Insights derived from “real-life” data are being billed as the next phase of pharma. But are payers convinced?
When a significant breakthrough makes headlines, the tendency to focus on the upshot often belies the rigours, towering expectations and hardball negotiations that have gone on behind the scenes, aka the nitty-gritty.
Pharma’s data revolution will be subject to this same fate. Insights derived from new data sources are expected to dazzle the world and disarm pharma’s fiercest critics, but the industry must first demonstrate its value to payers and other key stakeholders — a sobering challenge.
How can pharma leverage these emerging insights in payer negotiations? For Kristine Mullen, Advisor to Evidation Health, the answer lies in “real-life data”: behavioural data captured from patients in their daily lives, as opposed to solely in the clinic. “It's hard to evidence risk and evaluate interventions or segment and personalise treatments for patients when you don't know the impact on outcomes in the real-world. It's things like wearables, sensors and apps that help us surface novel, real-time data with which we can deeply understand consumers health contexts and longitudinal patterns. There's an overwhelming amount of new data generating from people today that can allow us to deeply understand the consumer health context.”
Augmenting this real-life data with more traditional forms of data collection, such as clinical trials and claims databases creates a more holistic picture of the patient journey, says Mullen. An attractive proposition to payers.
However, for Lisa Egbuonu-Davis, Vice President, Global Patient Centred Outcomes and Solutions, Sanofi, real-life data is frequently not “mature” enough to be used as a viable bargaining chip in payer negotiations, with somewhat limited access to data sources, she suggests.
For real-life data to be legitimised in key conversations, there are important elements to be considered first, says Egbuonu-Davis. Citing a careful appraisal of its application in different disease areas and patient populations. “Certain types of areas, potentially like rheumatoid arthritis are very symptomatic and certain variable areas maybe more amenable than others.”
An understanding of the relevant stakeholders is also critical, she says. “I think it is pretty clear that most of us in the industry try to discuss with the relevant payers and other key stakeholders including providers and patients what types of data they think are going to be most useful in demonstrating what is happening to key outcomes as we try to improve patients’ health. I think understanding who our stakeholders are and what types of data are going to be most convincing to them is probably the most important question to start with before we start gathering data.”
Andy Gunn, Global Head of Evidence Generation UCB, echoes the sentiment that real-life data leveraging is currently more notional. Feedback from KOL’s on payer negotiations has pointed to “payers relying more on claims database data than real-life or EMR data and as a result when you are having negotiations payers are less reluctant to make decisions based on other forms of data,” he says.
Why the reluctant uptake? Weighing in with a payer’s perspective is Everett Neville, Senior Vice President, Supply Chain & Speciality, Express Scripts. “Most of the data we see is interesting but it’s not distinguishing or differentiating or very usable when making formulary decisions. We do give a little more credence to the claims database information that we get but only so far as it supports what we see in the clinical studies,” he says.
If your product is not underpinned by a clinical study, we are not going to sign up purely on the merits of real-world data, asserts Neville.
Insights derived from EMR’s and wearables may pique the payer’s interest, but it ultimately doesn’t carry the same weight as a published study, says Neville. “It's not clinical research. It’s doesn’t share the same patient populations, there's a lot of bias to make a decision off that; it would be very fraught with mistakes, so still it comes down to the published studies and the packaged inserts, with some support from the claims data and very little credence given to anything else.”
The times are changing however, says Mullen. Pharma is taking its first tentative steps in examining real-world data and the payer community has historically relied on claims data but their increasing access to the information contained in medical record’s signals a shift in direction, she says.
Constrained resources are also necessitating data discussions, which will play a prominent role in future payer conversations, she predicts. “Remote patient monitoring” is a particular area ripe for exploring,” she says.
“There has been this influx in consideration for things like remote patient monitoring particularly for patients with congestive heart failures, who think there are many health plans as well many providers that are leveraging scales because it is an earlier predictor if the weight is going up by two pounds. That's a trigger to triage and intervene with that patient more rapidly.”
