Big Data and analytics have transformed the way we look at data. While the healthcare industry is no stranger to tools that capture and store data (Electronic Health Records), the industry is swiftly moving towards deep healthcare analytics, to radically change the way healthcare is delivered. The focus is shifting from merely treating, to prediction, and subsequently prevention. The move from volume-based to value-based models creates a strong case for deep analytics that provides personalized medicines tailored to the needs of individual patients, while widening the understanding of population health, to spot trends and prescribe effective solutions to the forecasted issues.
What is Deep Healthcare Analytics?
Gartner predicts that “automated healthcare could account up to 85% of all healthcare customer experiences by 2020”. This automation could range from a simple patient form to detecting diseases from an MRI or CAT scan (AI-based).
Under the umbrella of AI-based healthcare analytics, predictive analytics though in a nascent stage itself, is giving way to cognitive analytics, which includes conversational AI and related complex technologies. But one shouldn’t look at them as independent technologies, rather as levels on the scale of analytics adoption. Predictive analytics uses data from the past and present to forecast future scenarios, thus, allowing healthcare providers to plan, manage, and even preempt. Cognitive analytics using machine and deep learning techniques has the capability to provide actionable insights in real-time, by piecing together relevant data and applying the findings to a particular context. It is meant to supplement human decision-making.