I was reading a very interesting article (“Architects of Value”, EyeforPharma) on the vital role of real world data to demonstrate value of pharma drugs. There were many good points made that I want to delve into.
The background on this topic is that proof of drug effectiveness has gone beyond proving something in phase III trials. The article mentions that more than two-thirds of medicines have to prove themselves post-approval. This is where the onus of proof is shifted from a controlled trial to the wilds of real world evidence (RWE). RWE uses observational data, often claims data, to see how a drug works in the real world, under real conditions, with all the confounding factors mixed in.
Data generation game
What does this this shift to post-approval proof mean for pharma? It means that pharma need to become, as Dr Ameet Nathwani, Chief Medical Officer at Sanofi says, “an agile evidence generating engine.” Through phase III there is already a large amount of data collected that needs to be analyzed and understood. The challenge post-phase III is how to continue being agile and cost effective in generating the data needed to provide the evidence of effectiveness and safety.
Ameet mentions that this extension of the evidence gathering process will involve “a fundamental overhaul” of the whole go-to-market model, requiring a “seamless and integrated approach” that includes R&D through clinical trials to the collection and analysis of RWE in patient and outcome data. And this approach needs to be responsive, clearly communicated, and actively shared in a “timely, easy-to-understand, user-friendly, cost-effective way.”
I recently listend to a webinar on digital health in clinical trials. The majority of the attendees were patient engagement execs from pharma. One thread that came up in the discussion was around the collection of patient data to improve clinical trials, but from the perspective of the burden that places on the patient. One of the speakers, a patient advocate, described the burden of data collection in the context of the life of the patient, not only clashing with the demands of work and family, but also the impact of their disease. The concern is that in trials and beyond, patients are already overwhelmed with the data collection imposed by pharma. How can we even think of requesting more data?
Part of the issue, I feel, is that we are greedy with what we ask and frugal with what we share. We should approach data and those we collect data from by being frugal with what we ask and generous with what we give.
The pharma speakers at the webinar were interested in how digital health could be used to get more data, especially to get more insight into patient lives. That got me thinking: how can digital health be a connection between a therapy and the individual?
I feel, though, that “patient-centric” data collection is really about the pharma learning about the patient’s disease and therapy, not the patient learning about their own life and condition. Though it was heartening to hear the pharma speakers in the webinar mention how they are involving patients more in trial design. I sure hope the voice of the individual, not just as a patient, is heard.
But what got this whole brainwave going was that Ameet, from the article, kept referring to continuous efforts, to “continuous evidence generation.” This brought back how we used to talk about contrails of data that we are constantly streaming off of us (as a scientist, I do not like the term “exhaust”). There are interesting signals in all those contrails, and digital health stands to be a strong participant in collecting, aggregating, and surfacing the insights in that data.
In short, continuous evidence is the fusion of RWE, digital health, post-approval proof, and patient insight.
But in a world of continuous evidence, we cannot forget why we do what we do: to help folks live fuller lives. Let us not get distracted by the ability to build systems of engagement that continuously collect data for us to be able to prove our drugs to payers, providers, and regulatory bodies. Yes, those are necessary, but we also need to make all that data available to the ones who give it to us so that we can help them make life decisions, gain some insight into their health, and be happy.
What are you doing to bring on the world of continuous evidence generation? How do you balance your needs for better insights with the needs and interests of the individuals you are helping?
Let me know.
Image from Steffen239