Remote, patient-centered technologies, like sensors and wearables, have become an essential part of clinical research, especially in the age of Covid-19, when physical restrictions placed on patients and clinical sites have accelerated the adoption of virtual clinical trials. Beyond the context of the pandemic, collecting high-fidelity sensor data continues a trend in the virtualization of clinical trials. In fact, sensor usage in clinical trials is projected to surge up to 70 percent by 2025. Sensors can help to track a wide variety of key metrics including respiratory rate, sleeping patterns, blood pressure, heart rate, and other critical health functions that historically have been monitored during traditional onsite visits and check-ins.
That said, the expansion and advancement of wearables and sensors underscores the potential for this technology to revolutionize human health. The true difference-making is going to come when these new technologies are not only used on their own but in concert with scientific drug and device development. Indeed, the evolution of sensor technology and cloud data management will be a key driver of the personalization and overall digital transformation in life sciences.
Ten years from now, there won’t be a clinical trial that isn’t measuring potential biomarkers continuously rather than at discrete points in time, whenever they can. This can be done with a biochemical or physical replacement for something done in-clinic, or with the integration of digital biomarkers that cause no additional cost or pain—to the patient or anyone involved—using phones, watches, or other sensing infrastructure that’s already there. This isn’t simply a nice-to-have new set of tools; it’s a paradigm shift away from needing physical interaction with a patient at a particular moment in time toward being able to conduct trials remotely, at a fraction of the cost with more reliable results. With sensor data, we are eliminating the need for labor-intensive, time-intensive physical access to the body and allowing for measurements at scale to be instantly transmitted to the cloud and analyzed by a powerful algorithm.
Closing the gap between research and patient care
The marriage of cloud and sensor technology allows us to move from the staccato rhythm of traditional medical care to a continuous stream, from a low-frequency data environment to a high-frequency one; one where real-time access to those data allows for instant action. Ideally, we’ll be able to see right when alarming trends begin rather than hoping to catch them before it’s too late. Plus, with a well-instrumented patient, we can be measuring the things we don’t yet know we need to measure in order to find evidence that we’re not yet even looking for. Being able to standardize disparate data sets across various domains, machine learning (ML) and natural language processing (NLP) will continue to be foundational in making this type of insight generation a reality, across the board.
The doctor is as much a beneficiary as the patient here, as we try to understand the nature of diseases. With sensor technology, patients can be monitored continuously—not just at those discrete moments every six months when they have an appointment, and data can alert physicians of something requiring their attention, a change in a critical measurement, or a progressing trend that could be otherwise missed. Of course, you can’t do a full-body PET scan every day. But, a passive device working in the background can collect data 24/7. And on a patient’s end, high-frequency feedback can help optimize medication dosing and manage conditions as effectively as possible, alerting them when damage is about to or has just occurred.
But the game changer isn’t wearables themselves—it’s the algorithms behind the scenes, armed with sufficient data and computing power to do the work needed to draw powerful connections and meaningful insights. There is potential for sensor-generated data to be useful in diagnosis, prognosis, and even as a way to measure the value of therapies. Can we catch something earlier with cognitive or behavioral data than we can with traditional measures? Can we quantitatively and objectively detect changes in behavior that might give additional insight into what is happening at the molecular or cellular level in a patient—is their tumor burden growing or shrinking, for example? Can we use a measurement—again, an objective and quantitative one that is free from the biases of a patient’s self-assessment or the limitation that a health care professional can’t observe patients 24/7—as a proxy for quality of life or socioeconomic engagement?
What’s happening in the world of sensors is exciting, but what is most compelling is the potential for these technologies to propel life sciences toward patient-centric operating models. The science of using sensor data to define, measure, and create mathematical models of disease can lead to better outcomes—and huge benefits—for everyone in healthcare and life sciences. Doctors are going to have more successful outcomes with patients, biopharmaceutical companies are going to have more targeted and more successful drugs, and payers are going to have more evidence-based outcomes data. The point is to create an equation that patients, physicians and even payers can use to ensure that desired outcomes are achieved as much as possible. These equations are going to help us uncover breakthrough new drug approaches, epidemiological discoveries that are going to change health across populations, and new ways to engineer clinical trials that will bring us into the twenty-first century.
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