Companies are increasingly “looking at things like loneliness, the impact of loneliness and not having a social network,” she says. “It is actually causing patients with diabetes for example to have 144 dollars more per month of spend than individuals who have a good social network and that is really brought to life based on the real-life data.”
Neville doesn’t discount the value of these behavioural insights, but it ultimately doesn’t impact the bottom line when it comes to payer negotiations, he says. “It's pretty much non-usable in that arena. Do not confuse that with interest in the actual data - we are very involved in patient wearables and the ability to impact an individual patient and improve outcomes through a wearable - what I am suggesting is that if you have three drugs and one of the manufacturers says ‘I have got this great data and this data proves my drug is better, pay me more or put me on the formulary’ — it is not being used for that and I do not see it being used for that anytime in the near future.”
Neville is keen to stress the distinction between using data to improve patient outcomes and assessing the value of drugs. “Are we using this data, are we improving patient outcomes? Absolutely. One patient at a time, but that doesn't suggest that the drugs are different, that suggests that monitoring a patient and knowing when a diabetic patient is doing an injection and the blood glucose level at the time of the injection, did they overdose? Underdose? Are they in control? Those are extremely important, but they don't necessarily make me suggest a Novo over a Lilly on a formulary, that's what I was getting at when it comes to payer’s negotiations.”
Could RWE hold much sway in the reimbursement of a device if it demonstrates safety and effectiveness?
“Should manufacturers be paid if their device is better than their counterparts? Yes.” Affirms Neville. “I do believe these things will become a factor, but we are not there yet. We need some energisation, we need some independent collection of the data. We are beginning to do a lot of that ourselves, we are monitoring our own patients, we are using these devices, so the advice I would give manufacturers; if you are doing this through embedded devices because you want to collect information, you need to focus on open-source codes and ways that providers and payers can get access to this.
“Being furnished with results from a manufacturer versus being able to monitor that patient for ourselves and see the difference in the results is a world of difference and will make a big difference in the long term. Will we get to the point where we can see differences in devices and differences in outcomes? Will manufacturers be willing to put a portion of that cost at the risk that those are going to be duplicated in a patient population? I believe that’s where we are headed.”
Gunn, UCB, is also optimistic RWE could legitimise both device and drug reimbursement in payer discussions. “I would suggest that when it comes to these combination products, where companies and pharma companies have developed reusable devices in addition to the safety and efficacy data that is that generated via the clinical trial, and you can evaluate compliance, adherence, persistence and are able to meet certain primary and secondary endpoints, and you use a comparator that may already be on formulary in the study, you may generate the evidence needed to have that formulary discussion.”
What are the challenges to making data a viable bargaining chip in payer negotiations? For Egbuonu-Davis, the challenge is “getting high quality linked data across a significantly sized population,” she says. “Our experience has been, that when data vendors start with populations they tell us that they have large numbers of patients with disease x y and z or say multiple sclerosis but then when you look at linking high quality data across the databases, the population size shrinks significantly, so suddenly you start with millions of patients, then you have hundreds of thousands and then maybe 5 or 10,000.”
These data discrepancies stifle progress much earlier than payer conversations, she says. “We need adequate numbers even just to get data to drive internal decisions about where to go, what to do and how to develop services and show product value,” laments Egbuonu-Davis.
Mullen chimes with the view that data disparity is a core challenge for pharma. It feeds into the broader issue of “interoperability,” she says. Getting all the multiple stakeholders looking at the data in the same way, getting the health plan, provider and consumer all on the same page is a key challenge but one that pharma can ultimately surmount, she says.
How? “Create predictive models, triggers and tools that help you inform and influence how best to intervene and how best to leverage the information with much more rigour, as well as well normalising the data,” says Mullen.
Companies have also traditionally used data metrics and analytics for “business intelligence”, but these sophisticated tools should also be utilised to probe deep into the patient and the patient community, she says. It will require a “different mindset and skillset, a different view of deeply understanding the consumer,” she adds.
Pharma is also bringing in experts from other industries, such as the technology industry, to reform data processes and better understand the consumer, Mullen says. Combining these approaches, alongside AI advancements, will profoundly impact the way data is packaged and presented to payers in the future, she asserts.
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