Defining Clinical Value in Wearable Technology

carlos rodarte

Carlos Rodarte, PatientsLikeMe

PatientsLikeMe recently published a report that clearly illustrates the potential clinical value in wearable biosensor technology. Project HoneyBee is premised on the belief that continuous monitoring will enable critical insight into disease prevention and treatment that will ultimately lower costs while improving health outcomes. In order to dive a little deeper into some of the challenges in the wearable technology industry,we interviewed Carlos Rodarte, director of business development and wearables at PatientsLikeMe.

How can continuous monitoring technology better enable a co-operated healthcare system between patients and providers? In what way, if at all, would the “culture of health” shift in your mind?

Patients are increasingly gaining access to, and incorporating, wearable technology into their daily lives, and finding significant health benefits as they do so. The patient mindset that comes with such change—the belief that they can influence their health status—is incredibly powerful. If we learn how to best apply these technologies, and can overcome challenges such as data accuracy and ownership to maximize their value, wearables and the data that comes from them will help create a strong bridge between the individual and the broader healthcare system. In the end we will have created a system in which individuals are better equipped to directly improve their lives.

Validating wearable technology data requires keeping up with rapidly evolving hardware iterations and improvements. What is your opinion on the importance of data being validated for clinical use?

carlos_featuredData from wearables currently faces many challenges, and as noted, the space is currently undergoing a rapid evolution. To compound the problem, some consumer wearables were never created for use in clinical environments, and as a result they don’t have a sufficiently high level of evidence. As these technologies become widely adopted, and other novel analytic methods emerge, the signal-to-noise ratio will improve.

It’s important to think about validation in the context of a specific use case. There are times where “good enough” may be sufficient, but a collaborative and transparent research model is needed to determine where that threshold is. As users gain access to their data, they become the central point that enables data transmission. In the wearables studies we have done, we’ve noticed how eager patients are to share their data with each other and for research purposes.

Do you see the future of wearables as diagnostic or more a tool for treatment or prevention?

By having a more complete picture of an individual through more continuous measurement, we can enter an era of healthcare where we can begin to predict risk and likely outcomes, and as a result intervene earlier. Shifting away from a “sick care” system has numerous implications from a population health perspective and in how individuals interact with their own health behaviors.

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