Imagine a world where as soon as something medically useful could be detected, we would be prompted to take meaningful action—anything from a lifestyle change to taking a medical diagnostic test or the administration of a therapeutic agent. Imagine real-time, population-tested, scientifically-valid, difference-making, actionable recommendations—whether you’re fighting cancer or just trying to maintain a healthy, active life.
It’s coming. How soon and how dispersed depends on measuring and monitoring a broad array of data, including conventional blood work, genetic markers, behavioral patterns, mobility, sleep, and others that we may not yet even fully appreciate. But this represents the future of medical research and practice. The ability to collect and analyze diverse forms of information is forming the basis of what I call “patient equations.” Patterns uncovered by sophisticated analytic tools will have a dramatic impact on our ability to map and treat conditions with unprecedented precision.
Precision medicine for a pandemic
Patient equations are a representation of what life scientists and medical professionals have been working toward for a number of years — bringing the right treatments to the right patients at the right time. But the Covid-19 pandemic has provided new stimulus to making this paradigm commonplace. The use of technology is accelerating because it has to. Neutralizing the next threat to public health requires a new mindset, a new development path that uses computers and algorithms working in concert with patients, physicians, and scientists.
Precision medicine has been most closely associated with rare diseases or cancers, but it is no less applicable in the case of infectious disease—not to mention the thousands of ongoing ailments we must confront—as the current pandemic has so poignantly illustrated. We now know that there are multiple manifestations of the disease that occur due to SARS-CoV-2 infection. What was first called Covid-19 may be an umbrella term for several syndromes that the virus can cause. More time and research will better determine the potential relationships and how to best manage them but the idea of subtyping Covid-19 across many dimensions is exactly how precision medicine is just as relevant here as it has been in developing therapies for any other condition. The answers will await in the algorithms running behind the scenes and how they can be translated into actionable information.
More (data) sharing, more caring
The global spread and damage of Covid-19 have kicked-off a historical and scientific investigation for decades to come. Despite significant advancements in medical knowledge and technology, our ability to understand, prevent, and even contain a pandemic such as Covid-19 isn’t far from how generations before managed the 1918 Spanish flu.
It may sound harsh but, collectively, the life science industry missed an opportunity to make a huge difference helping to manage, and perhaps even contain, Covid-19. This is an unfortunate product of how little data is shared today. There are thousands upon thousands of research studies running around the world, with millions of patients actively participating in them. These studies contain patients with some of the most exquisitely curated data sets in the world of medicine. How much more would we know about possible treatments and outcomes, if we knew which patients taking part in clinical trials—regardless of therapeutic area—came down with Covid-19? If we had a way to view those data, looking for signs and symptoms, testing hypotheses once the very first patients were diagnosed then we would have not just a potential early warning system, but the ability to better determine what really matters when diagnosing patients and selecting therapies.
I hope the pandemic serves as a call to action in our industry to figure out how to use these data so that next time we are better prepared. Not only can we help contain the spread of future infections, but we can discover so much more about the management of health across broad populations, thanks to the high-fidelity and high-resolution view of individuals. We need a better way to look at research data through a broader lens and to connect with patients who are volunteering to participate in research beyond the confines of the research itself.
To do this, we can supplement all of our traditional medical knowledge with the digital trails we leave behind us every day. We can use the phenomenal connectivity and computational power available today to figure out what dimensions of information are relevant and predictive of our health futures. We can simplify the problem of trying to measure and comprehend it all. We can combine the known paths for patients, both in and outside of research programs, into a map—a multidimensional map, to be sure, but one with borders, with lines that delineate when and how health can be maintained, and diseases can be managed or cured. These paths, these maps, are patient equations. The digital ways we create, harvest, and combine data at scales never seen before in the history of medicine will help us discover them, and will help us reverse-engineer them. Perhaps not perfectly right away, and not in a way that will automate every decision we make for our care—but certainly in a way that is additive to our quality of life, and to our longevity, and that will help us to emerge from this pandemic with a better plan for the future.
Photo: metamorworks, Getty Images
