Dr Amy Abernethy: Collaboration Key to Optimize Interpretation of Data in the Evolving HEOR Landscape

As novel collaborations form in the health economics and outcomes research (HEOR) landscape, working with these new entrants will be key to optimally assess and understand emerging data and apply findings in the real-world setting, said Amy Abernethy, MD, PhD.

Transcript

A plenary session at ISPOR 2021 discusses the reinvention of HEOR. Can you speak on aspects of these new collaborations and what goals or challenges are being addressed?

We're seeing a lot of new entrants into the HEOR space. So, first of all, remember that as I think about [HEOR], I'm really broadening my thinking to this space that we've largely now started to call real-world data and real-world evidence. And what we're seeing now is that digital health companies, biosensor manufacturers, people who make devices where data is also a direct output—they're starting to come into this space.

Think about, again, COVID-19, where, for example, biosensors like the pulse oximeter helped us understand oxygen saturation in the home. These data sets now can be linked to other data sets to understand COVID-19 in new ways, as well as the impact of treatments for COVID-19, including vaccines. So, these new collaborations are helping us imagine new ways that we can make sense of the data.

Now, importantly, as I think about the new collaborations, I think about all the new actors that are coming into the space and learning how to work together in new ways. So, health tech companies, government, academic researchers, we're starting to see new actors working together and that means we're going to have to develop essentially a common language, lingua franca, to be able to understand the same concepts from many different lenses.

So, as the new entrants or collaborators come into the space, we're going to need to develop new ways of essentially understanding and respecting each other, collaborating, and then making sense of the data that comes into the space.

There's a lot of work to do, and one of the things I would highlight is that we can't just assume the analytic methods of the past are always going to work as these new capabilities are coming into our HEOR landscape. We've got a lot of work to do to get this right, and COVID-19 taught us that.

How is big data being used to apply HEOR in the real world to improve value-based care?

So, as I think about this language of big data, I really go back to this concept of the richness of data sets. So, when we first started talking about big data, 10-15 years ago, I think most of the data sets that we were talking about were what I would call voluminous, difficult to store, difficult to work with.

These days, I think of big data as many different data sets ultimately coming together to build new ways and enrich insights. And so big data to me these days means rich data. Sometimes they're small data sets, sometimes they're voluminous data sets, but really, this is about creating much more of a 360 holistic picture of an individual patient told through data, and then also a 360 holistic picture of cohorts of patients.

Ultimately, I would argue that richer data, better data, higher-quality data, data that we know how to work with ultimately leads to better decisions and better value. But ultimately, as I think about this kind of landscape of big data, what I am really thinking about is the richness of the data.