Wearables have gained massive popularity over the past few years, and wearable tech market shipments are forecast to exceed 240 million by 2021. This effectively bolsters the Big Data paradigm shift, with wearables becoming an increasingly essential source of personal data to be leveraged for a range of purposes. Wearables are bound to have significant impact specifically on healthcare, where data aggregated can drastically reform the way in which patients and care providers interact.
Smartwatches and smartbands already help people stay fit by tracking their activity levels, sleep cycles, nutrient intake, step and calorie counts, and more. The real game changer, however, lies in the ability to make previously untapped information accessible to medical service providers. And with a market expected to grow to 27.11 $B U.S. by 2023, it’s evident that remote monitoring and control will define the future of healthcare.
From curing to caring
Interactions with physicians traditionally involve on-demand curing, with patients visiting clinics, or meeting doctors face-to-face when something goes wrong. They typically seek treatment for whatever they may be suffering at a given moment, with little to no relationship maintained with caregivers in between visits.
This is already changing and is set to evolve to 24/7 doctor/patient connection in the imminent future. Doctors and caregivers – continuously “fed” medical parameters on patients– will be able to view and validate general wellbeing, and determine illnesses and other irregularities, even before those under their care become aware of them.
The switch from curing to caring will significantly enhance healthcare. It will also produce massive healthcare-related cost savings – an objective well acknowledged by regulators increasingly investing in improved reimbursements that incentivize remote patient monitoring and new telemedicine codes.
Machine learning and the future of diagnosis
While wearable-assisted monitoring will help transform patient/caregiver relationships, it won’t necessarily be sufficient on its own to revolutionize diagnosis and healthcare. Data by itself is, after all, useless, and must not only be collected, but also analyzed, interpreted and ultimately acted on. That’s exactly where machine learning and its advanced algorithms come into play.
It’s important to note that medicine is largely a statistics-based discipline. The more information collected and analyzed, the more accurate the diagnosis. Yet, as the amount of information collected grows exponentially with the proliferation of wearables, the task of processing it can become insurmountable.
Machine learning can take over much of this effort and complexity. As detailed in the previous blog post in this series, machine learning can help filter out “noise” and extract non-explicit information, discover hidden connections, determine trends, and even forecast emerging patterns – all critical to effective diagnosis, on the basis of vast amounts of data.
Where can all this lead? It could, for example, enable physicians currently relying on general statistics derived from many thousands of patients, to base their diagnoses on individual patient-specific intelligence.
Making the machine learning-driven healthcare revolution a reality
The shift from curing to caring – or reactive medicine to proactive – is an inevitability that must take place for better, more impactful medical diagnosis and treatment to be achieved. AI and machine learning are crucial for this healthcare revolution to succeed.
Machine learning advances already well underway will enable the technology to approach wearable-generated data much like doctors progressing through residency – learning and deriving rules as it goes. Its algorithms will start out by making patient-level observations, then sift through staggering numbers of variables and predictors no human doctor or caregiver could realistically handle, and uncover combinations and patterns that contribute to reliable outcome prediction.
Best of all, machine learning technology will support experience-based improvement, with medical analysis and determination increasing in efficiency and accuracy with each subsequent diagnosis.
In the next posts, we’ll examine real-world examples of AI and machine learning technology bearing disruptive impact, starting with developments in a range of industry segments, and culminating in healthcare-specific applications.
Posted by: Uri Schatzberg, Somatix CTO & co-founder
