unlike other types of care.
00:20:41.200 -->
00:20:52.000We also have an uncertain evidence base with them at the beginning because, of course, you know, you know, how durable is a treatment effect for these short clinical trials, we just simply don't have the data to show it.
00:20:52.000 -->
00:20:58.559So, for a number of reasons, cell engine therapies are unique and challenging.
00:20:58.559 -->
00:21:07.440Now, what we found in the paper that you mentioned, which was in JAMA's a research letter last fall, was we looked at the 25 cell engine therapies in the market.
00:21:07.440 -->
00:21:16.480And I say that broadly because we included all of the CAR T therapies and some cell therapies like Provenge, but also the non-oncology gene therapies.
00:21:16.480 -->
00:21:22.880And what we found there was that again, uh, you mentioned covered versus not at the beginning of the podcast.
00:21:22.880 -->
00:21:29.279We found less than 2% of the time payers denied coverage entirely of these products.
00:21:29.279 -->
00:21:35.200So typically it varied by product, but typically the pay's covered in some way.
00:21:35.200 -->
00:21:40.640The majority of the policies in our sample were restricted for celling gene therapy.
00:21:40.640 -->
00:21:46.559So it's a you know, even if we're among oncology, that is a big predictor of which oncology therapies would get restricted.
00:21:46.559 -->
00:21:49.359Is it a CAR T therapy, essentially?
00:21:49.359 -->
00:21:58.480Um we found that 60% of those restrictions closely reflected the eligibility criteria for the clinical trials as well.
00:21:58.480 -->
00:22:07.680Um so just what we What we discussed earlier on, this relationship for oncology to the to the ECOG, you know, is the big one there.
00:22:07.680 -->
00:22:29.680And to bring it right back to oncology, again we found that among those cell and gene therapies, the CAR T therapies and the other cancer cell therapies were more generously covered than the gene therapies for non-oncology indications, such as the sickle cell disease therapies or hemophilia, spinal muscular atrophy, Deschannes disease, and so on.
00:22:30.079 -->
00:22:50.480Well, and there I you unfortunately you have the problem of a relatively small sample size, and especially when you look at you know the straight-up gene therapies as opposed to to the cell therapies, you've got a real heterogeneous mix and a real interesting uh um yeah, when when it comes to the strength of the data, so to speak.
00:22:50.880 -->
00:22:51.440Absolutely.
00:22:51.440 -->
00:22:52.079Absolutely.
00:22:52.079 -->
00:22:55.759Particularly in the non-oncology space, these is such wide variation there.
00:22:55.759 -->
00:22:58.960Like so, you know, you compare and yeah.
00:22:58.960 -->
00:23:01.119Yeah, good example of that.
00:23:02.079 -->
00:23:18.480So look, it i if you if you kind of take a step back and look kind of both across all specialty drugs, across the oncology as a subset, across uh cell and gene therapy as a subset, we seem to have a line that goes um up and to the right when it comes to restrictions.
00:23:18.480 -->
00:23:22.880Is there any reason why, you know, again, uh that that's not going to change in the future?
00:23:22.880 -->
00:23:25.759You know, your JCO paper will look through 2024.
00:23:25.759 -->
00:23:34.799Is there anything you've seen as you pull this data down on a regular basis to suggest that the curve is changing in any way?
00:23:35.279 -->
00:23:46.559Across the board, so not just thinking of oncology here, I would imagine we'll see a continued uptick in the number of restrictions on access to many of these therapies.
00:23:46.559 -->
00:23:55.039I mean, it's driven by, you know, for rare disease, we often get asked the question well, why would payers restrict access to this particular rare disease therapy?
00:23:55.039 -->
00:23:59.440There's so few patients out there, how does that impact the bottom line in the meaningful way?
00:23:59.440 -->
00:24:03.440But the issue is it's just so many rare disease therapies approved now.
00:24:03.440 -->
00:24:11.440You know, we did a paper earlier this year that showed the vast majority of FDA approvals, especially therapies now, so these high-cost treatments.
00:24:11.440 -->
00:24:17.839And it's this sort of cumulative impact of all of these treatments is putting pressure on payers to find ways to pay for them.
00:24:17.839 -->
00:24:26.400And one of the reasons why I find this such a fascinating area to work in is that it really is where the rubber meets the road in terms of how decisions are made.
