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How Edge Computing and IoT will Impact Healthcare

Updated: May 15, 2019

Healthcare devices generate a tremendous amount of data. Too much, in the minds of some doctors, who worry the flood of information generated not only by devices in healthcare settings but also by emerging "recreational" health monitors and in-home health tech, can lead to "data overload" that will cause providers to lose the forest for the trees and misdiagnose patients. Healthcare IT professionals also fret about the challenge of storing and analyzing this fire hose-volume stream of data. And for good reason. The so-called "internet of everything" – networked products which in the healthcare field run the gamut from pacemakers to MRI machines – is expected to generate a staggering 507.5 zettabytes (that's 507.5 trilliongigabytes) of data by 2019.

Until now, the default solution for storing and analyzing these geometrically increasing volumes of data has been to house storage and processing power in the cloud. Soon, however, the answer to healthcare's data crunching woes may be what's known as "the edge" or "edge computing".

Think of the edge as the inverse of the cloud. Whereas cloud solutions represent a hub-and-spoke data storage and analytics system in which remote machines generate data that they send to cloud-based servers acting as the central informatics hub, edge computing pushes some data storage and analytics tasks to devices that are furthest out on the spokes and closest to the points of data collection – in other words, to the "edge" of the network.

As the industry publication Medical Design and Outsourcing observed last year, edge computing offers potentially significant improvements over existing health data storage and analysis solutions. To begin with, the edge is speedier because data processing happens closer to the source of collection, eliminating several steps in the current, multi-stage process in which providers collect data, send it to a central cloud server, and await a result. 

While this added efficiency may not seem significant in the abstract, in real life it adds up. Ask any emergency medicine doctor and she will tell you that how long it takes to process EKG data collected by an EMT on the drive from an accident scene to the emergency department may, quite literally, make the difference between a patient's life and death. Processing critical data out at the edge of the network at the point of collection makes a material difference in outcomes when minutes, even seconds, count.

Edge computing also saves space and energy by reducing the size and power needs of cloud servers and by leveraging the computing strength of increasingly powerful handheld devices like smartphones and wearable tech. In essence, the edge represents a "return to distributed computing" in which a "mesh network" of computing nodes processes critical data at the source.

An example of this is Bio-Lert.  Bio-Lert is an epilepsy seizure monitoring and tracking app installed on a patient's smartphone that receives and analyzes data from smartwatches to detect when a patient has a seizure. The processing of the raw health data happens at the "edge" of the network on the patient's phone, which then sends alerts to medical providers and uploads processed data to the cloud for storage and access by medical providers seeking deeper insight.

Infoedge has the expertise and insight to guide healthcare enterprises in the selection, adoption, and enterprise-wide integration of edge computing technology. Our "illuminate business experience" suite of services (iBX, for short) helps healthcare business leaders leverage innovations in the information technology arena to streamline hospital, urgent care, and medical office operations, create efficiencies, facilitate diagnoses, and improve patient outcomes. To learn about how your health enterprise can not just manage, but innovate and profit from, the volumes of data it and its patients collect every day, contact our team to start a conversation about Infoedge's iBX services.

#healthcare #bigdata #datafootprint #IoT

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