Breaking the Ivory Tower
Or 'why cost-effectiveness models may not be cost-effective in a future healthcare marketplace'
tl;dr-
Market access - the process of obtaining & maintaining 3rd party reimbursement for healthcare products & services - is a common & central component of today’s healthcare business models
Obtaining market access has been supported by various analyses & tools to demonstrate value; chief among them the Budget Impact Model (BIM) and the Cost-effectiveness Model (CEM);
BIMs look to estimate the financial impact of a product or service on a payer’s overall budget or spend
CEMs, also referred to as Cost-effectiveness analyses (CEAs) are methodological approaches to compare the economic & clinical benefit of different healthcare products & services against each other
Both analyses are often done in tandem - and are generally sponsored by innovator companies (i.e. biopharma, medtech) to support overall commercialization & market access strategy
While these analyses are heavily used in ex-US markets (e.g. UK, South Korea, increasingly France, etc.), the impact of health economic analyses on US payer decision-making is somewhat unclear
Innovator-sponsored models are often perceived to be biased towards the innovator’s business objectives; according to a 2021 study examining payer perceptions of innovator-sponsored economic models in oncology, 31% of the N = 102 sample will not consider an innovator-sponsored model in their decision-making; 43% will conduct their own analysis based on their own data to validate any sponsored model findings
While an increase in the use of third party methodologies - such as ICER - has been suggested anecdotally, the realized impact of these analyses on decision-making varies dramatically based on payer expertise & capabilities and the therapeutic area
In addition to perception barriers, widespread use of economic analyses face challenges on their accuracy, which may continue to limit their use if not refined; this need for validation may explain the previously cited 43% figure surrounding oncology economic models
From a big picture perspective, economic analyses face the common problems all models face in that they provide only a snapshot of reality - hence the catchy aphorism ‘All models are wrong, but some are useful.’
One potential reason behind the use of models for market access despite their challenges is the lack of a viable alternative; healthcare data infrastructure is notoriously fragmented, making it historically difficult to conduct real-world outcomes research
However, as the healthcare data ecosystem continues to consolidate & grow more interoperable, the viability of real-world evidence analyses compared to model-based approaches will continue to grow, resulting in greater importance of real-world data for economic analyses (and consequently, market access strategy)
Over time, the value of a prospective model (i.e. the BIMs / CEMs of today) may shift to be increasingly for its marketing value whereas real-world evidence & outcomes analyses become the bedrock for market access decision-making
This ‘breaking of the ivory tower’ suggests that innovator companies should strategically invest in real-world data analytic capabilities in-house - not simply access to RWD - in order to collaborate & engage with third party payers to achieve access for their innovations
Digital health companies, by virtue of being natively technology-based organizations, should see this as an opportunity both to tactically achieve access through evidence generation for their own products and services and as a long-term initiative to further dictate the conversation surrounding evidence generation in the future to erect defensive barriers
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I actually wanted to write my take on BIMs / CEMs for a while since demonstration of benefit - either clinical or economic - is such a hot topic in both the biopharma & the digital health (specifically, DTx, virtual care models, etc.) spheres. While the past two or three years have been digital health companies realizing that value demonstration is critical to a payer-reimbursed business model (which, in all fairness, still remains more sustainable than relying solely on B2C approaches), the next few years will likely be many of these companies trying to figure out exactly how to demonstrate that value.
Biopharma and medtech have had a head start here but I realistically don’t think it’s that much vs. digital health companies. This is because the current approaches of using prospective models - often built rather academically & strongly dependent on access to reliable, good data - is generally seen by payers as just another marketing tool vs. a collaboration opportunity. Across my career, I’ve heard payers refer to these models somewhat derisively as ‘just showing what they want to show us,’ making the widespread use of these models resemble more of a confrontation than a point of further cooperation. To writ, it’s clear that the demand-side need for tools that help assess the economic impact of new healthcare products & services is there, but the currently supply-side offering may simply just not be the best-fit.
The increasing excitement around real-world evidence as a better window into what really is increasing outcomes and driving costs represents a potentially larger paradigm shift than many biopharma stakeholders may realize. Implicitly, access to reliable real-world data - with of course, the right set of analytic capabilities - will beg the question, why should we as payers even consider a potentially biased, prospective model in the first place? Isn’t payer-conducted RWE more of a ‘competitor’ to innovator-led economic analyses; one that is already advantaged by being more comprehensive and more tailored to that payer’s patient population?
This core truth - that real world demonstration of value is likely more valuable to a payer in their decision-making vs. a prospective model built somewhat in an ‘ivory tower’ - is why I don’t think the gap for evidence generation expertise between biopharm and digital health is really that large. In fact, many digital health companies have already began or are in the midst of this journey; after all, what do you think a pilot is for? In many ways, a pilot is just a real-world evidence study that a payer conducts to assess the value of a given intervention - so long as a digital health company is able to continue to refine those capabilities & processes, they’re actually already doing ‘next level’ evidence generation compared to biopharma counterparts.
Actually, this ‘next level’ evidence generation - the use of real world evidence to assess economic value for market access - already sort of exists even in the biopharma space. Take for example, the German AMNOG process - which allows for manufacturers to set free pricing for one year while the German health authorities assess the value of continuing to pay for that drug (and at what price). We are increasingly seeing a willingness to incorporate RWE into these assessment methodologies, which may - at some point in the future - transform the entire market access process in Germany into something that resembles a digital health pilot today. Neat, right?
It doesn’t really take a far leap of logic to see how this process - an ‘innovation pilot,’ let’s call it - becomes something US payers may adopt for all new innovations at some point in the future. While many prerequisites have to be fulfilled first - the largest one being data interoperability - the demand for certainty on how a new innovation will meaningfully improve healthcare either on the cost or benefit side will be the central driver for these changes. One way or another, we’re all being pushed into this world - and you’re going to be outcompete if you’re still relying solely on BIMs / CEMs for your market access strategy.
This doesn’t mean BIMs / CEMs will be abandoned (and certainly not in the next 5-10 years, depending on how optimistic you are about widespread data interoperability), but it does mean innovator companies want to be smart about their use. At the end of the day, the goal of market access is to foster a sense of certainty between the payer & the innovator that a given product or service is going to do what the innovator says it will do. With that aim in mind, is throwing a prospective model over the fence the best way? Probably not.
Is throwing only a prospective model over the fence the best way? Most certainly not. Crafting economic analyses in an ivory tower is increasingly a thing of the past; so as an innovator, just try not to get caught up in the debris.
-WY