00:24:26.400 -->
00:24:35.039You know, we have all of the clinical trials, we have to get the guidelines, we've got cost-effectiveness analyses, you have competing drugs in the same marketplace.
00:24:35.039 -->
00:24:43.039And the payer has to decide what to do given their budget, given the cost of these therapies, and they have to make trade-offs.
00:24:43.039 -->
00:24:47.680And really, what we're trying to understand in the spec database is where these trade-offs are.
00:24:47.680 -->
00:24:54.240Now it's a it's it's it's hard, you know, the the line can't go up forever, I suppose.
00:24:54.240 -->
00:24:55.680At some point, there's a kink.
00:24:55.680 -->
00:25:05.359I think what we'll see is that there's increasing restrictions on novel therapies and a relaxing relaxation of restrictions as therapies remain on the market for longer.
00:25:05.359 -->
00:25:07.920I think that's something that we would find.
00:25:07.920 -->
00:25:17.119But in the short term, for newly approved innovations, I think pairs sort of bulk at those and they um will continue to add those limits.
00:25:17.680 -->
00:25:21.119And and I'm curious what the concern is about new therapies.
00:25:21.119 -->
00:25:30.720Is that and it I'm sure that it's not all one thing, but you can imagine there being concerns about um the robustness of the data at approval.
00:25:30.720 -->
00:25:37.359You can imagine there being questions about the budget impact at approval that that may resolve over time.
00:25:37.359 -->
00:25:45.200I mean, there's a lot of great examples of of payers sounding the alarm on things that are or are not actual problems.
00:25:45.200 -->
00:25:51.119Like, is there a sense of kind of what where that uncertainty is at the beginning, or is that kind of all the above?
00:25:51.440 -->
00:25:53.920Yeah, I mean, I th we mentioned cell and gene therapies there.
00:25:53.920 -->
00:26:02.799I mean, that's a that's where there's a lot of uncertainty, you know, for some of the gene therapies and even CAR T therapies, when people use the the cure word, you know, is this a cure?
00:26:02.799 -->
00:26:04.000Then we just don't know.
00:26:04.000 -->
00:26:06.799We don't know what that durability of treatment effect is.
00:26:06.799 -->
00:26:14.400And if the drug is priced in a way that's assuming a cure, then you it's I mean payers might push back on that, you know.
00:26:14.400 -->
00:26:27.440Um I think I don't want to say lack of preparation for the payer, but maybe you know, it's the the immediate budget impact, it's the lack, you know, it's what that budget impact would be in that particular year.
00:26:27.440 -->
00:26:38.240Um we see delays in access sometimes and that the payers are on issue in policies for a period of time, you know, I guess to try to push back that budget impact a little bit.
00:26:38.240 -->
00:26:46.720But mostly I think it is that the therapy is new, and we know least about that therapy on the day that it's approved.
00:26:46.720 -->
00:27:00.880And over time, physicians and payers and guideline developers become more familiar with it, and we see greater access as uh evidence accumulates, and simply familiarity with the product also improves.
00:27:01.279 -->
00:27:05.599Well, I like to end things on a happy note, and that actually feels rational, right?
00:27:05.599 -->
00:27:12.000That you're gonna have the most restrictions when we know the least, and as the evidence develops, we see things open up.
00:27:12.000 -->
00:27:16.559Please do not contradict me by telling me that that is not a universal truth.
00:27:16.559 -->
00:27:24.559I'm certain that there are some places where that runs in reverse, or where we get the evidence and we never get the uh we never get that the restrictions taken off.
00:27:24.880 -->
00:27:36.160It is a very reassuring finding of our analyses that a predictor of coverage generosity is the longer the length of time the drug is in the market, or length time from FDA approval.
00:27:36.160 -->
00:27:40.880So that is consistent across our model, so you're correct on that one.
00:27:42.240 -->
00:27:53.920So, on that legitimately somewhat happy note, um you know that wraps up this episode of Value-Based Voices brought to you by the Association of Value-Based Cancer Care.
00:27:53.920 -->
00:28:02.480I really, really appreciate your time today, James, sharing your insights and appreciate the attention of our listeners for joining this conversation.
00:28:02.480 -->
00:28:09.519If you found today's discussion thought provoking, please subscribe to Value Based Voices wherever you get your podcasts.
00:28:09.519 -->
00:28:16.000Stay tuned for more episodes that will spotlight the voices shaping the future of oncology.
