The Wearable Revolution

The rapid rise of wearable technology has fundamentally altered many facets of daily life for millions of consumers around the world. Smartphone-enabled mobile apps and sensors have significantly increased the amount of health-relevant data generated by consumers. As sensor hardware becomes smaller and more advanced we are now able to track a variety of metrics, ranging from digital biometrics (heart-rate, blood pressure), to blood-based biomarkers (glucose levels, cholesterol) and dietary and lifestyle choices. Consumers now seek self-quantification and today’s sensor proliferation arms race has enabled aggregation of vast amounts of consumer-generated behavioral, medical and contextual data. The question now becomes, what to make of all this data and how best can it be utilized?

Healthcare insurers are taking note
The surge in wearables and consumer-generated data is not going unnoticed. Large healthcare providers are recognizing the importance of this growing data pool and are working to find ways to leverage these emerging health datasets to lower their expenses. Analysis of these large volumes of data can be utilized to identify trends and behavior for individuals as well as larger segments of the population. Correlation of these patterns with clinical health-related outcomes will better enable healthcare players to more effectively identify and manage patients with high-risk conditions as well as provide more effective treatments.

Earlier this year United Healthcare announced a collaborative partnership with Qualcomm Life Inc. aimed at developing a health and wellness program that would encourage consumers to adopt positive behaviors in their daily routines. The program focuses primarily on utilizing wearables and medical-grade connected devices to aggregate health-related data around a user’s daily activity and provides financial reimbursement incentives for users who achieve certain activity-related milestones. Employees are motivated to engage in health-promoting activities, employers gain access to premium savings based on employee participation and insurance companies benefit from lower costs as participants are more actively engaged in managing their health. Everybody wins.

More recently Aetna announced their partnership with Apple in which the insurance giant would subsidize the cost of the latest generation Apple Watch for its customers. The program seeks to transform the existing consumer health experience by combining the seamless user experience and data-collection capabilities of Apple products with Aetna’s own analytics-based care management platforms. On the surface, the collaboration is yet another example of large healthcare payers leveraging wearable technologies to adjust insurance premiums and more accurately tailor their services and products to individual consumers, however a closer look at the Apple side of the fence reveals the true significance of this partnership. The data collected will be shared between both companies, which means that Apple’s collaboration with Aetna provides yet another method for Apple products to collect consumer health data.

Challenges to adoption
A number of companies are actively seeking to utilize big data to revolutionize the healthcare industry, however a considerable gap exists in the accuracy and reliability of the consumer-generated health data, and the medical-grade data collected by physicians and medical professionals. The recent class-action lawsuit against consumer wearable segment leader Fitbit, highlights the challenges that wearable technologies face when attempting to correlate lifestyle and activity data to meaningful health outcomes. To be fair, both Fitbit and Apple are not marketed as “medical-grade” devices, but if the data is to be used to provide medically-relevant insights, then the expectation of reliability and accuracy comes with the territory.

Within the healthcare industry, data is only relevant when it is supported by a robust body of clinical evidence. This remains one of the major challenges facing data collected by wearable devices. The ability to correlate consumer-generated wearable data to specific health benefits or disease phenotypes relies heavily on standardization of the data collected as well as normalization across large segments of the population. Integration of these novel data sets into existing research and clinical environments will go a long way in helping these emerging technologies obtain the clinical validation necessary to gain the regulatory approval. It is an uphill battle, but fortunately there are organizations already working to solve these challenges.

Bridging the gulf
Apple has continued to expand its presence in the rapidly emerging digital health space. The Cupertino tech giant has already made significant inroads into the healthcare industry through development of its HealthKit, CareKit and ResearchKit software frameworks. The recent acquisition of the personal health data startup Gliimpse is an attempt to address the chasm that exists between consumer-generated and medical-grade data. Gliimpse has built a personal health data platform that enables patients and medical professionals to easily share patient data. This move reinforces Apple’s position in the digital healthcare arena as they will now have access to digital data streams from both sides of the divide, consumer-generated data from iOS enabled devices and medical-grade data for physicians and other medical professionals.

The road ahead
In today’s connected world data is the new currency. Wearable devices provide a direct pathway to the consumer as healthcare providers seek a deeper understanding of the ways in which patient physiology and behavior affect health-related outcomes. If wearable technologies can improve patient outcomes and lower costs, healthcare providers and insurers stand to benefit significantly. However, in order to truly gain acceptance, data collection must become more reliable and accurate and additional technologies will be required identify patterns and correlations in the data.

Significant data-analytics capabilities must be developed in order to process and make sense of the overwhelming volume of data currently being collected. Work is already underway in these areas as companies like Intel, Apple and Alphabet are bringing their vast resources and artificial intelligence (A.I.) algorithms to help parse meaningful insights from the health data currently available. Early results are promising but there is still a ways to go before A.I.-enabled systems can replace conventional medical practices. Until then we will continue to wait for the day that our smartphones tell us to “Take two of these and call me in the morning.”

Stevan Samuel, PhD