I’m writing this blog as a follow up to my in-class discussion on how advances in wearable devices, computing power, and connected networks are ushering disruption across the health care continuum. There’s a growing variety of technologies and devices competing in wearable technology category – I’m specifically focusing on devices designed for biometric data collection. I’ll touch on today’s consumer wearable landscape, and then dig deeper into the opportunities presented by wearables to improve the quality and outcomes of health care.
It’s clear that wearable devices have exploded in the consumer space. A report from CCS Insights estimated 123 million wearable devices were sold worldwide in 2016, generating approximately $14 billion in value. CCS Insights projects by 2020 those numbers will grow to 411 million and $34.2 billion respectively. Consumer-grade activity trackers (https://www.wareable.com/fitness-trackers/the-best-fitness-tracker) provide a wide customer digraphic with the opportunity to capture, access, and easily digest personal activity data. Two benefits are clear: A) activity trackers provide readily-available access to otherwise non-avaialble historical and realtime activity data, and B) transparency into personal performance motivates users to become more engaged with their physical wellness. While consumer wearables available today are far from perfect – accuracy has been proven less than impressive and information output is criticized as basic notifications (ie. 10,000 steps reached) rather than actionable insight – the operating model, alongside growing interest in engaging with digital monitoring technologies, serve as key building blocks for realizing the full potential of wearable’s in the context of health care.
The current state of clinical biometric data collection is pretty dismal. Despite the unquestionable importance of the Healthcare industry, it has been one of the slowest to adopt new digital technologies. The “gold standard” technologies used for clinical physiological data collection are obtrusive and inhibiting. Not only in the sense of restricting normal physical activity, but often the monitoring process is required to be conducted in a clinical setting. A polysomnography machine (pictured left) used for sleep studies is a common example. Getting through a normal night’s sleep in a hospital bed while hooked up to a loud, beeping machine via multiple leads is difficult at best – and data collected often results in an inaccurate representation of a patient’s typical sleep pattern. The Holter monitor, used for 24-48 hour cardiac monitoring, is a technology that has been used in practice for two decades.
Although the Holter data-collection setting isn’t restricted to in-patient monitoring, the large and uncomfortable device inhibits normal activity and reduces compliance. Digging beyond the unpleasant patient experience and antiquated data capture methods, the model to then take collected patient data and transform it into actionable insight is fragmented and costly. Going beginning to end often involves working with multiple third party processing businesses – and ultimately, the data ends up stored away deep in the silos of hospital record systems.
I had the privilege of working for a medical technology startup and pioneer in the healthcare wearable space called MC10. The Company aims to modernize clinical biometric data capture via a seamless end-to-end hardware + software platform. The primary device and platform is the BioStampRC. It’s a small, soft, and flexible biometric sensing patch designed to conform to the human body. Described as the “work-horse” of MC10 hardware, the patch is packed with technology – electrodes to monitor cardiac activity, a gyroscope and accelerometer to measure movements of the body, 32MB of memory, BTLE data transmission are a few highlights. It’s waterproof, and has a battery life of up to 36 hours. The end-to-end software component includes mobile interfaces, cloud storage, and advanced analytical tools. The BioStamp is capable of capturing over 50 different cardiac and movement metrics, and in a very real way is re-defining the boundaries of physiological data capture. If you had to use a cardiac monitoring device for 48 hours, which technology would you prefer- A 5-lead Holter Monitor or a single patch that’s 0.3 cm think and just a bit larger than a band-aid?
Right now Pfizer and IBM are collaborating on an initiative called Project Blue Sky. A team of doctors and researchers are using MC10 technology to continuously monitor the biometric data of over 200 Parkinson’s patients. They are using the data collected to define the “digital signature” unique to each patient based off of their movements in real-world environments. The team then is able to quantitatively characterize how each patient responds to medications, events, and activities. IBM’s advanced machine learning capabilities are being leveraged to sort through the massive volumes of data, find trends among individual patient data, and make connections to other clinical data points. The ability to define a patients ‘digital signature’ is just one of many real world applications that I believe grounds an understanding for innovation in the personalization of care that lies ahead.Unobtrusive, continuous biometric monitoring of patients at on a large scale represents the crooks of a departure from patient-reported to objective data, population-based medicine to personalized-medicine, and responsive treatment to preventative treatment. Adoption of these technologies will enable doctors to understand the picture of a patient’s health on a more granular level than ever before, and will result in more collective, informed, and effective healthcare ecosystem.