Trends like artificial intelligence (AI), neural networks, cloud computing, machine learning, deep learning, wearables, and Internet of Things (IoT) are defining a new technological era. While one can’t be blamed for thinking this might be more hype than substance, the changes emerging from these trends are quite real.
The traditional healthcare system has enormous amounts of patient data (medical records, images, videos, and ICU signals) funneling into predictive-analytics systems that learn and detect trends to improve patient care. Besides collecting medical data at the point-of-care (at hospitals and clinics), engineers and scientists can now acquire, store, and work with large amounts of data from wearable medical devices in ways that weren’t conceivable even 10 years ago.
With all of this data, though, comes the very real challenge of transforming it into actionable insights. Often, this involves applying some of the latest analytics to your data to develop an innovative product or service that positively affects patient outcomes and delivers commercial growth. Beyond that, getting your product or service approved, and then quickly pushing it out into to the market, becomes another significant challenge.
Key enablers of success in overcoming these challenges are engineering software tools like MathWorks' MATLAB. They let medical-device engineers and researchers prototype and implement advanced algorithms, analyze large amounts of varied types of data quickly and effectively, and develop/deploy new machine-learning models without coding them from scratch.
How the Landscape Looks
To better understand this Big Data challenge, you can view this emerging landscape using two different perspectives: the IoT system framework along with the infrastructure enabling it to be possible; and the data-analytics (machine learning) framework, which focuses on the smart algorithms that help physicians and patients make more informed, data-driven decisions.