by Carlos Rodarte, Founder & Managing Director, Volar Health, LLC
This is part one of a three-part series that will discuss how enhanced access to in-depth health data is impacting our understanding of personhood, the environment around us, and the pharma model.
A New Understanding of Personhood
Our understanding and perception of health and disease is shaped by the data that we have available to us. This is true for the individual searching Dr. Google, an urban informaticist measuring the impact of a proposed mixed-use development, or the prototypical researcher on the hunt for the elusive but groundbreaking medical discovery.
As a society we uncovered previously hidden pathogens causing virulent diseases to creating personalized therapeutics in breast cancer based on mutation status. The data and methods underpinning these discoveries now appear obvious, particularly as they have become mainstream. And now, there is yet another paradigm forming that will impact how health and disease are defined, how interventions are assembled, and how individuals are engaged.
What’s driving the shift? It’s the data – and everything that extends from it. We’ve long known about the complexity of human behavior and the many factors that influence health, but only recently has data generation centrally focused on the person. To ensure we start seeing the previously hidden facets of our lives, we’ll need to also see processing and algorithmic improvements, greater specificity on the problems we’re solving, and amplify the insights that are generated. Done well, these insights enable self-discovery while also improving the population as a whole.
Health’s Digital Mirror Image
While the inpatient setting will continue to create a certain type of clinical healthcare data, we know that everyday situations and lived experience drive health outcomes in many cases. Through a variety of means, we are now able to collect, analyze, and act on heaps of “real life” data that have been applied to health.
There is certainly going to be a lot of “noise” when casting a net wide enough to encompass all aspects of our life, but we are seeing more and more traditional health and disease concepts reflecting themselves in our digital world.
Our New Digital World
Phenotype >> Digital phenotype
Biomarkers >> Digital biomarkers
Therapeutics >> Digital therapeutics
Technology and sensors in particular are not only bridging the inpatient and outpatient setting, but the digital and non-digital world. For example, digital versions of phenotype, biomarkers, and therapeutics have given rise to new models of virtual research and remote care.
Today there are initiatives to understand and measure health and disease in a more holistic way as well – a “full stack” model that elucidates relationships amongst data that were previously unknown, or hypothesized, but yet not computable.
One such example is the One Brave Idea project in cardiovascular disease, a next-generation Framingham heart study with additional data inputs such as patient-generated health data (PGHD) and environmental factors, alongside more advanced analytic capabilities. This project required a unique partnership model including AstraZeneca, American Heart Association, and Verily – each bringing a unique set of perspectives and capabilities.
These increasingly prevalent projects and partnerships are adding sophistication to a field historically viewed as needing more robust evidence. Startups, for example, are running more studies; and, regulators, such as the Food and Drug Administration, (FDA) are starting to join the national discussion, and in some cases providing much needed guidance.
The Needle in the Haystack
Techniques to capture objective data from digital devices are emerging, as are the applications of digital biomarkers. In order to make the best use of the data, we have to understand the person in a great level of detail (digital biomarkers) in the context in which they reside (social ecological model).
The ability to toggle between these two frames of reference opens the opportunity to look at various patterns, relationships, and biases.
Digital biomarkers may shed light into how gait in Parkinson’s disease can elucidate disease progression and treatment effectiveness, but in isolation, without a sound social ecological model such innovation is unlikely to have a broader impact.
These micro and macro frames also help us ensure we’re being representative with our research to enable personhood across the board. The promise to improve the health of large cohorts with smart data sets and analytical rigor in part depends on ensuring that there is a representative population to draw from. As was stated earlier, our understanding of health and disease is shaped by the data that we have available to us; but the availability of these data increasingly hinge on gaining access from the individuals who generate it.
There are skews in the population that have been whole genome sequenced or who can afford next-generation sensor tools to measure their health. Going forward, it is everyone’s role, and particularly those designing studies and methods, to ensure there is an appropriate level of representation and diversity included – whether it be ethnic, sexual orientation, socioeconomic status, etc.
What is pharma’s role?
Pharma is ultimately in the business of discovering, developing, and launching new interventions based on an increasing knowledge base of how disease manifests and how individuals behave. The use of real life data, for example – patient-generated health data (PGHD) that companies like Validic enable, opens up several opportunities across the pharma value chain.
Through this, pharma is in a position to know much more about the needs of patients, and as such, pharma has a responsibility to reflect back to patients’ learnings that arise. A few examples where pharma can apply novel forms of data with the potential to simultaneously help individuals learn more about themselves.
Understanding the progression of disease and determining the factors that influence treatment outcomes is a need for pharma. The digital phenotype is increasingly quantified and, as a result, computable—whether it be from a human behavior standpoint or detailed physiology. The meshing of patient-reported outcome measures (PROMs) with new sensor streams will further increase computational models that reveal new subtypes of disease and responders. This opens up opportunities to engage patients more empathetically in their care and in research studies. A publication titled Building a learning health community: By the people, for the people, does a nice job of itemizing the principles that should be considered when building patient-centric health measures – ensuring that the measure is clear, that it respects people’s time, is harmless, and is ultimately actionable, to name a few.
The use of digital endpoints is part of a much larger effort to digitize the entire drug development process, and these endpoints will have a pronounced impact on how patients are engaged and how trials are conducted. For example, the six-minute walk test (6MWT) can be greatly enhanced in controlled, in-clinic environments to include measures of gait and balance, while modified versions assisted with remote technology can be conveniently deployed in outpatient and home environments. The Clinical Trials Transformation Initiative (CTTI) has a long history of bringing cohesion to efforts that aim to modernize clinical trials.
Determining who needs a particular intervention and whether that intervention is working in the real world can be analyzed in entirely different ways. Rather than over-relying on billing and claims data to identify needs and assess drug effectiveness, geography and social determinants of health, for example, enable a new, more accurate calculus. In addition, after a drug’s launch, there may be observational studies that are initiated, and these types of studies are particularly exciting as the number of observations that can now be made are much greater than to what we have been accustomed.
As pharma continues to answer these questions and build internal infrastructure to accommodate novel and disparate data sources, organizations will encounter many additional challenges. These challenges will be technical in some cases, organizational in others, and everything else in between. Part two and three of this series will go deeper into quantifying our everyday environments and the smart home, and the future data-heavy pharma model.
If you are interested in learning more about digital health’s implications in pharma, download the industry report on the implications and considerations for digital health devices and data in clinical trials.
Carlos Rodarte is founder and managing director of Volar Health LLC, a digital health strategy consulting practice enabling a range of innovators to better utilize novel data sources to enhance their products and services. Disclosure: Carlos Rodarte is on the editorial board of Digital Biomarkers; Volar Health, LLC has a client relationship with Validic.